GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER I

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 101

Course Title: Fundamental of computer and Introduction to Programming

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: I

7. Category of Course: DC

8. Pre- requisite: Basic Knowledge of Mathematics

9. Course CO1: Learn the concepts of IT and understand the fundamentals of

Outcome**: basic building blocks of computer science.
CO2: Understand basic data types and syntax of C programming.
CO3: Propose solution to problem by using tools like algorithm and
flowcharts.
CO4: Analyze and select the best possible solution for decision-
based problems using decision making skills and develop the
aptitude to solve iterative problems using different types of looping
statements.
CO5: Implement complex problems as a collection of sub problems
by applying modularization in applications using functions.
CO6: Apply and implement the concept arrays for providing solution
to homogenous collection of data types.

** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10. Details of the Course:

Sl. Contents Contact
No. Hours
8
UNIT- I
Generation of computers, Computer system memory hierarchy,
Input/Output, RAM/ROM, Software & Hardware, Understand bit, byte,
KB, MB, GB and their relations to each other, Operating System
1 overview, Computer Networks Overview
Algorithms and Flow Charts – Examples of Flow charts for loops and
conditional statements

10
UNIT- 2
First C program - Hello world, How to open a command prompt on
Windows or Linux. How to read and print on screen -
printf(),scanf(),getchar(), putchar()

Variables and Data types - Variables, Identifiers, data types and

2 sizes, type conversions, difference between declaration and definition
of a variable, Constants

Life of a C program (Preprocessing, Compilation, Assembly, Linking,

Loading, Execution), Compiling from the command line, Macros,

Operators – equality and assignment, Compound assignment

operators, Increment and decrement operators, Performance
comparison between pre and post increment/decrement operators,
bitwise operators, Logical Operators, comma operator, precedence
and associativity.

UNIT- III
Conditional statements: if statement, if-else statement, ternary
statement or ternary operator, nested if-else statement, switch
3 8
statement, Difference between performance of if else and switch,
Advantages of if else and switch over each other

Loops: ‘for’ loops, ‘while’ loops, ‘do while’ loops, entry control and exit
control, break and continue, nested loops
UNIT- IV
Functions: Function prototype, function return type, signature of a
function, function arguments, call by value, Function call stack,
Recursion v/s Iteration, passing arrays to functions,
4 7
Storage classes: Automatic, Static, Register, External, Static and
Dynamic linking implementation, C program memory (show different
areas of C program memory and where different type of variables are
stored), scope rules.

5 10
UNIT- V
Arrays: Single-dimensional arrays, initializing arrays, computing
address of an element in array, character arrays, segmentation fault,
bound checking, Searching and Sorting.

Total 43
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publicati
on/Repri
nt
Text Books
1. • Peter Prinz, Tony Crawford,”C in a Nutshell”, Oreilly 1st 2011
Publishers,
2. • Peter Norton, “Introduction to computers”, TMH, 6th 2009

• E.Balagurusamy,”Programming in ANSI C”,McGraw 6th 2015

Hill
Reference Books
1. Steve Oualline, “Practical C programming”, Orielly 3rd 2011
Publishers, 2011.
2. Brian W Kernighan, Dennis M Ritcie, ”The C Programming 2nd 2000
Language”, Prentice Hall, 1988. R3. Herbert Schildt,” C:
The Complete Reference”, 4thEdition.TMH, 2000.
3. Yashwant Kanetkar,”Let Us C”,BPB Publication 8th 2007

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam / Lab Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER II

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 201

Course Title: Programming for Problem Solving

2. Contact Hours: L: 3 T: 0 P 0
3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: II

7. Category of Course : DC

8. Pre- requisite: Basic Knowledge of Mathematics and Computer Fundamentals, TCS

101

9. Course CO1: Learn and apply concepts of strings and multi-dimensional

Outcome**: array for providing solutions to homogenous collection of data
types
CO2: Propose solution to problem by using tools like algorithm and
flowcharts.
CO3: Apply the concept of pointers to optimize memory
management by overcoming the limitations of arrays.
CO4: Process and analyze problems based on heterogeneous
collection of data using structures.
CO5: Apply concepts of file handling to implement data storage and
retrieval tasks.
CO6: Implement the basic real life problems using python.

** Describe the specific knowledge, skills, or competencies the students are expected to
acquire or demonstrate.

10. Details of the Course:

Sl. Contents Contact
No. Hours
1 6
UNIT- I
Multi-Dimensional Arrays- Initializing arrays , row major and column
major form of an array, character strings and arrays,
Strings – Declaration of strings, Initialization of strings using arrays
and pointers, Standard library functions of string.
2 10
UNIT- 2
Pointers –Basic of pointers and addresses, Pointers and arrays,
Pointer arithmetic, passing pointers to functions, call by reference.
Accessing string through pointers.

Dynamic memory management in C - malloc(), calloc(), realloc(),

free(), memory leak,Dangling, Void, Null and Wild pointers

Structures - Structures, array of structures, structure within structure,

union, typedef, self-referential structure, pointer to structure

3 8
UNIT- III

File Handling - Opening or creating a file, closing a file, File modes,

Reading and writing a text file using getc(), putc(), fprintf()
,fscanf(),fgets(), fputs(), Reading and writing in a binary file, counting
lines in a text file, Search in a text file, Random file accessing
methods- feof(), fseek(), ftell() and rewind() functions.
4 10
UNIT- IV
Introduction to Python-

History of Python, Need of Python Programming, Python features,

First Python Program, Running python Scripts, Variables, Reserved
words, Lines and indentation, Quotations, Comments, Input output.

Data Types, Operators and Expressions: Standard Data Types –

Numbers, strings, Boolean, Operators – Arithmetic Operators,
comparison Operators, assignment Operators, logical Operators,
Bitwise Operators.
5 10
UNIT- V
Control flow – if, if-elif-else, for, while, break, continue, pass, range(),
nested loops.

Functions – Handling functions in Python

File Handling – Reading text file, writing text file, copying one file to
another

Total 44
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication
/Reprint
Text Books
1. • Peter Prinz, Tony Crawford,”C in a Nutshell”, 1st 2011
Oreilly Publishers,
2. • Yashwant Kanetkar,”Let Us C”,BPB Publication 8th 2007

Reference Books
1. • Steve Oualline, “Practical C programming”, Orielly 3rd 2011
Publishers, 2011.
2. • Brian W Kernighan, Dennis M Ritcie,”The C 2nd 2000
Programming Language”,Prentice Hall, 1988. R3.
Herbert Schildt,” C: The Complete Reference”,
4thEdition.TMH, 2000.
3. • E.Balagurusamy,”Programming in ANSI C”, 6th 2015
McGraw Hill

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam / Lab Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-308 Course Title: Logic Design & Computer Organization

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 3

7. Category of Course: DC

8. Pre-requisite: Basic Electronics Engineering (TEC 101/201)

9. Course After completion of the course the students will be able to:
Outcome: CO1: Understand the process of minimizing Boolean function and
obtaining the combinational logic circuits from Boolean functions.
CO2: Analyze the basic storage elements in digital circuits and develop
sequential circuits by applying them.
CO3: Evaluate the design of different types of register, counter, and
programmable logic devices.
CO4: Apply the concept of digital logic circuits in computer organization
& architecture and evaluate the computer performance.
CO5: Create the arithmetic logic used in computer and describe the machine
instruction execution.
CO6: Understand the memory hierarchy of computer and how different I/O
devices interact with the processing unit.
** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Simplification of Boolean Function using K-map method (upto 5 variables)
and Quine-Mc Clusky method. Nand and Nor Implementation.
1 Combinational Logic: Introduction, Analysis & Design Procedure, Binary 10
Adder & Subtractor, Decimal Adder, Binary Multiplier, Magnitude
Comparator, Decoders, Encoders, Multiplexers, Demultiplexers, code
conversion. Introduction to HDL description of combinational logic circuits.
Unit 2:
2 Sequential Logic: Introduction, Types of Sequential circuits, Basic storage 8
elements (Latch and Flip-flops), Characteristic equations & tables, excitation
table, Flip-flop conversion, Analysis and design of synchronous sequential
circuits.
Unit 3:
Registers, Shift register, Universal shift register, Counters (Ripple &
Synchronous): Introduction & Design, Introduction to memory, types of
memory, PLD: PAL, PLA, ROM
3 10
Introduction to Computer Organization & Architecture, Von Neumann and
Harvard Architecture, RISC and CISC machines, Evolution of Intel x86 and
ARM architecture, Basic measures of computer performance, Amdahl’s
Law, Little’s Law.
Unit 4:
Computer Arithmetic (Integer and Floating Point): Representation, Addition,
4 Subtraction, Multiplication and Division. Machine Instruction 10
characteristics, Addressing Modes, Processor structure and operation,
Instruction Cycle, Instruction Pipelining: Strategy, performance, Hazards.
Control unit operation and microprogrammed control.
Unit 5:
Memory hierarchy: Locality and performance, Cache memory: Principles
and elements of design, Internal memory, External memory, I/O interface:
5 10
External devices, I/O modules, Programmed I/O, Interrupt driven I/O, Direct
Memory Access.
Introduction to alternative architectures.
Total 48
11. Suggested Books:
S.No Name of Authors/Books/Publishers Edition Year of Publication /
Reprint
Textbooks
1. M. Morris Mano, Digital Logic and Computer 1st 2016
Design, Pearson
2. W. Stalling, Computer Organization and 11th 2022
Architecture, Pearson
Reference Books
1. Charles H. Roth Jr., Fundamentals of Logic 5th 2005
Design, Wadsworth Publishing
2. John P Hayes, Computer Architecture and 3rd 2017
Organization, McGraw Hill

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

Data structures with
1. Subject Code: TCS 302 Course Title: C
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE
25 SEE 50

5. Credits: 3

6. Semester: III

7. Category of Course: DC

8. Pre-requisite:

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Describe the concept of Data Structures and assess how the choice of data
structures impacts the performance of programs

CO2: Compare and contrast merits and demerits of various data structures in terms
of time and memory complexity.

CO3: Identify and propose appropriate data structure for providing the solution to
the real world problems.

CO4: Implement operations like searching, insertion, deletion, traversing

mechanism etc. on various data structures

CO5: Be familiar with advanced data structures such as balanced search trees,
hash tables, AVL trees, priority queues, ADT etc.

CO6: To augment merits of particular data structures on other data structure to

develop innovation in subject of study.

** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
1 Unit 1:
Introduction: Basic Terminology, Pointer and dynamic memory allocation, 10
Elementary Data Organization, Data Structure operations, Algorithm
Complexity and Time-Space trade-off Arrays: Array Definition,
Representation and Analysis, Single and Multidimensional Arrays, address
calculation, application of arrays, Array as Parameters, Ordered List, Sparse
Matrices. Stacks:Array. Representation and Implementation of stack,
Operations on Stacks: Push & Pop, Array Representation of Stack, Linked
Representation of Stack, Operations Associated with Stacks, Application of
stack: Conversion of Infix to Prefix and Postfix Expressions, Evaluation of
postfix expression using stack. Recursion: Recursive definition and processes,
recursion in C, example of recursion, Tower of Hanoi Problem, tail recursion.
Unit 2:

Queues: Array and linked representation and implementation of queues,

2 Operations on Queue: Create, Add, Delete, Full and Empty. Circular queue,
Dequeue, and Priority Queue.
Linked list: Representation and Implementation of Singly Linked Lists, Two- 10
way Header List, Traversing and Searching of Linked List, Overflow and
Underflow, Insertion and deletion to/from Linked Lists, Insertion and deletion
Algorithms, Doubly linked list, Linked List in Array, Polynomial
representation and addition, Generalized linked list.

Unit 3:
Trees: Basic terminology, Binary Trees, Binary tree representation, algebraic
Expressions, Complete Binary Tree. Extended Binary Trees, Array and Linked
Representation of Binary trees, Traversing Binary trees, Threaded Binary trees.
3
Traversing Threaded Binary trees, Huffman algorithm & Huffman tree. 9
Searching and Hashing: Sequential search, binary search, comparison and
analysis, Hash Table, Hash Functions, Collision Resolution Strategies, Hash
Table Implementation

Unit 4:
Sorting: Insertion Sort, Bubble Sorting, Quick Sort, Two Way Merge Sort,
Heap Sort, Sorting on Different Keys, Practical consideration for Internal
4 Sorting. 9

Binary Search Trees: Binary Search Tree (BST), Insertion and Deletion in
BST, Complexity of Search Algorithm, Path Length, AVL Trees
Unit 5:
5 File Structures: Physical Storage Media File Organization, Organization of
records into Blocks, Sequential Files, Indexing and Hashing, Primary indices, 8
Secondary indices, B+ Tree index Files, B Tree index Files, Indexing and
Hashing Comparisons, Graph, Traversal(DFS,BFS) ,Minimum spanning tree
Total 46

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Horowitz and Sahani, “Fundamentals of data Structures”, Galgotia 2nd 2008
Publication Pvt. Ltd., New Delhi.

2 R. Kruse etal, “Data Structures and Program Design in C”, 2nd 2006
Pearson Education Asia,

3 A. M. Tenenbaum, “Data Structures using C & C++”, Prentice-Hall 2nd 2014

of India Pvt. Ltd., New Delhi.

4 K Loudon, “Mastering Algorithms with C”, Shroff Publisher & 1st 2000
Distributors Pvt. Ltd.

5 Bruno R Preiss, “Data Structures and Algorithms with Object 1st 1998
Oriented Design Pattern in C++”, Jhon Wiley & Sons, Inc.

6 Adam Drozdek, “Data Structures and Algorithms in C++”, 4th 2013

Thomson Asia Pvt

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

Object Oriented
1. Subject Code: TCS 307 Course Title: Programming with C++

2. Contact Hours: L: 3 T: 0 P: 0 ProfessionaCommuni

cation
3. Examination Duration (Hrs): Theory 3 Practical 0
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: III

7. Category of Course: DC

8. Pre-requisite: Subject Name with Code

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Demonstrate the C++ Program uses data types, operators, expressions,
array, strings and functions.
CO2: Implement Constructors (Parameterized, Copy), this pointer, friend
function, dynamic objects, arrays of objects.
CO3: Illustrate the Operator Overloading of +, -, preincrement,
postincrement, << and >>.
CO4: Implement the single, multiple, multilevel and hybrid inheritance in
C++.
CO5: Illustrate function overloading, Overriding and virtual functions.
CO6: Carry out exception handling techniques and provide solutions to
storage related problems using STL.
** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Introduction: Need of object-oriented programming, Overview of C++,
Header Files and Namespaces, Sample C++ program, Different data types,
1 operators, expressions, and statements, arrays and strings, pointers & user- 10
defined types function components, argument passing, inline functions,
recursive functions.
Unit 2:
Classes & Objects: Class Specification, Objects, Scope resolution operator,
Access members, defining member functions, Data hiding, Constructors,
Parameterized constructors, Destructors, Static data members, Friend
functions, passing objects as arguments, Returning objects, Arrays of objects, 9
2
Dynamic objects, Pointers to objects, Copy constructors, This Pointer.
Operator overloading: Fundamentals of Operator Overloading, Overloading
Binary Operators and unary operators, Operator overloading using friend
functions such as +, -, pre-increment, post-increment, overloading of << and
>>.

Unit 3:
Inheritance: Necessity of inheritance, Types of inheritance with examples,
3 Base Class and Derived class, Public, private and protected access modifiers, 9
inheriting multiple base classes, working of Constructors and Destructors in
Inheritance, Passing parameters to base class constructors, Virtual base classes
Unit 4:
Virtual functions and Polymorphism: Polymorphism, function overloading,
4 Overriding Methods, Virtual function, Calling a Virtual function through a 9
base class reference, Pure virtual functions, Abstract classes, Virtual
Destructors, Early and late binding
Unit 5:
I/O System Basics and STL: C++ stream classes, I/O manipulators, fstream
5 and the File classes, basic file operations, function templates Exception
9
Handling: Exception handling fundamentals, Throwing an Exception,
Catching an Exception, Re-throwing an Exception, An exception example.
STL: An overview, containers, vectors, lists, maps, Algorithms
Total 46

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Herbert Schildt,The Complete Reference C++, McGraw Hill 4th 2017

2 Balagurusamy E,Object oriented Programming with C++ 8th 2020

Reference Books
1. Paul Deitel and Harvey Deitel, C++: How to Program, 10th 2016
Pearson

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

Discrete Structures and
1. Subject Code: TMA 316 Course Title: Combinatorics

2. Contact Hours: L: 3 T: 1 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE SEE
25 ESE 50

5. Credits: 4

6. Semester: III

7. Category of Course: DC

8. Pre-requisite: TMA101 Engineering Mathematics-I

TMA201 Engineering Mathematics-II

9.Course After completion of the course the students will be able to:
Outcome**: CO1: Be able to specify and manipulate basic mathematical objects such as sets,
functions, and relations . Demonstrate an understanding of partial order
relations and Lattices.

CO2: Understand the basics of discrete probability and number theory, and be
able to apply the methods from these subjects in problem solving.

CO3: Produce convincing arguments, conceive and/or analyze basic

mathematical proofs and discriminate between valid and unreliable
arguments.

CO4: Discriminate, identify and prove the properties of groups and subgroups

CO5: Be able to apply basic counting techniques to solve combinatorial problems

CO6: Demonstrate different traversal methods for trees and graphs. Model
problems in Computer Science using graphs and trees.
** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
1 Relations and Functions: 11
Review of Sets,
Relations - properties, equivalence relation, matrix and Graph representation,
Closure operations Functions, Types of functions, Invertability, Composition of
functions and Inverse functions,Partially ordered Sets and Lattices. Lattice
Properties, Lattices as Boolean Algebra
Unit 2:
2 Probability Theory
Basics of Probability, Conditional Probability; Random Variables, probability
9
mass and density function, commutative distribution function, expected values,
mean, variance and standard deviation, Distributions: Binomial. Poisson, normal,
uniform,, exponential,
Unit 3:
Fundamentals of Logic: Basic Connectives and Truth Tables, Logical
3 Equivalence – The Laws of Logic, Logical Implication – Rules of Inference. The
9
Use of Quantifiers,
Methods of Proof: Different methods of proof – Direct Proof, Indirect Proof,
Counter examples, Principle of Induction.
Unit 4:
Groups: Definitions, Examples, and Elementary Properties, Homomorphism,
Isomorphism, permutation groups and cyclic Groups, subgroups, cosets, and
Lagrange’s Theorem
4 10
Counting:
Set cardinality and counting, Sum and Product Rules, Inclusion Exclusion
Principles, Pigeonhole principle, permutations and combinations, Basics of
recurrence relations and, generating Functions
Unit 5:
Graphs and Trees
5 Fundamentals of Graphs Graph types – undirected, directed, weighted; - 9
Representing graphs and graph isomorphism -connectivity-Euler and Hamilton
paths, Isomorphism Tree properties, traversal techniques;
Total 48

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Kenneth H. Rosen:” Discrete Mathematics and its 6th 2007
Applications”, , McGraw Hill,. Edition
2 JayantGanguly: “A Treatise on Discrete Mathematical 2nd 2011
Structures”, Sanguine-Pearson,.
Reference Books
1. D.S. Malik and M.K. Sen: “Discrete Mathematical 2nd 2004
Structures: Theory and Applications”, Thomson,.
2 Thomas Koshy:” Discrete Mathematics with Applications”, 1st 2005, Reprint 2008
Elsevier,.
3 Ralph P. Grimaldi:” Discrete and Combinatorial 5th 2004
Mathematics” Pearson Education,.
4 S.B.Singh, Jaikishor and Ekata, “Discrete Mathematics”, 3rd 2011
Khanna Publication,.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

Logic Design & Computer
1. Subject Code: PCS-308 Course Title: Organization Lab
ProfessionalCommunicati
2. Contact Hours: L: 0 T: 1 P: 2 on
3. Examination Duration (Hrs): Theory 0 Practical 3
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: 3

7. Category of Course: DC

8. Pre-requisite: Basic Electronics Lab

9. Course After completion of the course the students will be able to:
Outcome: CO1: Understand various logic gates and digital circuits.
CO2: Identify various digital ICs and understand its operation.
CO3: Design elementary digital circuits under real and simulated environment.
CO4: Simulate various logic circuits using simulation tool.
10. Details of the Course:
Sl. List of problems for which student should develop program and execute Contact
No. in the Laboratory Hours
To realize two and three variable Boolean functions using basic gates and
1. 2
universal gates digital IC.
2. To design and test a half/full adder circuit using digital IC gates. 2
3. To design and test a half/full subtractor circuit using IC gates. 2

4. To design, implement and test the function F(A,B,C,D) = m(1,3,5,7,9,15) + 2

d(4,6,12,13) using a NOR-OR implementation.
5. To design and test RS, JK, D and T flip flops using logic gates. 2
6. To design and test shift registers using flip-flops. 2
7. To design and test an asynchronous up/down counter. 2
8. To design, implement and test Boolean functions using a multiplexer. 2

9. To design and simulate the implementation of Binary to Gray code conversion 2

and vice versa using OrCAD/PSPICE.

10. To design and simulate the implementation of 4-bit binary adder-subtractor 2

circuit using OrCAD/PSPICE.
11. To design and simulate the implementation of 2-bit binary multiplier circuit 2
using OrCAD/PSPICE.

12. To design and simulate the implementation of Ring and Johnson counter using 2
OrCAD/PSPICE.
13. To design and simulate Booths Algorithm using Verilog HDL. 2
14. To design and simulate 32-bit Floating-Point multiplier using Verilog HDL. 2
15. To design and simulate 8-bit ALU using Verilog HDL. 2
Total 30

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. M. Morris Mano, Michael D. Ciletti, Digital Design: 6th 2018
With an Introduction to the Verilog HDL, VHDL, and
System Verilog, Pearson
Reference Books
1. John P Hayes, Computer Architecture and Organization, 3rd 2017
McGraw Hill

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

1. Subject Code: PCS 302 Course Title: Data structures Lab

2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: 3

7. Category of Course: DC

8. Pre-requisite: TCS101, TCS 201

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Implement Stack, Queues using array in C programming language.

CO2: Create Linked lists (single, double, circular) and perform various
operations on Linked lists and implement Stack, Queue using Linked list
in C programming language.

CO3: Create Binary Search tree and perform operations such as traversal, deletion
and execute Linear, Binary search, hashing and simple graph structure.

CO4: Implement the sorting algorithm (Bubble, insertion, selection, merge,

quick) and compare the performance of these algorithms

** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

Sl. List of problems for which student should develop program and execute in the Contact
No. Laboratory Hours
Array :
a) Write a C program to find out the sum all even elements from an array.
b) Write a C program to find out union of two given arrays.
16.
c) Write a C program to find out intersection of two given arrays.
d) Write a C program to store N elements into the array and the reverse the
contents of that array.
17. a. Write a C program to find element with maximum value from an array.
b. Write a C program to create a dynamic array.
c. Write a C program to Implementation Stack Using Array.

a. Write a C program to Implementation queue Using Array.

b. Write a C program to convert infix expression into postfix expression.
18.
c. Write a C program to evaluate any postfix expression.

a. Link list:
b. Create a Single Linked List with pointers left & right where new nodes
are always added after the right. Then user will input a key that should be
19. searched in the linked list & the element having the key value should be
deleted & linked list should be updated. If elements is not found then a
message “Unsuccessful Search" should be displayed.

a. Write a program to insert string in linked list in alphabetical order.

b. Write a program to search a node from a linked list.
20. c. Doubly Linked List: Write a C program to implement doubly linked list with
following operations.
i)Insert ii) delete ii) display.

a) Circular linked list: Write a C program to implement Circular.

21. b) linked list with following operations.
i)Insert ii) delete ii) display.

a. Write a C program to implement Stack Using inked List.

22. b. Write a program to implement queue using double pointers.
c. Write a C program to implement Queue Using Linked List.
Tree and Graphs:
a) Write a C program to create Binary search tree and perform following
23. operations on it.
i)Insert node ii) Delete node iii) Search node.
Execute simple graph traversals algorithms (DFS and BFS) .
SORTING TECHNIQUES and SEARCHING TECHNIQUES
a) Write a C program to sort an array using Bubble Sort technique.
24. b) Write a C program to sort an array using selection sort technique.

c) Write a C program to sort an array using Insertion sort technique.

d) Write a C program to sort an array using Merge Sort technique.
e) Write a C program to sort an array using Quick Sort technique.
25. f) Write a C program to implement Linear Search.
g) Write a C program to implement Binary Search.
h) Write a C program to implement Hashing.

Total

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Ellis Horowitz and Sartaj Sahni, Fundamentals of Data 2nd 2014
Structures in C, Universities Press
2. Data Structures Through C, Yashavant Kanetkar 3rd 2019
Reference Books
1. Seymour Lipschutz, Data Structures Schaum's Outlines, 1st 2014
McGraw Hill
2. 2021
Data Structures Using C A Practical Approach for Beginers

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

1. Subject Code: PCS 307 Course Title: OOP WITH C++ LAB

2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory Practical

4. Relative Weight: CIE 50 MSE 25 SEE 50

5. Credits: 2
6. Semester: III
7. Category of Course: DC

8. Pre-requisite: PCS 101, PCS 102

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Evaluate the basic difference between object-oriented programming
and procedural language and their data types.
CO2: Implement the programs using C++ features such as object
creation, compile time polymorphism, inheritance, abstraction,
encapsulation etc.
CO3: Design and solve programs that incorporates the use of object-
oriented techniques such as abstract classes, pure virtual functions,
and constructors.

CO4: Create programs based on the concepts of virtual base

classes, virtual functions and STL to solve real time problems
** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

Sl. List of problems for which student should develop program and execute in the Contact
No. Laboratory Hours
An electricity board charges the following rates to domestic users to discourage
large consumption of energy.
For the first 100 units: - 60 P per unit
1. For the next 200 units: -80 P per unit
Beyond 300 units: -90 P per unit
All users are charged a minimum of Rs 50 if the total amount is more than Rs 300
then an additional surcharge of 15% is added.
Implement a C++ program to read the names of users and number of units consumed
and display the charges with names
Construct a C++ program that removes a specific character from a given string and
2. return the updated string.
Typical Input: computer science is the future
Typical Output: compuer science is he fuure
Implement a C++ program to find the non-repeating characters in string.
3. Typical Input: graphic era university
Typical Output: c g h n p s t u v y
You are given an array of elements. Now you need to choose the best index of this
array. An index of the array is called best if the special sum of this index is maximum
across the special sum of all the other indices. To calculate the special sum for any
index you pick the first element that is and add it to your sum. Now you pick next
two elements i.e., and and add both of them to your sum. Now you will pick the
next elements, and this continues till the index for which it is possible to pick the
elements. Find the best index and in the output print its corresponding special sum.
Note that there may be more than one best index, but you need to only print the
maximum special sum.

Input
First line contains an integer as input. Next line contains space separated integers
denoting the elements of the array
4.
Output
In the output you have to print an integer that denotes the maximum special sum

Input/Output Format
Typical Input Expected Output

5 8
13125
10 9
2 1 3 9 2 4 -10 -9 1 3

5. Implement a C++ program to demonstrate the concept of data abstraction using the
concept of Class and Objects

Define a class Hotel in C++ with the following specifications

Private members
• Rno Data member to store room number
6.
• Name Data member to store customer name
• Tariff Data member to store per day charges
• NOD Data member to store number of days of stay
• CALC() Function to calculate and return amount as NOD*Tariff ,and if the value
of days* Tariff >10000,
then total amount is 1.05* days*Tariff.
Public members
• Checkin() Function to enter the content Rno, Name, Tariff and NOD
• Checkout() Function to display Rno, Name, Tariff,
NOD and Amount (amount to be displayed by calling function) CALC()

Implement a Program in C++ by defining a class to represent a bank account.

Include the following:
Data Members
● Name of the depositor
● Account number
● Type of account (Saving, Current etc.)
7.
● Balance amount in the account
Member Functions
● To assign initial values
● To deposit an amount
● To withdraw an amount after checking the balance
● To display name and balance
Anna is a contender for valedictorian of her high school. She wants to know how
many students (if any) have scored higher than her in the exams given during this
semester.
Create a class named Student with the following specifications:
➢ An instance variable named scores holds a student's 5 exam scores.
➢ A void input () function reads 5 integers and saves them to scores.
➢ An int calculateTotalScore() function that returns the sum of the student's
scores.
Input Format
In the void Student::input() function, you must read 5 scores from standard input
8. and save them to your scores instance variable.
Output Format
In the int Student::calculateTotalScore() function, you must return the student's total
grade (the sum of the values in scores).
The code in the editor will determine how many scores are larger than Anna’s and
print that number to the console.
Sample Input
The first line contains n, the number of students in Anna’s class. The n subsequent
lines contain each student's 5 exam grades for this semester.
3
30 40 45 10 10
40 40 40 10 10
50 20 30 10 10
Sample Output
1
Construct a Program in C++ to show the working of function overloading(compile
9. time polymorphism) by using a function named calculate Area () to calculate area
of square, rectangle and triangle using different signatures as required.
Create a class called Invoice that a hardware store might use to represent an invoice
for an item sold at the store. An Invoice should include four pieces of information
as instance.
Data Members ‐
• partNumber (type String)
• partDescription (type String)
• quantity of the item being purchased (type int)
10.
• price_per_item (type double)
Your class should have a constructor that initializes the four instance variables.
Provide a set and a get method for each instance variable. In addition, provide a
method named getInvoiceAmount() that calculates the invoice amount (i.e.,
multiplies the quantity by the price per item), then returns the amount as a double
value. If the quantity is not positive, it should be set to 0. If the price per item is not
positive,it should be set to0.0. Write a test application named invoiceTest that
demonstrates class Invoice’s capabilities.
Imagine a tollbooth with a class called TollBooth. The two data items are of type
unsigned int and double to hold the total number of cars and total amount of money
collected. A constructor initializes both of these data members to 0. A member
function called payingCar( )increments the car total and adds 0.5 to the cash total.
11. Another function called nonPayCar( ) increments the car total but adds nothing to
the cash total. Finally a member function called display( )shows the two totals.
Include a program to test this class. This program should allow the user to push one
key to count a paying car and another to count a non paying car. Pushing the ESC
key should cause the program to print out the total number of cars and total cash
and then exit.
Create a class called Time that has separate int member data for hours, minutes and
seconds. One constructor should initialize this data to 0, and another should
initialize it to fixed values. A member function should display it in 11:59:59 format.
12. A member function named add() should add two objects of type time passed as
arguments. A main ( ) program should create two initialized values together, leaving
the result in the third time variable. Finally it should display the value of this third
variable.
Create class SavingsAccount. Use a static variable annualInterestRate to store the
annual interest rate for all account holders. Each object of the class contains a private
instance variable savingsBalance indicating the amount the saver currently has on
deposit. Provide method calculateMonthlyInterest() to calculate the monthly
interest by multiplying the savingsBalance by annualInterestRate divided by
13. 12.This interest should be added tosavingsBalance. Provide a static method
modifyInterestRate() that sets the annualInterestRate to a new value. Write a
program to test class SavingsAccount. Instantiate two savingsAccount objects,
saver1 and saver2, with balances of Rs2000.00 and Rs3000.00, respectively. Set
annualInterestRate to 4%, then calculate the monthly interest and print the new
balances for both savers. Then set the annualInterestRate to 5%, calculate the next
month’s interest and print the new balances for both savers
Create a class Complex having two int type variable named real & img denoting
14. real and imaginary part respectively of a complex number. Overload +, - , ==
operator to add, to subtract and to compare two complex numbers being denoted by
the two complex type objects
Using the concept of operator overloading. Implement a program to overload the
following:
15. a. Unary –
b. Unary ++ preincrement, postincrement
c. Unary -- predecrement, postdecrement
Using the concept of operator overloading. Implement a program to overload the
following:
With the help of friend function
16.
a. Unary –
b. Unary ++ preincrement, postincrement
c. Unary -- predecrement, postdecrement
Create a Base class that consists of private, protected and public data members and
member functions. Try using different access modifiers for inheriting Base class to
17. the Derived class and create a table that summarizes the above three modes (when
derived in public, protected and private modes) and shows the access specifier of
the members of base class in the Derived class.
You are given three classes A, B and C. All three classes implement their own
version of func. In class A, func multiplies the value passed as a parameter by 2. In
class B, func multiplies the value passed as a parameter by 3. In class C, func
multiplies the value passed as a parameter by 5.You are given class D such that You
18. need to modify the class D and implement the function update_val which sets D's
val to new_val by manipulating the value by only calling the func defined in classes
A, B and C.It is guaranteed that new_val has only 2, 3 and 5 as its prime factors.
Implement class D's function update_val. This function should update D's val only
by calling A, B and C's func.
Sample Input
new_val = 30
Sample Output
A's func called 1 times
B's func called 1 times
C's func called 1 times
Create a class called Student that contains the data members like age, name,
enroll_no, marks. Create another class called Faculty that contains data members
like facultyName, facultyCode, salary,deptt, age, experience, gender. Create the
19. function display() in both the classes to display the respective information. The
derived Class Person demonstrates multiple inheritance. The program should be
able to call both the base classes and displays their information. Remove the
ambiguity (When we have exactly same variables or same methods in both the base
classes, which one will becalled?) by proper mechanism

20. Implement a real case scenario by a proper C++ code to provide the solution to
Diamond Problem in C++
Create a base class called shape. Use this class to store two double type values that
could be used to compute the area of figures. Derive two specific classes called
triangle and rectangle from base shape. Add to the base class, a member function
get_data() to initialize base class data members and another member function
display_area() to compute and display the area of figures. Make display_area() as a
virtual function and redefine this function in the derived class to suit their
21. requirements. Using these three classes, design a program that will accept
dimensions of a triangle or a rectangle interactively and display the area. Remember
the two values given as input will be treated as lengths of
two sides in the case of rectangles and as base and height in the case of triangle and
used as follows:
Area of rectangle = x * y
Area of triangle = ½ *x*y
Create a base class called CAL_AREA(Abstract). Use this class to store float type
values that could be used to compute the volume of figures. Derive two specific
classes called cone, hemisphere and cylinder from the base CAL_AREA. Add to
the base class, a member function getdata ( ) to initialize base class data members
and another member function display volume( ) to compute and display the volume
21. of figures. Make display volume ( ) as a pure virtual function and redefine this
function in the derived classes to suit their requirements. Using these three classes,
design a program that will accept dimensions of a cone, cylinder and hemisphere
interactively
and display the volumes. Remember values given as input will be and used as
follows:
Volume of cone = (1/3)πr2h
Volume of hemisphere = (2/3)πr3
Volume of cylinder = πr2h
The task is to debug the existing code to successfully execute all provided test files.
You are required to extend the existing code so that it handles the
std::invalid_argument exception properly. More specifically, you have to extend the
implementation of the process_input function. It takes integer n as an argument and
has to work as follows:
1.It calls function largest_proper_divisor(n).
2.If this call returns a value without raising an exception, it should print in a single
line result=d where d is the returned value.
3.Otherwise, if the call raises an invalid_argument exception, it has to print in a
single line the string representation of the raised exception, i.e., its message.
4.Finally, no matter if the exception is raised or not, it should print in a single line
returning control flow to the caller after any other previously printed output.

Input Format
The input is read by the provided locked code template. In the only line of the input,
there is a single integer n, which is going to be the argument passed to function
process input.

22.
Output Format

The output should be produced by the function process_input as described in the

statement.
Sample Input
0
Sample Output
the largest proper divisor is not defined for n=0
returning control flow to the caller
Explanation 1
In the first sample, n = 0, so the call largest_proper_divisor(0) raises an exception.
In this case, the function process_input prints two lines. In the first of them, it prints
the string representation of the raised exception, and in the second line, it prints
returning control flow to the caller.
Sample Input
9
Sample Output
result=3
Templates are the foundation of generic programming, which involves writing code
in a way that is independent of any particular type. Write a program that can create
23.
a list (create a class list) of given type (int, float, char etc.) and perform insertion
and deletion on list object.
Construct a C++ program to demonstrate different methods of List, Vector and Map
24.
in STL (Standard Template Library)
You are provided with a vector of N integers. Then, you are given 2 queries. For the
first query, you are provided with 1 integer, which denotes a position in the vector.
The value at this position in the vector needs to be erased. The next query consists
of 2 integers denoting a range of the positions in the vector. The elements which fall
under that range should be removed. The second query is performed on the updated
vector which we get after performing the first query.
Input Format
The first line of the input contains an integer N. The next line contains N space-
separated integers (1-based index). The third line contains a single integer x,
denoting the position of an element that should be removed from the vector. The
fourth line contains two integers a and b denoting the range that should be erased
from the vector inclusive of a and exclusive of b.
Output Format
Print the size of the vector in the first line and the elements of the vector after the
25.
two erase operations in the second line separated by space.
Sample Input
6
146289
2
24
Sample Output
3
189
Explanation
The first query is to erase the 2nd element in the vector, which is 4. Then, a modified
vector is {1 6 2 8 9}, we want to remove the range of 2~4, which means the 2nd and
3rd elements should be removed. Then 6 and 2 in the modified vector are removed
and we finally get {1 8 9}
Total

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Herbert Schildt,“The Complete Reference C++”, 4th Edition, 4th 2003
Tata McGraw Hill, 2003.
2. Balagurusamy.” Object Oriented Programming with 8th 2020
C++”,8th Edition, Tata McGraw Hill,2020
Reference Books
1. Paul Deitel and Harvey Deitel,” C++: How to Program”,10 10th 2016
Edition, Pearson

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

Fundamental of Cloud
1. Subject Code: TCS 351 Course Title: Computing and Bigdata

2. Contact Hours: L: 3 T: P:

3. Examination Duration (Hrs): Theory Practical

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3
6. Semester: III
7. Category of Course: DE
8. Pre-requisite: NA

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Identify the importance of cloud computing services for the digital age
technologies.
CO2: Differentiate the services and deployment models of cloud computing.
CO3: Evaluate the case studies of the different types of cloud computing
applications.
CO4: Analyze the cloud computing services management techniques, providers, and
standards.
CO5: Distinguish the cloud computing services using Bigdata and big data analytics.
CO6: Design and deploy a cloud based web application.

** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

Contact
Sl. No. Contents
Hours
Unit 1:
1 Introduction to Cloud Computing, Vision, History, Evolution, and Characteristics of 9
Cloud Computing (NIST), Characteristic, Advantages and Disadvantages of Cloud
Computing, Cloud computing vs. Cluster computing vs. Grid computing, Importance
of Open Standards for digital age technologies.

Unit 2:
Working of Cloud Computing, Cloud Computing comparison with traditional
computing architecture (client/server), Impact of Networks, Web Development and
2 User Interface (UI) on Cloud computing. 9
Cloud Deployment Models: Public cloud, Private cloud, Hybrid cloud, Community
cloud.

Unit 3:
Cloud Service Models: Infrastructure as a Service (IaaS), Platform as a Service
(PaaS), Software as a Service (SaaS).
Infrastructure as a Service (IaaS): IaaS definition, Virtualization, Hypervisors,
Machine Image, Virtual Machine (VM), Resource Virtualization, Server, Storage,
Networking, Virtual Machine (resource) provisioning and manageability, Data
centre physical plant/building, Networking firewalls/security, Data storage in cloud
computing (storage as a service), Amazon Elastic Compute Cloud (EC2), Eucalyptus,
3 Open Stack, Case Study of IaaS. 9
Platform as a Service (PaaS): PaaS definition, Service Oriented Architecture (SOA),
Cloud Platform and Management, Development tools, database management, business
analytics, Operating systems, Google App Engine, Microsoft Azure, and Salesforce,
Case Study of PaaS.
Software as a Service (SaaS): SaaS definition, Web services, Web 2.0, Case Study of
SaaS.

Unit 4:
Introduction to Big Data, Characteristics, Architectures, Technologies, Applications,
Advantages and Disadvantages of Big Data, Tools and Techniques applied in Big Data:
Association rule learning, Classification tree analysis, Genetic algorithms, Machine
learning, Regression analysis, Sentiment analysis, Social network analysis, Difference
4 between big data and big data analytics. 9
Introduction to Big Data analytics, Data Analysis Techniques: A/B testing, Data fusion
and data integration, Data mining, Machine learning, Natural language processing
(NLP), Statistics. Case study of Big Data.

Unit 5: Foundations Services of AWS: Savings, Security, Compliance and DRaaS

Development Operations. AWS Services: Amazon Lambda, Amazon Relational
Database Service (Amazon RDS), Amazon S3, Amazon CloudFront, Amazon Glacier,
and Amazon SNS.
Service Management in Cloud Computing: Service Level Agreements (SLAs), Billing
5 & Accounting. 9
Economics of Cloud Computing: SWOT Analysis and Value Proposition, General
Cloud Computing Risks, (Performance, Network Dependence, Reliability, Outages,
Safety Critical Processing Compliance and Information Security.
Design and Deploy an Online Video Subscription Application on the Cloud.

Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint
Textbooks
1. Rajkumar Buyya, Cloud Computing Principles and Paradigms, 1st 2013
Wiley,

2 Kannammal, Fundamentals of Cloud Computing, Cengage 1st 2015

Learning,
3 Cloud Computing Bible, Barrie Sosinsky, Wiley-India, 1st 2011

Reference Books
1. Jared Dean, Bigdata Data Mining and Machine Learning, 1st 2014
Wiley,
2 Vince Reynolds, Bigdata for Beginners, Create space 1st 2016
Independent Publishing Platform,

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

Introduction to Cryptography
1. Subject Code: TCS 392 Course Title:

2. Contact Hours: L: 3 T: P:

3. Examination Duration (Hrs): Theory Practical

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3
6. Semester: III
7. Category of Course: DE
8. Pre-requisite: NA

9. Course After completion of the course the students will be able to:
Outcome**: CO1:Classify security vulnerabilities involved in data communication
over Internet and makeuse of classical algorithms to address the
vulnerabilities.
CO2: Apply symmetric block ciphers to secure data transmission and storage
CO3: Analyze the various public key cryptographic systems and usage of hashing
CO4 Appreciate the design of Public Key algorithms, mathematical
background and make useof the same for data communication and
message authentication
CO5: Categorize types of viruses, worms, intrusion and decide measures to
counter thethreats.
CO6: Understand the legal aspects related to Cybercrime,
Intellectual Property, Privacy,Ethical Issues.

** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit – 1:
Introduction: Computer Security Concepts: The OSI SecurityArchitecture,
Security Attacks, Security Services, Security
1 Mechanisms, a Model for Network Security, Standards Cryptography 8
fundamentals and terminology; Cryptanalysis and Brute-Force Attack,
Fundamental techniques of cryptography Substitution and Transposition;
Classical Ciphers; Basics of
Steganography.
Unit – 2:
2 9
Modern Cryptography: Symmetric Encryption and MessageConfidentiality:
Symmetric Encryption Principles, Fiestal structure. Symmetric Block
Encryption Algorithms, Simple DES, double DES, Stream Ciphers and
RC4, Random and
Pseudorandom Numbers.
Unit – 3:
Symmetric key distribution using symmetric encryption: A Key Distribution
Scenario, Session Key Lifetime, A Transparent Key Control Scheme,
Decentralized Key Control, Controlling Key UsageMathematical Background for
3 cryptography: prime number, Euclidean algorithm for GCD, Extended Euclidean 10
algorithm for multiplicative inverse, Euler’s totient function, their programming
implementation.

Unit 4:
Public-Key Cryptography: Public-Key Encryption Structure, Applications for
Public-Key Cryptosystems, Requirements for Public-Key Cryptography, The
4 RSA Public-Key Encryption Algorithm. 9
Message Authentication: Approaches to Message Authentication,
Authentication Using Conventional Encryption, Message
Authentication without Message Encryption, MD5 Hash Algorithm.
Unit 5: System Security: Intruders, Intrusion Detection, Password
Management, Types of Malicious Software, Viruses, Virus Countermeasures,
Worms and Principles of Firewalls
5 Legal and Ethical Aspects: Cybercrime and Computer Crime, Intellectual 8
Property, Privacy, Ethical Issues.

Total 44
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. William Stallings, Network Security Essentials 6th 2018
Applications and Standards, ,Pearson Education,
2 William Stallings , Cryptography and Network Security, 7th 2017
Pearson Education,
Reference Books
1. Behrouz Forouzan , Cryptography and Network Security, 3rd 2015
McGraw Hill,
2 Atul Kahate, "Cryptography and Network Security", 3rd 2017
McGraw Hill Education,,

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 331 Course Title: Fundamental of IoT

2. Contact Hours: L: 3 T: 0 P: 0 C
3. Examination Duration (Hrs): Theory 3 Practical 0
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: III

7. Category of Course: DE

8. Pre-requisite: NA

9.Course After completion of the course the students will be able to:
Outcome**: CO1: Explain the terms used in IoT.
CO2: Describe key technologies in Internet of Things.
CO3: Identify components needed to provide a solution for certain applications.
CO4: Analyze security requirements in an IoT system.
CO5: Design wireless sensor network architecture and its framework along with
WSN applications.
CO6: Understand business models for the Internet of Things.
** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
INTRODUCTION
1 Introduction to Internet of Things: History of IoT, About IoT, Overview and 8
Motivations, Examples of Applications, Internet of Things Definitions and
Frameworks: IoT Definitions, IoT Architecture, General Observations, ITU-
T Views, Working Definition, IoT Frameworks, Basic Nodal Capabilities.
Unit 2:
2 FUNDAMENTAL IoT MECHANISMS AND KEY TECHNOLOGIES
Identification of IoT Objects and Services, Structural Aspects of the IoT,
Environment Characteristics, Traffic Characteristics, Scalability, 10
Interoperability, Security and Privacy, Open Architecture, Key IoT
Technologies, Device Intelligence, Communication Capabilities, Mobility
Support, Device Power, Sensor Technology, RFID Technology, Satellite
Technology.
Unit 3:
RADIO FREQUENCY IDENTIFICATION TECHNOLOGY
RFID: Introduction, Principle of RFID, Components of an RFID system,
Issues EPCGlobal Architecture Framework: EPCIS & ONS, Design issues,
Technological challenges, Security challenges, IP for IoT, Web of Things.
3 Wireless Sensor Networks: History and context, WSN Architecture, the
10
node, Connecting nodes, Networking Nodes, Securing Communication
WSN specific IoT applications, challenges: Security, QoS, Configuration,
Various integration approaches, Data link layer protocols, routing protocols
and infrastructure establishment.

Unit 4:
RESOURCE MANAGEMENT IN THE INTERNET OF THINGS
Clustering, Software Agents, Clustering Principles in an Internet of Things
4 Architecture, Design Guidelines, and Software Agents for Object
Representation, Data Synchronization. Identity portrayal, Identity 10
management, various identity management models: Local, Network,
Federated and global web identity, user-centric identity management, device
centric identity management and hybrid-identity management, Identity and
trust.
Unit 5:
INTERNET OF THINGS PRIVACY, SECURITY AND
GOVERNANCE
Vulnerabilities of IoT, Security requirements, Threat analysis, Use cases and
misuse cases, IoT security tomography and layered attacker model, Identity
5 establishment, Access control, Message integrity, Non-repudiation and 10
availability, Security model for IoT.
Internet of Things Application: Smart Metering Advanced Metering
Infrastructure, e-Health Body Area Networks, City Automation, Automotive
Applications, Home Automation, Smart Cards.
Total 48

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Daniel Minoli, “Building the Internet of Things with IPv6 1st 2013
and MIPv6: The Evolving World of M2M
Communications”, ISBN: 978-1-118-47347-4, Willy
Publications
2 Bernd Scholz-Reiter, Florian Michahelles, “Architecting the 1st 2011
Internet of Things”, ISBN 978-3- 642-19156-5 e-ISBN 978-
3-642-19157-2, Springer
3 Parikshit N. Mahalle&Poonam N. Railkar, “Identity 1st 2015
Management for Internet of Things”, River Publishers,
ISBN: 978-87-93102-90-3 (Hard Copy), 978-87-93102-91-
0 (ebook).
Reference Books
1. HakimaChaouchi, “The Internet of Things Connecting 1st 2010
Objects to the Web” ISBN : 978-1- 84821-140-7, Willy
Publications
2 Olivier Hersent, David Boswarthick, Omar Elloumi, The 1st 2015
Internet of Things: Key Applications and Protocols, ISBN:
978-1-119-99435-0, 2 nd Edition, Willy Publications
3 Daniel Kellmereit, Daniel Obodovski, “The Silent 1st 2014
Intelligence: The Internet of Things”,. Publisher: Lightning
Source Inc; ISBN-10: 0989973700, ISBN-13: 978-
0989973700.
4 Fang Zhaho, Leonidas Guibas, “Wireless Sensor Network: 1st 2055
An information processing approach”, Elsevier, ISBN: 978-
81-8147-642-5.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

Python Programming for
1. Subject Code: TCS- 341 Course Title: Computing

2. Contact Hours: L: T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 4

6. Semester: 3rd

7. Category of Course: DE

8. Pre-requisite: TCS 101, TCS 201

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Describe the principles of structured programming and be able to describe, design,
implement, and test structured programs using currently accepted methodology.

CO2: Explain what an algorithm is and its importance in computer programming.

CO3: Recognize and construct common programming idioms: variables, loop, branch,
subroutine, and input/output.
CO4: Define and demonstrate the use of the built-in data structures 'list' and 'dictionary'.
CO5: Apply idioms to common problems such as text manipulation, web page building,
and working with large sets of numbers.
CO6: Design and implement a program to solve a real-world problem using the language
idioms, data structures, and standard library.

** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Introduction: Introduction to Python: Importance of Python, Installing and working
with Python in Windows, Linux and Mac, Using Python as calculator, Comments, how
to define main function in Python.
1 9
The concept of data types - Variables, Arithmetic Operators and Expressions
String manipulations - Subscript Operator, Indexing, slicing a string, Converting
strings to numbers and vice versa, split function.
Control flow - if statements, for and while loops, nested loops, Short-
circuit (lazy evaluation), range () function, break and continue statements, pass
statements
Unit 2:
Data Structures:
Lists - Basic list operations, Replacing, inserting, removing an element; Searching and
sorting a list, Methods of list objects, using lists as Stacks and Queues, List, and nested
2 9
list Comprehensions.
Tuple, Sets, Difference between list and tuple
Dictionary - adding and removing keys, accessing, and replacing values, traversing
dictionaries
Unit 3
File and Exception Handling in Python
Reading config files in python, Writing log files in python, Understanding read
functions, read (), readline() and readlines(), Understanding write functions, write()
3 and writelines(), Manipulating file pointer using seek, Programming using file 11
operations

Exception Handling - Exceptions, why use exceptions, raising an exception, try and
except, try, except and else clause; try and finally
Unit 4:
Regular Expressions and Python Packages
Regular Expressions - re module, searching a string (match and search), Finding a
4 string (findall), Break string into substrings (split), Replace part of a string (sub) 9

Python packages: Simple programs using the built-in functions of packages matplotlib,
NumPy, Pandas
Unit 5:
Python Functions and OOP Concepts
Python functions and modules - OS and SYS modules, defining python functions,
calling a function, function arguments, Lambda, and map function, Importing python
5 module. 10
Classes and OOP - Class definition syntax, objects, class, and instance
variables, Inheritance and multiple inheritance, Polymorphism, Overloading,
Overriding, Data Hiding
Total 48

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Kenneth A. Lambert, “The Fundamentals of Python: First 1st 2011
Programs”, Cengage Learning.,

2. 2nd 2015
Think Python: How to think like a Computer Scientist

3. 1st 2017
Python Programming using Problem Solving Approach

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

Fundamental of Information
1. Subject Code: TCS 332 Course Title: Security and Blockchain

2. Contact Hours: L: 3 T: P:

3. Examination Duration (Hrs): Theory Practical

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 4
6. Semester: III
7. Category of Course: DE
8. Pre-requisite: NA

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Explain information security and blockchain
CO2: Know the working of information security techniques
CO3: Analyze the different information security protocols
CO4 Use Blockchain to implement information security protocols
CO5 Apply information security techniques in different applications
CO6: Develop blockchain enabled information security protocols

** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit – 1:
Introduction to information security
What is information security, why we need information security, the zero trust
model, overview of ethical hacking
Protection against- unauthorised modification, unauthorised deletion and
unauthorised access, different types of user authentication techniques, access
1 9
control techniques
Pillars of information security - confidentiality, availability and integrity Steps
to fix a cyber crime - Identify cyber threats, analyse and evaluate threat, treatment
Type of hackers - white hat, grey hat, black hat
Penetration testing and its phases - reconnaissance, scanning, gaining access,
maintaining access, covering tracks
Unit – 2:
2 8
Linux Basics and Scripting for Information Security
Bash, linux commands, man page, adding and deleting, users and adding them to
sudo group, switching users, creating, copying, moving and removing file,
writing and appending text to a file, file permissions, working with editors, grep,
cut command, starting and stopping services Introduction to bash scripting-
basics of bash or shell scripting, conditional statements, loops, manipulating files
Introduction to python - Basics of python, conditional statements, loops, list,
tuple, dictionary, functions
Unit – 3:
Basics of Network and Web Security
TCP 3-way handshake, netcat - The Swiss Army Knife of TCP/IP Connections,
3 use netcat to Listen on a port, pushing a command shell 10
back to listener, transfer files, ICMP and Ping command Cross site scripting (XSS)
attack, Phishing, Spear Phishing, Sql Injection Attack, Wireshark - A Packet
Sniffing Tool
Unit 4:
Overview of blockchain- Overview of blockchain, structure of a block, block
4 header, block identifiers: block header hash and block height, genesis block, 10
linking of blocks, merkle trees, and use of
merkle root in payment verification
Unit 5:
5 Blockchain mechanisms: Details of distributed ledger, smart contracts, bitcoins 8
networks, mining process, consensus algorithms, proof of work, proof of stake,
proof of weight, proof of capacity.
Total 45
11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication

No. / Reprint
Textbooks
1. Georgia Weidman,“Penetration Testing: A Hands- 1st 2020
on Introduction to Hacking”, No Starch Press,
2 George Icahn, “Blockchain: the complete guide to 1st 2020
understanding blockchain technology”,
3 Antony lewis, “The basics of bitcoins and blockchains: an 3rd 2020
introduction to cryptocurrencies and the technology that
powers them”
Reference Books
1. Andreas M. Antonopoulos, “Mastering Bitcoin: unlocking 2nd 2017
digital cryptocurrencies”, O'Reilly Media,
2 Roger Wattenhofer, “Distributed Ledger Tehnology, The 2nd 2017
science of the Blockchain”, Inverted Forest Publishing,
Antonopoulos, Andreas M. and Wood, Gavin. Mastering 2nd 2018
Ethereum. O’Reilly Media,.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering Mathematical Foundations

1. Subject Code: TCS-343 Course Title: for Artificial Intelligence
ProfessionaCommunicati
2. Contact Hours: L: 3 T: 1 P: on
0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE SEE

25 50

5. Credits: 4

6. Semester: III

7. Category of Course: DC

8. Pre-requisite: TMA 101 Engineering Mathemaics I, TMA 201 Engineering

Mathemaics II

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the basic concepts of Linear Algebra such as System
of Linear Equation, Matrices, Vector Space, Rank, etc.
CO2: Understand the basic principles of probability, Bayes theorem,
understand the definitions of discrete, continuous, and joint random
variables, compute the mean, variance and covariance of random
variables.
CO3: Solve problems on matrix decompositions such as Choleskey
Decomposition, Eigen Decomposition and Diagonalization, Singular
Value Decomposition
CO4: Describe the vector calculus concepts such as differentiation of
Univariate Function, Partial Differentiation and Gradients.
CO5: Analyze various mathematical concepts, that are required to build
AI & ML models.
CO6: Create an AI & ML models by applying the concepts of
mathematics such as Linear Algebra, Analytical Geometry, Matrix,
Calculus, Probability, etc.
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate
10. Details of the Course:
Sl. Contact
Contents
No. Hours
Unit 1:
Linear Algebra:
1 System of Linear Equation, Matrices, Solving system of Linear Equation, 10
Vector Spaces, Linear Independences, Basis and Rank, Linear Mappings,
Affine Space.
Unit 2:
Analytic Geometry:
2 Norms, Inner Products, Lengths and Distances, Angles and Orthogonality, 10
Orthonormal basis, Orthogonal Compliment, Inner Product of Function,
Orthogonal Projections, Rotations.
Unit 3:
Matrix Decomposition
3 Determinant and Trace, Eigen Values and Eigen Vectors, Choleskey 10
Decomposition, Eigen Decomposition and Diagonalization, Singular
Value Decomposition, Matrix Approximation, Matrix Pylogency
Unit 4:
Vector Calculus
4 Differentiation of Univariate Function, Partial Differentiation and 10
Gradients, Gradients of Vector-Valued Functions, Gradients of Matrices,
Linearization and Multivariate Taylor Series
Unit 5:
Probability and Distribution
5 10
Discrete and Continuous Probability, Sum Rule, Product Rule, Bayes’
Theorem, Gaussian Distribution, Change of Variables/Inverse Transform
Total 50
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Marc Peter Deisenroth , A. Aldo Faisal, Cheng Soon Ong, 1st 2020
MATHEMATICS FOR MACHINE LEARNING, Cambridge
University Press
2. Jay Dawani, Hands-On Mathematics for Deep Learning: Build 1st 2020
a solid mathematical foundation for training efficient deep
neural networks, Packt Publishing Limited
Reference Books
1. Tamoghna Ghosh , Shravan Kumar Belagal Math, Practical 1st 2022
Mathematics for AI and Deep Learning, BPB Publications

12. Mode of Test / Quiz / Assignment / Mid Term Exam / End Term Exam
Evaluation
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 344 Course Title: Probability and

Random Processes
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3 0
6. Semester: 3

7. Category of Course: DE

8. Pre-requisite: TMA 101 Engineering Mathematics I, TMA 102 Engineering

Mathematics II

9. Course After completion of the course the students will be able to:
Outcome**: CO1: To provide necessary basic concepts in probability and random processes
for applications such as random signals, linear systems in communication
engineering.
CO2: To understand the basic concepts of probability, one-dimensional and two-
dimensional random variables and to introduce some standard distributions
applicable to engineering which can describe real life phenomenon.
CO3: To understand the basic concepts of random processes which are widely
used in IT fields.
CO4: To understand the concept of correlation and spectral densities.
CO5: To understand the significance of linear systems with random inputs.
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate
10. Details of the Course:
Sl. Contact
Contents
No. Hours
Unit 1: PROBABILITY AND RANDOM VARIABLES
Probability – Axioms of probability – Conditional probability – Baye‘s
1 theorem - Discrete and continuous random variables – Moments – 9
Moment generating functions – Binomial, Poisson, Geometric,
Uniform, Exponential and Normal distributions.
Unit 2: TWO - DIMENSIONAL RANDOM VARIABLES
2 distributions – Marginal and conditional distributions – Covariance – 9
Correlation and linear regression – Transformation of random
variables – Central limit theorem (for independent and identically
distributed random variables).

Unit 3: RANDOM PROCESSES Classification

Stationary process – Markov process - Markov chain - Poisson
3 9
process – Random telegraph process.

Unit 4: CORRELATION AND SPECTRAL DENSITIES

Auto correlation functions – Cross correlation functions – Properties
4 – Power spectral density – Cross spectral density – Properties. 9

Unit 5: LINEAR SYSTEMS WITH RANDOM INPUTS

Linear time invariant system – System transfer function – Linear
5 9
systems with random inputs – Auto correlation and cross correlation
functions of input and output.
Total 45

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Oliver C. Ibe, “Fundamentals of Applied Probability and 1st 2007
Random Processes", Elsevier, Indian Reprint

2 Peebles. P.Z., "Probability, Random Variables and Random 4th 2017

Signal Principles", Tata McGraw Hill
Reference Books
1. Yates. R.D. and Goodman. D.J., "Probability and 3rd 2021
Stochastic Processes", Wiley India
2. Stark. H., and Woods. J.W., "Probability and Random 3rd 2002
Processes with Applications to Signal Processing", Pearson
Education

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering

Information Security
1. Subject Code: TCS-324 Foundations
Course Title:

2. Contact Hours: L: T: 0 P: 0
3
3. Examination Duration (Hrs): Theory
3 Practical
0
4. Relative Weight: CIE 25 MSE 25 SEE
50
5. Credits: 3 Ja
va
6. Semester: 3 Pr
ogr
7. Category of Course: DE am
8. Pre-requisite: Fundamental of Computers (TCS 101),mi Programming for problem
solving (TCS 201) ng
La
9. Course After completion of the course the students
b will be able to:
Outcome: (P
CO1: Explain symmetric and asymmetricCS key cryptosystems.

CO2: Know the working of cryptography-techniques.

40
8)
CO3: Analyze the different types of cryptosystems.

CO4: Use cryptographic techniques to implement information

security protocols.

CO5: Apply cryptographic techniques in different applications.

CO6: Develop symmetric and asymmetric key cryptosystems.

** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate
10. Details of the Course:
Sl. Contact
Contents
No. Hours
Unit 1:
Introduction to information security
1 What is information security, why we need information security, the zero trust 10
model, overview of ethical hacking
Protection against- unauthorized modification, unauthorized deletion and
unauthorized access, different types of user authentication techniques,
access control techniques
Pillars of information security - confidentiality, availability and integrity Steps
to fix a cyberc rime - Identify cyber threats, analyze and evaluatethreat,
treatment
Type of hackers - white hat, grey hat, black hat
Penetration testing and its phases - reconnaissance, scanning, gaining
access, maintaining access, covering tracks. SSL and Transport layer
security.
Unit 2:
Basics of cryptography
What is cryptography, what is confidentiality, data integrity, authentication,
and nonrepudiation, applications of cryptography - chip based payment
cards, digital currencies, computer passwords, digital communications,
2 plaintext, cipher-text, cipher - characteristics of a good cipher, encryption,
10
decryption, Key - significance of key length, symmetric and asymmetric key
cryptography, cryptanalysis, OSI security architecture- security attacks,
security services, security mechanisms

Unit 3:
Mathematics applied in information security
Concept of divisibility, prime numbers, importance of prime numbers in
3 cryptography, euclid theorem for GCD, extended euclidean algorithm, 8
modular arithmetic, random number generators, deterministic and
nondeterministic random number generators, XOR, bit shifts, euler's
totient theorem, chinese remainder theorem.
Unit 4:
Symmetric key cryptosystem
Secret Key (symmetric) cryptography - stream and block ciphers,
4 10
additive and multiplicative ciphers, rail fence technique, playfair cipher,
hill cipher, vernam cipher, Vigenère Cipher, RC4 algorithm, DES,
2DES, 2-3DES, 3DES, AES, block cipher modes of operations.
Unit 5:
Asymmetric key cryptosystem, digital signature, and message
integrity
5 RSA, Diffie Hellman key exchange protocol, Elliptic curve cryptography 8
(ECC), ElGamal encryption system. DSS algorithm, RSADS algorithm,
ECDSA algorithm, Message integrity, hash functions, MAC functions,
HMAC
Total 46

11. Suggested Books:

S.No Name of Authors/Books/Publishers Edition Year of
Publication /
Reprint
Textbooks
1. William Stallings, “Cryptography and Network 8th 2020
Security: Principles and Practice”, Pearson
publication, 2020.
Reference Books
1. Charles P. Pfleeger, Shari Lawrence Pfleeger, 5th 2015
Jonathan Margulies, “Security in Computing”, 5th
Edition, Prentice Hall.

2. William Stallings, “Network Security Essentials: 6th 2016

Applications and Standards”, Prentice Hall.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER III

Name of Department: - Computer Science and Engineering Introduction to Statistical

Data Science
1. Subject Code: TCS-342 Course Title:
ProfessionaCommunication
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 25

5. Credits: 3

6. Semester: 3

7. Category of Course: DE

8. Pre-requisite: Primary knowledge of statistics and statistical tools

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Critically analyze statistical methodologies in order to assess best
practice guidance when applied to real-world problems in specific
contexts
CO2: Investigate and evaluate key concepts of statistics and data
science techniques and assess when to apply such techniques in
practical situations
CO3: Contextualize, implement statistical models using different
statistical tools
CO4: Develop the ability to build and assess data-based models.
CO5: Understand fundamental principles of statistics and data science
applications and technologies in order to provide strategies to address
processing of datasets with a variety of characteristics.
CO6: Apply knowledge about algorithms for statistical analysis, machine
learning or data extraction in new areas within data science.
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10. Details of the Course:

Contact
Sl. No. Contents
Hours
Unit 1:
Introduction to data science: Introduction to modern data analysis, Data
science components, Data Science Applications.
Fundamental Statistics- Probability Distribution: Introduction to
1 10
Probability, Probability Distribution
(Continuous and discrete- Normal, Bernoulli, Binomial, Negative
Binomial, Geometric and Poisson distribution)
Unit 2:
Discrete random variables, Continuous random variable, Markov-chain
2 10
Monte Carlo, Descriptive Statistics- Sample covariance,
Sample covariance matrix, Outlier.
Unit 3:
Concepts of Correlation, Regression, Linear square estimation, Simple
3 9
Linear Regression, Multiple Regression

Unit 4:
Naïve Bayes’ Theorem, Bayesian classification, Central Limit theorem,
4 Data Exploration & preparation, Confidence Interval, The hypothesis- 8
testing, Z-Score.

Unit 5:
5 Parametric Testing: t-Test and Z-Test, Non-parametric Testing: ANOVA 10
and chi-Square
Total 47
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Douglas C. Montgomery , George C. Runger , Applied 6th 2016
Statistics and Probability for Engineers, Wiley
2. M. Ross, Introduction to Probability and Statistics for 4th 2009
Engineers and Scientists, Academic Press
3. James D. Miller , Statistics for Data Science, Packt 1st 2017
Publishing Limited
Reference Books
1. Dr. D.C. Agarwal & Dr. Pradeep K. Joshi, Probability 1st 2022
& Statistics for Data Science, Shree Sai Prakashan

2. Dr. Mark Gardener, “Beginning R: The Statistical 1st 2012

Programming Language”, John willey& Sons

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-408 Course Title: Programming in Java

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE. 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: IV

7. Category of Course: DC

8. Pre-requisite: TCS 307 Object oriented Programming with C++

9. Course CO1. Explain the Java programming features and develop programs to
Outcome**: demonstrate the same.
CO2. Make use of object-oriented concepts to develop applications CO3.
Classify exceptions and demonstrate applications for file handling
and multithreading.
CO4. Analyze collection framework and develop applications using GUI.
CO5. Compare and utilize collection framework for programming
applications.
CO6. Design applications for event handling and accessing databases
using Java features.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Introduction to Java: Importance and features of Java, Concepts of Java
Virtual machine (JVM) Keywords, Constants, Variables and data types,
operators and expressions, Control statements, Conditional statements, loops
and iterations, Wrapper classes, Scanner Class: Scanner class methods
1 10
(next(),nextLine() etc.
Concept of class: Class definition, adding variables and methods, creating
objects, constructors, defining methods, calling methods, Arrays,String
Handling in java( String, StringBuffer classes)

Unit 2:
Object Oriented Programming concepts: Inheritance, super classes,
2 multilevel hierarchy, abstract and final classes, overloading and overriding 9
Packages and interfaces: Packages, Defining Packages, Using Packages,
import and static import, Access protection.
Interface: Defining Interfaces, abstract methods declarations,
implementing interfaces, extended interfaces, interface references.
Unit 3: Exception handling: Exception Types, Exception class,
RuntimeException Class, Error Class, Checked and uncheced Exceptions,
Defining new exceptions; Handling: try, catch and finally; throw statement,
throws clause.
Input/Output:Basics, Byte and Character Streams, reading and writing
3 9
from console and file.
Multithreaded programming: Java thread model, synchronization,
messaging, thread class, Runnable interface, inter thread communication,
Producer/ consumer problems, Wait () and notify ().

Unit 4: Collection and Generic Framework: Introduction to Collection

and Generic Framework: Object class and typesafety, Generics, Interfaces
Iterator, List, Set, ArrayList, LinkedList HashSet and ArrayDeque classes
4 AWT & Swing: Introduction to AWT and Swings, Swings advantages over 9
AWT, Swing applications, Swing Controls : JButton ,JLabel , JCheckBox ,
JRadioButton , JList , JComboBox, JTextFiled, JTextArea , JScrollBar,
JTable, Graphics in swing
Unit 5: Event Handling:Event delegation model, classes, Event Listener
Interfaces, Adapter classes.

5 Java Database Connectivity (JDBC):The Concept of JDBC, JBDC 9

drivers(Type1 Driver,Type4 Driver), Connection interface, Statement
interface, ResultSet interface, Creating and executing SQL statements.

Total 46

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Patrick Naughton and Herbert Schildt, “Java 2 The 9th 2017
Complete Reference”, , McGraw Hill Education
2 E. Balaguruswamy, “Programming with Java a Primer”, 6th 2019
Tata McGraw Hill
Reference Books
1. Core Java-Head First Publication 3rd 2018
2. Cay S Horstmann and Gary Cornell, “Core Java 2019
Volume –I and II”, Sun Microsystems,

12. Mode of Test / Quiz / Assignment / Mid Term Exam / End Term Exam
Evaluation
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-402 Course Title: Finite Automata and

Formal Languages

2. Contact Hours: L: 3 T: 1 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 4

6. Semester: IV

7. Category of Course: DC

8. Pre-requisite: TMA 101 / 201 Engineering Mathematics

9. Course CO1. Understand the concept of abstract machines and their power to
Outcome**: recognize languages.
CO2. Formulate DFA, RE and FA with output.
CO3. Design CFG and check the language is not CFL.
CO4. Design PDA and convert n-PDA into D-PDA.
CO5. Design and be familiar with Turing machines and computability.
CO6: Formulate finite machines, push down automata and Turing machines
for automated functioning of devices.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Detailed Syllabus

Contact
UNIT CONTENTS
Hrs

Introduction; Alphabets, Strings and Languages; Automata and

Grammars, Deterministic finite Automata (DFA)-Formal Definition,
Unit - I Simplified notation: State transition graph, Transition table,
10
Language of DFA, Nondeterministic finite Automata (NFA), NFA
with epsilon transition, Language of NFA, Equivalence of NFA and
DFA, Minimization of Finite Automata, Myhill-Nerode Theorem.

Regular expression (RE), Definition, Operators of regular expression

and their precedence, Algebraic laws for Regular expressions,
Unit - II Regular expression to FA, DFA to Regular expression, Arden 10
Theorem, Non Regular Languages, Pumping Lemma for regular
Languages. Application of Pumping Lemma, Closure properties of
Regular Languages, Decision properties of Regular Languages, FA
with output: Moore and Mealy machine, Equivalence of Moore and
Mealy Machine, Applications and Limitation of FA.

Context free grammar (CFG) and Context Free Languages (CFL):

Definition, Examples, Derivation, Derivation trees, Ambiguity in
Unit – III Grammar, Useless symbols, Simplification of CFGs, Normal forms
9
for CFGs: CNF and GNF, Closure proper ties of CFLs, Decision
Properties of CFLs, Pumping lemma for CFLs, Applications and
Limitation of CFL and CFG.

Push Down Automata (PDA): Description and definition,

Unit – IV Instantaneous Description, Language of PDA, Acceptance by Final
10
state, Acceptance by empty stack, Deterministic PDA, Equivalence
of PDA and CFG, CFG to PDA and PDA to CFG, Two stack PDA.

Turing machines (TM): Basic model, definition and representation,

Instantaneous Description, Language acceptance by TM, Variants of
Turing Machine, TM as Computer of Integer functions, Universal
Unit – V TM, Church’s Thesis, Recursive and recursively enumerable 8
languages, Halting problem, Introduction to Undecidability,
Undecidable problems about TMs. Post correspondence problem
(PCP), Modified PCP, Introduction to recursive function theory.

Total 47

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. J. Hopcroft, R. Motwani, and J. Ullman. Introduction to 3rd 2007
Automata Theory, Languages, and Computation, 3rd
edition, 2007, Pearson/Addison-Wesley.
2 KLP Mishra and N. Chandrasekaran, “Theory of 3rd 2006
Computer Science: Automata, Languages and
Computation”, PHI Learning Private Limited, Delhi
India.
Reference Books
1. Michael Sipser, Introduction to the Theory of 1st 2013
Computation, 3rd edition (or 1st edition), 2013, Cengage
Learning
2. P. Linz. Introduction to Formal Languages and 6th 2017
Automata, 6th edition, 2017, Jones and Barlett.

12. Mode of Test / Quiz / Assignment / Mid Term Exam / End Term Exam
Evaluation
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-403 Course Title: Microprocessors

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory Practical 0

3
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: IV

7. Category of Course: DC

8. Pre-requisite: TEC-101 Basic Electronics, TCS-301-Logic design

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Remember the concept of microcomputer system.
CO2: Understand microprocessor 8085, 8086 and microcontroller 8051 hardware.
CO3: Apply the concepts of assembly language programming of 8085 and 8086 to
fulfil different tasks.
CO4: Examine the application of 8085 and 8086 microprocessor with interrupt
system, real time timer and counter.
CO5: Test different interfacing ICs and memory for defined tasks with 8085 and
8086 microprocessor.
CO6: Integrate the knowledge of 8085, 8086and 8051 in various embedded
systems.

** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10. Details of the Course:

Contact
Sl. No. Contents
Hours
Unit1: Microprocessor Architecture: Introduction to Microprocessor and
1 Microcomputer Architecture, Brief Evolution of Microprocessor, 8085 10
Register Organization, Internal architecture, Pins & Signals, 8085 Interrupts.
Unit 2: Programming with 8085: Programming model of 8085, 8085
instructions set, Addressing modes, Instruction Timing & Execution, Assembly
2 10
language Programs using Instruction of 8085. Stack and subroutine.

Unit 3: Interfacing with 8085, Memory Interfacing: Interfacing

3 10
EPROM&RAM memories. Address Decoding, Memory & I/O mapped I/O,
Programmable Interfaces: - 8255, 8251, 8253, 8259, Overview of DMA &
DMA controller, ADC and DAC interfacing.
Unit 4: Intel 8086 (16 bit processor): 8086 Architecture, Physical address,
segmentation, memory organization, Instruction set of 8086, Addressing
4 10
Modes, Pins &Signals, 8086 Interrupts. Simple Assembly Language Programs
using Instruction of 8086.
Unit 5: 8051 Microcontroller: Microprocessor Vs Microcontroller,
5 Embedded Systems, 8051 Architecture- Registers, Pin diagram, I/O ports 08
functions, Memory organization. Interrupts of 8051.
Total 48

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint

Textbooks
1. Ramesh Gaonkar, Microprocessor Architecture, 6th 2013
Programming, and Applications with the 8085, Penram
International Publication (India) Pvt. Ltd
2 A. K. Ray & K. M. Bhurchandi, Advanced 3rd 2012
Microprocessors and peripherals, Tata McGraw Hill
3. Muhammad Ali Mazidi, Janice Gillispie Mazidi, The 2nd 2007
8051 Microcontroller & Embedded System, Pearson /
PHI publication
Reference Books
1. Douglas V. Hall, Microprocessors and Interfacing, Tata 3rd 2012
McGraw Hill
2. Barry B. Brey, The Intel Microprocessors Architecture 8th 2012
Programming and interfacing, Pearson

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

Design and Analysis of
1. Subject Code: TCS 409 Course Title: Algorithms
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4.Relative Weight: CIE 25 MSE 25 SEE 50
5. Credits: 3

6. Semester: IV

7. Category of Course: DC

8. Pre-requisite: TCS-302 Data Structures with C, Any programming language

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand various asymptotic notations to analyze time and space complexity of
algorithms.

CO2: Analyze the various paradigms for designing efficient algorithms using concepts of
design and conquer, greedy and dynamic programming techniques.

CO3: Provide solutions to complex problems using the concept of back tracking and
branch and bound techniques.

CO4: Apply algorithm design techniques to predict the complexity of certain NP complete
problems.

CO5: Implement Dijkstra’s, Bellman-ford, Prims, Kruskal’s algorithms to solve the real
world problems like traveling salesman problem, job sequencing, packet routing etc

CO6: Apply pattern matching algorithms like Rabin Karp Algorithm, Brute-force
techniques etc to find a particular pattern.
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10.Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Asymptotic Notations and Searching Algorithms
1 8
Introduction to Algorithms - What is an Algorithm, Rate of growth, Commonly
used rate of growths, Types of analysis, Asymptotic Notations, Master theorem
Searching - Linear search (sorted and unsorted), Iterative and recursive binary
search, Tower of Hanoi and solving its recursion, Fibonacci and solving its
recursion
2 Unit 2:
Sorting Algorithms
Sorting - Bubble sort, Insertion sort, selection sort, quick
sort, randomized quick sort, merge sort, heap sort, counting sort, External sorting

10
Divide sorting algorithms into following types - online sort, stable sort, in place
sort, Comparison of sorting algorithms on the basis of number of swaps, by number
of comparisons, recursive or iterative nature, time and space complexity
Unit 3:
Graph Algorithms
Representation of Graphs, Breadth-first search (BFS), depth-first search (DFS),
topological sort, Difference between BFS and DFS
Data structures for disjoint
3 sets - Finding cycle in a graph, Finding strongly connected components 12
Minimum spanning trees - Kruskal and Prim algorithms (Greedy
Algorithms)
Single source shortest paths - Dijkstra (Greedy Approach) and
Bellman ford (Dynamic Programming) algorithms
All pair shortest paths - The
Floyd Warshall algorithm
Unit 4:
Algorithm Design Techniques - Greedy and Dynamic Programming
4 Greedy algorithms - Activity selection problem, Job sequencing problem,
Huffman codes, fractional knapsack problem 10
Dynamic Programming - Overlapping substructure property, Optimal
substructure property, Tabulation vs Memoization, Fibonacci numbers, 0/1
Knapsack problem, Longest common subsequence, Matrix chain multiplication
Unit 5:
Hashing, String Matching and NP-Completeness
Hashing Data Structure - Introduction to Hashing, Hash function, Collision and
collision handling, Collision handling - Chaining, Open addressing
5 String Matching - Naive string-matching algorithm, The Rabin-Karp algorithm, 10
The Knuth-Morris-Pratt algorithm
NP-Completeness - Importance of NP-completeness, P, NP, NP Complete and NP
hard problems, Polynomial time and polynomial time verification, The subset-sum
problem, The traveling salesman problem
Total 50

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Thomas H. Cormen, Charles E. Leiserson, Ronal L. Rivest, 2nd 2006.
Clifford Stein:” Introduction to Algorithms”, 2nd Edition,
PHI,
Reference Books
1. Donald E.Knuth:”The Art of Computer Programming: Volume 3rd 1998
1: Fundamental Algorithms”,3rd Edition
2 Ellis Horowitz, Sartaj Sahni, SanguthevarRajasekaran:” 2nd 2007
Fundamentals of Computer Algorithms”, 2nd Edition,
University press,.
3 Anany Levitin: “Introduction to the Design & Analysis of 2nd 2007
Algorithms”, 2nd Edition, Pearson Education,.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY),
DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

Virtualization and Cloud
1. Subject Code: TCS 451 Course Title: Computing
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50
5. Credits: 3

6. Semester: IV

7. Category of Course:DE

8. Pre-requisite: TCS-351 Fundamental of Cloud Computing and Big data

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Identify the concept of virtualization technique in cloud computing
platform.
CO2: Demonstrate the use case of the virtual machines.
CO3: Analyze the use case of parallel and distributed computing.
CO4: Evaluate the architectures of cloud computing.
CO5: Assess the viability of developing, deploying, maintaining and securing
cloud computing solutions using a variety of resiliency virtualization
testing tools.
CO6:Create cloud-computing virtualization strategies using virtualization tools
to solve identified business needs on behalf of a client.

** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO Hours
Unit 1:
Virtualization
1 Introduction, Characteristics of Virtualized Environments, Taxonomy of 9
Virtualization Techniques, Execution Virtualization, Types of hardware
virtualization: Full virtualization - partial virtualization - para
virtualization
Application Virtualization, Network Virtualization, Desktop
Virtualization, Storage Virtualization, Server Virtualization, Data
Virtualization.
Virtualization Solution Providers: V2 Cloud, VMware, Citrix, Microsoft,
Oracle VM VirtualBox.
Advantages and Disadvantages of Virtualization, Examples, Xen: Para
virtualization, VMware: Full Virtualization, Microsoft Hyper-V, Case
study of Virtualization Technique
Unit 2:
Virtual Machines
2 Virtual machines basics, Process virtual machines: Memory architecture
emulation, Instruction emulation, Operating system emulation, Dynamic 9
binary optimization, High level VN architecture, System virtual machines:
Resource virtualization (Processors, Memory, Input/Output), Case Study
of Intel VT-x
Unit 3:
Parallel and Distributed Computing
Eras of Computing, Parallel vs. Distributed Computing, Architectural
Styles for Distributed Computing, Models for Inter-Process
Communication, Technologies for Distributed Computing, Remote
Procedure Call, Distributed Object Frameworks, Service Oriented
Computing, Parallel programming, from laptops to supercomputers to the
3 cloud, Parallel architectures and programming languages and models, 9
including shared memory (eg OpenMP on your multicore laptop),
distributed memory (MPI and UPC on a supercomputer), GPUs (CUDA
and OpenCL), and cloud (MapReduce, Hadoop and Spark). Parallel
algorithms and software tools for common computations (eg dense and
sparse linear algebra, graphs, structured grids). Tools for load balancing,
performance analysis, debugging. How high level applications are built (eg
climate modeling). Case study of Parallel and Distributed Computing.

Unit 4:
Cloud Computing Architecture
Fundamental Cloud Architectures, Workload Distribution Architecture,
Resource Pooling Architecture, Dynamic Scalability Architecture, Elastic
Resource Capacity Architecture, Service Load Balancing Architecture,
4 Cloud Bursting Architecture, Elastic Disk Provisioning Architecture, 9
Redundant Storage Architecture.
Cloud Computing Reference Architecture (CCRA): Introduction, Benefits
of CCRA, Migrating into a Cloud: Introduction, Challenges while
migrating to Cloud, Broad approaches to migrating into the cloud, Seven-
step model of migration into a cloud, Migration Risks and Mitigation. Case
study of Cloud Computing Architecture.
Unit 5:
Virtualization Tools and Techniques
Parasoft Virtualize, Mountebank, Hoverfly cloud, MicroFocus Data
5 Simulation Software, CA service Virtualization, Mocklab, Rational Test 9
Virtualization Server, Tricentis Tosca, Docker, Kubernetes, OpenShift,
and Istio. Assess the viability of developing, deploying, maintaining and
securing cloud computing solutions using a variety of resiliency
virtualization testing tools, Create cloud-computing virtualization
strategies using virtualization tools to solve identified business needs on
behalf of a client, Case study of Virtualization Tools and Techniques.
Design and Deploy an Online Healthcare Application on the Cloud.
Total 45 Hrs.

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publica
tion /
Reprint
Textbooks
1. Mastering Cloud Computing by Rajkumar Buyya 1st 2017
etc., Published by McGraw Hill,
2 Virtual Machines by James E. Smith, Ravi Nair, 1st 2005
Published by MK Publishers,
3 V K Pachghare, Cloud Computing, PHI, 2016

Reference Books
1. Barrie Sosinsky , Cloud Computing Bible, Wiley 1st 2011
Publishing Inc.,

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End
Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY),
DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

Statistical Data
1. Subject Code: TCS 471 Course Title: Analysis with R
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: IV

7. Category of Course: DE

8. Pre-requisite: TMA101-Engineering Mathematics, TCS-201 Programming for

problem solving, TCS 351 Fundamental of Cloud Computing and Big data

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Define the concepts of statistics.
CO2: Understand the probability distribution techniques in different applications.
CO3: Identify the needs of data preprocessing.
CO4: Implement the manipulation and processing of data in R
CO5: Apply the concepts of functions in R
CO6: Evaluate the use of R in data analytics
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Statistics: Introduction to Statistics- Descriptive Statistics,
Summary Statistics, Basic probability theory, Statistical Concepts (uni-variate and
1 bi-variate sampling, distributions, re-sampling, statistical Inference, prediction 9
error)

Unit 2: Probability Distribution: Introduction to Probability, Probability

Distribution (Continuous and discrete- Normal, Bernoulli, Binomial, Negative
Binomial, Geometric and Poisson distribution) , Bayes’ Theorem, Central Limit
2 10
theorem, Data Exploration & preparation, Concepts of Correlation, Regression,
Covariance, Outliers.
Unit 3: Introduction to R and Data Preprocessing: Introduction & Installation of
R, R Basics, Finding Help, Code Editors for R, Command Packages, Manipulating
3 and Processing Data in R, Reading and Getting Data into R, Exporting Data from 10
R

Unit 4: Data Objects-Data Types & Data Structure. Viewing Named Objects,
Structure of Data Items, Manipulating and Processing Data in R (Creating,
4 Accessing, Sorting data frames, Extracting, Combining, Merging, reshaping data 8
frames), Control Structures

Unit 5: Functions in R (numeric, character, statistical), working with objects,

Viewing Objects within Objects, Constructing Data Objects, Building R Packages,
Running and Manipulating Packages, Non parametric Tests- ANOVA, chi-Square,
5 t-Test, U-Test, Introduction to Graphical Analysis, Using Plots(Box Plots, Scatter 10
plot, Pie Charts, Bar charts, Line Chart), Plotting variables, Designing Special
Plots, Simple Liner Regression, Multiple Regression

Total 47
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Dr. Mark Gardener, “Beginning R: The Statistical 1st 2012
Programming Language”, John willey& Sons, 2012
2. Gareth James, Daniela Witten, Trevor Hastie and Robert 2nd 2021
Tibshirani, “An Introduction to Statistical Learning with
Applications in R,” Springer, Germany,
Reference Books
1. N.G Das, “Statistical Methods (Combined edition 1st 2017
volume 1 & 2),” Mc Graw Hill

12. Mode of Test / Quiz / Assignment / Mid Term Exam / End Term Exam
Evaluation
GRAPHIC ERA (DEEMED TO BE UNIVERSITY),
DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

Microcontroller and Its
1. Subject Code: TCS 431 Course Title:
Interfacing
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: IV

7. Category of Course: DE

8. Pre-requisite: TEC101/201 Basic Electronics Engineering, TCS-308Logic Design and

Computer Organization

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understanding the concept of embedded system.

CO2: Assembly language programming of 8051

CO3: Study of Arduino.

CO4: Interfacing of different IC with 8051.

CO5: Design and develop systems based on 8051 microcontroller and its interfaces.

CO6: Understand the working of interrupts

** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10.Details of the Course:

Contact
S.NO. Contents
Hours
Unit 1: 10
MICROCONTROLLER: Difference between Microprocessors and Micro-
controllers, Types of Micro-controllers, Memory structure of 8051, Processor
1
Architecture – Harvard v/s Von Neumann, CISC v/s RISC, 8051 Architecture
,Micro-controller Memory types – control storage, variable area, stack, hardware
register space, SFR,8051 pin diagram..
2 Unit 2: 9
8051 Instruction Set:
Addressing modes, external addressing, Instruction execution, Instruction set –
data movement, arithmetic, bit operators, branch, Software development tools
like assemblers, simulators, O/P file formats. Assembling and running an 8051
program, 8051 data types, 8051 flag bits and the PSW register, 8051 register
banks and stack
Unit 3: 9
PROGRAMMING OF 8051 and INTERRUPTS:
3 Programming of 8051, I/O bit manipulation. Timer, counter, programming of
timer, 8051 interrupts, Interrupts priority in the 8051, and interrupts
programming.
Unit 4: 9
INTRODUCTION TO ARDUINO IDE PLATFORM

4 Introduction to ATMEGA328 microcontroller and to Arduino IDE,

Hardware,Characteristics,Interfacing with different peripheral
devices,Debugging hardware errors,Using PWM I/O pins,Interfacing Arduino
hardware with Internet of Things
Unit 5: 8
INTERFACING:
5 Interfacing with 8051: LCD, Keyboard, ADC, DAC interfacing, Sensor
interfacing and Signal Conditioning, Stepper motor and DC motor, Basics of
serial communications, 8051 connection to RS-232, 8051 serial port
programming assembly.
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Editio Year of Publication
No. n / Reprint
Textbooks
1. Mazidi,”The 8051 Microcontrollers & Embedded 1st 2007
Systems”, Pearson Education,
MykePredko ,“Programming and Customizing the 1st , 2003
8051 Micro-controller”,Tata McGraw-Hill edition
Brad Kendall,”Arduino Make use of:A complete 3rd 2013
beginner guide”,

Reference Books
1. Kenneth Ayala, “The 8051 Microcontroller”, West 1st 1993
Publishing Company,
Julien Bayle,”C-Programming for Arduino” , 2nd 2013

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

Fundamental of Cyber
1. Subject Code: TCS 492 Course Title: Security

2. Contact Hours: L 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: IV

7. Category of Course: DE

8. Pre-requisite: TCS-332 Fundamentals of Information Security and Block Chain,

TCS-392 Introduction to Cryptography

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Explain the three pillars of cyber security, types of hackers and penetration
testing.
CO2: . Implement the scripting concepts used in cyber security.
CO3: Use the netcat, ping and wireshark tools to analyze the security of network.
CO4: Use the Javascript, php, sql to analyze the web security.
CO5: Explain the use of cyber security protocols for cyber threats.
CO6: Analyze the security level of web applications.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Introduction to Cyber Security
What is Cyber security, Why we need Cyber security, The Zero Trust Model,
Ethical Hacking
Protect Against - Unauthorised Modification, Unauthorised Deletion and
Unauthorised Access
1 9
Three pillars of Cyber Security - Confidentiality, Availability and Integrity
Steps to fix a crime - Identify Cyber Threats, Analyse and Evaluate Threat,
Treatment
Type of Hackers - White Hat, Great Hat, Black Hat
Penetration Testing and its Phases - Reconnaissance, Scanning, Gaining Access,
Maintaining Access, Covering Tracks
Unit 2: 9
Linux Basics and Scripting for Ethical Hacking
2 Bash, Linux commands, man page, Adding and deleting, users and adding them
to sudo group, switching users, creating, copying, moving and removing file,
Writing and appending text to a file, File permissions, working with editors, grep,
cut command, Starting and stopping services, Automating tasks with cron jobs
Introduction to Bash Scripting - Basics of Bash or Shell Scripting, conditional
statements, loops, Manipulating files
Introduction to Python - Basics of Python, conditional statements, loops,list,
tuple, dictionary, functions
Unit 3:
Networking Basics for Ethical Hacking
Virtualization - Installing and configuring virtual machine, Configuration of
network - NAT, Bridged and Host only, Dual boot system
TCP/IP - IPv4 and IPv6, IP Address, Mac Address, Subnets, TCP 3-way
10
3 handshake, DNS
Communication between VMs or Setting up network between machines,
netcat - The Swiss Army Knife of TCP/IP Connections, use netcat to Listen on a
port, pushing a command shell back to listener, transfer files, Ping command
Wireshark - A Packet Sniffing Tool
Unit 4:
Basics of Web and Web Security
4 Introduction to Java Script - Basics of Javascript, Input validation, Cross site 9
scripting (XSS) attack
Introduction to PHP and SQL - Basics of PHP, Input Validation, Phishing,
Spear Phishing, Sql Injection Attack
Unit 5:
Introduction to Cyber Threats and System Hacking
Cyber Threats - Malware, password attacks, Distributed denial-of-service
5
(DDos), Ransomware attack, Eavesdropping attack (man in the middle attack), 10
Birthday attack, buffer overflow attack, IP and Mac address spoofing,
Steganography, Anonymous browsing, Introduction to tor browser, Introduction
to VPN, Secure Sockets Layer (SSL), Secure Shell (SSH)
Total 47

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Georgia Weidman ,“Penetration Testing: A Hands-On 1st 2014.
Introduction to Hacking,” No Starch Press Inc.,
2 Nina Godbole ,SunitBelapure, “Cyber 1st 2011
Security:Understanding Cyber Crimes, Cyber Forensics
and Legal Perspectives,” Wiley India,.
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

Fundamental of
1. Subject Code: TCS 421 Course Title: Statistics and AI
and AI
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: IV

7. Category of Course: DE

8. Pre-requisite: TMA101/TMA Engineering Mathematics, TCS-341 Python

Programming for Computing

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Demonstrate knowledge of statistical and exploratory data analysis data
analysis techniques utilized in decision making.
CO2: Apply principles of Data Science to the analysis of business problems.

CO3: To use Machine Learning Algorithms to solve real-world problems.

CO4: To provide data science solution to business problems and visualization.

CO5: To learn the basic concepts and techniques of AI and machine learning

CO6: To explore the various mechanism of Knowledge and Reasoning used for
building expert system

** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Introduction to AI
1
Definition, Problem, State space representation. Intelligent Systems: 10
Categorization of Intelligent System, Components of AI Program,
Foundations of AI, Applications of AI, Current trends in AI, Intelligent
Agents: Anatomy, structure, Types
Unit 2:
Problem solving
2 Solving problem by Searching: Problem Solving Agent, Formulating
Problems. Uninformed Search Methods: Breadth First Search (BFS),
Depth First Search (DFS), Depth Limited Search, Depth First Iterative 9
Deepening (DFID), Informed Search Methods: Greedy best first Search,
A* Search, Memory bounded heuristic Search. Local Search Algorithms
and Optimization Problems: Hill climbing search Simulated annealing,
Local beam search.

Unit 3:
An Introduction to Data Science, Data Processing and Visualization

Definition, working, benefits and uses of Data Science, Data science vs.
3 Business Intelligence, The data science process, Role of a Data Scientist.
Data Processing and Visualization: Data Formatting, Exploratory Data Analysis, 9
Filtering, and hierarchical indexing using Pandas. Data Visualization: Basic
Visualization Tools, Specialized Visualization Tools, Seaborn Creating and
Plotting Maps.

Unit 4:
Statistical Data Analysis & Inference

Populations and samples, Statistical modelling, probability distributions, fittings 9

4 a model, Statistical methods for evaluation, Exploratory Data Analysis, Getting
started with R, Manipulating and Processing data in R, working with function in
R, working with descriptive Statistics, Working with graph plot in R.
Unit 5:
Statistical Applications
8
8
Basic Statistical operations, Linear Regression Analysis, Logistic
andExponential Regression, Time Series Analysis, Probability Distribution,
ANOVA, Correlation and Covariance.
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Tom M. Mitchell. "Machine Learning" McGraw-Hill,. 1st 2017

2 “Statistical programming in R”, Oxford University Press 1st 2017

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

Introduction to Big Data
1. Subject Code: TCS 462 Course Title:

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 4

6. Semester: IV

7. Category of Course: DE

8. Pre-requisite: TCS-342, TCS-302

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Outline the theory of big data, and explain challenges of big data
CO2: Understand the types of Big data and its characteristics
CO3: Compare Business Intelligence vs Big Data
CO4: Get the idea of NoSQL databases, different types of
NoSQL/NewSQL datastores
CO5: Discuss various types of Big Data analytics
CO6: Elaborate a Big Data management architecture
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Big Data and its Challenges
Defining Big Data, Characteristics of Big Data, Evolution of Big Data,
Traditional Business Intelligence vs Big Data, The Evolution of Data
1 Management, Understanding the Waves of Managing Data, creating 9
manageable data structures, Web and content management, Managing big
data. Building a Successful Big Data Management Architecture, beginning
with capture, organize, integrate, analyze, and act, Setting the architectural
foundation, Performance matters, Traditional and advanced analytics.
Unit 2:
Big Data Types and its Sources
Defining Structured Data Exploring sources of big structured data,
2 Understanding the role of relational databases in big data Defining 8
Unstructured Data, exploring sources of unstructured data, Understanding
the role of a CMS in big data management. Looking at Real-Time and Non
Real-Time Requirements, Putting Big Data Together, managing different
data types, integrating data types into a big data environment.

Unit 3:
Technology Foundations of Big Data
Exploring the Big Data Stack: - Layer 0: Redundant Physical Infrastructure
- Physical redundant networks, Managing hardware: Storage and servers,
3 9
Infrastructure operations - Layer 1: Security Infrastructure, Interfaces and
Feeds to and from Applications and the Internet- Layer 2: Operational
Databases. Layer 3: Organizing Data Services and Tools. Layer 4:
Analytical Data Warehouses, Big Data Analytics, Big Data Applications.
Unit 4:
Introduction to NoSQL and NewSQL
Introduction to NoSQL, Uses, Features and Types, Need, Advantages,
Disadvantages and Application of NoSQL, Overview of NewSQL. RDBMSs
4 Are Important in a Big Data Environment. PostgreSQL relational database. 8
Nonrelational Databases. Key-Value Pair Databases - Riak keyvalue
database. Document Databases MongoDB, CouchDB . Columnar
Databases, HBase columnar database. Graph Databases- Neo4J graph
database.
Unit 5:
Big Data Analytics
Basic analytics, Advanced analytics, Operationalized analytics, Monetizing
5 8
analytics. Modifying Business Intelligence Products to Handle Big Data,
Studying Big Data Analytics Examples, Terminologies used in Big Data
environment.
Total 42
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Judith Hurwitz, Alan Nugent , Fern Halper , 1st 2013

Marcia Kaufman, Big Data for Dummies, Wiley

2. Subhashini Chellappan Seema Acharya, Big Data 2nd 2019

and Analytics, Wiley
3. Big Data, Black Book, Dreamtech Press 1st 2016

Reference Books
1. Michele Chambers, Michael Minelli , Ambiga 1st 2013
Dhiraj ,Big Data, Big Analytics: Emerging Business
Intelligence and Analytic Trends for Today's
Businesses, Wiley

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

1. Subject Code: PCS-408 Course Title: Java Programming Lab

2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: IV

7. Category of Course: DC

8. Pre-requisite: PCS-307 OOPS with C++ Lab, TCS 307 Object oriented Programming
with C++

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Understand the object-oriented approach in programming along with
the purpose and usage principles of inheritance, polymorphism,
encapsulation, and method overloading etc.

CO2: Demonstrate ability to test and debug Java programs using IDE.

CO3: Analyze, design, and develop small to medium sized application

programs that demonstrate professionally acceptable programming
standards.
CO4: Demonstrate skills of developing event-driven programs using
graphical user interfaces.

CO5: Demonstrate skills of developing event-driven programs using graphical

user interfaces.

** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate.

10. Details of the Course:

Sl. List of problems for which student should develop program and execute Contact
No. in the Laboratory Hours
Taking input from Command line and convert objects into primitivedata
type:
22. Write a java program to take input as a command line argument. Your name, 2
course, universityrollno and semester. Display the information.

Name: UniversityRollNo:Course:
Semester:

Concepts of Java Control statements, Conditional statements, loops and

iterations, Wrapper classes, Scanner Class:

Using the switch statement, write a menu-driven program to calculate the

maturity amount of a bank deposit.
The user is given the following options:
(i) Term Deposit
(ii) Recurring Deposit
23. 2
For option (i) accept Principal (p), rate of interest (r) and time period in years
(n). Calculate and output the maturity amount (a) receivable using the formula
a = p[1 + r / 100]n.

For option (ii) accept monthly installment (p), rate of interest (r) and time period
in months (n). Calculate and output the maturity amount (a) receivable using
the formula a = p * n + p * n(n + 1) / 2 * r / 100 * 1 / 12. For an incorrect option,
an appropriate error message should be displayed.
[ Use Scanner Class to take input ]
Program to find if the given numbers are Friendly pair or not (Amicable or
not). Friendly Pair are two or morenumbers with a common abundance.
Input & Output format:

• Input consists of 2 integers.

• The first integer corresponds to number 1 and the second integer
corresponds to number 2.
If it is a Friendly Pair display Friendly Pair or displays Not Friendly Pair.

For example,6 and 28 are Friendly Pair.

(Sum of divisors of 6)/6 = (Sum of divisors of 28)/28.
24. Steps to check whether the given numbers are friendly pair or not 2

▪ Input the numbers num1 and num2.

▪ Initialize sum1 = sum2 = 0.
▪ sum1 = sum of all divisors of num1.
▪ sum2 = sum of all divisors of num2.
▪ If (sum1 == num1) and (sum2 == num2), then print
"Abundant Numbers".
▪ Else, print "Not Abundant Numbers".

Program to check whether the given numbers are friendly pair or not
Program to replace all 0's with 1 in a given integer. Given an integer as an input,
all the 0's in the number has to be replaced with 1.
For example, consider the following number Input: 102405
25. Output: 112415 2
Input: 56004
Output: 56114
Steps to replace all 0's with 1 in a given integer
• Input the integer from the user.
• Traverse the integer digit by digit.
• If a '0' is encountered, replace it by '1'.
• Print the integer.
Array in Java:
Printing an array into Zigzag fashion. Suppose youwere given an array of
integers, and you are told to sort the integers in a zigzag pattern. In general, in
a zigzag pattern, the first integer is less than the second
integer, which is greater than the third integer, which is less than the fourth
integer, and so on. Hence, the converted array should be in the form of e1 < e2
> e3 < e4 > e5 < e6.
Test cases: Input 1:
7
4378621
26. 2
Output 1:
3748261

Input 2:
4
1432

Output 2:
1423
The problem to rearrange positive and negative numbers in an array .
Method: This approach moves all negative numbers to the beginning and
positive numbers to the end but changes the order of appearance of the
elements of the array.
Steps:

1. Declare an array and input the array elements.

27. 2. Start traversing the array and if the current element is negative, 2
swap the current elementwith the first positive element and
continue traversing until all the elements have been encountered.
3. Print the rearranged array.

Test case:

• Input: 1 -1 2 -2 3 -3
Output: -1 -2 -3 1 3 2
Program to find the saddle point coordinates in a given matrix. A saddle point
28. is an element of the matrix,which is the minimum element in its row and the 2
maximum in its column.
For example, consider the matrix given belowMat [3][3]
123
456
789
Here, 7 is the saddle point because it is the minimum element in its row and
maximum element in its column.
Steps to find the saddle point coordinates in a givenmatrix.

1. Input the matrix from the user.

2. Use two loops, one for traversing the row andthe other for
traversing the column.
3. If the current element is the minimum element inits row and
maximum element in its column, then return its coordinates.
Else, continue traversing.
String Handling in Java (using String and StringBuffer class):
Program to find all the patterns of 0(1+)0 in the given string. Given a string
containing 0's and 1's, find the total number of 0(1+)0 patterns in the string
and output it.
0(1+)0 - There should be at least one '1' between the two 0's.
29. 2
For example, consider the following string.
Input: 01101111010
Output: 3
Explanation: 01101111010 - count = 1

30. Write a java program to delete vowels from given string using StringBuffer 2
class
Class definition, creating objects and constructors:
Write a java program to create a class named 'Bank ' with the following data
members:
• Name of depositor
• Address of depositor
• Account Number
• Balance in account

31. 2
Class 'Bank' has a method for each of the following:
1. Generate a unique account number for each depositor.
2. For first depositor, account number will be 1001, for second depositor
it will be 1002 and so on
3. Display information and balance of depositor
4. Deposit more amount in balance of any depositor
5. Withdraw some amount from balance deposited.
6. Change address of depositor
After creating the class, do the following operations.
1. Enter the information (name, address, account number, balance) of the
depositors. Number of depositors is to be entered by the user.
2. Print the information of any depositor.
3. Add some amount to the account of any depositor and then display
final information of that depositor.
4. Remove some amount from the account of any.
depositor and then display final information of that depositor.
5. Change the address of any depositor and then display the final
information of that depositor.
6. Randomly repeat these processes for some other
bank accounts.
Define a class Word Example having the followingdescription:
Data members/instance variables:

private String strdata: to store a sentence.

Parameterized Constructor WordExample(String) : Accept a

sentence which
32. may be terminated by either’.’, ‘? ’or’!’ only. The wordsmay be separated by 2
more than one blank space and are in UPPER CASE.

Member Methods:
void countWord(): Find the number of wordsbeginning and
ending with a vowel.

void placeWord(): Place the words which begin andend with a vowel at the
beginning, followed by the remaining words as they occur in the sentence
Method overloading (Compile time Polymorphism):
Write a Java program to create a class called
ArrayDemo and overload arrayFunc() function.

void arrayFunc(int [], int) To find all pairs of elements in an

Array whose sum is equal to a givennumber :

Array numbers= [4, 6, 5, -10, 8, 5, 20], target=10

Output :

33. Pairs of elements whose sum is 10 are :4 + 6 = 10 2

5 + 5 = 10
-10 + 20 = 10
void arrayFunc(int A[], int p, int B[], int q) Giventwo sorted arrays A
and B of size p and q, Overload method arrayFunc() to merge elements of
A with B bymaintaining the sorted order i.e. fill A with first p smallest
elements and fill B with remaining elements.

Example:
Input :
int[] A = { 1, 5, 6, 7, 8, 10 }
int[] B = { 2, 4, 9 }
Output:
Sorted Arrays:
A: [1, 2, 4, 5, 6, 7]
B: [8, 9, 10]
(Use Compile time Polymorphism MethodOverloading)
Method overriding (Runtime Polymorphism), Abstract class and Abstract
method, Inheritance, interfaces:
Write a java program to calculate the area of a rectangle, a square and a circle.
Create an abstract class 'Shape' with three abstract methods namely
rectangleArea() taking two parameters, squareArea() and circleArea() taking
34. one parameter each. 2
Now create another class ‘Area’ containing all the three methods
rectangleArea(), squareArea() and circleArea() for printing the area of
rectangle, square and circle respectively. Create an object of class Area and call
all the three methods.
(Use Runtime Polymorphism)
Write a java program to implement abstract class andabstract method with
following details:

Create a abstract Base Class TemperatureData members:

double temp;
Method members:
void setTempData(double) abstact void changeTemp()

Sub Class Fahrenheit (subclass of Temperature) Data members:

double ctemp;
method member:
35. Override abstract method changeTemp() to convert Fahrenheit temperature into 2
degree Celsius by using formula C=5/9*(F-32) and display converted
temperature

Sub Class Celsius (subclass of Temperature)

Data member:
double ftemp;
Method member:
Override abstract method changeTemp() to convert degree Celsius into
Fahrenheit temperature by using formula F=9/5*c+32 and display converted
temperature
Write a java program to create an interface that consists of a method to display
36. 2
volume () as an abstract method and redefine this method in the derived
classes to suit their requirements.
Create classes called Cone, Hemisphere and Cylinder that implements the
interface. Using these three classes, design a program that will accept
dimensions of a cone, cylinder and hemisphere interactively and display the
volumes.

Volume of cone = (1/3)πr2h Volume of hemisphere = (2/3)πr3 Volume of

cylinder = πr2h

Write a java program to accept and print the employee details during runtime.
The details will include employee id, name, dept_ Id. The program should
raise an exception if user inputs incomplete or incorrect data. The entered
value should meet the following conditions:

37. a. First Letter of employee name should be in capital letter. 2

b. Employee id should be between 2001 and 5001
c. Department id should be an integer between 1 and 5.

If the above conditions are not met, then the application should raise specific
exception else should complete normal execution.
Create a class MyCalculator which consists of a single method power (int, int).
This method takes two integers, n and p, as parameters and finds np. If either
n or p is negative, then the method must throw an exception which says, "n
and p should be non- negative".

Input Format
Each line of the input contains two integers, n and p. Output Format
Each line of the output contains the result, if neither of n and p is negative.

Otherwise, the output contains "n and p should be non- negative".

Sample Input
35
38. 2 4 2
00
-1 -2
-1 3
Sample Output
243
16
java.lang.Exception: n and p should not be zero. java.lang.Exception: n or p
should not be negative. java. lang. Exception: n or p should not be negative.

Explanation
In the first two cases, both n and p are positive. So, the power function returns
the answer correctly.
In the third case, both n and p are zero. So, the exception, "n and p should not
be zero.” is printed.

In the last two cases, at least one out of n and p is negative. So, the exception,
"n or p should not be negative.” is printed for these two cases.
File Handling in Java:
Write a java file handling program to count and display the number of
palindromes present in a text file "myfile.txt".
39. Example: If the file "myfile.txt" contains the following lines, 2
My name is NITIN
Hello aaa and bbb wordHow are You
ARORA is my friendOutput will be => 4
Multithreaded programming:
Write a program MultiThreads that creates two threads-one thread with the
name CSthread and the other thread named ITthread.
40.
Each thread should
display its respective name and execute after a gap of 500 milliseconds. Each
thread should also display a number indicating the number of times it got a
chance to execute.
Write a java program for to solve producer consumer problem in which a
41. producer produces a value and consumer consume the value before producer 2
generate the next value
Collection and Generic Framework:
Write a method removeEvenLength that takes an ArrayList of Strings as a
42.
parameter and that removesall the strings of even length from the list.
(Use ArrayList)
Write a method swapPairs that switches the order of values in an ArrayList of
Strings in a pairwise fashion. Your method should switch the order of the first
two values, then switch the order of the next two, switch the order of the next
two, and so on.

For example, if the list initially stores these values: {"four", "score", "and",
"seven", "years",
"ago"} your method should switch the first pair, "four", "score", the second
43. pair, "and", "seven", and the third pair, "years", "ago", to yield this list: 2
{"score", "four", "seven", "and", "ago", "years"}

If there are an odd number of values in the list, the final element is not moved.

For example, if the original list had been: {"to", "be", "or", "not", "to", "be",
"hamlet"} It would again switch pairs of values, but the final value, "hamlet"
would not be moved, yielding this list: {"be", "to", "not", "or", "be", "to",
"hamlet"}
Write a method called alternate that accepts two Listsof integers as its
parameters and returns a
new List containing alternating elements from the twolists, in the
following order:
• First element from first list
• First element from second list
• Second element from first list
• Second element from second list
44. • Third element from first list 2
• Third element from second list

If the lists do not contain the same number of elements, the remaining elements
from the longer list should be placed consecutively at the end. For example,
for a first list of (1, 2, 3, 4, 5) and a second
list of (6, 7, 8, 9, 10, 11, 12), a call of alternate (list1,
list2) should return a list containing (1, 6, 2, 7, 3, 8, 4,
9, 5, 10, 11, 12). Do not modify the parameter lists passed in.
AWT & Swing, Event Handling:
Write a GUI program to develop an application that receives a string in
one text field, and count number ofvowels in a string and returns it in
another text field, when the button named “CountVowel” is clicked.
When the button named “Reset” is clicked it will resetthe value of
textfield one and Textfield two.
When the button named “Exit” is clicked it will closed the application.

45. 2

Java Database Connectivity (JDBC):

Create a database of employee with the following fields.

46. • Name 2
• Code
• Designation
• Salary
a) Write a java program to create GUI java application to take
employee data from the TextFields and store it in database
using JDBC connectivity.
b) Write a JDBC Program to retrieve all the records from the
employee database.

Total 48

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Patrick Naughton and Herbert Schildt, “Java 2 The 9th 2017
Complete Reference”, McGraw Hill Education
E. Balaguruswamy, “Programming with Java a Primer”, 6th 2019
Tata McGraw Hill
Reference Books
1. Core Java-Head First Publication 3rd 2018
Cay S Horstmann and Gary Cornell, “Core Java Volume Standard 2019
–I and II”, Sun Microsystems, edition

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

1. Subject Code: PCS-403 Course Title: Microprocessors lab

2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: 4

7. Category of Course: DC

8. Pre-requisite: PEC151/251 Basic Electronics Engineering Lab, PCS 308 Logic design lab

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Remember 8085 and 8086 instruction set.
CO2: Understand different assembly language programs on
microprocessor-based microcomputer kit.
CO3: Apply the programming concepts to test and debug assembly
language programs in the laboratory.
CO4: Assemble various devices and memories with microprocessor for any
defined task.
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate
10. Details of the Course:
Sl. List of problems for which student should develop program and execute Contact
No. in the Laboratory Hours
1. Write program in 8085 to swap two 8-bit numbers. 2
Write a program in 8085 to move a block of data bytes from one location to 1
2.
another location.
Write programs in 8085 to perform addition & subtraction of 8-bit number 1
3.
with carry / borrow.
4. Write a program in 8085 for addition of 16 bits numbers with carry. 1
5. 1
Write a program for multiplication of two 8-bit numbers in 8085.
6. 1
Write an ALP in 8085 to add two 8-bit BCD data.
(a) Write an ALP in 8085 to find larger number between two numbers. 2
7.
(b) Write an ALP in 8085 to find smaller number between two numbers.
8. 1
Write an ALP in 8085 to find largest /smallest in a series of n number.
9. 1
Write a program to find square root of a number in 8085.
10. Write a program for division of two 8 bit numbers in 8085. 1
11. Write a program in 8085 to count number of ones in an 8 bit number. 1

12. Write a program in 8085 to find sum of digits of an 8 bit number. 1

(a) Write a program in 8086 to add two 16-bit numbers given by the user. 2
13.
(b) Write a program in 8086 to subtract two 16-bit numbers given by the user.
(a) Write a program in 8086 to multiply two 8-bit data. 2
14.
(b) Write a program in 8086 to divide: 8-bit data by 8-bit data.
Write a program in 8086 to find the largest no. from an array of n numbers 1
15. stored in an array.

16. Write a program in 8086 to add and subtract two 8-bit BCD numbers. 1
Write a program in 8086 to convert a BCD number to its Binary code 1
17. equivalent.

18. Write a program in 8086 to perform sorting of given set of numbers. 1

1
19. Write a program in 8086 to convert a Binary number to its gray code
equivalent.
1
20. Write a program in 8086 to convert a BCD number to its ASCII code
equivalent.
Total 24

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publica
tion /
Reprint
Textbooks
1. Ramesh Gaonkar, Microprocessor Architecture, Programming, 6th 2013
and Applications with the 8085, Penram International Publication
(India) Pvt. Ltd
2. A. K. Ray & K. M. Bhurchandi, Advanced Microprocessors and 3rd 2012
peripherals, Tata McGraw Hill
Reference Books
1. Douglas V. Hall, Microprocessors and Interfacing, Tata McGraw 3rd 2012
Hill
2. Barry B. Brey, The Intel Microprocessors Architecture 8th 2012
Programming and interfacing, Pearson

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

1. Subject Code: PCS-409 Course Title: Design and Analysis of

Algorithms Lab
2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 1 Practical 2

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: IV

7. Category of Course: DC

8. Pre-requisite: PCS-302 Data Structures Lab, Any programming language

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Analyze algorithmic time and space complexity using asymptotic
notations.

CO2: Design efficient algorithms utilizing techniques such as divide and

conquer, greedy, and dynamic programming.

CO3: Solve complex problems using backtracking and branch-and-bound

techniques.

CO4: Predict the complexity of NP-complete problems and propose

algorithmic approaches.

CO5: Apply Dijkstra's, Bellman-Ford, Prim's, and Kruskal's algorithms to

real-world problems.

CO6: Implement pattern matching algorithms like Rabin-Karp and brute-

force techniques for pattern identification.:
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10. Details of the Course:

Sl. List of problems for which student should develop program and execute Contact
No. in the Laboratory Hours
Week 1:

1. Note: Input, output format for problem I, II and III is same and is given at the 2
end of this exercise.
I. Given an array of nonnegative integers, design a linear algorithm and
implement it using a program to find whether given key element is present in
the array or not. Also, find total number of comparisons for each input case.
(Time Complexity = O(n), where n is the size of input)

Sample I/O Problem - 1:

Input: Output:
3 Present 6
8 Present 3
34 35 65 31 25 89 64 30 Not Present 6
89
5
977 354 244 546 355
244
6
23 64 13 67 43 56
63

II. Given an already sorted array of positive integers, design an algorithm

and implement it using a program to find whether given key element is present
in the array or not. Also, find total number of comparisons for each input case.
(Time Complexity = O(nlogn), where n is the size of input).

III. Given an already sorted array of positive integers, design an algorithm

and implement it using a program to find whether a given key element is present
in the sorted array or not. For an array arr[n], search at the indexes arr[0], arr[2],
arr[4],. ,arr[2k] and so on. Once the interval (arr[2k] <
key < arr[ 2k+1] ) is found, perform a linear search operation from the index 2k
to find the element key. (Complexity < O(n), where n is the number of elements
need to be scanned for searching): Jump Search

Input format:
The first line contains number of test cases, T. For each test case, there will be
three input lines. First line contains n (the size of array).
Second line contains n space-separated integers describing array.
Third line contains the key element that need to be searched in the array.

Output format:
The output will have T number of lines.
For each test case, output will be “Present” if the key element is found in the
array, otherwise “Not Present”.
Also for each test case output the number of comparisons required to search the
key.

Sample I/O Problem - 2, 3:

Input: Output:
3 Present 3
5 Not Present 4
12 23 36 39 41 Present 3
41
8
21 39 40 45 51 54 68 72
69
10
101 246 438 561 796 896 899 4644 7999 8545
7999
Week 2:

I. Given a sorted array of positive integers containing few duplicate

elements, design an algorithm and implement it using a program to find whether
the given key element is present in the array or not. If present, then also find the
number of copies of given key. (Time Complexity = O(log n))

Input format:
The first line contains number of test cases, T. For each test case, there will be
three input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array.
Third line contains the key element that need to be searched in the array.

Output format:
The output will have T number of lines.
For each test case T, output will be the key element and its number of copies in
the array if the key element is present in the array otherwise print “ Key not
present”.

2. Sample I/O Problem I: 2

Input: Output:
2 981 - 2
10 75 - 3
235 235 278 278 763 764 790 853 981 981
981
15
1 2 2 3 3 5 5 5 25 75 75 75 97 97 97
75

II. Given a sorted array of positive integers, design an algorithm and

implement it using a program to find three indices i, j, k such that arr[i] + arr[j]
= arr[k].

Input format:
The first line contains number of test cases, T. For each test case, there will be
two input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array.
Output:
The output will have T number of lines.
For each test case T, print the value of i, j and k, if found else print “No sequence
found”.

Sample I/O Problem II:

Input: Output:
3 No sequence found.
5 2, 7, 8
1 5 84 209 341 1, 6, 9
10
24 28 48 71 86 89 92 120 194 201
15
64 69 82 95 99 107 113 141 171 350 369 400 511 590 666

III. Given an array of nonnegative integers, design an algorithm and a

program to count the number of pairs of integers such that their difference is
equal to a given key, K.

Input format:
The first line contains number of test cases, T. For each test case, there will be
three input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array. Third line
contains the key element.

Output format:
The output will have T number of lines.
For each test case T, output will be the total count i.e. number of times such pair
exists.

Sample I/O Problem III:

Input: Output:
2 2
5 4
1 51 84 21 31
20
10
24 71 16 92 12 28 48 14 20 22
4
Week 3:

I. Given an unsorted array of integers, design an algorithm and a program

to sort the array using insertion sort. Your program should be able to find
3. 1
number of comparisons and shifts ( shifts - total number of times the array
elements are shifted from their place) required for sorting the array.

Input Format:
The first line contains number of test cases, T. For each test case, there will be
two input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array.

Output Format:
The output will have T number of lines.
For each test case T, there will be three output lines. First line will give the
sorted array.
Second line will give total number of comparisons.
Third line will give total number of shift operations required.

Sample I/O Problem I:

Input: Output:
3 -31 -23 32 45 46 65 76 89
8 comparisons = 13
-23 65 -31 76 46 89 45 32 shifts = 20
10 21 32 34 46 51 54 65 76 78 97
54 65 34 76 78 97 46 32 51 21 comparisons = 28
15 shifts = 37
63 42 223 645 652 31 324 22 553 -12 54 65 86 46 325 -12 22 31 42 46 54
63 65 86 223 324 325 553 645 652
comparisons = 54
shifts = 68

II. Given an unsorted array of integers, design an algorithm and implement

a program to sort this array using selection sort. Your program should also find
number of comparisons and number of swaps required.

Input Format:
The first line contains number of test cases, T. For each test case, there will be
two input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array.

Output Format:
The output will have T number of lines.
For each test case T, there will be three output lines. First line will give the
sorted array.
Second line will give total number of comparisons. Third line will give total
number of swaps required.

Sample I/O Problem II:

Input: Output:
3 -21 -13 12 45 46 65 76 89
8 comparisons = 28
-13 65 -21 76 46 89 45 12 swaps = 7
10 21 32 34 46 51 54 65 76 78 97
54 65 34 76 78 97 46 32 51 21 comparisons = 45
15 swaps = 9
63 42 223 645 652 31 324 22 553 12 54 65 86 46 325 12 22 31 42 46 54
63 65 86 223 324 325 553 645 652
comparisons = 105
swaps = 14

III. Given an unsorted array of positive integers, design an algorithm and

implement it using a program to find whether there are any duplicate elements
in the array or not. (use sorting) (Time Complexity = O(n log n))

Input Format:
The first line contains number of test cases, T. For each test case, there will be
two input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array.

Output Format:

The output will have T number of lines.

For each test case, output will be 'YES' if duplicates are present otherwise ‘NO’.

Sample I/O Problem III:

Input: Output:
3 NO
5 YES
28 52 83 14 75 NO
10
75 65 1 65 2 6 86 2 75 8
15
75 35 86 57 98 23 73 1 64 8 11 90 61 19 20
Week 4:

I. Given an unsorted array of integers, design an algorithm and implement

it using a program to sort an array of elements by dividing the array into two
subarrays and combining these subarrays after sorting each one of them. Your
program should also find number of comparisons and inversions during sorting
the array.

Input Format:
The first line contains number of test cases, T. For each test case, there will be
two input lines. First line contains n (the size of array).
4. Second line contains space-separated integers describing array. 1

Output Format:
The output will have T number of lines.
For each test case T, there will be three output lines. First line will give the
sorted array.
Second line will give total number of comparisons. Third line will give total
number of inversions required.

Sample I/O Problem I:

Input: Output:
3 21 23 32 45 46 65 76 89
8 comparisons = 16
23 65 21 76 46 89 45 32 inversions =
10 21 32 34 46 51 54 65 76 78 97
54 65 34 76 78 97 46 32 51 21 comparisons = 22
15 inversions =
63 42 223 645 652 31 324 22 553 12 54 65 86 46 325 12 22 31 42 46 54
63 65 86 223 324 325 553 645 652
comparisons = 43
inversions =

II. Given an unsorted array of integers, design an algorithm and implement

it using a program to sort an array of elements by partitioning the array into two
subarrays based on a pivot element such that one of the sub array holds values
smaller than the pivot element while another sub array holds values greater than
the pivot element. Pivot element should be selected randomly from the array.
Your program should also find number of comparisons and swaps required for
sorting the array.

Input Format:

The first line contains number of test cases, T. For each test case, there will be
two input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array.

Output Format:
The output will have T number of lines.
For each test case T, there will be three output lines. First line will give the
sorted array.
Second line will give total number of comparisons. Third line will give total
number of swaps required.

Sample I/O Problem II:

Input: Output:
3 21 23 32 45 46 65 76 89
8 comparisons = 14
23 65 21 76 46 89 45 32 swaps = 10
10 21 32 34 46 51 54 65 76 78 97
54 65 34 76 78 97 46 32 51 21 comparisons = 29
15 swaps = 21
63 42 223 645 652 31 324 22 553 12 54 65 86 46 325 12 22 31 42 46 54
63 65 86 223 324 325 553 645 652
comparisons = 45
swaps = 39

III. Given an unsorted array of integers, design an algorithm and implement

it using a program to find Kth smallest or largest element in the array. (Worst
case Time Complexity = O(n))

Input Format:
The first line contains number of test cases, T. For each test case, there will be
three input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array. Third line
contains K.
Output Format:
The output will have T number of lines.
For each test case, output will be the Kth smallest or largest array element. If
no Kth element is present, output should be “not present”.

Sample for Kth smallest:

Input: Output:
3 123
10 78
123 656 54 765 344 514 765 34 765 234
3
15
43 64 13 78 864 346 786 456 21 19 8 434 76 270 601
8
Week 5:

I. Given an unsorted array of alphabets containing duplicate elements.

Design an algorithm and implement it using a program to find which alphabet
has maximum number of occurrences and

print it. (Time Complexity = O(n)) (Hint: Use counting sort)

Input Format:
The first line contains number of test cases, T. For each test case, there will be
two input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array.

Output:
5. 2
The output will have T number of lines.
For each test case, output will be the array element which has maximum
occurrences and its total number of occurrences.
If no duplicates are present (i.e. all the elements occur only once), output should
be “No Duplicates Present”.

Sample I/O Problem I:

Input: Output:
3 a–3
10 No Duplicates Present
aedwadqafp l-4
15
rkpgvyumqadjcze
20
gtlltcwawglcwdsaavcl

II. Given an unsorted array of integers, design an algorithm and implement

it using a program to find whether two elements exist such that their sum is
equal to the given key element. (Time Complexity = O(n log n))

Input Format:
The first line contains number of test cases, T. For each test case, there will be
two input lines. First line contains n (the size of array).
Second line contains space-separated integers describing array. Third line
contains key

Output Format:
The output will have T number of lines.
For each test case, output will be the elements arr[i] and arr[j] such that
arr[i]+arr[j] = key if exist otherwise print 'No Such Elements Exist”.

Sample I/O Problem II:

Input: Output:
2 10 40
10 No Such Element Exist
64 28 97 40 12 72 84 24 38 10
50
15
56 10 72 91 29 3 41 45 61 20 11 39 9 12 94
302

III. You have been given two sorted integer arrays of size m and n. Design
an algorithm and implement it using a program to find list of elements which
are common to both. (Time Complexity = O(m+n))

Input Format:
First line contains m (the size of first array).
Second line contains m space-separated integers describing first array. Third
line contains n (the size of second array).
Fourth line contains n space-separated integers describing second array.
Output Format:
Output will be the list of elements which are common to both.

Sample I/O Problem III:

Input: Output:
7 10 10 34 55
34 76 10 39 85 10 55
12
30 55 34 72 10 34 10 89 11 30 69 51

Note: Consider the following input format in the form of adjacency matrix for
graph based questions (directed/undirected/weighted/unweighted graph).

Input Format: Consider example of below given graph in Figure (a).

A boolean matrix AdjM of size V X V is defined to represent edges of the graph.
Each edge of graph is represented by two vertices (start vertex u, end vertex v).
That means, an edge from u to v is represented by making AdjM[u,v] and
AdjM[v,u] = 1. If there is no edge between u and v then it is represented by
making AdjM[u,v] = 0. Adjacency matrix representation of below given graph
is shown in Figure (b). Hence edges are taken in the form of adjacency matrix
from input. In case of weighted graph, an edge from u to v having weight w is
represented by making AdjM[u,v] and AdjM[v,u] = w.

Input format for this graph is shown in Figure (c).

First input line will obtain number of vertices V present in graph.
After first line, V input lines are obtained. For each line i in V, it contains V
space separated boolean integers representing whether an edge is present
between i and all V.
6. 3
Figure (a) Figure (b) Figure (c)

Week 6:

I. Given a (directed/undirected) graph, design an algorithm and implement

it using a program to find if a path exists between two given vertices or not.
(Hint: use DFS)

Input Format:
Input will be the graph in the form of adjacency matrix or adjacency list.
Source vertex number and destination vertex number is also provided as an
input.

Output Format:
Output will be 'Yes Path Exists' if path exists, otherwise print 'No Such Path
Exists'. Sample I/O Problem I:
II. Given a graph, design an algorithm and implement it using a program
to find if a graph is bipartite or not. (Hint: use BFS)

Input Format:
Input will be the graph in the form of adjacency matrix or adjacency list.

Output Format:
Output will be 'Yes Bipartite' if graph is bipartite, otherwise print 'Not Bipartite'.
Sample I/O Problem II:

III. Given a directed graph, design an algorithm and implement it using a

program to find whether cycle exists in the graph or not.

Input Format:
Input will be the graph in the form of adjacency matrix or adjacency list.

Output Format:
Output will be 'Yes Cycle Exists' if cycle exists otherwise print 'No Cycle
Exists'. Sample I/O Problem III:

Week 7:

Note: Input, output format along with sample input output for problem I and II
is same and is provided at the end of problem II.

I. After end term examination, Akshay wants to party with his friends. All
his friends are living as paying guest and it has been decided to first gather at
Akshay’s house and then move towards party location. The problem is that no
one knows the exact address of his house in the city. Akshay as a computer
science wizard knows how to apply his theory subjects in his real life and came
up with an amazing idea to help his friends. He draws a graph by looking in to
location of his house and his friends’ location (as a node in the graph) on a map.
He wishes to find out shortest distance and path covering that distance from
7. each of his friend’s location to his house and then whatsapp them this path so 3
that they can reach his house in minimum time. Akshay has developed the
program that implements Dijkstra’s algorithm but not sure about correctness of
results. Can you also implement the same algorithm and verify the correctness
of Akshay’s results? (Hint: Print shortest path and distance from friends’
location to Akshay’s house)

II. Design an algorithm and implement it using a program to solve previous

question's problem using Bellman- Ford's shortest path algorithm.

Input Format:
Input will be the graph in the form of adjacency matrix or adjacency list. Source
vertex number is also provided as an input.
Output Format:
Output will contain V lines.
Each line will represent the whole path from destination vertex number to
source vertex number along with minimum path weigth.

Sample I/O Problem I and II:

Input: Output:
5 1:0
04100 231:3
00004 31:1
02040 431:3
00004 5231:7
00000
1

III. Given a directed graph with two vertices ( source and destination).
Design an algorithm and implement it using a program to find the weight of the
shortest path from source to destination with exactly k edges on the path.

Input Format:
First input line will obtain number of vertices V present in the graph.
Graph in the form of adjacency matrix or adjacency list is taken as an input in
next V lines.

Next input line will obtain source and destination vertex number. Last input line
will obtain value k.

Output Format:
Output will be the weigth of shortest path from source to destination having
exactly k edges. If no path is available then print “no path of length k is
available”.

Sample I/O Problem III:

Input: Output:
4 Weight of shortest path from (1,4) with 2 edges : 9
0 10 3 2
0007
0006
0000
14
2
Week 8:

Note: Input, output format along with sample input output for problem I and II
is same and is provided at the end of problem II.

8. I. Assume that a project of road construction to connect some cities is

3
given to your friend. Map of these cities and roads which will connect them
(after construction) is provided to him in the form of a graph. Certain amount
of rupees is associated with construction of each road. Your friend has to
calculate the minimum budget required for this project. The budget should be
designed in such a way that the cost of connecting the cities should be minimum
and number of roads required to connect all the cities should be minimum (if
there are N cities then only N-1 roads need to be constructed). He asks you for
help. Now, you have to help your friend by designing an algorithm which will
find minimum cost required to connect these cities. (use Prim's algorithm)

II. Implement the previous problem using Kruskal's algorithm.

Input Format:
The first line of input takes number of vertices in the graph.
Input will be the graph in the form of adjacency matrix or adjacency list.

Output Format:
Output will be minimum spanning weight

Sample I/O Problem I and II:

Input: Output:
7 Minimum Spanning Weight: 39
0075000
0085000
7809700
5 0 9 0 15 6 0
0 5 7 15 0 8 9
0 0 0 6 8 0 11
0 0 0 0 9 11 0

III. Assume that same road construction project is given to another person.
The amount he will earn from this project is directly proportional to the budget
of the project. This person is greedy, so he decided to maximize the budget by
constructing those roads who have highest construction cost. Design an
algorithm and implement it using a program to find the maximum budget
required for the project.

Input Format:
The first line of input takes number of vertices in the graph.
Input will be the graph in the form of adjacency matrix or adjacency list.

Output Format:
Out will be maximum spanning weight.

Sample I/O Problem III:

Input: Output:
7 Maximum Spanning Weight: 59
0075000
0085000
7809700
5 0 9 0 15 6 0
0 5 7 15 0 8 9
0 0 0 6 8 0 11
0 0 0 0 9 11 0
Week 9:

I. Given a graph, Design an algorithm and implement it using a program

9. to implement Floyd- Warshall all pair shortest path algorithm. 2

Input Format:
The first line of input takes number of vertices in the graph.
Input will be the graph in the form of adjacency matrix or adjacency list. If a
direct edge is not present between any pair of vertex (u,v), then this entry is
shown as AdjM[u,v] = INF.

Output Format:
Output will be shortest distance matrix in the form of V X V matrix, where each
entry (u,v) represents shortest distance between vertex u and vertex v.

Sample I/O Problem I:

Input: Output:
5 Shortest Distance Matrix:
0 10 5 5 INF 0 10 15 5 15
INF 0 5 5 5 INF 0 5 5 5
INF INF 0 INF 10 INF INF 0 15 10
INF INF INF 0 20 INF INF INF 0 20
INF INF INF 5 0 INF INF INF 5 0

II. Given a knapsack of maximum capacity w. N items are provided, each

having its own value and weight. You have to Design an algorithm and
implement it using a program to find the list of the selected items such that the
final selected content has weight w and has maximum value. You can take
fractions of items,i.e. the items can be broken into smaller pieces so that you
have to carry

only a fraction xi of item i, where 0 ≤xi≤ 1.

Input Format:
First input line will take number of items N which are provided.
Second input line will contain N space-separated array containing weights of
all N items. Third input line will contain N space-separated array containing
values of all N items.
Last line of the input will take the maximum capacity w of knapsack.

Output Format:
First output line will give maximum value that can be achieved.
Next Line of output will give list of items selected along with their fraction of
amount which has been taken.

Sample I/O Problem II:

Input: Output:
6 Maximum value : 22.33
6 10 3 5 1 3 item-weight
621835 5-1
16 6-3
4-5
1-6
3-1

III. Given an array of elements. Assume arr[i] represents the size of file i.
Write an algorithm and a program to merge all these files into single file with
minimum computation. For given two files A and B with sizes m and n,
computation cost of merging them is O(m+n). (Hint: use greedy approach)

Input Format:
First line will take the size n of the array. Second line will take array s an input.

Output Format:
Output will be the minimum computation cost required to merge all the
elements of the array.

Sample I/O Problem III:

Input: Output:
10 960
10 5 100 50 20 15 5 20 100 10

Solved example: Consider arr[5] = { 10, 5, 100, 50, 20, 15}. As per the brute
force approach, first of all merge first two files (having 10 and 5 file size).
Cost of merging will be = 10+5=15. List will become {15, 100, 50, 20, 15}.
Similarly, again merging first two files ( i.e. having 15 and 100 file size). Cost
of merging will be = 15+100=115.
List will become {115, 50, 20, 15}.
For the subsequent steps the list becomes, (165, 20, 15}, {185, 15} and {200}.
Therefore total cost of merging = 15+115+165+185+200 = 680.
But this is not minimum computation cost. To find minimum cost, consider the
order arr[5] = {5, 10, 15, 20, 50, 100}. By applying the same approach, the total
cost of merging =
15+30+50+100+200 = 395.
Week 10:

I. Given a list of activities with their starting time and finishing time. Your
goal is to select maximum number of activities that can be performed by a single
person such that selected activities must be non-conflicting. Any activity is said
to be non-conflicting if starting time of an activity is greater than or equal to the
finishing time of the other activity. Assume that a person can only work on a
single activity at a time.

Input Format:
First line of input will take number of activities N.
Second line will take N space-separated values defining starting time for all the
N activities. Third line of input will take N space-separated values defining
finishing time for all the N activities.

Output Format:
10.Output will be the number of non-conflicting activities and the list of selected 2
activities.

Sample I/O Problem I:

Input: Output:
10 No. of non-conflicting activities: 4
1 3 0 5 3 5 8 8 2 12 List of selected activities: 1, 4, 7, 10
4 5 6 7 9 9 11 12 14 16

II. Given a long list of tasks. Each task takes specific time to accomplish it
and each task has a deadline associated with it. You have to design an algorithm
and implement it using a program to find maximum number of tasks that can be
completed without crossing their deadlines and also find list of selected tasks.

Input Format:
First line will give total number of tasks n.
Second line of input will give n space-separated elements of array representing
time taken by each task.
Third line of input will give n space-separated elements of array representing
deadline associated with each task.

Output Format:
Output will be the total number of maximum tasks that can be completed.

Sample I/O Problem II:

Input: Output:
7 Max number of tasks = 4
2 1 3 2 2 2 1 Selected task numbers : 1, 2, 3, 6
2386253

III. Given an unsorted array of elements, design an algorithm and

implement it using a program to find whether majority element exists or not.
Also find median of the array. A majority element is an element that appears
more than n/2 times, where n is the size of array.

Input Format:
First line of input will give size n of array.

Second line of input will take n space-separated elements of array.

Output Format:
First line of output will be 'yes' if majority element exists, otherwise print 'no'.
Second line of output will print median of the array.

Sample I/O Problem III:

Input: Output:
9 yes
442322322 2

Week 11:

I.Given a sequence of matrices, write an algorithm to find most efficient way

to multiply these matrices together. To find the optimal solution, you need to
find the order in which these matrices should be multiplied.

Input Format:
First line of input will take number of matrices n that you need to multiply.
For each line i in n, take two inputs which will represent dimensions aXb of
matrix i.
1
Output Format:
Output will be the minimum number of operations that are required to
multiply the list of matrices.

Sample I/O Problem I:

Input: Output:
3 4500
10 30
30 5
5 60
Solved Example: Consider a sequence of three matrices A of size 10X30, B of
size 30X5, C of size 5X60. Then,
(AB)C = (10*30*5) + (10*5*60) = 4500 operations A(BC) = (30*5*60) +
(10*30*60) = 27000 operations. Hence the ouput of the program must be 4500

II.Given a set of available types of coins. Let suppose you have infinite supply
of each type of coin. For a given value N, you have to Design an algorithm
and implement it using a program to find number of ways in which these coins
can be added to make sum value equals to N.

Input Format:
First line of input will take number of coins that are available. Second line of
input will take the value of each coin.
Third line of input will take the value N for which you need to find sum.

Output Format:
Output will be the number of ways.

Sample I/O Problem II:

Input: Output:
4 5
2563
10

Solved Example: Let coin value set is C = {2,3,6,5} and the value N = 10.
There are five solutions: {2,2,2,2,2}, {2,2,3,3}, {2,2,6}, {2,3,5} and {5,5}.
Hence the output is 5.

III.Given a set of elements, you have to partition the set into two subsets such
that the sum of elements in both subsets is same. Design an algorithm and
implement it using a program to solve this problem.

Input Format:
First line of input will take number of elements n present in the set. Second
line of input will take n space-separated elements of the set.

Output Format:
Output will be 'yes' if two such subsets found otherwise print 'no'.

Sample I/O Problem III:

Input: Output:
7 yes
1 5 4 11 5 14 10

Solved Example: Let set is S = {1, 5, 4, 11, 5, 14, 10}. Sum of the elements =
1+5+4+11+5+14+10 = 50. Now dividing the set into two halves such that sum
of elements of both the subsets = (50/2) = 25. Therefore, subsets are {1, 5, 5,
14} and {4, 11, 10}.
Week 12:
2
I.Given two sequences, Design an algorithm and implement it using a program
to find the length of longest subsequence present in both of them. A
subsequence is a sequence that appears in the same relative order, but not
necessarily contiguous.

Input Format:
First input line will take character sequence 1. Second input line will take
character sequence 2.

Output Format:
Output will be the longest common subsequence along with its length.

Sample I/O Problem I:

Solved Example: Consider two input strings “AGGTAB” and “GXTXAYB.

Then the length of longest common subsequence is 4 i.e. for subsequence
“GTAB”.

II.Given a knapsack of maximum capacity w. N items are provided, each

having its own value and weight. Design an algorithm and implement it using
a program to find the list of the selected items such that the final selected
content has weight <= w and has maximum value. Here, you cannot break an
item i.e. either pick the complete item or don't pick it. (0-1 property).

Input Format:
First line of input will provide number of items n.
Second line of input will take n space-separated integers describing weights
for all items. Third line of input will take n space-separated integers
describing value for each item.
Last line of input will give the knapsack capacity.

Output Format:
Output will be maximum value that can be achieved and list of items selected
along with their weight and value.

Sample I/O Problem I:

Input: Output:
5 Value = 16
23346 Weights selected : 3 3 4
12594 Values of selected weights : 2 5 9
10

III.Given a string of characters, design an algorithm and implement it using a

program to print all possible permutations of the string in lexicographic order.

Input Format:
String of characters is provided as an input.

Output Format:
Output will be the list of all possible permutations in lexicographic order.

Sample I/O Problem II:

Input: Output:
CAB ABC
ACB
BAC
BCA
CAB
CBA

Week 13:

I.Given an array of characters, you have to find distinct characters from this
array. Design an algorithm and implement it using a program to solve this
problem using hashing. (Time Complexity = O(n))

Input Format:
First line of input will give the size n of the character array.
Second line of input will give n space-separated elements to character array.

Output Format: Output will be the list of characters present in the array in
alphabetical order and frequency of each character in the array.

Sample I/O Problem I:

II.Given an array of integers of size n, design an algorithm and write a

program to check whether this array contains duplicate within a small window
of size k < n.

Input Format:
First input line contains number of test cases T. For each test case T, there will
be three input lines. First line contains size n of array.
Second input line contains n space-separated array elements. Third input line
2
contains value k.

Output Format:
Output will have T number of lines.
For each test case, output will be “Duplicate present in window k” if the
duplicate element is found in the array, otherwise “Duplicate not present in
window k”.

Sample I/O Problem II:

Input: Output:
2 Duplicate not present in window 3.
10 Duplicate present in window 4.
1234123412
3
12
123123123123
4

III.Given an array of nonnegative integers, Design an algorithm and

implement it using a program to find two pairs (a,b) and (c,d) such that a*b =
c*d, where a, b, c and d are distinct elements of array.

Input Format:
First line of input will give size of array n.
Second line of input will give n space-separated array elements.
Output Format:
First line of output will give pair (a,b) Second line of output will give pair
(c,d).

Sample I/O Problem III:

Input: Output:
10 42
31 23 4 1 39 2 20 27 8 10 18
Week 14:

I.Given a number n, write an algorithm and a program to find nth ugly

number. Ugly numbers are those numbers whose only prime factors are 2, 3 or
5. The sequence 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24,. is sequence of
ugly numbers.

Input:
First line of input will give number of test cases T. For each test case T, enter
a number n.

Output:
There will be T output lines.
For each test case T, Output will be nth ugly number.
1
Sample I/O Problem I:

II.Given a directed graph, write an algorithm and a program to find mother

vertex in a graph. A mother vertex is a vertex v such that there exists a path
from v to all other vertices of the graph.

Input:
Graph in the form of adjacency matrix or adjacency list is provided as an
input.

Output:
Output will be the mother vertex number. Solved Example: Consider a
directed graph: In this graph, vertex 0 is mother vertex.
Total 24
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Thomas H. Cormen, Charles E. Leiserson, Ronal L. 2nd 2006.
Rivest, Clifford Stein:” Introduction to Algorithms”,
2nd Edition, PHI,
Reference Books
1. Donald E.Knuth:”The Art of Computer Programming: 3rd 1997
Volume 1: Fundamental Algorithms”,3rd Edition
2 Ellis Horowitz, Sartaj Sahni, SanguthevarRajasekaran:” 2nd 2007
Fundamentals of Computer Algorithms”, 2nd Edition,
University press,.

12. Mode of Test / Quiz / Assignment / Mid Term Exam / End Term Exam
Evaluation
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 442 Course Title: Automated

Reasoning
2. Contact Hours:L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 4

7. Category of Course: DE

8. Pre-requisite: TCS 344 Probability and Random Process

9. Course After completion of the course the students will be able to:
Outcome**: CO1: represent mathematical and other knowledge using logical formalism
CO2: understand the history of formalizing mathematical knowledge
CO3: know and understand the advantages and limitations of the main approaches
and techniques in automated reasoning of mathematical knowledge
CO4: apply different automated reasoning techniques to new problems

** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate
10.Details of the Course:
Contact
Sl. No. Contents
Hours
Unit 1: Introduction and motivation: Role of logic in Computer Science, problem
1 representation. Basic notions: language, models, interpretations, validity, proof, 8
decision problems in logic. decidability.
Unit 2: Propositional logic: Syntax, semantics, proof systems, Validity,
2 satisfiability and unsatisfiability, soundness and completeness. 8

Unit 3: Mechanization: truth tables, normal forms, semantic tableau, resolution,

proof by contradiction, example. First order predicate logic theory: Quantifiers,
3 8
first order models, validity and satisfiability, semantic tableaux.

Unit 4: Normal forms, skolemization: Elimination of quantifiers, unification,

4 resolution and various resolution strategies, equality axioms and para-modulation. 8
Horn formulas and programs.
Unit 5: Prolog as a restricted resolution-based theorem prover. Undecidability
5 8
and incompleteness in logic, compactness Theorem.
Total 40

11. Suggested Books:

SL. Name of Authors/Books/Publishers Editio Year of Publication /
No. n Reprint
Textbooks
1 Michael Huth and Mark Ryan, “Logic in Computer 2nd 2005
Science: Modelling and Reasoning about Systems”,
Cambridge University Press
2 Arindama Singh, “Logics for Computer Science”, 2nd 2020
Prentice Hall of India.
Reference Books
1. M. Ben-Ari, “Mathematical Logic for Computer 3rd 2012
Science”, Springer
2 Elliott Mendelson , “Introduction to Mathematical 6th 2015
Logic”, CRC Press

12. Mode of Test / Quiz / Assignment / Mid Term Exam / End Term Exam
Evaluation
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

Introduction to cryptography
1. Subject Code: TCS 493 Course Title: and PKC
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 4th

7. Category of Course: DE

8. Pre-requisite: TCS 332 Fundamental of Information Security and Blockchain.

9. Course After completion of the course the students will be able to:
Outcome**: CO1 Explain symmetric and asymmetric key cryptosystems.
CO2: Know the working of cryptography techniques.
CO3: Analyze the different types of cryptosystems.
CO4: Use cryptographic techniques to implement information
security protocols.
CO5: Apply cryptographic techniques in different applications.
CO6: Develop symmetric and asymmetric key cryptosystems.

** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate
10.Detailed Syllabus
UNIT CONTENTS Contact
Hrs

Unit-I
Basics of cryptography

What is cryptography, what is confidentiality, data integrity, authentication,

and nonrepudiation, applications of cryptography - chip based payment
cards, digital currencies, computer passwords, digital communications,
1 plaintext, cipher-text, cipher - characteristics of a good cipher, encryption,
decryption, Key - significance of key length, symmetric and asymmetric key
cryptography, cryptanalysis, OSI security architecture- security attacks, 10
security services, security mechanisms
2. Unit-II 8
Mathematics for cryptography

Concept of divisibility, prime numbers, importance of prime numbers in

cryptography, euclid theorem for GCD, extended euclidean algorithm,

modular arithmetic, random number generators, deterministic and

nondeterministic random number generators, XOR, bit shifts, euler's totient
theorem, chinese remainder theorem
3. Unit-III 10
Symmetric key cryptosystem

Secret Key (symmetric) cryptography - stream and block ciphers, additive

and multiplicative ciphers, rail fence technique, playfair cipher, hill cipher,
vernam cipher, Vigenère Cipher, RC4 algorithm, DES, 2DES, 2-3DES,
3DES, AES, block cipher modes of op
4. Unit-IV 8
Asymmetric key cryptosystem

RSA, Diffie Hellman key exchange protocol, Elliptic curve cryptography

(ECC), ElGamal encryption system

5. Unit-V 10
Digital signature and message integrity mechanisms

DSS algorithm, RSADS algorithm, ECDSA algorithm, Message integrity,

hash functions, MAC functions, HMAC, secure electronic transaction, use of
ECDSA in blockchain implementation
TOTAL 46

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of

No. Publication /
Reprint
Textbooks
1. William Stallings, “Cryptography and Network 7th 2016
Security: Principles and Practice”, Pearson
publication, 2020
Reference Books
1. Charles P. PFleeger, Shari Lawrence Pfleeger, Jonathan 5th 2018
Margulies, “Security in Computing ”, 5th Edition,
Prentice Hall
2. Roger Wottenhofer, “ Distributed Ledger Technology, 2nd 2017
The science of Blockchain”, Invested Forest
Publishing (2e), 2017

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

Information Security and
1. Subject Code: TCS 426 Course Title: Risk Management
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 4

7. Category of Course: DE

8. Pre-requisite: Information Security foundations and programming

9. Course After completion of the course the students will be able to:
Outcome**: CO1Information Security and Risk Management

CO2: Information Security and Risk Management

CO3: Analyze the different types of information security risk management

techniques.

CO4: Use information security risk management techniques to

implement informationsystems.

CO5 Apply information security risk management techniques in different

applications.

CO6: Develop information security risk management techniques.

** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate
10. Detailed Syllabus

UNIT CONTENTS Contact

Hrs
Overview of information security and cryptography
What is information security, why we need information security, zero trust
model
Protection against- unauthorised modification, unauthorised deletion and
unauthorised access, different types of user authentication techniques,
access control techniques
Pillars of information security - confidentiality, availability and integrity
What is cryptography, what is confidentiality, data integrity, authentication,
Unit-I and nonrepudiation, applications of cryptography, network security model, 10
plaintext, ciphertext, cipher - characteristics of a good cipher, encryption,
decryption, Key - significance of key length, symmetric and asymmetric
key cryptography, cryptanalysis, OSI security architecture- security
attacks, security services, security mechanisms

Unit-II Risk management and analysis 8

Overview of risk management, risk identification, identifying the assets,
threats and vulnerabilities, risk control strategies, selection of a risk control
strategy, planning for risk analysis, performing risk analysis and assessment
Unit-III Information security planning and implementations 8
Information security policy, standards and practices, information security
blueprint, security education, training and awareness program, project
management for information security, technical topics of implementation,
nontechnical aspects of implementation

Unit-IV Disaster recovery and risk monitoring 8

What is disaster in information security, disaster recovery planning, disaster
recovery plan, risk monitoring, requirement of risk monitoring, various
phases of risk monitoring

Unit-V Vulnerabilities and security assessment 8

What is vulnerability, sources of vulnerabilities, vulnerability assessment,
system security policy, building a security policy, security requirement
specification, threat identification, threat models (Dolev Yao model and
CK adversary model), threat analysis, vulnerability identification and
assessment, security certification, security monitoring and auditing
TOTAL 46

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication /

No. Reprint
Textbooks
1. William Stallings, “Cryptography and Network Security: 8th 2020
Principles and Practice”, Pearson publication, 2020
2 Michael E. Whitman and Herbert J. Mattord , Principles of 2nd 2007
Information Security, (2e), Thomson Learning, 2007
3. NIIT, Introduction to Information Security Risk 2nd 2004
Management, PHI, 2004
4 Joseph Migga Kizza , A Guide to Computer Network 3rd 2013
Security, Springer-Verlag London Limited.
Reference Books
1. Charles P. Pfleeger, Shari Lawrence Pfleeger, Jonathan 5th 1989
Margulies, “Security in Computing”, 5th Edition, Prentice
Hall.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER IV

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-484 Course Title: Biometric Security

2. Contact Hours: L: T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 03

6. Semester: 4

7. Category of Course: 4DC

8. Pre-requisite: Mathematical Foundations for Artificial Intelligence (TCS343)

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Basic information on the fundamental physical and organic science.
CO2: Understand designing standards of biometric frameworks.
CO3: Understand biometric frameworks and be able to examine and design for essential
biometric framework applications.
CO4: Understand various Biometric security issues.
CO5: Describe Cryptography security
CO6: Understanding of Fuzzy model

** Describe the specific knowledge, skills or competencies the students are expected to acquire
or demonstrate
10.Details of the Course:
Sl. Contact
Contents
No. Hours
Unit 1: Introduction- Authentication systems, Development of biometric
authentication. Basic terms, biometric data, biometric characteristics,
biometric features, biometric templates and references. Expected properties of
1 09
biometric identifiers. Basics in biometric errors estimation. Enrolment,
verification and identification. Applications of Biometrics.

Unit 2: Fingerprints and Hand Geometry: Technical description,

Characteristics, Competing technologies, Strengths–Weaknesses,
2 Deployment. Face and Voice Recognition: Technical description, 09
Characteristics, Strengths-Weaknesses, Deployment.

Unit 3:
3 Biometric System Security: Secure transfer of biometric data. Secure storage, 10
use of smart cards, principles of match-off-card and match-on-card
techniques. Biometrics in the cloud. Points of attack. Privacy models.
Spoofing: Static and dynamic liveness features. Liveness detection in
biometrics. Selected liveness detection techniques, frequency analysis for
paper printouts detection.
Unit 4:
4 Protection: Overview of principles from cryptography to secure fuzzy data. 08
Template protection strategies: feature protection, key-binding, key-
generating, hybrids.
Unit 5:

5 Overview of fuzzy vaults, fuzzy commitment, fuzzy extractors and revocable 08

bio tokens. Bio cryptographic infrastructures for secure template
management.
Total 44
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Jiankun Hu, David Chek Ling Ngo, Andrew Beng Jin Teoh, 2015
“Biometric Security”, Cambridge Scholars Publishing
2. Khalid Saeed, Jerzy Pejas, Romuald Mosdorf. “Biometrics, 2010
Computer Security Systems and Artificial Intelligence
Application, Springer.
3. John D. Woodward, Jr. Nicholas M. Orlans Peter T. Higgins, 2003
“Biometrics”, dream tech

Reference Books
1. 1. John Chirillo and Scott Blaul,” Implementing Biometric 2005
Security”, Wiley Eastern Publications, 2005

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 501 Course Title: System Software

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: V

7. Category of Course: DC

8. Pre-requisite: TCS-402 Finite Automata and Formal Languages

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Define system software and differentiate system software with other software’s.
CO2: Assess the working of Assembler, Loader/Linker and Macroprocessor.
CO3: Understand the concept of passes in translators.
CO4: Determine the purpose of linking, and types of linking.
CO5: Develop the system software according to machine limitations.
CO6: Compare and Contrast the various text editors.
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
1 Machine Architecture: Introduction, System Software and its relation to Machine
9
Architecture, Simplified Instructional Computer (SIC), Architecture of SIC
Machine , SIC Programming Examples
Unit 2:
Assemblers: Basic Assembler Functions, A Simple SIC Assembler, Algorithm and
2 Data Structures for Assemblers, Machine Dependent Assembler Features -
Instruction Formats & Addressing Modes, Program Relocation.
9
Machine Independent Assembler Features – Literals, Symbol-Definition
Statements, Expression, Program Blocks, Control Sections and Programming
Linking, Assembler Design Operations - One-Pass Assembler, Multi-Pass
Assembler
Unit 3:
3 Loaders and Linkers: Basic Loader Functions - Design of an Absolute Loader, A 10
Simple Bootstrap Loader, Machine-Dependent Loader Features – Relocation,
Program Linking, Algorithm and Data Structures for a Linking Loader; Machine-
Independent Loader Features - Automatic Library Search, Loader Options, Loader
Design Options - Linkage Editor, Dynamic Linkage, Bootstrap Loaders

Editors and Debugging Systems: Text Editors - Overview of Editing Process,

User Interface, Editor Structure, Interactive Debugging Systems - Debugging
Functions and Capabilities, Relationship With Other Parts Of The System, User-
Interface Criteria
Unit 4:
Macro Processor: Basic Macro Processor Functions - Macro Definitions and
Expansion, Macro Processor Algorithm and Data Structures, Machine-Independent
4 Macro Processor Features - Concatenation of Macro Parameters, Generation of 8
Unique Labels, Conditional Macro Expansion, Keyword Macro Parameters, Macro
Processor Design Options, General-Purpose Macro Processors, Macro Processing
Within Language Translators
Unit 5:
Lex and Yacc :Lex and Yacc - The Simplest Lex Program, Recognizing Words
With LEX, Symbol Tables, Grammars, Parser-Lexer Communication, The Parts of
Speech Lexer, A YACC Parser, The Rules Section, Running LEX and YACC, LEX
and Hand- Written Lexers, Using LEX - Regular Expression, Examples of Regular
5 Expressions, A Word Counting Program, Parsing a Command Line. 10

Using YACC – Grammars, Recursive Rules, Shift/Reduce Parsing, What YACC

Cannot Parse, A YACC Parser - The Definition Section, The Rules Section,
Symbol Values and Actions, The LEXER, Compiling and Running a Simple
Parser, Arithmetic Expressions and Ambiguity, Variables and Typed Tokens.
Total 46
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks/ Reference Books
1. Leland.L.Beck: “ System Software: an introduction to 3rd 1997
systems programming”, 3rd Edition, Addison-Wesley,.
2 John.R.Levine,” Tony Mason and Doug Brown: Lex 1st 1998
and Yacc”, O'Reilly, SPD,.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

1. Name of Department: - Computer Science and Engineering

Operating Systems
TCS 502
2. Subject Code: Course Title:

3. Contact Hours: L: 3 T: 0 P: 0
0
4. Examination Duration (Hrs): Theory 3 Practical

5. Relative Weight: CIE 25 MSE 25 SEE 50

6. Credits: 3

7. Semester: 4

8. Category of Course: DC

9. Pre-requisite: TCS 301, TCS 302, TCS 404

9. Course After completion of the course the students will be able to:
Outcome CO1 Understand the concept and design issues associated with an operating system.
**:
CO2: Identify the problems related to process management, synchronization and apply
learned methods to solve basic problems.

CO3. Explain the basics of memory management and the use of virtual memory in
modern operating systems.

CO4. Understand the concept deadlock avoidance, prevention, and detections

techniques.

CO5: Implementation of process management, memory management and file

management using system calls.

CO6: Analyze the data structures and algorithms used for developing an operating
system.
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.
10.Details of the Course:
Sl. Contact
Contents
No. Hours
Introduction to Operating Systems, UNIX: What operating systems do; Operating
System structure; Operating System Services; User - Operating System interface; System
1 8
calls; Types of system calls; Operating System structure; Unix command: Command
Structure, Internal and External commands, filters; vi editor.
Process Management: Process concept; Operations on processes; Multithreading
models; Threading issues. Process Scheduling: Basic concepts; Scheduling criteria;
Scheduling algorithms; Multiple-Processor scheduling; Thread scheduling.
2 10
Process Synchronization: Inter-process communication; Synchronization: The Critical
section problem; Peterson’s solution; Synchronization hardware; Semaphores; Classical
problems of synchronization.
Deadlocks: Deadlocks: System model; Deadlock characterization; Methods for handling
deadlocks; Deadlock prevention; Deadlock avoidance; Deadlock detection and recovery
from deadlock.
3 Memory Management: Memory Management Strategies: Background; Swapping; 10
Contiguous memory allocation; Paging; Structure of page table; Segmentation. Virtual
Memory Management: Background; Demand paging; Page replacement; Allocation of
frames; Thrashing
File System, Implementation of File System: File System: File concept; Access
methods; Directory structure; Protection. File system structure; Directory
implementation; Allocation methods; Free space management.
4 8
Secondary Storage Structures: Mass storage structures; Disk structure; Disk scheduling;
Disk management; Swap space management. Protection: Goals of protection, Principles
of protection, Access matrix.
File System, Implementation of File System: File System: File concept; Access
methods; Directory structure; Protection. File system structure; Directory
implementation; Allocation methods; Free space management.
5 8
Secondary Storage Structures: Mass storage structures; Disk structure; Disk
scheduling; Disk management; Swap space management. Protection: Goals of protection,
Principles of protection, Access matrix.
Total 44
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint
Textbooks
1. Abraham Silberschatz, Peter Baer Galvin, Greg Gagne: 7st 2006
Operating System Principles, 7th edition, Wiley India,
2006.
2. Abraham Silberschatz, Peter Baer Galvin, Greg Gagne: 7th 2006
Operating System Principles, 7th edition, Wiley India, 2006.
3. Unix concepts and applications – Sumitabha Das 1st 2005

Reference Books
1. Andrew S Tanenbaum: Operating Systems: Design and 3rd 2006
Implementation, 3rd edition, Prentice Hall, 2006
2. Stuart E. Madnick, John Donovan: Operating Systems, 2008
Tata McGraw Hill, 2008
10.
12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V
Name of Department: - Computer Science and Engineering
Database Management
1. Subject Code: TCS-503 Course Title: System
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3
6. Semester: 5th
7. Category of Course: DC
8. Pre-requisite: TCS 302, TCS 404

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the different issues involved in the design and implementation of a
database system.

CO2: Study the physical and logical database designs, database modeling, relational,
hierarchical, and network models.

CO3: Understand and use data manipulation language to query, update, and manage a
database.

CO4: Develop an understanding of essential DBMS concepts such as: database security,
integrity, concurrency.

CO5: Design and build a simple database system and demonstrate competence with the
fundamental tasks involved with modeling, designing, and implementing a DBMS.

CO6: Evaluate a business situation and designing & building a database application
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.
10.Details of the Course:
Sl.
Contents Contact Hours
No.
Unit 1:
Introduction: An overview of DBMS; Advantages of using DBMS
approach; Database systems vs File Systems, Database system concepts
and architecture
1 Data models, schemas, and instances; Three-schema architecture and data 9
independence; Database languages and interfaces; The database system
environment; Centralized and client-server architectures; Classification of
Database Management systems.

Unit 2:
Entity-Relationship Model: Using High-Level Conceptual Data Models
2 9
for Database Design; An Example Database Application; Entity Types,
Entity Sets, Attributes and Keys; Relationship types, Relationship Sets,
Roles and Structural Constraints; Weak Entity Types; Refining the ER
Design; ER Diagrams, Naming Conventions and Design Issues;
Relationship types of degree higher than two.

Relational Model and Relational Algebra: Relational Model Concepts;

Relational Model Constraints and Relational Database Schemas; Update
Operations, Transactions and dealing with constraint violations; Unary
Relational Operations: SELECT and PROJECT; Relational Algebra and
Calculus Operations from Set Theory; Binary Relational
Operations: JOIN and DIVISION; Additional Relational Operations.
Examples of Queries in Relational Algebra; Relational Database Design
Using ER- to-Relational Mapping.
3 11
SQL – 1: SQL Data Definition and Data Types; Specifying basic constraints
in SQL; Schema change statements in SQL; Basic queries in SQL; More
complex SQL Queries.

Insert, Delete and Update statements in SQL; Specifying constraints as

Assertion and Trigger; Views (Virtual Tables) in SQL; Additional features
of SQL; Database programming issues and techniques; Embedded SQL,
Dynamic SQL; Database stored procedures.

Optimization of SQL Queries through Indexes, Concepts of NoSQL.

Unit 4: Database Design – 1: Informal Design Guidelines for Relation
Schemas; Functional Dependencies; Normal Forms Based on Primary Keys;
General Definitions of Second and Third Normal Forms; Boyce-Codd
Normal Form
4 9
Properties of Relational Decompositions; Algorithms for Relational
Database Schema Design; Multivalued Dependencies and Fourth Normal
Form; Join Dependencies and Fifth Normal Form; Inclusion Dependencies;
Other Dependencies and Normal Forms

Unit 5:
Transaction Management: The ACID Properties; Transactions and
Schedules; Concurrent Execution of Transactions; Lock- Based
Concurrency Control; Performance of locking; Transaction support in SQL;
5 Introduction to crash recovery; 2PL, Serializability and Recoverability; 10
Lock
9Management; Log Files; Checkpointing; Recovering from a System Crash;
Media Recovery
Total 48
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. McGraw-Hill. Date K., Swamynathan S. An Introduction to 2nd 2012
Database Systems. Eight Edition. Pearson.
2. Elmasri R. and Navathe S.B., Fundamentals of Database 2nd 2012
Systems.
3. Fifth Edition.Pearson. Singh S.K., Database Systems- 2nd 2011
Concepts, Designs and Application. 2nd Edition. Pearson
4. Date, C.J. Introduction to Database Systems (Vol I & II) 8th 8th 2004
Edition. Addison- Wesley.

Reference Books
1. Silberschatz A. Korth H. F. Sudarshan S., Database System 1st 2014
Concepts. Sixth Edition

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering Computer Based Numerical

1. Subject Code: TMA 502 Course Title: and Statistical Technique

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 5

7. Category of Course: DC

8. Pre-requisite: TMA 101, TMA 201

9. Course After completion of the course the students will be able to:
Outcome* CO1: Develop the notation of errors, finding of errors, roots and apply them in problem
*: solving in concern subject.
CO2: Understand the methods of interpolation techniques and apply them.
CO3: Elaborate the basics of numerical differentiation and integration and implement
them.
CO4: Explain the concepts of differential equation.
CO5: Elaborate the basics of correlation and regression, curve fitting and be able to
apply the methods from these subjects in problem solving.
CO6: Examine statistical techniques and able to relate these to real problems.

** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction to Numbers and their accuracy, Computer Arithmetic,
Mathematical preliminaries, Errors and their Computation, General error formula,
Order of Approximation.
Solution of Algebraic and Transcendental Equation: Bisection Method,
1 Iteration method, Secant Method, Newton Raphson method, Rate of convergence 10
and their algorithms.
Solution of system of linear equations: Gauss Elimination method, Gauss Jordan
method and Gauss Seidel method and their algorithms.

Unit 2: Interpolation: Introduction to Finite Differences, Difference

2 09
tables, Polynomial Interpolation: Newton’s forward and backward
formula, Central difference formulae: Gauss forward and backward
formula. Interpolation with unequal intervals: Lagrange’s interpolation,
Newton divided difference formula and their algorithms.

Unit 3: Numerical Differentiation and Integration: Numerical Differentiation

for Interpolation Formulae, General Quadrature formula, Trapezoidal rule,
Simpson’s 1/3 and 3/8 rule and their algorithms.
3 09
Numerical Solution of Differential Equations: Euler’s explicit and implicit
methods, modified Euler’s method, Runge-Kutta Method, Solution of Boundary
Value Problem by Finite Difference Method and their algorithms.

Unit 4: Statistical Computation: Introduction to Method of least squares, Curve

4 08
Fitting of different types of curves. Data fitting with Cubic spline Interpolation.

Unit 5: Correlation and Regression Analysis: Introduction of correlation and

5 regression, Correlation coefficient and it’s application in computer science, Linear 09
and Nonlinear Regression, Multiple Regression.

Total 45
11. Suggested Books:
SL Name of Authors/Books/Publishers/Place of Edition Year of
. Publication Publication /
N Reprint
o.
Textbooks
1. Gupta C. B. Singh S. R. and Kumar Mukesh First 2016
“Engineering Mathematics for Semesters III and IV” edition
McGraw Hill Education,
2. Rajaraman V, “Computer Oriented Numerical First 2020
Methods”, Pearson Education. edition
Reference Books
1. Sastry, S. S, “Introductory Methods of Numerical Second 2009
Analysis”, Pearson Education.
2. Jain, Iyengar and Jain, “Numerical Methods for Fourth 2003
Scientific and Engineering Computations”, New Age
Int.

3. Steven C Chapra, “Applied Numerical Methods with Second 2007

Matlab”.

12 Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

Database Management
1. Subject Code: PCS-503 Course Title: System Lab

2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: CIE 25 MSE 25 SEE 50
5. Credits: 2

6. Semester: 5th

7. Category of Course: DC

8. Pre-requisite: TCS 302, TCS 404

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Students get practical knowledge on designing and analysis of conceptual model
and mapping of conceptual model to relational database systems.
CO2: Design and implement SQL queries using DDL and DML concepts for updation
and managing a database.
CO3: Design and implement advance SQL queries such as relational constraints,
joins, set operations, aggregate functions, and views.
CO4: Design and implement queries using optimization techniques.
CO5: Application of transaction control language (TCL), data control language
(DCL) in SQL to evaluate practical implications of DBA such as transaction,
recovery, and security.
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10.Details of the Course:

Sl. List of problems for which student should develop program and execute in Contact
No. the Laboratory Hours
Problem Statement 1:
Granting Permissions: Data Control Language (DCL) Commands: 2
1.
Grant/Revoke.

Problem Statement 2:
Creation of database/tables for different applications (DDL commands):
2. 2
Creating tables (without constraints)

3. Problem Statement 3: 2
Creation of database/tables for different applications (DDL commands):
Creating tables (with Column level and Table level constraints)

Problem Statement 4:
Inserting data into database (DML Commands): updating / deleting records in a
4. table. 2

Problem Statement 5:
5. TCL command: saving (commit) and undoing (rollback) 2

Problem Statement 6:
6. Data retrieval (DR) command: Fetching data from database using SELECT, 2
FROM and WHERE command (Projection and Selection)
Problem Statement 7:
7. 2
Perform the following: Altering a Table, Dropping/ Truncating/ renaming
Tables, backing up/ restoring a database
Problem Statement 8:
For a given set of relational schemas, create tables and perform the following:
8. Simple queries; Simple queries with aggregate functions (group by and having 2
clause).

Problem Statement 9:
Queries involving, Date functions, string functions (character manipulations and
9. 2
case manipulation functions)

Problem Statement 10:

10. 2
Math functions, CASE, DECODE, Implicit and explicit typecasting functions.
Problem Statement 11:
11. Join Queries: Inner join, Equi-join, natural join, Outer join (LEFT-OUTER 3
JOIN, RIGHT OUTER JOIN and FULL OUTER JOIN)
Problem Statement 12:
12. Subqueries-with IN clause, with EXISTS clause 2

Problem Statement 13:

For a given set of relation tables perform the following: Creating Views (with and
13. without Check options), Dropping a view, Selecting data from a view. 3

Problem Statement 14:

For a given set of relation tables perform the following: Creating Views (with
14. and without Check options), Dropping a view, Selecting data from a view. 2

Problem Statement 15:

15. Generate primary key values with Sequence. 2
Problem Statement 16:

16. optimization of queries with Indexes 2

Problem Statement 17:

17. Applying SYNONYMS on database objects. 2

Problem Statement 18:

18. Introduction to Dynamic SQL 2

Problem Statement 19:

19. Introducing Triggers on data objects 3

Problem Statement 20:

20. 3
Introducing Procedures on data objects for optimization of queries
Total 40
11. Suggested Books:
S. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint

Text Books

1. RamezElmasri, Shamkant, B. Navathe, “Database 7th 2015

Systems”, Pearson Education, 7Th Edition.

3. M L Gillenson, “Introduction to Database 2nd 2012

Management”, Wiley Student Edition

Reference Books

1. Mary Beth Roeser: [1Oracle® Database] SQL 2nd 2017

Language Reference 12c Release 1 (12.1) E41329-
25

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

Cloud-Based Application
1. Subject Code: TCS 552 Course Title: Development and Management

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 5

7. Category of Course: DE

8. Prerequisite: TCS-451

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Recognize the cloud based application development platforms and
economic benefits.
CO2: Analyze the use case of various cloud service provider’s applications and
platforms.

CO3: Apply the advanced cloud computing application’s concepts.

CO4: Analyze the use case of cloud-based application deployment and
management concepts.
CO5: Explore the use case of various cloud platforms, offered services and
security aspects.

CO6: Develop and deploy the cloud based server-side application using Node.js
and the front-end using React.

** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10.Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Fundamental of Cloud Based Applications
Cloud Data centers, Software stack, Virtualization, software defined networks
1 and storage, cloud storage, and programming models, Clouds Based Application 9
development motivating factors, benefits, challenges, service models, SLAs and
security. Concepts behind data center design and management, Economic and
technological benefits of the cloud paradigm.
2 Unit 2:
Cloud Platforms in Industry 9
Amazon Web Services: Compute Services, Storage Services, Communication
Services.
Google App Engine: Architecture, Core Concepts, Application Life Cycle, Cost
Model, Observations.
Microsoft Azure: Azure Core Concepts, SQL Azure, Azure Compute and
Storage, Azure Database and Networking, Monitoring and Managing Azure
Solutions.
IBM Cloud (Kyndryl), Salesforce, Heroku, Alibaba Cloud, Oracle Cloud,
Tencent Cloud, OVHcloud, DigitalOcean, and Linode (Akamai).
Case study on available Cloud Platforms in Industry.
Unit 3:
Advanced Cloud Computing
Energy Efficiency in Clouds, Green Cloud Computing Architecture, Market
based Management of Clouds, Market-Oriented Cloud Computing, Reference
Model for MOCC.
Federated Clouds/Intercloud: Definition, Characterization, Cloud Federation
3 Stack, Technologies for Cloud Federation. 9
Third Party Cloud Services, MetaCDN, Spot Cloud, Cloud Authentication
Protocols, Cloud Security Threats with Cloud Apps.
Virtualized CPU, memory and I/O resources, network (SDN) and storage
(SDS), Key role of virtualization to enable the cloud. Cloud storage concepts
like data distribution, durability, consistency and redundancy.
Case study on Advanced Cloud Computing services.
Unit 4:
Cloud Management
Fundamentals of Cloud Management, Management Services, Cloud properties,
Multi-tier Application Deployment in Clouds, Challenges, Requirements,
4 Service Level Agreements (SLAs),Billing& Accounting.
9
Cloud Policy and Governance: Risk Management and Regulatory Practices.
Cloud Analytics and Cost Metrics.
Case study on Cloud Management Services, Distributed file systems, NoSQL
databases, object storage using HDFS, CephFS, HBASE, MongoDB,
Cassandra, DynamoDB, S3, and Swift.
Unit 5:
Cloud Based Secured Applications Development
Current trends in cloud computing i.e. IoT, Big Data, Machine Learning. Cloud
Infrastructure Security, Network level security, Host level security, Application
level security, Access management and control.
MapReduce, Spark and GraphLab programming models, Develop and deploy
5 the cloud based server-side application using Node.js and the front-end using
React, Case Study on Open Source and Commercial Clouds applications: 9
Amazon EC2, Amazon S3,Amazon Redshift, GitHub Repository, AWS IoT
Core, AWS IoT Device Defender, AWS IoT Device Management, AWS IoT
FleetWise, AWS IoT SiteWise, AWS IoT Events, AWS IoT TwinMaker, AWS
IoT Analytics, Azure IoT Hub, Azure IoT Central, Azure Digital Twins, Azure
IoT Edge, Azure Percept, Azure Sphere, and Azure RTOS.
Design and Deploy a Restaurant Application to Cloud.
Total 45 Hrs.
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Mastering Cloud Computing by Rajkumar Buyya, 1st 2013.
Vecchiola & Selvi (Published by McGraw Hill Education
Pvt. Ltd),
2 Cloud Management & Security by Imad. M. Abbadi 3rd 2014.
(WILEY Publication
Reference Books
1. Cloud Computing – A Hands-On Approach by Arshdeep 1st 2014
Bahga, Vijay Madisetti.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 571 Course Title: Big Data Visualization

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50
5. Credits: 3

6. Semester: V

7. Category of Course: DE

8. Pre-requisite: Fundamental of Cloud Computing and Bigdata TCS 351

9. Course After completion of the course, the students will be able to:
Outcome**: CO1: Create and adapt visualizations to represent complex data sets and
emphasize targeted concepts for effective communication
CO2: Analyze and interpret large volumes of data to identify patterns, trends,
and insights.
CO3: Apply data visualization techniques to communicate complex data sets
effectively.
CO4: Develop skills in storytelling with data, effectively conveying narratives
through visual representations.
CO5: Demonstrate proficiency in using tools and technologies for big data
visualization.
CO6: Use leading open-source and commercial software packages (Tableau) to
create and publish visualizations that enable clear interpretations of big,
complex, and real-world data
** Describe the specific knowledge, skills, or competencies the students are expected to acquire or
demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Techniques for visual data representations: Data Visualization,
Information Visualization, Concept Visualization, Strategic Visualization,
Metaphor Visualization, and Compound Visualization.
1 Visualization design objectives: Methodology, Establishing intent, 10
The visualization's function-explain, explore, exhibit; Tone-analytical and
abstract, key factors in a visualization project, The eight hats of data visualization
design

2 Unit 2: Demonstrating Editorial Focus: Importance of editorial focus, 10

Preparing and familiarizing of data, Refining the editorial focus, Using visual
analysis to find stories
Conceiving and Reasoning: Preparing data, Refining, The Visualization
anatomy - Data Representation: choosing correct visualization method, physical
properties of data, degree of accuracy in interpretation, creating an appropriate
design metaphor, choosing the final solution; The Visualization anatomy-
Data presentation: Interactivity, Annotation, and Arrangement;

Unit 3: Taxonomy of Data Visualization: Choosing appropriate chart type:

Dot plot, Column chart, Floating bar, pixelated bar chart, Histogram,
Slopegraph, Radial chart, Glyph chart, Sankey diagram, Area size chart;
Assessing hierarchies and part-to-whole relationships: Pie chart, Stacked bar
chart, Square pie, Treemap, Circle packing diagram, Bubble hierarchy, Tree
3 Hierarchy; Showing changes over time: Line chart, Sparklines, Area chart, 9
Horizon chart, Stacked area chart, Candlestick chart, Barcode chart, Flow map;
Plotting connections and relationships: Scatter plot, Bubble plot, Scatter plot
matrix, Heatmap, Parallel sets, Radial network, Network Diagram; Mapping
geospatial data: Choropleth map, dot plot map, Bubble plot map, Isarithmic map

Unit 4: Tools for data visualization: Tableau, Google Charts, Datawrapper,

4 9
Chartio, IBM Watson Analytics, and Sisense

Unit 5: Data Visualization through Tableau: Tableau basics, connecting

Tableau to various datasets, creating bar charts, area charts, maps, scatterplots,
pie charts, and tree maps; Create Interactive Dashboards, storylines, Joins, Data
5 Blending, Table calculations, parameters, Dual axis charts, Export results from 8
Tableau to other software, Work with time-series data, Creating data extracts,
Aggregation, Granularity and Level of detail, Adding filters, create data
hierarchies, Adding actions to dashboards
Total 46
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Andy Kirk, Data Visualization: a successful design process, 1st 2015
Packt Publishing
Reference Books
1. Tamara Munzer, Visualization Analysis and Design, CRC 2nd 2014
Press

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

Communication
1. Subject Code: TCS 531 Course Title: models and protocols

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 ESE 50

5. Credits: 3

6. Semester: V

7. Category of Course: DE

8. Pre-requisite: NA

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Understand the common network communication primitives as part of
programming tasks in various languages.

CO2: Discuss the various Protocols used in Communication

CO3: Analyze more complex protocol engineering and network management tasks

CO4: Understand terminology, concepts, and technologies required for

telecommunication in local area networks (LANs) and on the global Internet

CO5: Describe and analyze the Data Encoding and Transmission techniques.

CO6: Use of network management tools

** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10. Details of the Course:

Contact
S.NO. Contents
Hours
Unit 1:
Introduction and Overview:Key elements of communications and networking,
Layered protocol model, Network edge, End systems, access networks, links,
1 9
Network core, Packet switching, circuit switching, network structure,
Multiplexing, Delay, loss and throughput in networks, Protocol layers, service
models, Networks under attack: security, History.
2 Unit 2:
9
Application Layer: Principles of network applications, Web and HTTP, FTP,
Electronic Mail, SMTP, POP3, IMAP, DNS, P2P applications, Video streaming
and content distribution networks, Ethereal (network packet sniffer), Socket
programming with UDP and TCP

Unit 3:
Data Encoding and Transmission: Data encoding and transmission concepts,
Digital data transmission over digital signal: NRZ encoding, Multilevel binary
encodings, Biphase encodings, Scrambling techniques, Digital data transmission
over analog signal: Public telephone system, Amplitude Shift Keying (ASK),
3 Frequency Shift Keying (FSK), Phase Shift Keying (PSK), Performance of digital 10
to analog modulation schemes, Quadrature Amplitude Modulation (QAM), Analog
data transmission over digital signal: Digitization, Pulse Code Modulation, Non-
linear encoding, Delta modulation, Analog data transmission over analog signal:
Asynchronous transmission, Synchronous transmission, Ethernet link layer frame
example.
Unit 4:
4 Data Link Control: Introduction and services, Error detection and correction, 8
Multiple access protocols, LANs, Addressing & ARP, Ethernet, Switches, VLANs,
PPP, Link virtualization, MPLS, Data center networking, Web request processing.
Unit 5: Wireless and Mobile Networks

5 Wireless, Wireless links, characteristics, CDMA, IEEE 802.11 wireless LANs

(“Wi-Fi”), Cellular Internet Access: Architecture, Standards (e.g., 3G, LTE), 9
Mobility, Principles: addressing and routing to mobile users, Mobile IP, Handling
mobility in cellular networks, Mobility and higher-layer protocols
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. 1.Douglas E. Comer,” Internetworking with TCP/IP 6th 2014
Volume One - 6th Edition” Publisher is Pearson, © 2014
2. Protocol specifications (RFCs) and other readings will 1st 2008
also be assigned

Reference Books
1. Seymour Lipschutz, Data Structures Schaum's Outlines, 1st 2014
McGraw Hill

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V
Name of Department: - Computer Science and Engineering
Computer System Security
1. Subject Code: Course Title:
TCS 591

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50
5. Credits: 3
6. Semester: V
7. Category of Course: DE
8. Pre-requisite: TCS-491

9. Course After completion of the course, the students will be able to:
Outcome**: CO1: Explain different security threats and attacks.

CO2: Know the working of different attacks and security protocols.

CO3: Analyze the different security protocols.

CO4: Use programming to implement security protocols.

CO5: Use programming to implement security protocols.

CO6: Develop system security protocols

** Describe the specific knowledge, skills, or competencies the students are expected to acquire or
demonstrate.
10.Details of the Course:
Sl. Contact
Contents
No. Hours
Unit 1:
Introduction to System security:
Control hijacking attacks buffer overflow, integer overflow, bypassing browser
1 memory protection, Sandboxing and Isolation, Tools and techniques for writing 10
robust application software, Security vulnerability detection tools, and techniques
program analysis (static, concolic and dynamic analysis), Privileges, access
control, and Operating System Security, Exploitation techniques, and Fuzzing
Unit 2:
Software security:
2 Vulnerabilities, Attacks, and Countermeasures: Privileged programs (Set-UID 10
programs) and vulnerabilities & Privilege Separation, Buffer Overflow
vulnerability and defences, Return-to-libc attack, Race, Condition vulnerability
and attack, Dirty COW attack, Format String vulnerability and attack, Shellshock
attack, Heartbleed attack Interactivity, Annotation, and Arrangement;

Unit 3:
Web Security:
Same origin Policy, Cross site scripting attack, Cross site request forgery attack,
3 10
Sql Injection attack, Clickjacking attack, Content Security Policies (CSP) in
web, Web Tracking, Session Management and User Authentication, Session
Integrity, Https, SSL/TLS, Threat Modelling
Unit 4:
Smartphone Security:
Android vs. ioS security model, threat models, information tracking, rootkits,
4 9
Access control in Android operating system, Rooting android devices,
Repackaging attacks, Attacks on apps, Whole- disk encryption, hardware
protection, Viruses, spywares, and keyloggers and malware detection
Unit 5:
Hardware and system security:
Meltdown Attack, spectre attack, Authentication and password, Access control
5 6
concept, Access control list, Capability, Sandboxing, Threats of Hardware
Trojans and Supply Chain Security, Side Channel Analysis based Threats, and
attacks. Issues in Critical Infrastructure and SCADA Security.
Total 45

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint
Textbooks
1. Security in Computing, Book by Charles P Pfleeger and 5th 2015
Shari Lawrence Pfleeger, V edition
2. Cryptography and Network Security: Principles and 7th 2017
Practice, Book by William Stallings, VII edition
Reference Books

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 509 Course Title: Machine Learning

2. Contact Hours: L: 3 T: P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: V

7. Category of Course: DE

8. Pre-requisite: Design and Analysis of Algorithm, Fundamental of Statistics and AI (TCS 421 /
Statistical Data Analysis with R (TCS 471), Discrete Structures and Combinatorics (TMA 316)

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Acquire concepts and methods in statistical machine learning
CO2: Analyze fundamental principles of machine learning algorithms
CO3: Understand machine learning motivated by case-studies
CO4: Investigate and evaluate key topics in machine learning
algorithms for data science industry
** Describe the specific knowledge, skills, or competencies the students are expected to acquire or
demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Machine learning foundation
Review of logic and knowledge system - language, axiom, hypothesis, theorem,
logic & types, what is ML, Inductive bias in ML, AI pyramid, Pattern
classification pipeline, Linear algebra in ML, Probabilistic logic and statistical
1 inference (Random expt./ variable, CDF, WLLN, Bayes, Markov & Chernoff 14
bound, Hypothesis testing and performance indices - ROC, Estimation -
detection, Optimality of Bayes, bias-variance, underfit-overfit, entropy as
Information, Cover’s packing lemma, Curse of dimensionality, Case study:
Wealth – optimal payoffs in portfolios (stock market)

Unit 2:
Unsupervised Learning
Clustering, Clustering methods – Partition vs. Hierarchical, k-Means and k-
2 8
Medoids, Hierarchical: Agglomerative & Divisive, Error Analysis in Clustering,
Ensemble - clustering, Case study: Clustering in Health care, Causal cluster,
Graph cluster
Unit 3:
Supervised Learning
Main objectives and types of Supervised methods (Parametric, Semi parametric,
3 Non-parametric), Linear Regression and Weiner filter, Grammar based/
10
Inductive learning - Decision Trees – CART, ID-3, Pruning metrics for tree; D-
tree examples, Linear SVM (basics and V-C bound), k-NN rule and examples,
Learning as Factorization, Ensemble learning: Bagging, Boosting. Case studies:
covered for mentioned Supervised learning techniques.
Unit 4:
Reinforcement & Interaction Learning
4 Basic model of Reinforcement Learning as game (Agent, Critic, Environment),
8
Optimal policy & Q – values, Bellman equation, Case studies on R Learning
Active learning, Deep Reinforcement, Transfer learning with examples,
Federated Machine Learning with examples.
Unit 5:
Special topics in Machine Learning
Sentiment Mining: NLP pipeline process, Data Analytics – Big data and Hadoop
5 8
model, Business Analytics – Competitive Machine Learning, ANN building
blocks (problem solving), Deep learning, Feed forward, Backpropagation, C-
NN, Recurrent-NN.
Total 48

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Pattern classification – R, Duda, P. Hart and D. Stork, 2nd. 2007
Wiley Publisher
2. The Elements of Statistical Learning – J. Friedman, R. 3rd. 2017
Tibshirani and T. Hastie, Springer Publisher
3. Pattern Recognition and Machine Learning – C. Bishop, 2nd. 2016
Springer Publisher

4. Deep Learning – A. Courville, I. Goodfellow, Y. Bengio, 2nd. 2016

MIT Press

Reference Textbooks
1. Machine Learning – Tom M. Mitchell, Mc Graw Hill 1st 2017
Publisher
2. Introduction to Machine Learning – E. Alpaydin, PHI 3rd. 2015
Publisher

3. Elements of Information Theory – T M. Cover, J A. Thomas, 2nd. 2006

Wiley Publisher

12. Mode of Internal Seminar – presentation on topic in ML & internal viva

OR
Evaluation
Simulation of ML method with real dataset & internal viva
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering Introduction to

Artificial Intelligence
1. Subject Code: TCS 562 Course Title:
and Data Science
2. Contact Hours: L: 3 T: 0 P: 0 ProfessionaCommunic
ation
3. Examination Duration (Hrs.): Theory 3 Practical 0
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 5

7. Category of Course: DC

8. Pre-requisite: TCS 342, TCS 462

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the concepts of Artificial Intelligence and Data Science
with their related terminologies.
CO2: Analyze and Apply various programming skills for understanding
Data nature and its requirements.
CO3: Analyze and apply various modelling techniques for basic data
Analytics.
CO4: Demonstrate Problem Solving using AI algorithms.
CO5: Understand, Apply and Demonstrate different techniques and tools
for Data Analysis.
CO6: Analyze Real World Case Studies on Applications of Data Science.

** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10. Details of the Course:

Sl.
Contents Contact Hours
No.
Unit 01:
Data Science History, Data Science and Related Terminologies,
Types of Analytics, Applications of Data Science, Data Science
Process Models.
1 Introduction to AI, History and Foundation of AI, Intelligence, and 10
it’s type,
Categorization of Artificial Intelligent based System, Agents &
Environments, Applications, and Current trends in AI

Unit 02:
2 Introduction to Data, Types, Data Preprocessing, Understanding 10
Data Requirements, Dealing with Erroneous/Missing Values,
Standardizing Data, Steps involved in EDA using Python
Programming/R.
Knowledge and Reasoning in AI: Knowledge based Agents, Syntax
and Semantics, Forward Chaining, Backward Chaining, Knowledge
Engineering, Belief Network
Unit 3:
Introduction to Modelling Techniques, Supervised Learning
Algorithms- Regression, Classification, and Unsupervised Learning
3 Algorithms- Clustering, Association Rule Mining 10
Feature Selection, Dimensionality Reduction, Independent and
Dependent Variables, Relationship between Variables: Correlation,
Multicollinearity, Factor Analysis, Treatment of Outliers
Unit 4:
4 Problem Solving Agent, Formulating Problems, Example Problems, 10
Uninformed Search Methods, Informed Search Method, Local
Search Methods, Genetic algorithms, Adversarial Search
Unit 5:
Applications of Analytics in Healthcare, Applications of Analytics in
5 Agriculture, Applications of Analytics in Business, Applications of 8
Analytics in Sports, Forms of Learning, Introduction to Expert
Systems, Expert System Architecture, Capstone Project
Total 48
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Deepak Khemani, A First Course in Artificial 6th 2018
Intelligence, McGraw Hill Education
2. N. P. Padhy, Artificial Intelligence and 1st 2005
Intelligent Systems, Oxford
3 B.Uma Maheshwari, R.Sujatha, Introduction to Data 1st 2021
Science, Wiley
4 Jake VanderPlas, Python Data Science Handbook, 1st 2022
O’Reilly
Reference Books
1. Stuart J. Russell and Peter Norvig, Artificial Intelligence 3rd 2009
a Modern Approach, McGraw Hill

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

Multi-Modal Data Processing
1. Subject Code: TCS-563 Course Title: and Learning

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 PRS 0 MSE 25 SEE 50 PRE 0

5. Credits: 3

6. Semester: 5

7. Category of Course: DE

8. Pre-requisite:

9. Course After completion of the course the students will be able to:
Outcome**: CO1: To discuss multimodal data and its applications
CO2: To apply text processing techniques in the relevant applications
CO3: To analyze various speech processing approaches
CO4: To create a model based on digital image and video processing
CO5: To analysis data of imbalance for multimodal design
CO6: To compare various types of processing such as Text process,
Speech processing, Image and Video processing
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
1 Introduction: Introduction to Multimodal data and applications, 8
Challenges of multimodal data, Data collection & cleaning.
Unit 2:
Text Processing: Text normalization, Lemmatization, Morphology,
2 Subword tokenization; Text processing and statistics: TFIDF, BM-25, 9
Zipf’s law, Hipf’s law; Language models and smoothing techniques; Vector
space models.
Unit 3:
Speech Processing: Speech production and perception, Acoustic and
3 articulatory phonetics; Short-term analysis: Need and windowing, Energy, 9
Zero-crossing rate, Autocorrelation function, Fourier transform,
Spectrogram; Short-term synthesis: Overlap-add method; Cepstrum
analysis: Basis and development, mel-cepstrum.
Unit 4:
Digital Image and Video Processing: Point processing, Neighborhood
4 processing, Enhancement, Edge detection, Segmentation, Feature 9
descriptors, Restoration, Morphological operations, Image transforms,
Spatial and temporal data handling.
Unit 5:
Multi-modal data synchronization and fusion: Data understanding and
5 quality estimation, meta data filtering, amount of data estimation for 8
multimodal design, data synchronization and fusion, imbalance data
analysis for multimodal design.
Total 43

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. R. C. Gonzalez, R. E. Woods, Digital Image 1st 2008
Processing, Pearson, Prentice-Hall
2. R. Klette, Concise Computer Vision: An Introduction 1st 2014
Into Theory And Algorithms, Springer
3. L. R. Rabiner, R. W. Schafer, Introduction To Digital 1st 2007
Speech Processing, Now Publishers Inc
4. A But, A Miasnikov, G Ortolani, Multimodal Deep 1st 2019
Learning With Tensorflow: Translate Mathematics Into
Robust Tensorflow Applications With Python, Packt
Publishing Limited
5. M Yang, B Rosenhahn, V Murino, Multimodal Scene 1st 2019
Understanding: Algorithms, Applications And Deep
Learning, Academic Press Inc
Reference Books
1. D. Jurafsky, J.H. Martin, Speech And Language 3rd 2022
Processing, (Online Available
At Https://Web.Stanford.Edu/~Jurafsky/Slp3/)
2. J-P Thiran, F Marqués And H Bourlard, Multimodal 1st 2009
Signal Processing: Theory And Applications For
Human-Computer Interaction, Academic Press

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

Data Science: Visualization with
TCS-549 Tableau Specialization
1. Subject Code: Course Title:

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 5

7. Category of Course: DE

8. Pre-requisite:

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Clean up, format and analyze data to prepare for interactives
CO2: Design visualizations that represent the relationships contained
in complex data sets and adapt them to highlight the ideas we want to
communicate
CO3: Use principles of human perception and cognition in
visualization design.
CO4: Identify the statistical analysis needed to validate the trends
present in data visualizations.
CO5: Critically evaluate visualizations and suggest improvements and
refinements.
CO6: Use leading open source and commercial software packages
(Tableau) to create and publish visualizations that enable clear
interpretations of big, complex and real world data
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
UNIT 01:
Context of Data Visualization: Visualization as a discovery tool, Bedrock
of visualization as a discovery tool, Visualizing the past, Different Data
Visuals for Different Needs, The classic case of London 1855 cholera
1 10
epidemic and how it changed the face of visualization, The 20th Century
advancements, Computer-based Visualization, The Power of Human
Perception, Different Data Calls for Different Views, Leveraging
Composition and Interactivity, Using Data to Tell Stories
Visualization design objectives: Methodology, Establishing intent, The
visualization's function-explain, explore, exhibit; Tone-analytical and
abstract, key factors in a visualization project, The eight hats of data
visualization design
UNIT 02:

Demonstrating Editorial Focus: Importance of editorial focus, Preparing

and familiarizing of data, Refining the editorial focus, Using visual analysis
to find stories

Conceiving and Reasoning: Preparing data, Refining, The Visualization

2 10
anatomy - Data Representation: choosing correct visualization method,
physical properties of data, degree of accuracy in interpretation, creating
an appropriate design metaphor, choosing the final solution; The
Visualization anatomy- Data presentation: Interactivity, Annotation and
Arrangement;
Unit 03:
Taxonomy of Data Visualization: Choosing appropriate chart type: Dot
plot, Column chart, Floating bar(Gantt chart), pixelated bar chart,
Histogram, Slopegraph, Radial chart, Glyph chart, Sankey diagram, Area
size chart; Assessing hierarchies and part-to-whole relationships: Pie
chart, Stacked bar chart, Square pie, Tree map, Circle packing diagram,
3 Bubble hierarchy, Tree Hierarchy; Showing changes over time: Line chart, 9
Sparklines, Area chart, Horizon chart, Stacked area chart, Candlestick
chart (or box and whiskers plot, OHLC chart), Barcode chart, Flow map;
Plotting connections and relationships: Scatter plot, Bubble plot, Scatter
plot matrix, Heatmap, Parallel sets, Radial network, Network Diagram;
Mapping geo-spatial data: Choropleth map, dot plot map, Bubble plot map
, Isarithmic map
Unit 04:

Collaborative Visual Analysis: Supporting Asynchronous Collaborative

Information Visualization, Designing for social data analysis, Design
considerations for collaborative visual analytics

Constructing and Evaluating the Design Solution: Nested model for

4 9
visualization design and validation, Challenge of information visualization
evaluation, Visualization software, applications and programs; Charting
and statistical analysis tools, programming environments, tools for
mapping, The construction process, Approaching the finishing line, Post-
launch evaluation, Developing the capabilities
Unit 05:

Data Visualization through Tableau: Tableau basics, connecting tableau

to various datasets, creating bar charts, area charts, maps, scatterplots,
pie charts, tree maps; Create Interactive Dashboards, storylines, Joins,
5 8
Data Blending, Table calculations, parameters, Dual axis charts, Export
results from Tableau to other software, Work with timeseries data,
Creating data extracts, Aggregation, Granularity and Level of detail,
Adding filters, create data hierarchies, Adding actions to dashboards
Total 46

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Andy Kirk, “Data Visualization: a successful design 1st 2012
process”, Packt Publishing, 2012
Reference Books
1. Tamara Munzer, “Visualization Analysis and Design”, 1st 2014
CRC Press

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-582 Course Title: Introduction to AI & ML

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical

0
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 5

7. Category of Course: DC

8. Pre-requisite:

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Ability to compare different AI algorithms in terms of design issues,
computational complexity, and
assumptions
CO2: Apply basic search techniques and AI algorithms for problem solving
CO3: Identify the machine learning algorithms which are more appropriate for
various types of learning tasks in
various domains
CO4: Analyse and Differentiate various classification approaches
CO5: Implement machine learning algorithms on real datasets
CO6: The student will learn about the basic concepts of ANN and CNN
** Describe the specific knowledge, skills or competencies the students are expected to acquire or
demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction to AI: Definitions, Foundations AI, History of AI,
Intelligent Agents, Structure of Intelligent Agents, Environments; Problem
1 8
solving Agents, Problem Formulation, Search Strategies, Constraint
Satisfaction Search, Informed search Methods
Unit 2: Knowledge representation and reasoning: Agents that Reason
Logically, Propositional Logic and Inference,
First-Order Logic, Inference in First-Order Logic
2 Planning and Learning: Introduction to Planning, Types, Learning from 8
observations, Forms of Learning,
Inductive Learning, Learning decision trees, Reinforcement Learning

Unit 3: Data Preprocessing, Machine Learning process, Feature scaling,

3 8
Simple Linear Regression, Multiple Linear Regression, Polynomial
Regression, Support Vector Regression, Decision Tree Regression,
Evaluating the Regression Models Performance

Unit 4: Logistic Regression, K-Nearest Neighbors, Support Vector

4 8
Machine, Decision Tree Classification, Random Forest Classification

Unit 5: K-Means clustering, Hierarchical Clustering, Apriori, Eclat, Artificial

5 Neural Networks, Activation Function, Gradient Descent, Stochastic 8
Gradient Descent, Backpropagation, Convolutional Neural Networks
Total 40

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Laurence Moroney, AI and Machine Learning for 1st 2020
Coders, O’REILLY Media
2. Stuart Russell and Peter Norvig, Artificial Intelligence: 3rd 2015
A Modern Approach, Pearson Education India
3. Elaine Rich, Kevin Knight and Shivashankar B. Nair, 3rd 2017
Artificial Intelligence, McGraw-Hill
Education

Reference Books
1. Dan W. Patterson, Introduction to Artificial Intelligence 1st 2015
and Expert Systems, Pearson
Education India
2. Devroye L., Gyorfi L., Lugosi G., A Probabilistic 1st 1996
Theory of Pattern Recognition, Springer

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 543 Course Title: Knowledge

Representation
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 5

7. Category of Course: DE

8. Pre-requisite: TCS 409, Design Analysis and Algorithm, TCS 343

Mathematical Foundations for Artificial Intelligence

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Use logic programming and knowledge representation languages for
modelling simple application domains in Artificial Intelligence
CO2: Apply reasoning mechanisms in knowledge representation languages to test
the correctness of models and to formulate more expressive queries.
CO3: Design ontology-based knowledge systems with reasoning mechanism;
integrate with other systems for building applications.
CO4: Understand the entire process of how to design, construct, and query a
knowledge graph to solve real-world problems.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Contact
Sl. No. Contents
Hours
Principles of knowledge representation, Propositional Logic- Proof
Systems, Natural Deduction, Tableau Method, Resolution Method. First
1 Order Logic Syntax and Semantics, Unification, Forward Chaining, Horn 10
Fragments of First Order Logic.

Rule based systems, The Rete Algorithm, Rete example, Programming

2 rule Based Systems, Description Logics, Reasoning in Description 9
Logics, Structure Matching, Classification, Extensions of DL

The ALC Language, Ontology Representation languages, Ontology

3 9
Languages- RDF, RDFS-Rule Interchange Format, Logic programming
with OWL: OWL-Building OWL ontology- SPARQL- RDF/OWL
ontology processing using Graph databases

Non monotonic logics 4 hours Classical vs non-monotonic logic. Ways to

4 achieve non-monotonicity-Stable Model Semantics querying Semantic 8
Nets and Frames.

5 Discussions on Contemporary Issues in knowledge representation 6

Total 42
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Franz Baader, Ian Horrocks, Carsten Lutz, Uli Sattler, An 1st 2017
Introduction to Description Logic, Cambridge University
Press
2. Ronald Brachman & Hector Levesque, Knowledge 1st 2004
Representation and Reasoning, Morgan Kaufmann
3. Frank van Harmelen, Vladimir Lifschitz and Bruce Porter 1st 2008
(Eds), Handbook of Knowledge Representation
Foundations of Artificial Intelligence
4. Ian Robinson, Jim Webber, Emil Eifrem, Graph Databases, 2nd 2015
O'Reilly Media
Reference Books
1. Pascal Hitzler, Markus Kroetsch, and Sebastian Rudolph, 1st 2009
Foundations of Semantic Web Technologies, Chapman &
Hall/ CRC Textbooks in Computing

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering Introduction to Artificial

Intelligence
1. Subject Code: TCS 542 Course Title:

2. Contact Hours: L: 3 T: 1 P: 0

3. Examination Duration (Hrs.): Theory 3 Practical 0

4. Relative Weight: CIE 25 PRS 0 SEE 50

5. Credits: 3

6. Semester: V

7. Category of Course: DC

8. Pre-requisite: TCS 343 Mathematical Foundations for Artificial Intelligence

9. Course After completion of the course the students will be able to:
Outcome**: CO 1: Understand the basics of the theory and practice of Artificial Intelligence.
CO 2: Learn the basics of Artificial Intelligence programming.
CO 3: Understand various searching techniques use to solve the AI problems.
CO 4: Apply knowledge representation techniques and problem solving strategies to
common AI applications.
CO 5: Build self-learning and research skills to tackle a topic of interest on his/her
own or as part of a team.
CO 6: Apply the knowledge of AI and agents in developing multidisciplinary real
world projects
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction
What is AI? , Foundation of AI, State space representation. Intelligent Systems:
1 Categorization of Intelligent System, Components of AI Program, Types of AI, 8
Foundations of AI, Applications of AI, Current trends in AI, Intelligent Agents:
Anatomy, structure, Types.
Unit 2: Problem Solving
2 Solving problem by Searching: Problem Solving Agent, Formulating Problems. 10
Uninformed Search Methods: Breadth First Search (BFS), Depth First Search
(DFS), Depth Limited Search, Depth First Iterative Deepening (DFID), Informed
Search Methods: Greedy best first Search, A* Search, Memory bounded heuristic
Search. Local Search Algorithms and Optimization Problems: Hill climbing
search Simulated annealing, Local beam search
Unit 3: Uncertain Knowledge and Reasoning
Acting under uncertainty, Basic Probability Notation, Inference using full joint
3 8
distributions, Bayes Rule and its use.

Unit 4: Knowledge Representation

First order predicate calculus, Horn Clauses, Introduction to PROLOG, Semantic
4 10
NetsPartitioned Nets, Minskey frames, Case Grammar Theory, Production Rules
KnowledgeBase, The Inference System, Forward & Backward Deduction
Unit 5: Expert System and Programing Language
Expert System Existing Systems (DENDRAL, MYCIN), domain exploration,
5 Meta Knowledge, Expertise Transfer, Self Explaining System 12
Programming Language: Introduction to programming Language, LISP,
PROLOG
Total 48

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. G F. Luger, Artificial Intelligence: Structures and Strategies 6th 2021
for Complex Problem Solving,
2. Dan W. Patterson, Introduction to Artificial Intelligence and 1st 1990
Expert Systems, PHI
3 1st 2012
Eileen Mc Daniel, Stephen McDaniel, The
Accidental Analyst: Show Your Data Who’s Boss
Freak Analytics

Reference Books
1. Stuart J. Russell and Peter Norvig, Artificial Intelligence a 3rd 2009
Modern Approach, McGraw Hill

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

1. Subject Code: Course Title: Artificial Intelligence Lab

PCS 542
2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3 hr

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2
6. Semester: V

7. Category of Course: DC

8. Pre-requisite: TCS 409 Design and Analysis of Algorithm, TCS 341Python

programming for computing

9. Course After completion of laboratory the students will be able to:

Outcome**:
CO1: Implement methods in AI
CO2: Analyze AI algorithms and applications

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Contact
S. N. Contents
Hours
To program problem-solving by path traversal search over graph
1 2
using BFS, DFS, UFS
To program problem-solving by path traversal using A*
2 2
algorithm (Informed Search)
To use lobe.ai user interface and perform manual labeling,
2 2
training and testing for supervised object recognition in Image

To understand and develop basic KR (Knowledge Representation) tools

3 from practical AI problem definitions: KR methods 2
To take two - category input data file and use thresholding to
4 design binary classifier for dataset 1 dimensional, 2 dimensional 2
dataset
To register and use monkeylearn.com and create model, train
and classify sentiments that lead to sentiment prediction using
5 2
corpus of hotel reviews as part of NLU

To register and use Teachable machine (Google API) and perform multiple
6 2
class / pose analysis and classification.
To use quillbot.com and study basic machine transcription roles in
7 summarization: as NLP application 2

To use quillbot.com and study basic machine transcription roles

8 2
in grammar checker (syntax): as NLP application
To use quillbot.com and study basic machine transcription roles
9 2
in paraphrasing: as NLP application
Program probabilistic model given: it is Friday and that a
student is absent is 3 %. Since there are 5 school days in a
10 week, the probability that it is friday is 20 %. What is the 2
probability that a student is absent given that today is friday.
Program Bayesian rule in python to get the result.
To extract intra-day stock market data for 4 stocks and write
11 program that: plots the values, develops linear regression, 2
derive mean and correlation.
To program best fit distributions for at least 2 discrete
12 2
distributions and estimate mean and variance for the dataset.
To program best fit distributions for at least 2 continuous
13 2
distributions and estimate mean and variance for the dataset.
To program method using Z score, DB for detection of
14 2
Outliers in dataset
To study role of correlation as strong, weak and moderate
15 2
between underlying features as function of sample size
To develop knowledge discovery and association rule map for
16 2
healthcare dataset

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER V

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 511 Course Title: Computer Networks

IProfessionaCommu
2. Contact Hours: L: 3 T: 0 P: 0
nication
3. Examination Duration (Hrs):Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: V

7. Category of Course: DC

8. Pre-requisite: TCS 101, Fundamental of Computer & Introduction to Programming

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Apply and Characterize computer networks from the view point of
components and from the view point of services.
CO2: Display good understanding of the flow of a protocol in general and a
network protocol in particular
CO3: Evaluate and Select the most suitable Application Layer protocol (such as
HTTP, FTP, SMTP, DNS, BitTorrent) as per the requirements of the network
application and work with available tools to demonstrate the working of these
protocols.
CO4: Design a Reliable Data Transfer Protocol and incrementally develop
solutions for the requirements of Transport Layer
CO5: Describe the essential principles of Network Layers and use IP addressing to
create subnets for any specific requirements
CO6: Evaluate and select the appropriate technology to meet Data Link Layer
requirements and design a framework to implementing TCP/IP protocol suite.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction: Computer Networks and the Internet, Overall view: As
components and as services; What is a protocol, what is a network protocol,
Access Networks and Physical Media, Circuit and Packet Switching, Internet
1 Backbone, Delays: Processing, Queuing, Transmission and Propagation delays, 11
The Layered Architecture: Protocol Layering, The OSI Reference Model and
the TCP/IP protocol stack, History of Computer Networking, and the Internet.

Unit 2: Application Layer: Principles and Architectures of Network

Applications, Client and Server processes, the idea of socket, Transport
services available to Application Layer especially in the internet Application
Layer Protocols: The Web and http: Persistent and Nonpersistent connections,
http message format, cookies, proxy server, conditional GET, File Transfer
2 11
Protocol, Email: smtp, mail message formats, mail access protocols: pop3,
imap, MIME, DNS: Services, How it works, Root, Top-Level and Authoritative
DNS servers, Resource Records, DNS messages A simple introduction to p2p
file distribution: BitTorrent

Unit 3: Transport Layer: Introduction and Services, The Transport layer in

internet, Difference between Connection Oriented and Connectionless services,
UDP: Segment structure, checksum in UDP, stop-and-wait, Go Back N,
3 6
Selective Repeat, TCP: Connection Establishment, TCP header, Sequence and
acknowledgement numbers, Round Trip Time, Flow Control, Congestion,
Control.
Transport Layer: Introduction and Services, The Transport layer in internet,
Difference between Connection Oriented and Connectionless services UDP:
Segment structure, checksum in UDP

Unit 4: Network Layer: Introduction, Packet Forwarding and Routing,

Difference between Virtual Circuits and Datagram networks, The internals of a
router: Input ports, output ports, switching architecture The Internet
Protocol(IP), Datagram format, IP fragmentation, IPv4, addressing, subnets,
CIDR, classful addressing, DHCP, Network Address Translation(NAT),
Universal Plug and Play as a provider of NAT, Internet Control Message
4 Protocol(ICMP), IPv6 Header, Moving from IPv4 to IPv6: tunnelling. Routing 6
Algorithms: Introduction, global vs decentralized routing, The Link State(LS)
Routing Algorithm, The Distance Vector (DV) Routing Algorithm,
Hierarchical Routing, Introduction to Routing in the Internet: RIP, OSPF, BGP;
Introduction to Broadcast and Multicast Routing.

Unit 5: Link Layer and Local Area Networks: Introduction to Link Layer and
its services, Where Link Layer is implemented? Error detection and correction
techniques: Parity checks, Checksum, CRC; Multiple Access protocols:
5 Channel Partitioning, Random Access (Slotted Aloha, Aloha, CSMA), Taking 10
Turns; Link Layer Addressing: MAC addresses, ARP, Ethernet, CSMA/CD,
Ethernet Technologies, Link Layer Switches, Switches vs Routers, VLANS

Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publicati
on /
Reprint
Textbooks
1. Computer Networking: “A Top Down Approach (5th edition)”, 7th 2017
Ross and Kurose, Pearson/Addison-Wesley
Reference Books
1. Andrew Tanenbaum and David Wetherhall, “Computer Networks”, 5th 2010
Prentice Hall
2. Peterson and Davie, “Computer Networks: A System Approach”, 4th 2007
Elsevier

3. Forouzan, “Data Communication and Networking”, McGraw Hill 5th 2013

4. William Stallings: “Data and Computer Communication”, Pearson 8th 2007

Education, 2007
5. Nader F. Mir:” Computer and Communication Networks”, 1st 2007
Pearson Education.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER Vth

Name of Department: - Computer Science and Engineering

Computer Networks Lab
1. Subject Code: PCS 511 Course Title:

2. Contact Hours: L: 0 T: 0 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: PRS 25 MSE 25 PRE 50

5. Credits: 2

6. Semester: 5th

7. Category of Course: DC

8. Pre-requisite: Computer networks

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand various components that make up a computer network,
including routers, switches, hubs, servers, and clients and learn about the basic
commands used troubleshooting.
CO2: Design UTP cable for cross and direct connection using crimping tool.
CO3: Implement the common network protocols such as TCP/IP, UDP, HTTP,
DNS, DHC and FTP Understand how these protocols function and their role in
facilitating communication between devices using network simulation tool like
Packet tracer.
CO4: Apply the static and dynamic routing concepts in the network core and
monitoring network traffic using Wireshark and develop skills in
troubleshooting network connectivity issues.
CO5: Design network applications using UDP and TCP socket programing
concepts and network design principles and test these applications using real
or virtual network devices.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. List of problems for which student should develop program and execute Contact
No. in the Laboratory Hours
Problem Statement 1:
2
1. Familiarization of Network Environment, Understanding and using network
utilities: ipconfig, netstat, ping, telnet, ftp, traceroute etc.
Problem Statement 2:

2. Familiarization with Transmission media and tools: Co-axial cable, UTP 2

cable, Crimping tool, Connectors etc.
Preparing the UTP cable for cross and direct connection using crimping tool.
Problem Statement 3:
3. Installation and introduction of simulation tool. (Packet Tracer) 2

Problem Statement 4:

4. To configure a basic network topology consisting of routers, switches, and 2

end devices such as PCs or laptops. Configure IP addresses and establish
connectivity between devices. (Using packet Tracer)
Problem Statement 5:
To configure a DHCP server on a router or a dedicated DHCP server device.
5. Assign IP addresses dynamically to devices on the network and verify 2
successful address assignment. (Using packet Tracer)

Problem Statement 6:
6. To configure a local DNS server to resolve domain names within a network. 2
(Using packet Tracer)
Problem Statement 7:
7. To analyze complete TCP/IP protocol suite layer’s headers using Wire 2
Shark
Problem Statement 8:
Static Routing: Configure static routes on multiple routers to enable
8. 2
communication between different networks. Test the connectivity by pinging
between hosts in different networks. (Using packet Tracer)
Problem Statement 9:
Dynamic Routing (RIP): Configure routers to use the Routing Information
Protocol (RIP) for dynamic routing. Enable RIP on the interfaces connected
9. 2
to different networks and verify that routes are being learned and propagated.
Test the connectivity between hosts in different networks. (Using packet
Tracer)
Problem Statement 10:
Dynamic Routing (OSPF): Configure routers to use the Open Shortest Path
First (OSPF) routing protocol. Set up OSPF on the routers and advertise
10. 2
network information. Verify that OSPF is establishing neighbor relationships
and propagating routes. Test connectivity between hosts in different
networks. (Using packet Tracer)
Problem Statement 11:
TCP Client-Server Communication:
Implement a TCP client program that sends a message to a TCP server
program.
11. Implement the corresponding TCP server program that receives the message 2
and displays it.
Test the communication between the client and server by exchanging
messages
(Using ‘C’ Language)
Problem Statement 12:
UDP Client-Server Communication:
Implement a UDP client program that sends a message to a UDP server
12. program. 2
Implement the corresponding UDP server program that receives the message
and displays it
(Using ‘C’ Language)
Optional programs for advanced learner
1. 2
Problem Statement 1:
File Transfer using TCP:

Implement a TCP server program that listens for incoming connections.

Implement a TCP client program that sends a file to the server.
The server should receive the file and save it on the local machine.
Verify the successful transfer by comparing the original file with the received
file
Problem Statement 2:
Chat Application using TCP:
Implement a TCP client program for a chat application.

2. Implement the corresponding TCP server program. 2

Multiple clients should be able to connect to the server and exchange
messages.
Test the chat application by simulating multiple clients communicating with
each other.
Problem Statement 3:
DNS Lookup using UDP:

Implement a UDP client program that sends a domain name to a DNS server.
3. 2
Implement the corresponding DNS server program that resolves the domain
name to an IP address.
The server should send the resolved IP address back to the client.
Test the program by performing DNS lookups for different domain names
Problem Statement 4:
HTTP Server using TCP:
Implement a TCP server program that acts as an HTTP server.
4. 2
The server should be able to handle HTTP requests and send back appropriate
HTTP responses.
Test the server by accessing it through a web browser and requesting different
resources.
Problem Statement 5:

5. Virtual LANs (VLANs): Create multiple VLANs and configure inter-VLAN 2

routing using a router or Layer 3 switch. Assign hosts to different VLANs and
test communication between hosts in different VLANs.
6. Problem Statement 6: 2
Access Control Lists (ACLs): Implement access control lists on routers to
control traffic flow based on source/destination IP addresses, port numbers,
or protocols. Test the ACLs by allowing or denying specific types of traffic
between hosts.
Problem Statement 7:
Network Address Translation (NAT): Configure Network Address
7. Translation on a router to translate private IP addresses to public IP addresses 2
and vice versa. Test connectivity between hosts with private IP addresses and
hosts on the public internet.
Total 38

11. Suggested Books:

S. No. Name of Authors/Books/Publishers Edition Year of
Publicati
on /
Reprint

Text Books

1. Behrouz A. Forouzan, “Data Communications and Networking 6th 2022

with TCPIP Protocol Suite, 6/e”, McGraw Hill

Reference Books

1. Ross and Kurose, Computer Networking: “A Top-Down 5th 2017

Approach (5th edition)”, Pearson/Addison-Wesley

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER V

Name of Department: - Computer Science and Engineering

Computer System
TCS 597
1. Subject Code: Course Title:
Security

3 0 0 ProfessionaCommuni
2. Contact Hours: L: T: P:
cation

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE

50
5. Credits: 3

6. Semester: 5th

7. Category of Course: DE

8. Pre-requisite: TCS-492 Fundamental of Cyber Security

9. Course After completion of the course, the students will be able to:
Outcome**: CO1: Explain different security threats and attacks.
CO2: Know the working of different attacks and security protocols.
CO3: Analyze the different security protocols.
CO4: Use programming to implement security protocols.
CO5: Use programming to implement security protocols.
CO6: Develop system security protocols
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Introduction to System security:
Control hijacking attacks buffer overflow, integer overflow, bypassing
browser memory protection, Sandboxing and Isolation, Tools and techniques
1 for writing robust application software, Security vulnerability detection tools, 10
and techniques program analysis (static, concolic and dynamic analysis),
Privileges, access control, and Operating System Security, Exploitation
techniques, and Fuzzing
Software security:
Vulnerabilities, Attacks, and Countermeasures: Privileged programs (Set-
UID programs) and vulnerabilities & Privilege Separation, Buffer Overflow
vulnerability and defences, Return-to-libc attack, Race, Condition
2 vulnerability and attack, Dirty COW attack, Format String vulnerability and 10
attack, Shellshock attack, Heartbleed attack Interactivity, Annotation, and
Arrangement;

Web Security:
Same origin Policy, Cross site scripting attack, Cross site request forgery
3 attack, Sql Injection attack, Clickjacking attack, Content Security Policies 10
(CSP) in web, Web Tracking, Session Management and User Authentication,
Session Integrity, Https, SSL/TLS, Threat Modelling
Smartphone Security:
Android vs. ioS security model, threat models, information tracking, rootkits,
4 Access control in Android operating system, Rooting android devices, 9
Repackaging attacks, Attacks on apps, Whole- disk encryption, hardware
protection, Viruses, spywares, and keyloggers and malware detection
Hardware and system security:
Meltdown Attack, spectre attack, Authentication and password, Access
5 control concept, Access control list, Capability, Sandboxing, Threats of 6
Hardware Trojans and Supply Chain Security, Side Channel Analysis based
Threats, and attacks. Issues in Critical Infrastructure and SCADA Security.
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Security in Computing, Book by Charles P Pfleeger and 5th 2011
Shari Lawrence Pfleeger, V edition
2. Cryptography and Network Security: Principles and 7th 1998
Practice, Book by William Stallings, VII edition
Reference Books

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term
Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER V

Name of Department: - Computer Science and Engineering Computer System

1. Subject Code: PCS-597 Course Title: Security Lab

2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: PRS 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: 5th

7. Category of Course: DC

8. Pre-requisite:

9. Course After completion of the course the students will be able to:
Outcome**: 1. Explain different security threats and attacks

2. Know the working of different attacks and security protocols

3. Analyse the different security protocols

4. Use programming to implement security protocols

5. Apply security mechanisms to secure various applications

6. Develop system security protocols

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Ex. NAME OF THE PROGRAM Hours

No.
1. Practical demonstration of buffer overflow vulnerability and attack. Also 2
write down the solutions available to mitigate the buffer overflow attack.

2. Practical demonstration of race condition and vulnerability and attack. What 2

are possible solutions for race condition vulnerability.

3. Practical demonstration of dirty cow vulnerability and attack. 2

4. Installation and demonstration of burp suite tool. 2
5. 2
Installation and demonstration of metasploit tool.

6. Practical demonstration of XSS using burp suite tool. 2

7. Practical demonstration of CSRF vulnerability and attack. What are the 2

possible solutions for CSRF?

8. Practical demonstration of SQL injection vulnerability and attack. What are 2

the possible solutions for SQLi?

9. Installation and demonstration of wireshark tool. 2

10. Practical demonstration of HTTPs using the wireshark tool. 2

11. Practical demonstration of ICMP using the wireshark tool. 2
12. Case study of hardware security and attacks like Stuxnet and hardware 2
trojan.

13. Case study of side channel attack. 2

Total 26
11.Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication
/ Reprint
Textbooks
1. Security in Computing, Book by Charles P Pfleeger and 5th 2011
Shari Lawrence Pfleeger, V edition
2. Cryptography and Network Security: Principles and 7th 1998
Practice, Book by William Stallings, VII edition
Reference Books

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER V

Name of Department: - Computer Science and Engineering

Blockchain for
1. Subject Code: TCS 556 Course Title: IoT systems

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 5

7. Category of Course: DC

8. Pre-requisite: Fundamental of IoT (TCS 331), Fundamental of Cyber

Security (TCS 391)
9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Explain blockchain and its uses.

CO2: Know the mechanisms of IoT-based systems CO3: Identify the

needs of data preprocessing.

CO3: Know the mechanisms of IoT-based systems.

CO4. Use blockchain in the designing of IoT-based systems.

CO5: Apply blockchain to different IoT-based application frameworks.

CO6: Implement IoT-based secure application frameworks.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
UNIT-1

Introduction of IoT
1 Overview of IoT, Motivations, Applications of IoT, Internet of Things IoT 8
Architecture, IoT Frameworks, Vulnerabilities of IoT, Security
requirements, Threat analysis, IoT security tomography and layered
attacker model, Security model for IoT.
UNIT-2

Security and blockchain

Threats and threat models, data confidentiality, data integrity,

2 8
authentication and access control, non-repudiation and availability,
overview of blockchain, what is block, data structure of the blockchain,
structure of a block, block header, block identifiers: block header hash,
genesis block, linking of blocks, merkle trees, and use of merkle root.
UNIT-3
Cryptography in blockchain
Concept of divisibility, prime numbers, importance of prime numbers in
cryptography, euclid theorem for GCD, extended euclidean algorithm,
modular arithmetic, random number generators, deterministic and
3 12
nondeterministic random number generators, XOR, bit shifts, euler's
totient theorem, chinese remainder theorem, RSA, Diffie Hellman key
exchange protocol, Elliptic curve cryptography (ECC), ElGamal
encryption system. DSS algorithm, RSADS algorithm, ECDSA
algorithm, Message integrity, hash functions, MAC functions, HMAC
UNIT-4

Blockchain technology

Use of ECDSA in blockchain implementation, use of merkle root in

payment verification, use of hash functions to chain blocks, use of
digital signatures to sign transactions, differences between ethereum
4 and bitcoin, block format, mining algorithm, Proof of Work (PoW), Proof 10
of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Burn (PoB),
Byzantine Fault Tolerance (BFT), account management, contracts and
transactions, decentralized applications using ethereum proof-of-stake
(PoS) algorithm, smart construct, structure of a smart contact, use of a
smart contract, remix platform, blockchain implementation through
smart contact.

UNIT-5

Blockchain enabled IoT security.

Applications and uses of blockchain enabled IoT security, Challenges,

5 and issues of blockchain enabled IoT security, blockchain in banking, 10
use of blockchain in e-commerce, marketing, and logistic operations,
blockchain in IoT ecosystem, blockchain in IoT-based smart healthcare
applications, use of blockchain in IoT-enabled smart farming,
blockchain in transportation system and future research aspects.
Total 48

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. William Stallings, “Cryptography and Network Security: 1st 2020
Principles and Practice”, Pearson publication, 2020.

2. William Stallings, “Cryptography and Network 2nd 2020

Security: Principles and Practice”, Pearson
publication, 2020.
3. Harshita Patel, Ghanshyam Singh Thakur, 1st 2020
“Blockchain Applications in IoT Security”, 1st Edition,
IGI Global, 2020.

Reference Books
1. Sudhir K. Sharma, Bharat Bhushan, Parma N. Astya, 1st 2021
Narayan C. Debnath, "Blockchain Applications for
Secure IoT Frameworks: Technologies Shaping the
Future," Bentham books, 2021.
2. William Stallings, “Network Security Essentials: 6th 2016
Applications and Standards”, 6th Edition, Prentice Hall,
2016

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER V

Name of Department: - Computer Science and Engineering

Block chain Technology
1. Subject Code: TCS-592 Course Title: and its application

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 5th

7. Category of Course: DE
8. Pre-requisite: TCS 302 Data Structure with C, TCS 332 Fundamental of
Information security and Block Chain

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Explain blockchain technology and its immutable property.
CO2: Know the working of distributed ledger.
CO3: Analyze the different consensus protocols.
CO4: Use Ethereum to implement Blockchain.
CO5: Apply blockchain techniques in different applications.
CO6: Develop blockchain based frameworks to secure a
communication environment
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Introduction to blockchain- Overview of blockchain, structure of a block, block header,
1 block identifiers: block header hash and block height, genesis block, linking of blocks, 10
merkle trees, and use of merkle root in payment verification
Application of cryptography to blockchain- Overview of ECDSA, DSA and RSADS, use
2 of hash functions to chain blocks, use of digital signatures to sign transactions 9

Distributed ledger- Introduction to distributed systems, fault tolerance and paxos,

byzantine agreement, authenticated agreement, eventual consistency & bitcoin
3 8
consistency- availability and partitions, bitcoin, smart contracts, weak consistency,
distributed storage, consistent hashing mechanism
Blockchain mining and consensus-Overview of various consensus algorithms,
decentralized consensus, independent verification of transactions, mining nodes,
aggregating transactions into blocks, constructing the block header, successfully
4 10
mining of block, validating a new block, assembling and selecting chains of blocks,
consensus attacks, DoS attack on blockchain, changing the consensus rules, soft fork
signaling with block version
Ethereum- Differences between ethereum and bitcoin, block format, mining algorithm,
proof-of-stake (PoS) algorithm, account management, contracts and transactions,
decentralized applications using ethereum proof-of-stake (PoS) algorithm, contracts,
5 and transactions. 8
Applications of blockchain technology- Blockchain in banking and marketing, smart
contracts, blockchain of Internet of Things, blockchain in healthcare, Future Research
directions of blockchain technology
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. George Icahn, “Blockchain: the complete guide to understanding 4th 2020
blockchain technology”, 2020.
2. Antony lewis, “The basics of bitcoins and blockchains: an 5th 2018
introduction to cryptocurrencies and the technology that
powers them” 2020.

Reference Books
1. Andreas M. Antonopoulos, “Mastering Bitcoin: unlocking digital 2nd 2017
cryptocurrencies”, O'Reilly Media,(2e) 2017.
2. Roger Wattenhofer, “Distributed Ledger Technology, 2nd 2017
The science of the Blockchain”, Inverted Forest
Publishing, (2e), 2017
12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 601 Course Title: Compiler Design

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VI

7. Category of Course: DC

8. Pre-requisite: TCS 402, TCS 302

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the various phases and fundamental principles of compiler
design like lexical, syntactical, semantic analysis, code generation and
optimization.

CO2: Compare and contrast various parsing techniques such as SLR, CLR,
LALR etc.

CO3: Use annotated tree to design the semantic rules for different aspects of
programming language.

CO4: Implement lexical analyzer and parser by using modern tools like Flex
and Bison.

CO5: Examine patterns, tokens & regular expressions for solving a problem in
the field of data mining.

CO6: Design a compiler for concise programming language.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Introduction, Lexical analysis: Compilers; Analysis of Source Program; The
Phases of a Compiler; Cousins of the Compiler; The grouping of phases; 9
1
Compiler- Construction toolsz Lexical analysis: The Role of Lexical Analyzer;
Input Buffering; Specifications of Tokens; Recognition of Tokens.

Unit 2:
2 Syntax Analysis – 1: The Role of the Parser; Context-free Grammars; Writing
a Grammar; Top-down Parsing; Bottom-up Parsing. Operator-Precedence 9
Parsing; LR Parsers; Using ambiguous grammars; Parser Generators

Unit 3:
Syntax-Directed Translation: Syntax-Directed definitions; Constructions of
3 Syntax Trees; Bottom-up evaluation of S-attributed definitions; L-attributed 8
definitions; Top-down translation. Run-Time Environments : Source
Language Issues; Storage Organization; Storage-allocation strategies, Storage-
allocation in C; Parameter passing
Unit 4:
Intermediate Code Generation: Intermediate Languages; Declarations;
Assignment statements; Boolean Expressions; Case statements; Back patching;
4 Procedure calls. 9
Code Generation: Issues in the design of Code Generator; The Target Machine;
Run-time Storage Management; Basic blocks and Flow graphs; Next-use
information; A Simple Code Generator; Register allocation and assignment; The
dag representation of basic blocks; Generating code from dags.
5 Unit 5: 9
Code Optimization, Compiler Development: Code Optimization:
Introduction; The principal sources of optimization; Peephole optimization;
Optimization of basic blocks; Loops in flow graphs.
Compiler Development: Planning a compiler; Approaches to compiler
development; the compiler development environment; Testing and
maintenance.
Total 44
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Alfred V Aho, Ravi Sethi, Jeffrey D Ullman: “Compilers- 2nd 2013
Principles, Techniques and Tools”, Pearson Education,
Reference Books
1. Charles N. Fischer, Richard J. leBlanc, Jr.:” Crafting a 1st 1991
Compiler with C”, Pearson Education,.
2 Andrew W Apple: “Modern Compiler Implementation in 1st 1997
C”, Cambridge University Press,.

3 Kenneth C Louden: “Compiler Construction Principles & 1st 1997

Practice”, Thomson Education,.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 611 Course Title: Software Engineering

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 6

7. Category of Course: DC

8. Pre-requisite: Basics of Programming

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Understand Software Development Life Cycle and importance of
engineering the software.

CO2: Development of efficient software requirement specification for desired

product.

CO3: Compare various software development methodologies ad conclude on

their applicability in developing specific type of product.

CO4: Construct an efficient design specification document for attainment of user

desired product.

CO5: Develop applications using the concepts of various phases of software

development life cycle.

CO6: Study various software testing techniques and identify their relevance to
developing a quality software.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Introduction:What is Software Engineering and its history, Software Crisis,
Evolution of a Programming System Product, Characteristics of Software,
Brooks’ No Silver Bullet, Software Myths
1 Software Development Life Cycles: Software Development Process, The Code- 10
and-Fix model, The Waterfall model, The Evolutionary Model, The Incremental
Implementation, Prototyping, The Spiral Model, Software Reuse, Critical
Comparisons of SDLC models, An Introduction to Non-Traditional Software
Development Process: Rational Unified Process, Rapid Application
Development, Agile Development Process
Unit 2:
Requirements: Importance of Requirement Analysis, User Needs, Software
Features and Software Requirements, Classes of User Requirements: Enduring
2 and Volatile; Sub phases of Requirement Analysis, Functional and Non-
functional requirements; Barriers to Eliciting User Requirements, The software 9
requirements document and SRS standards, Requirements Engineering, Case
Study of SRS for a Real Time System
Tools for Requirements Gathering: Document Flow Chart, Decision Table,
Decision Tree; Structured Analysis: DFD, Data Dictionary, Introduction to non-
traditional Requirements Analysis Tools: FSM, Statecharts and Petrinets;
Unit 3:
3 Software Design: Goals of Good Software Design, Design Strategies and 8
Methodologies, Data Oriented Software Design, Structured Design: Structure
Chart, Coupling, Cohesion,, Modular Structure, Packaging; Object Oriented
Design, Top-Down and Bottom-Up Approach, Design Patterns
Software Measurement and Metrics: Various Size Oriented Measures:
Halestead’s Software Science, Function Point (FP) Based Measures, Cyclomatic
Complexity Measures: Control Flow Graphs.
Development: Selecting a Language, Coding Guidelines, Writing Code, Code
Documentation
Unit 4:
Testing: Testing Objectives, Unit Testing, Integration Testing, Acceptance
Testing, Regression Testing, Testing for Functionality and Testing for
Performance, Top-Down and Bottom-Up Testing Strategies: Test Drivers and
4 Test Stubs, Structural Testing (White Box Testing), Functional Testing (Black 10
Box Testing), Test Data Suit Preparation, Alpha and Beta Testing of Products.
Static Testing Strategies: Formal Technical Reviews (Peer Reviews), Walk
Through, Code Inspection, Compliance with Design and Coding Standards,
Automated Testing
Unit 5:
Software Maintenance and Software Project Management: Software as an
Evolutionary Entity, Need for Maintenance, Categories of
Maintenance:Preventive, Corrective and Perfective Maintenance, Cost of
5 Maintenance, Software Re-Engineering, Reverse Engineering. Software
Configuration Management Activities, Change Control Process, Software 8
Version Control, An Overview of CASE Tools. Estimation of Various
Parameters such as Cost, Efforts, Schedule/Duration, Constructive Cost Models
(COCOMO), Resource Allocation Models, Software Risk Analysis and
Management.
Software Quality Assurance: SQA Plans, ISO 9000 models, SEI-CMM Model
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. R. S. Pressman,” Software Engineering: A Practitioners 7th 2010
Approach”, McGraw Hill.
2 P.K.J. Mohapatra,” Software Engineering (A Lifecycle 2010
Approach)”, New Age International Publishers
Reference Books
1. Ian Sommerville,” Software Engineering”, Addison Wesley. 9th 2011
2 Pankaj Jalote:” An Integrated Approach to Software 2005
Engineering”, Narosa Publishing House.

3 Carlo Ghezzi, M. Jarayeri, D. Manodrioli,” Fundamentals 2nd 2003

of Software Engineering”, PHI Publication.

4 Rajib Mall,” Fundamentals of Software Engineering”, PHI 5th 2018

Publication.
5 Pfleeger, “Software Engineering”, Macmillan Publication. 3rd 2006

6 Ian Sommerville,” Software Engineering”, Addison 9th 2011

Wesley.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-604 Course Title: Computer Networks I

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VI

7. Category of Course: DC

8. Pre-requisite: TCS 101, TCS 102

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Apply and Characterize computer networks from the view point of
components and from the view point of services.

CO2: Display good understanding of the flow of a protocol in general and a

network protocol in particular

CO3: Evaluate and Select the most suitable Application Layer protocol (such
as HTTP, FTP, SMTP, DNS, BitTorrent) as per the requirements of the
network application and work with available tools to demonstrate the
working of these protocols.

CO4: Design a Reliable Data Transfer Protocol and incrementally develop

solutions for the requirements of Transport Layer

CO5: Describe the essential principles of Network Layers and use IP addressing
to create subnets for any specific requirements

CO6: Evaluate and select the appropriate technology to meet Data Link Layer
requirements and design a framework to implementing TCP/IP protocol
suite.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction: Computer Networks and the Internet, Overall view: As
components and as services; What is a protocol, what is a network protocol,
Access Networks and Physical Media, Circuit and Packet Switching, Internet
1 Backbone, Delays: Processing, Queuing, Transmission and Propagation delays, 11
The Layered Architecture: Protocol Layering, The OSI Reference Model and the
TCP/IP protocol stack, History of Computer Networking, and the Internet.

Unit 2: Application Layer: Principles and Architectures of Network Applications,

Client and Server processes, the idea of socket, Transport services available to
Application Layer especially in the internet Application Layer Protocols: The
Web and http: Persistent and Nonpersistent connections, http message format,
2 cookies, proxy server, conditional GET, File Transfer Protocol, Email: smtp, mail 11
message formats, mail access protocols: pop3, imap, MIME, DNS: Services, How
it works, Root, Top-Level and Authoritative DNS servers, Resource Records,
DNS messages A simple introduction to p2p file distribution: BitTorrent

Unit 3: Transport Layer: Introduction and Services, The Transport layer in

internet, Difference between Connection Oriented and Connectionless services,
3 6
UDP: Segment structure, checksum in UDP, stop-and-wait, Go Back N, Selective
Repeat, TCP: Connection Establishment, TCP header, Sequence and
acknowledgement numbers, Round Trip Time, Flow Control, Congestion,
Control.
Transport Layer: Introduction and Services, The Transport layer in internet,
Difference between Connection Oriented and Connectionless services UDP:
Segment structure, checksum in UDP

Unit 4: Network Layer: Introduction, Packet Forwarding and Routing, Difference

between Virtual Circuits and Datagram networks, The internals of a router: Input
ports, output ports, switching architecture The Internet Protocol(IP), Datagram
format, IP fragmentation, IPv4, addressing, subnets, CIDR, classful addressing,
DHCP, Network Address Translation(NAT), Universal Plug and Play as a
4 provider of NAT, Internet Control Message Protocol(ICMP), IPv6 Header, 6
Moving from IPv4 to IPv6: tunnelling. Routing Algorithms: Introduction, global
vs decentralized routing, The Link State(LS) Routing Algorithm, The Distance
Vector (DV) Routing Algorithm, Hierarchical Routing, Introduction to Routing
in the Internet: RIP, OSPF, BGP; Introduction to Broadcast and Multicast
Routing.
Unit 5: Link Layer and Local Area Networks: Introduction to Link Layer and its
services, Where Link Layer is implemented? Error detection and correction
techniques: Parity checks, Checksum, CRC; Multiple Access protocols: Channel
5 10
Partitioning, Random Access (Slotted Aloha, Aloha, CSMA), Taking Turns; Link
Layer Addressing: MAC addresses, ARP, Ethernet, CSMA/CD, Ethernet
Technologies, Link Layer Switches, Switches vs Routers, VLANS
Total 45

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Computer Networking: “A Top Down Approach (5th 7th 2017
edition)”, Ross and Kurose, Pearson/Addison-Wesley
Reference Books
1. Andrew Tanenbaum and David Wetherhall, “Computer 6th 2022
Networks”, Prentice Hall
2. Peterson and Davie, “Computer Networks: A System 5th 2011
Approach”, Elsevier

3. Forouzan, “Data Communication and Networking”, 5th 2017

McGraw Hill

4. William Stallings: “Data and Computer Communication”, 8th 2007

Pearson Education, 2007
5. Nader F. Mir:” Computer and Communication Networks”, 1st 2007
Pearson Education.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS693 Course Title: Full stack web development

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 PRS 25 MSE 50

5. Credits: 3

6. Semester: VI

7. Category of Course: DC

8. Pre-requisite: TCS 408, TCS 503

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Apply HTML and CSS effectively to create interactive websites.

CO2: Implement client-side scripting using JavaScript to design dynamic websites.

CO3: Develop XML, AJAX and Jquery based web applications.

CO4: Implement server-side scripting using PHP.

CO5: Design PHP application with Database connectivity.

CO6: Ability to design and deploy simple web applications using MVC architecture.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Conta
Sl.
Contents ct
No.
Hours
Unit 1:
HTML
Basics of HTML, formatting and fonts, commenting code, color, hyperlink, lists, tables,
images, forms, XHTML, Meta tags, Character entities, frames and frame sets, Browser
1 architecture and Web site structure. Overview and features of HTML5. 8
CSS
Need for CSS, introduction to CSS, basic syntax and structure, using CSS, type of CSS,
background images, colors and properties, manipulating texts, using fonts, borders and
boxes, margins, padding lists, positioning using CSS, Introduction to Bootstrap.
Unit 2:
JavaScript: Client-side scripting with JavaScript, variables, functions, conditions,
loops and repetition, Pop up boxes.
2 8
Advance JavaScript: JavaScript and objects, JavaScript own objects, the DOM and
web browser environments, Manipulation using DOM, forms and validations, JSON.

Unit 3:
XML: Introduction to XML, uses of XML, simple XML, XML key components, DTD
and Schemas.
3 Ajax : Introduction to Ajax , XMLHttpRequest Methods and Properties, JavaScript 10
code for Ajax , Implementing Ajax techniques with a server scripting language ,
Handling the Response, Ajax with JSon
JQuery: jQuery Introduction, Install and Use jQuery Library, jQuery Syntax, Ajax
with jQuery, Load method, jQuery get and getJson methods.
Unit 4:
PHP
XAMPP Server Configuration, Introduction and basic syntax of PHP, decision and
4 looping with examples, PHP and HTML, Arrays, Functions, Browser control and 10
detection, string, Form processing, Files.
Advance Features: Cookies and Sessions, Basic commands with PHP examples,
Connection to server, creating database, selecting a database, listing database, listing
table names, creating a table, inserting data, altering tables, queries, deleting database,
deleting data and tables.

Unit 5:
MERN
Web Application Deployment, Content Management System (CMS). MERN Stack:
5 MongoDB: Overview , Environment, Data Modelling, Database Operations. Express: 12
Installing ExpressJS, Environment, Routing React: React Intro, , React Lifecycle,
Building Forms using React, states and components. Node: Install node, simple server,
HTML and JSON Response.
Total 48
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Learning PHP, MySQL, JavaScript, CSS & HTML5: A 3rd 2014
Step-byStep Guide to CreatingDynamic Websites by Robin
Nixon

2. Full Stack JavaScript: Learn Backbone.js, Node.js and 2nd 2018

MongoDB. Copyright © 2015 BYAZAT MARDAN
3.

Reference Books

1. Fritz Schneider, Thomas Powell, JavaScript – The 3rd 2017

Complete Reference, 2017, 3rd Edition, McGraw Hill.

2. Steven Holzener, PHP – The Complete Reference,2017, 1st 2017

1st Edition, Mc-Graw Hill

3. Learning PHP, MySQL & JavaScript: With jQuery, CSS 1st 2015
& HTML5 by Robin Nixon

4 Paul Deitel, Harvey Deitel, Abbey Deitel, Internet & World 6th 2020
Wide Web - How to Program, 2020 6th edition, Pearson
Education.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: PCS-601 Course Title: Compiler Design Lab

2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: 6

7. Category of Course: DC

8. Pre-requisite: TCS 402, PCS 251

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Construct lexical analyzer and parser layout by using modern tools like
Flex and Bison.
CO2: Explore the different finite automata problems with the help of tools.
CO3: Compare and contrast various parsing techniques such as SLR, CLR,
LALR with the help of bison tool.
CO4: Analyze the syntax rules by designing the syntax trees from different
aspects of programming languages.
** Describe the specific knowledge, skills, or competencies the students are
expected to acquire or demonstrate.

10. Details of the Course:

Conta
Sl. List of problems for which student should develop program and execute in the
ct
No. Laboratory
Hours
Design a LEX Code to count the number of lines, space, tab-meta character, and rest
1.
of characters in each Input pattern.
Design a LEX Code to identify and print valid Identifier of C/C++ in given Input
2.
pattern.
Design a LEX Code to identify and print integer and float value in given Input
3.
pattern.
4. Design a LEX Code for Tokenizing (Identify and print OPERATORS,
SEPERATORS, KEYWORDS, IDENTIFERS) in the C-fragment:

5. Design a LEX Code to count and print the number of total characters, words, white
spaces in given ‘Input.txt’ file.

6. Design a LEX Code to replace white spaces of ‘Input.txt’ file by a single blank
character into ‘Output.txt’ file.

7. Design a LEX Code to remove the comments from any C-Program given at run-time
and store into ‘out.c’ file.

8. Design a LEX Code to extract all html tags in the given HTML file at run time and
store into Text file given at run time.

9. Design a DFA in LEX Code which accepts string containing even number of ‘a’ and
even number of ‘b’ over input alphabet {a, b}.

10. Design a DFA in LEX Code which accepts string containing third last element ‘a’
over input alphabet {a, b}.

11. Design a DFA in LEX Code to Identify and print Integer &amp; Float Constants and
Identifier.

12. Design YACC/LEX code to recognize valid arithmetic expression with operators +,
-, * and /.
Design YACC/LEX code to evaluate arithmetic expression involving operators +, -,
13. * and / without operator precedence grammar &amp; with operator precedence
grammar.
Total
11. Suggested Books:
S Name of Authors/Books/Publishers Edition Year of
N Publication Reprint
Textbooks
1. 2nd 2012
Charles N. Fischer, Richard J. leBlanc, Jr.:” Crafting a Compiler
with C”, Pearson Education, 1991.
2. 2nd 2012
Andrew W Apple: “Modern Compiler Implementation in C”,
Cambridge University Press, 1997.
3. 6th 2011
Kenneth C Louden: “Compiler Construction Principles & Practice”,
Thomson Education, 1997.
Reference Books
1. 5th 2014
Alfred V Aho, Ravi Sethi, Jeffrey D Ullman: “Compilers-
Principles, Techniques and Tools”, Pearson Education, 2007

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

Computer Networks Lab
1. Subject Code: PCS 604 Course Title:

2. Contact Hours: L: 0 T: 0 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: CIE 0 PRS 25 MSE 25 SEE 0 PRE 50

5. Credits:

6. Semester: 6th

7. Category of Course: DC

8. Pre-requisite: Computer Networks

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand various components that make up a computer network,
including routers, switches, hubs, servers, and clients and learn about the basic
commands used troubleshooting.
CO2: Design UTP cable for cross and direct connection using crimping tool.
CO3: Implement the common network protocols such as TCP/IP, UDP, HTTP,
DNS, DHC, FTP and NAT Understand how these protocols function and their
role in facilitating communication between devices using network simulation tool
like Packet tracer.
CO4: Apply the static and dynamic routing concepts in the network core and
monitoring network traffic using Wireshark and develop skills in troubleshooting
network connectivity issues.
CO5: Design network applications using UDP and TCP socket programing
concepts and network design principles and test these applications using real or
virtual network devices.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. List of problems for which student should develop program and Contact
No. execute in the Laboratory Hours
Problem Statement 1:
2
1. Familiarization of Network Environment, Understanding and using network
utilities: ipconfig, netstat, ping, telnet, ftp, traceroute etc.
Problem Statement 2:

2. Familiarization with Transmission media and tools: Co-axial cable, UTP 2

cable, Crimping tool, Connectors etc.
Preparing the UTP cable for cross and direct connection using crimping tool.
Problem Statement 3:
3. Installation and introduction of simulation tool. (Packet Tracer) 2

Problem Statement 4:

4. To configure a basic network topology consisting of routers, switches, and 2

end devices such as PCs or laptops. Configure IP addresses and establish
connectivity between devices. (Using packet Tracer)
Problem Statement 5:
To configure a DHCP server on a router or a dedicated DHCP server device.
5. Assign IP addresses dynamically to devices on the network and verify 2
successful address assignment. (Using packet Tracer)

Problem Statement 6:
6. To configure a local DNS server to resolve domain names within a network. 2
(Using packet Tracer)
Problem Statement 7:
NAT (Network Address Translation): Set up NAT on a router to translate
7. private IP addresses to public IP addresses for outbound internet 2
connectivity. Test the translation and examine how NAT helps conserve
IPv4 address space. (Using packet Tracer)
Problem Statement 8:

8. Network Troubleshooting: Simulate network issues such as connectivity 2

problems, incorrect configurations, or routing failures. Use Packet Tracer's
simulation mode to diagnose and troubleshoot the network.
Problem Statement 9:
9. 2
To monitor network traffic using Wire Shark
Problem Statement 10:
10. To analyze complete TCP/IP protocol suite layer’s headers using Wire 2
Shark
Problem Statement 11:
TCP Client-Server Communication:
Implement a TCP client program that sends a message to a TCP server
program.
Implement the corresponding TCP server program that receives the message
11. 2
and displays it.
Test the communication between the client and server by exchanging
messages

(Using ‘C’ Language)

Problem Statement 12:
UDP Client-Server Communication:
Implement a UDP client program that sends a message to a UDP server
12. program. 2
Implement the corresponding UDP server program that receives the
message and displays it
(Using ‘C’ Language)
Optional programs for advanced learner
Problem Statement 1:

1.
File Transfer using TCP:

Implement a TCP server program that listens for incoming connections.

Implement a TCP client program that sends a file to the server.
The server should receive the file and save it on the local machine.
Verify the successful transfer by comparing the original file with the
received file
Problem Statement 2:
Chat Application using TCP:

Implement a TCP client program for a chat application.

2. Implement the corresponding TCP server program.
Multiple clients should be able to connect to the server and exchange
messages.
Test the chat application by simulating multiple clients communicating with
each other.
Problem Statement 3:
DNS Lookup using UDP:

Implement a UDP client program that sends a domain name to a DNS server.
3.
Implement the corresponding DNS server program that resolves the domain
name to an IP address.
The server should send the resolved IP address back to the client.
Test the program by performing DNS lookups for different domain names
Problem Statement 4:
HTTP Server using TCP:

Implement a TCP server program that acts as an HTTP server.

4. The server should be able to handle HTTP requests and send back
appropriate HTTP responses.
Test the server by accessing it through a web browser and requesting
different resources.

11. Suggested Books:

S. No. Name of Authors/Books/Publishers Year of
Publication /
Reprint
Text Books

1. Behrouz A. Forouzan, “Data Communications and Networking 2022

with TCPIP Protocol Suite, 6/e”, McGraw Hill

Reference Books

1. Ross and Kurose, Computer Networking: “A Top-Down 2017

Approach (5th edition)”, Pearson/Addison-Wesley

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: PCS693 Course Title: Web Programming Lab

2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: VI

7. Category of Course: DC

8. Pre-requisite: Subject Name with Code

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Design basic websites using HTML and Cascading Style Sheets.
CO2: Develop dynamic web pages with validation using Java Script objects and
by applying different event handling mechanisms.
CO3: Develop modern interactive web applications using PHP, XML and
MySQL
CO4: Understand client(JS) and server-side(PHP) scripting and their
applicability
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. List of problems for which student should develop program and Contact
No. execute in the Laboratory Hours
Basic Html Tags: To create a simple html file to demonstrate the use of
1. different tags.

Html Tags (List, Table) : To create a simple html file to demonstrate the
2. use of different tags.

3. Html Tags (Form)

Frames & iFrames : To create an html page with different types of frames
4.
such as floating frame, navigation frame & mixed frame.
Map: To create an html page with different types of image map such as
5. circle, rect , poly & mixed map.

CSS: Inline, Internal and External Style sheets To create an html file by
6. applying the different styles using inline, external & internal style sheets.

Input Output In JavaScript: To create an HTML page to explain input and

7. output using a calculator with the use of various predefined functions and
objects in Javascript.

Window Object methods alert() , prompt() , confirm(), open(), close() ,

8. print():To create an html page to explain the use of various predefined
functions in window object in java script.

Event Handling - Background Color Change: To create an html page to

9. change the background color for every click of a button using javascript.

Event Handling - calendar for the month and year by combo box: To
create an html page with 2 combo box populated with month & year, to
10. display the calendar for the selected month & year from combo box using
javascript.

Window object method setInterval, clearInterval: To create an html page

11. with three button START PAUSE and RESET for controlling stopwatch.

12. PHP XAMPP Server: Install and configure PHP, web server, MYSQL
(XAMPP), Write a program to print “Welcome to PHP”, Create a php
program to find odd or even number from given number. Write a php
program to find maximum of three numbers.

PHP Basic : Write a program to enter TWO numbers and print the Swap
13. Numbers using PHP Example.

Form Handling in PHP: Write a PHP Program to demonstrate the variable

function: gettype() and settype(),Write a PHP Program to demonstrate the
14. variable unction: isset() ,Write a PHP Program to demonstrate the variable
unction: unset()

15. Session Handling Using PHP: Create login page using session variables
Cookies Management: Write PHP program to implement a cookie and
16. session based counter. Create Cookies variable using PHP, Display the
cookies variable using PHP.

File Uploading Using PHP (To Understand File Uploading in PHP) : Create
17. PHP To upload the user input file and using constraints file type, file size.

PHP with MySQL: Write a PHP program to connect to a database and

retrieve data from a table and show the details in a neat format , a simple
18. application to Enter data into database, Develope a simple application to
Update, Delete table data from database.

File Handling Using PHP: Write a php program to Read from existing file.,
19. Write a php program to Write a file.

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Learning
1 PHP, MySQL, JavaScript, CSS & HTML5: A 3rd 2014
Step-byStep
. Guide to CreatingDynamic Websites by Robin
Nixon

2. Full
2 Stack JavaScript: Learn Backbone.js, Node.js and 2nd 2018
.
MongoDB. Copyright © 2015 BYAZAT MARDAN
Reference Books

1 Schneider, Thomas Powell, JavaScript – The

1. Fritz 3rd 2017
Complete
. Reference, 2017, 3rd Edition, McGraw Hill.

2. Steven
2 Holzener, PHP – The Complete Reference,2017, 1st 1st 2017
Edition,
. Mc-Graw Hill

3. Learning
3 PHP, MySQL & JavaScript: With jQuery, CSS & 1st 2015
HTML5
. by Robin Nixon

4. Paul
4 Deitel, Harvey Deitel, Abbey Deitel, Internet & World 6th 2020
Wide
. Web - How to Program, 2020 6th edition, Pearson
Education.
12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

Image Processing and
1. Subject Code: TCS 691 Course Title: Computer Vision

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 6th

7. Category of Course: DE

8. Pre-requisite: TCS 301, Any Programming Language

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the principals the Image Processing terminology used to
describe features of images.
CO2: Understand the mathematical foundations for digital manipulation of
images
CO3: Design, code and test digital image processing applications using
MATLAB.
CO4: Analyze a wide range of problems and provide solutions related to the
design of imageprocessing systems through suitable algorithms,
structures, diagrams, and other appropriate methods.
CO5: Plan and undertake a major individual image processing project.
CO6: Write programs in Matlab for digital manipulation of images; image
acquisition; preprocessing; segmentation.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
INTRODUCTION TO IMAGE PROCESSINGAND COMPUTER VISION: Pixels,
Intensity, Coordinate Conventions, Sampling and Quantization, Histogram Analysis,
Videos, Image Processing Pipeline, Image Processing and Computer Vision Research
1 Areas: Low-level, Mid-Level and High-Level Vision. INTRODUCTION TO MATLAB /
9
OCTAVE:Basic Opeartions, Image / Video handling, Flow Control, Vectorization.
INTRODUCTION TO PYTHON:Basic Opeartions, Lists, Tuples, Strings, Dictionaries,
Flow Control, Numpy, Image/Video handling, OpenCV, PIL, Orange.
Unit 2: IMAGE PROCESSING / LOW-LEVEL VISION: Image Enhancement in Spatial
Domain, Image Enhancement in Frequency Domain, Edge Detection, Image
Restoration, Color Image Processing, Wavelet Transform, Image Compression,
2 Morphological Image Processing, Color Image Processing, Stereo Vision, Motion 9
Analysis, Local and Image Features, Visual Saliency

Unit 3: MID-LEVEL VISION: Hough Transform, Otsu Thresholding, k-means, GraphCut,

3 GrabCut, Normalized Cut, Watersheds, Skeleton Extraction, Object Proposals, 11
Cosegmentation, Background Subtraction in Videos, Motion History Image

Unit 4: HIGH-LEVEL VISION: Image Classification, Object Localization, Object

4 Recognition, Object Detection, CNN, AlexNet, VGG, GoogleNet, DenseNet, FCN for 9
Semantic Segmentation, YOLO, Image Captioning, generative adversarial networks

Unit 5:
APPLICATIONS OF IMAGE PROCESSING AND COMPUTER VISION: Video
5 Surveillance Systems, Medical Diagnosis, Facial recognition system, Automatic activity 10
recognition system, Fire detection System, traffic sign detection and recognition

Total 48
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Digital Image Processing, by R. C. Gonzalez, R. E. Woods 4th 2017
and S. L. Eddins , Publisher: Pearson. Edition
2. 2nd 2017
Digital Image Processing using Matlab, by R. C. Gonzalez,
R. E. Woods and S. L. Eddins , Publisher: Pearson.
3. 1st 2018
Deep Learning for Computer Vision, by Rajalingappaa
Shanmugamani, Publisher: O Reilly
Reference Books
1. Deep Learning with Keras by Antonio Gulli, Sujit Pal, 1st 2017
Publisher: O Reilly
2. 1st 2012
Programming Computer Vision with Python", Jan Salem,
Publisher: O Reilly

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI
Name of Department: - Computer Science and Engineering
1. Subject Code: TCS 651 Cours Title: DevOps on Cloud

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3
6. Semester: VI
7. Category of Course: DE
8. Pre-requisite: Students should have a strong technology background, an
understating of cloud infrastructure and skill with a scripting language to master this
course.

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Define and understand ideas of DevOps.
CO2: Describe and demonstrate how DevOps relate to working in the cloud.
CO3: Describe and demonstrate how DevOps tools work together.
CO4: Use a public/private cloud environment as a framework to examine the ideas of
DevOps.
CO5: Examine some use cases, deployment, test automation, continuous delivery,
and the public/private cloud toolsets for DevOps .
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.
10. Details of the Course:
Sl. Contact
Contents
No. Hours
Unit 1:An introduction to DevOps, Gain insights of the DevOps environment, 9
1
DevOps Vs Agile, DevOps Ecosystem.
Unit 2: Version Control with Git, Install GIT and work with remote repositories,
GIT workflows, Branching and Merging in Git. Understand the importance of
2 Continuous Integration, Introduction to Jenkins, Jenkins management. Build and 9
automation of Test using Jenkins and Maven.
Unit 3:Continuous Testing, learn and Install Selenium, create test cases in
3 Selenium, Integrate Selenium with Jenkins, Continuous Deployment. 10

Unit 4:Introduction to Docker, understanding images and containers, Docker

Ecosystem, Introduction to Docker Networking, Monolith and Microservices,
4 8
features of Microservices Architecture, Advantages of Microservices.

Unit 5:Introduction of Kubernetes, Kubernetes Architecture, Docker Swarm and

5 Kubernetes, Application deployment using Docker and Kubernetes. 8

Total 44

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint
Textbooks
1. The Visible Ops Handbook by Kevin Behr, Gene Kim and 1st 2004
George Spafford, IT Process Institute
2. DevOps for Developers by Michael Hüttermann. 1st 2012

3. The Goal: A Process of Ongoing Improvement by Eliyahu 1st 2008

M. Goldratt, Jeff Cox Author, David Whitford (Other
Contributor)
4 Devops: a comprehensive beginners guide to learn devops 1st 2019
step by step, Ethan Thorpe,
ISBN-10 : 1081563672
5. Material provided by the instructor 1st 2007

Reference Books
1. Continuous Delivery: Reliable Software Releases 3rd 2010
through Build, Test, and Deployment Automation by
Jez Humble and David Farley
2. The Phoenix Project by Gene Kim, Kevin Behr, George 3rd 2013
Spafford

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

Bigdata Storage and
1. Subject Code: TCS 671 Course Title: Processing

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VI

7. Category of Course: DE
8. Pre-requisite: TCS-351 Fundamental of Cloud Computing and Bigdata,

TCS-571 Bigdata Visualization

9. Course After completion of the course, the students will be able to:
Outcome**: CO1: Understand the concepts and significance of big data, including its
capture, management, organization, and analysis
CO2: Utilize the HDFS command line interface to interact with the file system,
manage data nodes, and work with the data flow.
CO3: Describe the concept of MapReduce, its features, types, and formats, and
comprehend the workflow of a MapReduce job.
CO4: Set up a Hadoop cluster, considering system requirements, and understand
the different installation mode
CO5: Analyze and manage big data using Hadoop ecosystem tools and
techniques, such as HDFS, MapReduce, and NoSQL databases.
CO6: Apply critical thinking and problem-solving skills to address
technological challenges associated with big data and propose appropriate
solutions.
** Describe the specific knowledge, skills, or competencies the students are
expected to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Big Data Overview: Understanding Big Data, Capturing Big Data,
Benefitting from big data, management of big data, Big Data Architecture
1 and Characteristics, Organizing big data, Technological Challenges from 10
big data.

Unit 2: Hadoop Distributed File System (HDFS), HDFS design, HDFS

2 concepts: Data node, name node, Command line interface, File system, 10
Data flow, limitations
Unit 3: Hadoop I/O: Data integrity, compression, serialization, File based
data structures, Concept of Map Reduce, features, types, and formats,
3 Working of Map Reduce: Shuffle and sort, Task execution, Job tracker, 9
task tracker

Unit 4: Setting up a Hadoop cluster: Basic system requirements, installation

4 and cluster formation, Modes of installation: the standalone, pseudo-
9
distributed, and distributed, purpose of different modes of installations and
applications
Unit 5:
Hadoop Eco System and YARN: Hadoop ecosystem components,
5 schedulers, fair and capacity, Hadoop 2.0 Features 8
NoSQL Databases:- RDBMS Vs. NoSQL, Types of No SQL Databases,
Architecture of NoSQL Databases, CAP Theorem,
Total 46
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Tom White, Hadoop: A definitive guide, 3/e o'reilley 3rd 2012

Reference Books
1. Fei Hu, Big Data: Storage, Sharing and Security, CRC 1st 2016
Press, Taylor, and Francis.

12. Mode of Evaluation Test / Quiz / Assignment / Mid-Term Exam / End-Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

Network and System
1. Subject Code: TCS 619 Course Title: security
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50
5. Credits: 3

6. Semester: VI

DE
7. Category of Course:

8. Pre-requisite: TCS 591

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the basics of computer security
CO2: Elaborate the cryptographic techniques.
CO3: Discuss the transport layer security
CO4: Find the pros and cons of various key distribution methods
CO5: Analyze the wireless Network security

CO6: Find the level of system security

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Introduction
Computer Security Concepts, The OSI Security Architecture, Security Attacks, 9
1
Security Services, Security Mechanisms, Models for network security,
standards.
Unit 2:
Cryptography
Symmetric Encryption and Message Confidentiality Symmetric Encryption
Principles, Symmetric Block Encryption Algorithms, Random and
2
Pseudorandom Numbers, Stream Ciphers and RC4, Cipher Block Modes of
Operation. 9
Public-Key Cryptography and Message Authentication 61 Approaches to
Message Authentication, Secure Hash Functions, Message Authentication
Codes, Public-Key Cryptography Principles, Public-Key Cryptography
Algorithms, Digital Signatures
Unit 3:
Network security Application - I
Key Distribution and User Authentication
Symmetric Key Distribution Using Symmetric Encryption, Kerberos, Key Distribution Using
Asymmetric Encryption, X.509 Certificates, Public-Key Infrastructure, Federated Identity
3 Management 10
Transport-Level Security
Web Security Considerations, Secure Socket Layer and Transport Layer
Security, Transport Layer Security,
HTTPS, Secure Shell (SSH)
Unit 4:
Network security Application - II
Wireless Network Security
IEEE 802.11 Wireless LAN Overview, IEEE 802.11i Wireless LAN Security,
Wireless Application Protocol Overview, Wireless Transport Layer Security,
4 WAP End-to-End Security
8
Electronic Mail Security
Pretty Good Privacy, S/MIME, DomainKeys Identified Mail,
IP Security
IP Security Overview, IP Security Policy, Encapsulating Security Payload,
Combining Security Associations, Internet Key Exchange, Cryptographic Suites

Unit 5:
System Security
Intruders
Intruders, Intrusion Detection, Password Management,
Malicious Software
Types of Malicious Software, Viruses, Virus
5 Countermeasures, Worms, Distributed Denial of Service Attacks. 10
Firewalls
The Need for Firewalls, Firewall Characteristics, Types of Firewalls, Firewall
Basing, Firewall Location and Configurations,
Legal and Ethical Aspects
Cybercrime and Computer Crime, Intellectual Property, Privacy, Ethical Issues
Total 46

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. W. Stallings, “Network Security Essentials”, Prentice Hall, 6th 2017

2 Reference:-Ch. P. Pfleeger, S. L. Pfleeger,“ Security in 4th 2006

Computing”, 4th Edition Prentice Hall,
Reference Books

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 641 Course Title: Virtual Reality

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VI

7. Category of Course: DE
8. Pre-requisite: NA

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Demonstrate an understanding of techniques, processes, technologies and
equipment used in virtual reality
CO2: Identify appropriate design methodologies for immersive technology
development, especially from a physiological perspective

CO3: Exploit the characteristics of human visual perception in Virtual Reality

techniques

CO4: Provide rendering to VR specific problems

CO5: Effectively categorize the benefits/shortcomings of

available VR technology platforms.

CO6: Discuss the use of geometry in virtual reality

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Contact
S.NO. Contents
Hours
Unit 1:
Introduction: Goals, VR definitions, Birds-eye view (general, hardware,
software, sensation and perception), Applications of VR, Technical framework,
1 Mixed and Augmented Reality 8
Geometry of Virtual Worlds: Geometric modeling, Transforming models, Matrix
algebra, 2D and 3D rotations, Axis-angle representations, Quaternions, Converting
and multiplying rotations, Homogeneous transforms, Eye Transforms, Canonical
view transform, Viewport Transform
2 Unit 2:
Light and Optics: Interpretations of light, Refraction, Simple lenses, Diopters,
Imaging properties of lenses, Lens aberrations, Photoreceptors, Sufficient 9
resolution for VR, Light Intensity, Eye movements for VR, Neuroscience of vision

Unit 3:
Visual Perception and Tracking Systems: Depth perception, Motion Perception,
Frame rates and displays, Orientation Tracking, Tilt drift correction, Yaw drift
3 9
correction, Tracking with a camera, Perspective n-point problem, Filtering,
Lighthouse approach

Unit 4:
Visual Rendering: Shading models, rasterization, Pixel shading, VR
4 9
specific problems, Distortion shading, Post-rendering image wrap

Unit 5:
Audio: Physics and physiology, Auditory perception, Auditory
5 8
Localization, Rendering, Spatialization and display, Combining other
senses, Spatial Sound
Interfaces: Locomotion, Manipulation, System Control, Social Interaction,
VR Engines and Other Aspects of VR, Evaluation of VR systems
Total 43

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Grigore C. Burdea , Philippe Coiffet, “Virtual Reality 2nd 2003
Technology”, Wiley-IEEE press
2 Marschner, Shirley "Fundamentals of Computer Graphics", 4th 4th 2016
Edition, CRC Press
3 LaValle "Virtual Reality", Cambridge University Press, 1st 2016

4 “Virtual Reality”, Steve Lavalle (online open book) 1st 2016

Reference Books
1. 1. K. S. Hale and K. M. Stanney, “Handbook on Virtual 2nd 2015
Environments”, 2nd edition, CRC Press, 2015
2 George Mather,” Foundations of Sensation and 1st 2007s
Perception:” Psychology Press

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 662 Course Title:Machine Learning

LearninLearningProfe
2. Contact Hours: L: 3 T: 0 P: 0 ssionaCommunicatio

3. Examination Duration (Hrs): Theory n

Practical
3 0
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VI

7. Category of Course: DC
8. Pre-requisite: Design and Analysis of Algorithm, Fundamental of Statistics
and AI (TCS 421 / Statistical Data Analysis with R (TCS 471), Discrete Structures
and Combinatorics (TMA 316)

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Acquire concepts and methods in statistical machine learning
CO2: Analyze fundamental principles of machine learning algorithms
CO3: Understand machine learning motivated by case-studies
CO4: Investigate and evaluate key topics in machine learning
algorithms for data science industry
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Machine learning foundation
Review of logic and knowledge system - language, axiom, hypothesis,
theorem, logic & types, what is ML, Inductive bias in ML, AI pyramid,
Pattern classification pipeline, Linear algebra in ML, Probabilistic logic
and statistical inference (Random expt./ variable, CDF, WLLN, Bayes,
Markov & Chernoff bound, Hypothesis testing and performance indices
1 14
- ROC, Estimation - detection, Optimality of Bayes, bias-variance,
underfit-overfit, entropy as Information, Cover’s packing lemma, Curse
of dimensionality, Case study: Wealth – optimal payoffs in portfolios
(stock market)

Unit 2: Unsupervised Learning

Clustering, Clustering methods – Partition vs. Hierarchical, k-Means and
2 k-Medoids, Hierarchical: Agglomerative & Divisive, Error Analysis in 8
Clustering, Ensemble - clustering, Case study: Clustering in Health
care, Causal cluster, Graph cluster
Unit 3: Supervised Learning
Main objectives and types of Supervised methods (Parametric, Semi
parametric, Non-parametric), Linear Regression and Weiner filter,
Grammar based/ Inductive learning - Decision Trees – CART, ID-3,
3 10
Pruning metrics for tree; D-tree examples, Linear SVM (basics and V-C
bound), k-NN rule and examples, Learning as Factorization, Ensemble
learning: Bagging, Boosting. Case studies: covered for mentioned
Supervised learning techniques.
Unit 4: Reinforcement & Interaction Learning
4 Basic model of Reinforcement Learning as game (Agent, Critic, 8
Environment), Optimal policy & Q – values, Bellman equation, Case
studies on R Learning Active learning, Deep Reinforcement, Transfer
learning with examples, Federated Machine Learning with examples.

Unit 5: Special topics in Machine Learning

Sentiment Mining: NLP pipeline process, Data Analytics – Big data and
5 Hadoop model, Business Analytics – Competitive Machine Learning, 8
ANN building blocks (problem solving), Deep learning, Feed forward,
Backpropagation, C-NN, Recurrent-NN.
Total 48
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks

1. Pattern classification – R, Duda, P. Hart and D. Stork, 2nd. 2007

Wiley Publisher

2. The Elements of Statistical Learning – J. 3rd. 2017

Friedman, R. Tibshirani and T. Hastie, Springer

Publisher
3. Pattern Recognition and Machine Learning – C. 2nd.
Bishop, Springer Publisher 2016

4. Deep Learning – A. Courville, I. Goodfellow, Y. 2nd.

Bengio, MIT Press 2016

Reference Textbooks
1. Machine Learning – Tom M. Mitchell, Mc Graw Hill 1st 2017
Publisher
2. Introduction to Machine Learning – E. Alpaydin, 3rd. 2015
PHI Publisher
3. Elements of Information Theory – T M. Cover, J 2nd. 2006
A. Thomas, Wiley Publisher
12. Mode of Internal Seminar – presentation on topic in ML & internal viva
OR
Evaluation
Simulation of ML method with real dataset & internal viva

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

PCS 662 Machine Learning Lab

LLAbearninLearningProf
essionaCommunication
1. Subject Code: Course Title:

2. Contact Hours: L: T: 1 P: 2

3. Examination Duration (Hrs): Theory Practical 2 hr

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: VI

7. Category of Course: DC

8. Pre-requisite: Design and Analysis of Algorithm, Python programming or C++

programming or Java Programming

9. Course After completion of laboratory the students will be able to:

Outcome**:
CO1: Implement methods in statistical machine learning
CO2: Analyze data and machine learning algorithms
CO3: Understand machine learning motivated by case-studies

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Contact
S. N. Contents
Hours
To implement various similarity functions for attribute tuples stored as
1 2
.csv file – Euclidean, Manhattan, Cosine.
To use lobe.ai user interface and perform manual labeling, training
2 2
and testing for supervised object recognition in Image

To understand and develop basic KR (Knowledge Representation) Tools from

3 practical AI problem definitions: KR methods 2

To take two - category input file and use thresholding to design binary
4 2
classifier for 1 feature, for 2 feature dataset
To register and use monkeylearn.com and create model, train and
classify sentiments that lead to sentiment prediction using corpus of
5 2
hotel reviews as part of NLU
To register and use Teachable machine (Google API) and perform multiple class /
6 2
pose analysis and classification.
To use quillbot.com and study basic machine transcription roles in summarization:
7 as NLP application 2

To use quillbot.com and study basic machine transcription roles in

8 2
grammar checker (syntax): as NLP application
To use quillbot.com and study basic machine transcription roles in
9 2
paraphrasing: as NLP application
To create multiple clusters using PoS (parts of speech) data by
10 2
reading input text file
To create multiple clusters from column data entries of .csv file
11 2
using k-means algorithm
To create multiple clusters by using hierarchical clustering –
12 2
Agglomerative based on .csv file
To use healthcare dataset and form scatter plot with observed
13 2
statistical measures
To import tabular data for related clinical parameters and program a
14 2
basic linear regression model
Program probabilistic model given: it is Friday and that a student is 2

absent is 3 %. Since there are 5 school days in a week, the

15 probability that it is friday is 20 %. What is the probability that a
student is absent given that today is friday. Program Bayesian rule
in python to get the result.
To extract intra-day stock market data for 4 stocks and write 2
16 program that: plots the values, develops linear regression, derive
mean and correlation.
To write program that can import training samples for 3 labels and 2
17
perform k-NN for new queries and quantify error performance

Unit 1: Basic Concepts: Formulation of mathematical programming problems;

1 10
Classification of optimization problems; Optimization techniques – classical and
advanced techniques Optimization using Calculus: Convexity and concavity of
functions of one and two variables; Optimization of function of multiple variables
subject to equality constraints; Lagrangian function; Optimization of function of
multiple variables subject to equality constraints; Hessian matrix formulation
Unit 2: Linear Programming: Standard form of linear programming (LP) problem;
Canonical form of LP problem; Assumptions in LP Models; Graphical method for
two variable optimization problem; Motivation of simplex method, Simplex
2 10
algorithm and construction of simplex tableau; Revised simplex method; Duality in
LP; Primal dual relations; Dual Simplex Method; Sensitivity or post optimality
analysis; bounded variables
Unit 3: Dynamic Programming: Representation of multistage decision process;
3 Types of multistage decision problems; Concept of sub optimization and the 8
principle of optimality
Unit 4: Integer Programming: Integer linear programming; Branch and Bound
4 algorithm; Concept of cutting plane method; Mixed integer programming; Solution 8
algorithms.
Unit 5: Advanced Topics in Optimization: Direct and indirect search methods;
5 8
Heuristic and Meta-Heuristic Search methods; Multi objective optimization.
Total
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. S.S. Rao, “Engineering Optimization: Theory and 3rd 2013
Practice”, New Age International Publishers

2 H.A. Taha, “Operations Research: An Introduction”, 10th 2019

Pearson Education

3 Ravindran, K. M. Ragsdell and G. V. Reklaitis, 2nd 2006

“Engineering Optimization: Methods and Applications”,
Wiley India
Reference Books
1. R. Fletcher, “Practical Methods of Optimization”, Wiley 2nd 2009
India
2. K. Deb, “Optimization for Engineering Design”, Prentice 2nd 2012
Hall India

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VI

Name of Department: - Computer Science and Engineering

Big Data Analytics: Tools
1. Subject Code: TCS-663 Course Title: and Techniques

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VI

7. Category of Course: DE

8. Pre-requisite: Introduction to Big Data (TCS-462),

Big Data Visualization (TCS-571),
Data Base Management Systems (TCS-503),
Programming in Java (TCS-408),

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Investigate Hadoop related tools for Big Data Analytics and perform basic
Hadoop Administration
CO2: Analyse the technological foundations for Big data with Hadoop and design
of Hadoop distributed file system
CO3: Understand the concept of MapReduce workflow
CO4: Develop program using Hive and Apache Pig for large data processing
CO5: Outline the theory of big data, and explain applications of big data
CO6: Build Big Data Analytics application to solve real world problem
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Introduction to Hadoop: Introduction to Hadoop, Distributed Computing
1 Challenges, Hadoop Features, Hadoop Distributed File System (HDFS), Hadoop 9
Versions, Hadoop Installation, HDFS basic commands, Overview of Hadoop
Ecosystem, RDMS vs Hadoop
Unit 2:
Introduction to MapReduce Programming:
2 Introduction to MapReduce Framework, Mapper, Reducer, Combiner, Partitioner, 8
Searching, Sorting, Compression, Sample programs on MapReduce, Techniques to
optimize MapReduce Jobs
Unit 3: Hive and Apache Pig
Hive: Introduction to Hive, Hive Architecture, Hive Data Types, Hive Query
3 Language, User Defined Functions, Sample Programs 8
Apache Pig: Introduction to Pig, Pig Latin Overview, Data Types in Pig, Pig
Operators, User Defined Functions, Sample Programs
Unit 4:
4 Spark: Introduction to Spark, Features of Spark, Spark Architecture, Spark 9
Components, Spark RDD, Spark in-built functions, Sample Programs
Unit 5: Apache Flume, Sqoop and Big Data Applications
Flume: Introduction to Apache Flume, Flume Architecture, Data Flow,
Environment, Sample Exercise
Sqoop: Introduction to Sqoop, Sqoop Features, Sqoop Architecture, Sqoop
5 8
integration with Hadoop, Data import and export using Sqoop, Sqoop vs Flume,
Sample Exercise
Big Data Applications: Healthcare, Agriculture, Education, Media and
Entertainment, Travel, Retail, etc.
Total 42
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Subhashini Chellappan Seema Acharya, Big Data and 2nd 2019
Analytics, Wiley
2. Big Data, Black Book, Dreamtech Press 1st 2016

3. Raj Kamal, Big Data Analytics, Introduction to Hadoop, 1st 2019

Spark, and Machine-Learning, McGraw Hill Education
Reference Books
1. Hadoop: The Definitive Guide, Tom White, O'Reilly 4th 2015
2. Michele Chambers, Michael Minelli , Ambiga Dhiraj ,Big 1st 2013
Data, Big Analytics: Emerging Business Intelligence and
Analytic Trends for Today's Businesses, Wiley

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-682 Course Title: Advanced Machine Learning

AAdvancedProfessionaCommu
2. Contact Hours: L: 3 T: 0 P: nication
0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 0 SEE 25

5. Credits: 3

6. Semester: 6

7. Category of Course: DC

8. Pre-requisite:

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand and analyze the basic terminologies of Machine Learning
CO2: Understand the Parametric and non_parametric methods
CO3: Understand the concepts of various Kernel methods
CO4: Evaluate the working of different kernel and classifier models
CO5: Understand the different Graphical and Mixture Model techniques
CO6: Analyse and Differentiate various learning approaches
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Probability Theory, Overview of supervised learning, curse of
dimensionality, Decision Theory, Information Theory, Mini-Max Theory,
1 Bayesian Vs non-Bayesian approaches, Classification, Regression, 8
Desnsity estimation, Bias-variance, Lasso, MLE

Unit 2: Parametric versus non-Parametric methods, Generalized Linear

Models, Model selection, Hidden Markov Models, Linear smoothers,
2 Density estimation: cross-validation, Histogram, Kernel density 8
estimation, Bootstrap and sub-sampling, Non-parametric Bayes

Unit 3: Kernel Methods and Machines: Dual representations, Kernel

construction, Selecting the width of the kernel, Kernel density estimation
3 and classification, Radial basis functions and kernel, Gaussian processes, 8
Maximum margin classifiers, Relevance vector machines

Unit 4: Graphical and Mixture Models: Bayesian networks: Generative

models, Linear-Gaussian models; Conditional independence: D-
separation; Markov random fields: Factorization properties, Relation to
directed graphs; Inference in graphical models: Inference on a chain,
4 8
Trees, Factor graphs, Sumproduct & max-sum properties, Loopy belief
propagation; K-means clustering, Mixtures of Gaussians, EM, An
alternative view of EM

Unit 5: Other Learning Methods: Unsupervised learning, Semi-supervised

learning, Reinforcement learning, Ensemble learning, Online learning,
5 Active learning 8

Total 40
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Christopher M. Bishop, Pattern Recognition and 3rd 2016
Machine Learning, Springer
2. Stephen Marsland, Machine Learning: An Algorithc 2nd 2014
Perspective, CRC Press
Reference Books
1. Wasserman L., All of Statistics: A Concise Course in 1st 2010
Statistical Inference, Springer
2. Devroye L., Gyorfi L., Lugosi G., A Probabilistic 1st 1996
Theory of Pattern Recognition, Springer

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: PCS 682 Course Title: Advanced Machine Learning Lab

2. Contact Hours: L: 0 T: 1 P: 2

3. Examination Duration (Hrs): Theory 0 Practical 3

4. Relative Weight: CIE 25 MSE 25 SEE 25

5. Credits: 2

6. Semester: 6

7. Category of Course: DC

8. Pre-requisite: TCS-307 Object Oriented Programming with C++, PCS

582- ALML Lab

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the mathematics behind Machine Learning Model
CO2: Understand how Markov Model Work
CO3: Understand functioning of Convolutional Neural Network model
CO4: Understand the Regression and classification, associativity rules.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. List of problems for which student should develop program and execute Contact
No. in the Laboratory Hours
1. Implemention of Polynomial Regression in python 2
2. Implementation of Support Vector Regression in python 2
Write a python program to Implement Decision Tree and Random Forest
3. 2
Regression Algorithm
4. Write a program to demonstrate the working of the Naïve Bayes 2
algorithm

5. Build an Association Rule Learning Model by implementing the Apriori 2

and Eclat Algorithm

6. Write a program to implement the Upper Confidence Bound (UCB) and 2

Thomson Sampling Reinforcement Learning in python

7. Write a python program to Implement Convolutional Neural Network 2

Model
8. Write a program to implement the Principal Compoment Analysis. 2

9. Write a python program to Implement and Demonstrate Linear 2

Discriminant Analysis
10. Write a python program to Implement the Hidden Markov Model 2
Total 20
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Michael Learn, Python Programming, Mikcorp Limited 2nd 2021

2. Vijay Kumar Sharma, Vimal Kumar, Swati Sharma, and 2nd 2021
Shashwat Pathak, Python Programming: A Practical
Approach, CRC Press
Reference Books
1. Gowrishankar S., Veena A., Introduction to Python 1st 2018
Programming, CRC Press

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 643 Course Title: Theory of

Optimization
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 6

7. Category of Course: DE

8. Pre-requisite: Linear Algebra, Calculus

9. Course After completion of the course the students will be able to:
Outcome**: CO1: develop a knowledge in the field of optimization techniques and
their basic concepts, principles and algorithms.
CO2: understand fundamentals of linear programming, Integer
programming and Dynamic programming.
CO3: apply the theory of optimization methods for modelling various
types of decision-making problems.
CO4: solve the mathematical results and numerical algorithms of
optimization theory to concrete Engineering problems
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Basic Concepts: Formulation of mathematical programming
problems; Classification of optimization problems; Optimization
techniques – classical and advanced techniques Optimization using
1 Calculus: Convexity and concavity of functions of one and two variables; 10
Optimization of function of multiple variables subject to equality
constraints; Lagrangian function; Optimization of function of multiple
variables subject to equality constraints; Hessian matrix formulation
Unit 2: Linear Programming: Standard form of linear programming (LP)
problem; Canonical form of LP problem; Assumptions in LP Models;
Graphical method for two variable optimization problem; Motivation of
2 simplex method, Simplex algorithm and construction of simplex tableau; 10
Revised simplex method; Duality in LP; Primal dual relations; Dual
Simplex Method; Sensitivity or post optimality analysis; bounded
variables
Unit 3: Dynamic Programming: Representation of multistage decision
3 process; Types of multistage decision problems; Concept of sub 8
optimization and the principle of optimality
Unit 4: Integer Programming: Integer linear programming; Branch and
4 Bound algorithm; Concept of cutting plane method; Mixed integer 8
programming; Solution algorithms.
Unit 5: Advanced Topics in Optimization: Direct and indirect search
5 methods; Heuristic and Meta-Heuristic Search methods; Multi objective 8
optimization.
Total
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. S.S. Rao, “Engineering Optimization: Theory and 3rd 2013
Practice”, New Age International Publishers

2 H.A. Taha, “Operations Research: An Introduction”, 10th 2019

Pearson Education

3 Ravindran, K. M. Ragsdell and G. V. Reklaitis, 2nd 2006

“Engineering Optimization: Methods and Applications”,
Wiley India
Reference Books
1. R. Fletcher, “Practical Methods of Optimization”, Wiley 2nd 2009
India
2. K. Deb, “Optimization for Engineering Design”, Prentice 2nd 2012
Hall India

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering Network and System

1. Subject Code: TCS 679 Course Title: security
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VI

7. Category of Course: DE

8. Pre-requisite: TCS 591

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the basics of computer security
CO2: Elaborate the cryptographic techniques.
CO3: Discuss the transport layer security
CO4: Find the pros and cons of various key distribution methods
CO5: Analyze the wireless Network security

CO6: Find the level of system security

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Introduction
Computer Security Concepts, The OSI Security Architecture, Security Attacks, 9
1
Security Services, Security Mechanisms, Models for network security,
standards.
Unit 2:
Cryptography
Symmetric Encryption and Message Confidentiality Symmetric Encryption
Principles, Symmetric Block Encryption Algorithms, Random and
2
Pseudorandom Numbers, Stream Ciphers and RC4, Cipher Block Modes of
Operation. 9
Public-Key Cryptography and Message Authentication 61 Approaches to
Message Authentication, Secure Hash Functions, Message Authentication
Codes, Public-Key Cryptography Principles, Public-Key Cryptography
Algorithms, Digital Signatures
Unit 3:
Network security Application - I
Key Distribution and User Authentication
Symmetric Key Distribution Using Symmetric Encryption, Kerberos, Key Distribution Using
Asymmetric Encryption, X.509 Certificates, Public-Key Infrastructure, Federated Identity
3 Management 10
Transport-Level Security
Web Security Considerations, Secure Socket Layer and Transport Layer
Security, Transport Layer Security,
HTTPS, Secure Shell (SSH)

Unit 4:
Network security Application - II
Wireless Network Security
IEEE 802.11 Wireless LAN Overview, IEEE 802.11i Wireless LAN Security,
Wireless Application Protocol Overview, Wireless Transport Layer Security,
4 WAP End-to-End Security
8
Electronic Mail Security
Pretty Good Privacy, S/MIME, DomainKeys Identified Mail,
IP Security
IP Security Overview, IP Security Policy, Encapsulating Security Payload,
Combining Security Associations, Internet Key Exchange, Cryptographic Suites

Unit 5:
System Security
5 Intruders 10
Intruders, Intrusion Detection, Password Management,
Malicious Software
Types of Malicious Software, Viruses, Virus
Countermeasures, Worms, Distributed Denial of Service Attacks.
Firewalls
The Need for Firewalls, Firewall Characteristics, Types of Firewalls, Firewall
Basing, Firewall Location and Configurations,
Legal and Ethical Aspects
Cybercrime and Computer Crime, Intellectual Property, Privacy, Ethical Issues
Total 46

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. W. Stallings, “Network Security Essentials”, Prentice Hall, 6th 2017

2 Reference:-Ch. P. Pfleeger, S. L. Pfleeger,“ Security in 4th 2006

Computing”, 4th Edition Prentice Hall,
Reference Books

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VI

Name of Department: - Computer Science and Engineering

Network and System
1. Subject Code: PCS 679 Security Lab
Course Title:

2. Contact Hours: L: 0 T: 1 P: 2
3. Examination Duration (Hrs): Theory Practical
3
0
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 2

6. Semester: 6

7. Category of Course: DC
8. Pre-requisite: Java Programming Language (TCS 408), Database
Management System (TCS 503)

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Explain different threats and attacks on network and system.

CO2: Know the working of different cryptographic schemes.

CO3: Analyze the security of networks and systems.

CO4: Use programming to implement various security algorithms.

CO5: Apply security mechanisms to secure networks and systems.

CO6: Develop Networks and System Security Protocols.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

List of problems for which student should develop program and execute in Contact
Sl. No.
the Laboratory Hours

1 Write down a program for the encryption and decryption procedure of 3

affine cipher scheme.

2 Write down a program for the encryption and decryption procedure of hill 3
cipher scheme.

3 Write down a program for the encryption and decryption procedure of rail 3
fence technique.

4 Write down a program for the encryption and decryption procedure of RSA 3
algorithm.

5 Write down a program for the signature generation and verification 3

procedures of RSADS algorithm.

6 Write down a program for the signature generation and verification 3

procedures of DSA algorithm.
Design and implement a secure communication system, in which you have to
use symmetric key cryptography for the bulk data encryption. Use the RSA
7 algorithm for the encryption of shared secret key. Use RSADS algorithm for the 3
signature generation and verification. Further use SHA256 algorithm for the
calculation of the message digest value.
Use sample data of network anomalies and attacks, i.e., NSL-KDD and predict
8 about the various possibilities of attacks. You have to try this exercise with the 3
different machine learning algorithms.
Implement a simple security protocol that does encryption/ decryption using a
9 3
public key cryptographic algorithm through the scyther tool.
Implement the following problem using the scyther tool:

(i) Sender and receiver use symmetric key cryptography for the bulk data
secure exchange.

(ii) Use the RSA algorithm for the encryption of shared secret keys.

(iv) Use RSADS algorithm for signature generation and verification.

10 3
(v) Further use SHA256 algorithm for the calculation of the message
digest value.

(vi) Use various random secret values, pseudo identities and timestamp
values to get protection against the MITM attack, impersonation attack and
replay attack.

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. W. Stallings,” Network Security Essentials”. Prentice 6th 2003
Hall, 2003.
Reference Books
1. Ch. P. Pfleeger, S. L. Pfleeger „Security in Computing”, 4th 2006
4th Edition Prentice Hall, 2006

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VI

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-692 Cours Title: Block chain Platforms

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 6th

7. Category of Course: DE

8. Pre-requisite: TCS 332 Fundamental of Information Security and

Blockchain,TCS 493 Introduction of Cryptography and PKC, TCS 592
Blockchain technology and its applications

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Explain blockchain technology and its platforms.
CO2: Know the working of blockchain platforms.
CO3: Analyze the mechanism of various blockchain platforms.
CO4: Use different blockchain platforms to implement blockchain.
CO5: Apply security mechanism to secure the networks and system.
CO5: Apply blockchain platforms in different applications.
CO6: Develop blockchain platforms.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Blockchain mining - types of blockchain, mechanism of blockchain,
verification of transactions, mining nodes, aggregating transactions into
1 blocks, constructing the block header, successfully mining of block, 6
validating a new block, assembling and selecting chains of blocks,
consensus attacks
Consensus algorithms: Details of following consensus algorithms,
Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Activity (PoA)
2 8
Proof-of-Importance (PoI), Proof-of-Capacity (PoC), Proof-of-Burn
(PoB), Proof-of-Weight (PoWeight)
Advanced consensus algorithms: Details of following consensus
algorithms, Delegated Proof-of-Stake (DPoS), Proof of Elapsed Time
(PoET), Practical Byzantine Fault Tolerance (PBFT), Simplified
3 Byzantine Fault Tolerance (SBFT), Delegated Byzantine Fault 10
Tolerance (DBFT)

Blockchain platforms: Ethereum, Hyperledger

Fabric,Multichain,Hydrachain,Ripple,R3 Corda, BigChainDB,Open-
4 chain 16

Applications of blockchain platforms: Applications of blockchain

5 platforms in various domain, smart contract, smart cities, smart 6
healthcare system
Total 46
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. George Icahn, “Blockchain: the complete guide to 4th 2020
understanding blockchain technology”, 2020.
2. Antony lewis, “The basics of bitcoins and blockchains: 3rd 2020
an introduction to cryptocurrencies and the technology
that powers them” 2020
3. Imran Bashir, “Mastering Blockchain: Distributed 2nd
ledger technology, decentralization, and smart
contracts explained”, 2nd edition, Packt publishing.
Reference Books
1. Andreas M. Antonopoulos, “Mastering Bitcoin: 2nd 2017
unlocking digital cryptocurrencies”, O'Reilly Media,(2e)
2017.
2. Roger Wattenhofer, “Distributed Ledger Technology, 2nd 2017
The science of the Blockchain”, Inverted Forest
Publishing,(2e), 2017
3. Antonopoulos, Andreas M. and Wood, Gavin. Mastering 1st 2018
Ethereum. O’Reilly Media, 2018.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-703 Course Title: Computer Networks II

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VII

7. Category of Course: DC

8. Pre-requisite: TCS-604(Computer Networks-I)

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Analyze Global and Centralized Routing protocols and utilize tools (such
as NS2) to examine routing protocols of LS and DV types
CO2: Evaluate and select the appropriate technology to meet Data Link Layer
requirements
CO3: Specify the devices, components and technologies to build a cost-
effective LAN
CO4: Appreciate issues for supporting real time and multimedia traffic over
public network
CO5: Identify the availability strategies in a Network Management System that
will improve network availability and limit the effects of failures
CO6: Implement client server applications with TCP/UDP Socket Programming
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Routing Algorithms: Introduction, global vs decentralized routing, The
Link State(LS) Routing Algorithm, The Distance Vector (DV) Routing Algorithm,
1 Hierarchical Routing, Routing in the Internet: RIP, OSPF, BGP; Introduction to 9
Broadcast and Multicast Routing

Unit 2: Link Layer and Local Area Networks: Introduction to Link Layer and its
services, Where Link Layer is implemented? Error detection and correction
techniques: Parity checks, Checksum, CRC; Multiple Access protocols: Channel
2 Partitioning, Random Access (Slotted Aloha, Aloha, CSMA), Taking Turns; Link 10
Layer Addressing: MAC addresses, ARP, Ethernet, CSMA/CD, Ethernet
Technologies, Link Layer Switches, Switches vs Routers, VLANS

Unit 3: Multimedia Networking: Introduction, Streaming Stored Audio and Video,

Real Time Streaming Protocol (RTSP), Making the Best of the Best Effort Services,
3 Protocols for Real Time Interactive Applications: RTP, RTCP, SIP, H.323; 9
Providing multiple classes of service.

Unit 4: Network Management: What it is, Infrastructure of Network Management,

4 The Internet standard Management Framework, SNMP 9

Unit 5: Network Programming: Sockets-Address structures, TCP sockets, creating

sockets, bind, listen, accept, fork and exec function, close function; TCP client
server: Echo server, normal startup, terminate and signal handling, server process
5 termination, crashing and rebooting of server, host shutdown; Elementary UDP 8
sockets: UDP echo server, lack of flow control with UDP

Total 45

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. “Computer Networking A Top Down Approach, Kurose 7th 2017
and Ross”, 5th edition, Pearson
Reference Books
1. Douglas E. Comer, Pearson ,“Internetworking with TCP/IP 6th 2012
Volume 1 and 2 “.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-704 Course Title: Advanced Computer Architecture

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7th

7. Category of Course: DC

8. Pre-requisite: TCS 404

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Discuss the classes of computers, and new trends and developments in
computer architecture.
CO2: Study advanced performance enhancement techniques such as pipelines,
dynamic scheduling branch predictions, caches.
CO3: Compare and contrast the modern computer architectures such as RISC, Scalar,
and multi-CPU systems.
CO4: Critically evaluate the performance of different CPU architecture.
CO5: Improve the performance of applications running on different CPU
architectures.
CO6: Develop applications for high performance computing systems
** Describe the specific knowledge, skills, or competencies the students are
expected to acquire or demonstrate.

10. Details of the Course:

SN Contents Hours
Unit-1
Basics of Computing: Computer Architecture and Technology Trends, Moore's
Law and its Applications, Classification of parallel computers, Performance
based Computing, The Myopic View of Computer Architecture, Trends in
1 9
Technology, Energy, Power and Cost, Dependability, Processor Speed, Cost,
Power Consumption, Fabrication Yield
Performance Metrics and Evaluation: Measuring Performance, Benchmark
Standards, Amdahl's Law, Lhadma's Law
Unit-2
Memory Hierarchy Design: Basics of Memory Hierarchy, Coherence and
2 locality properties, Cache memory organizations, Advanced Optimization of 10
Cache Performance, Memory Technology and Optimization, Cache Coherence
and Synchronization Mechanism, Virtual Memory, Virtual Machines
Unit-3
Pipeline: Linear pipeline processor, nonlinear pipeline processor, Instruction
pipeline Design, Dynamic instruction scheduling, Branch Handling techniques,
3 branch prediction, Arithmetic Pipeline Design Data, and resource Dependences, 10
Performance Issues in Pipeline: Pipeline Hazards, Data Hazards Branch
hazards and Resource Hazards

Unit-4
Instruction Level Parallelism: Concepts and Challenges, Basic Compiler
4 techniques for exploiting ILP, Reducing the branch penalty with advanced branch 8
predictions, overcoming data hazards with dynamic scheduling, exploiting ILP
using multiple issues state scheduling
Unit-5
Multiprocessor architecture: Taxonomy of parallel architectures. Centralized
shared-memory, distributed shared-memory architecture, Message passing vs
Shared Memory
5 8
Thread and Process Level Parallel Architecture: Instruction Level Data
Parallel Architecture, SIMD Architecture, Fine Grained and Coarse-Grained
Associative and Neural Architecture, Data Parallel Pipelined and Systolic
Architectures, Vector Architectures
Total 45

11. Suggested Books:

S Name of Authors/Books/Publishers Edition Year of
N Publication Reprint
Textbooks
1. 2nd 2012
Kai Hwang,” Advance Computer Architecture” TMH 5.
Quinn, “Parallel Computing: Theory & Practice”, TMH
2. 2nd 2012
Matthew,” Beginning Linux Programming”, SPD/WROX

3. 1st 2011
Hennessy and Patterson,” Computer Architecture: A
Quantitative Approach”, Elsevier
4. 2nd 2004
Dezso and Sima,” Advanced Computer Architecture”,
Pearson
Reference Books
1. 1st 2014
Quinn, “Parallel Programming in C with MPI and Open MP”,
TMH

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII

Name of Department: - Computer Science and Engineering Research Methodology and

IPR
1. Subject Code: TRM-701 Course Title:

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50
5. Credits: 3

6. Semester: VII

7. Category of Course: DC

8. Pre-requisite: NA

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand research problem formulation
CO2: Analyze research related information
CO3: Follow research ethics
CO4: Understand that today’s world is controlled by Computer, Information
Technology, but tomorrow world will be ruled by ideas, concept, and
creativity.
CO5: Understanding that when IPR would take such important place in growth of
individuals & nation, it is needless to emphasis the need of information
about Intellectual Property Right to be promoted among students in general
& engineering in particular.
CO6: Understand that IPR protection provides an incentive to inventors for
further research work and investment in R & D, which leads to creation of
new and better products, and in turn brings about, economic growth and
social benefits.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Meaning of research problem, Sources of research problem, Criteria Characteristics
of a good research problem, Errors in selecting a research problem, Scope and
1 9
objectives of research problem. Approaches of investigation of solutions for
research problem, data collection, analysis, interpretation, Necessary
instrumentations
Unit 2:
2 Effective literature studies approaches, analysis Plagiarism, Research ethics. 8

Unit 3:
Effective technical writing, how to write report, Paper Developing a Research
3 9
Proposal, Format of research proposal, a presentation and assessment by a review
committee.
Unit 4:
Nature of Intellectual Property: Patents, Designs, Trade and Copyright. Process of
Patenting and Development: technological research, innovation, patenting,
4 9
development. International Scenario: International cooperation on Intellectual
Property. Procedure for grants of patents, Patenting under PCT.

Unit 5:
Patent Rights: Scope of Patent Rights. Licensing and transfer of technology. Patent
information and databases. Geographical Indications.
5 12
New Developments in IPR: Administration of Patent System. New developments
in IPR; IPR of Biological Systems, Computer Software etc. Traditional knowledge
Case Studies, IPR and IITs.
Total 47
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint
Textbooks
Stuart Melville, Wayne Goddard, Research Methodology: 1st 1996
1. An Introduction for Science & Engineering Students, Juta
& Co. Ltd.
Wayne Goddard, Stuart Melville, Research methodology: 2nd 2014
2.
An introduction, Juta Academic
Ranjit Kumar, Research Methodology: A Step by Step 2nd 2005
3.
Guide for Beginners, Pearson India
Halbert, Resisting Intellectual Property, Taylor & Francis 1st 2007
4.
Ltd ,
Reference Books
1. Robert P. Merges, Peter S. Menell, Mark A. Lemley, “ 2nd 2016
Intellectual Property in New Technological Age”, Wolters
Kluwer Law and Business
2. T. Ramappa, “Intellectual Property Rights Under WTO”, S. 1st 2008
Chand

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII
Name of Department: - Computer Science and Engineering
Cloud Orchestration and
1. Subject Code: TCS 750 Course Title: Load Balancing
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50
5. Credits:
3
6. Semester: 7
7. Category of Course: DE

8. Pre-requisite: TCS-351 Fundamental of Cloud Computing and Bigdata

TCS-451 Virtualization and Cloud Computing
TCS-552 Cloud Based Application Development and
Management

9. Course After completion of the course the students will be able to:
Outcome* CO1: Apply the concepts of cloud automation, orchestration, load balancing and resource
*: scheduling management techniques.

CO2: Demonstrate the cloud orchestration and automation tools in the cloud services.
CO3: Distinguish cloud management techniques in the cloud services.

CO4: Evaluate Heat Orchestration Services for cloud services deployment.

CO5: Evaluate the different orchestration and automation tools and services to achieve a
performing cloud-based web-service.

CO6: Design and deploy a cloud-based web-service that uses the RESTful API
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: 9
Introduction to automation, orchestration, and load balancing, Identify the need of
automation, orchestration and load balancing of cloud resources, Resource
1
scheduling mechanism in the cloud. Students will develop and evaluate scaling and
load balancing solutions, work with cloud storage systems, and develop
applications in several programming paradigms.
Unit 2: 9

Cloud orchestration tools: AWS CloudFormation, IBM Cloud Orchestrator,

2 RedHat Ansible, Microsoft Azure Automation, Terraform, Kubernetes,
Cloudify, and Morpheus.
DC/OS container orchestration, Mesos Containers, Docker Containers.
Cloud infrastructure automation tools: Chef Automate, Google Cloud Deployment
Manager, Puppet Enterprise, Red Hat Ansible Automation Platform, VMware
vRealize Automation.
Unit 3: 9

Cloud management techniques such as Cloud instances at scale, Cloud software

deployment considerations such as scaling strategies, load balancing, fault
3 tolerance, accounting for tail latencies and optimizing for cost.
Case study of the following cloud services: IBM Cloud Orchestrator, Ingram
Micro Cloud Orchestrator, Microsoft Azure Automation, IT Automation with
AWS Lambda, AWS Systems Manager Automation, Microsoft Cycle Computing,
Morpheus, OpenStack Heat orchestration engine, Saltstack, Zymr.
Unit 4: 9
Heat orchestration service, Heat orchestration Template (HoT), Architecture, Main
4
execution flow, Scheduling and fault tolerance concepts in the MapReduce
programming model, Cloud programming models (MapReduce, Storm, Spark,
GraphLab, Spark Streaming and Samza), OpenStack Heat Orchestration Service.
Unit 5: 9
5
RESTful API, Benefits of RESTful APIs, RESTful API client request, RESTful
API authentication methods, Design and deploy a cloud based web-service that
uses the REST interface to respond to queries that require running an analytics
job on a large data set which is stored in a database. The web-services are
evaluated through a load generator for a fixed time period (several hours)
by measuring the cost of cloud resources used and their system’s performance
(throughput).
Design and Deploy Smart Traffic Management System Application to Cloud.
Total 45 Hrs.
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint
Textbooks
1. Barrie Sisisky, “Cloud Computing Bible” ,Published by 2010
Wiley Publishing, Inc.
2 Felipe Gutierrez ,“Spring Cloud Data Flow: Native Cloud 1 2021
Orchestration Services for Microservice”

3 Adnan Ahmed Siddiqui, “OpenStack Orchestration”, Packt 2015

Publishing Ltd

4 “Practical Load Balancing: Ride the Performance Tiger (Expert's 1 2012

Voice in Networking)”, A press

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

TCS 771 Natural Language

1. Subject Code: Course Title:
Processing
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DE

8. Pre-requisite: Machine Learning

9. Course After completion of the course the students will be able to:
Outcome CO1: Understand basics of Natural Language Processing (NLP)
CO2: Analyze and Evaluate NLP models
CO3: Understand neural language models for NLP
CO4: Apply Recurrent neural network models in NLP
CO5: Understand transformers and self-attention models for NLP
CO6: Apply deep learning to create interesting NLP applications
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction to Natural Language Processing. Ambiguity in
Languages. Applications of NLP (Motivation). Traditional vs deep learning-
based NLP. Introduction to traditional NLP libraries: NLTK, Wordnet.
1 10
Regular expressions, Basic Steps of NLP: Tokenization, Stemming,
Lemmatization. Converting text to features, n-grams, corpora, text
normalization, smoothing, Bag of Words (BoW) model
Unit 2: vector space model, cosine similarity, tf-idf term weighting and its
variations, text classification, example of spam classifier using Naïve
2 Bayes. Sentiment classification. Logistic Regression. Evaluation 10
measures: Precision, Recall, F-score. Test sets and Cross-validation.
Static word embeddings: word2vec, GloVe.
Unit 3: Neural language models, Feedforward Neural Language Modeling,
Training neural nets: loss function, computing the gradient, computation
graphs, backward differentiation on computation graphs. Training the
3 neural language model. Sequence labeling for Parts of Speech (POS) and 10
Named Entities: English word classes, PoS tagging, named entities and
named entity tagging, Recurrent Neural Networks (RNNs) for NLP, LSTM

Unit 4: RNNs: inference in RNNs, training. RNNs as language models:

training RNN language model, RNNs for other NLP tasks: sequence
4 labeling, sequence classification, generation with RNN-based language 10
models. Stacked and Bidirectional RNN architectures, LSTM: gated units,
layers and networks. Encoder-Decoder models, Attention
Unit 5: Self-attention networks: Transformers, transformer blocks, multi-
head attention, modelling word order: positional embeddings.
Transformers as language models. Bidirectional transformer encoders.
5 8
Transfer learning through fine-tuning.
NLP applications: machine translation, question answering, chatbots and
dialog systems
Total 48
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint
Textbooks
1. Daniel Jurafsky, James H. Martin, Speech and Language Third 2023
Processing, edition
2. Uday Kamath, John Liu and James Whitaker, Deep First 2019
Learning for NLP and Speech Recognition edition

Reference Books
1. Steven Bird, Ewan Klein and Edward Loper, Natural First 2009
Language Processing with Python edition

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 731 Course Title: Computer Forensics

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VII

7. Category of Course: DE

8. Pre-requisite: NA

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the importance of a systematic procedure for investigation of data
found on digital storage media that might provide evidence of wrong-doing.
CO2: Identify and document potential security breaches of computer data that suggest
violations of legal, ethical, moral, policy and/or societal standards

CO3: Use tools for faithful preservation of data on disks for analysis and find data that
may be clear or hidden on a computer or another device
CO4: Work with computer forensics tools used in data analysis, such as searching,
absolute disk sector viewing and editing, recovery of files, password cracking, etc.
CO5: Present the results of forensics analysis as an expert.
CO6: Discuss the Cyber Laws and Cyber Crimes.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Cyber Crimes, Laws and Cyber Forensics: Introduction to IT laws & Cyber
Crimes, The World and India
1 Cyber Forensics Investigation: Introduction to Cyber Forensic Investigation,
9
Investigation Tools, eDiscovery, Digital Evidence Collection, Evidence
Preservation, E-Mail Investigation, E-Mail Tracking, IP Tracking, E-Mail
Recovery, Encryption and Decryption methods, Search and Seizure of
Computers, Recovering deleted evidences, Password Cracking
Unit 2:
Digital Forensics Fundamentals: Introduction to Incident response, digital
forensics stepwise procedure, Computer/network/Internet forensic and anti-
forensics , Unix/Linux incident response, Unix/Linux forensics investigation
steps and technologies, Memory forensics, Windows incident response tools ,
Windows forensics tools
2
Data and Evidence Recovery- Introduction to Deleted File Recovery,
Formatted Partition Recovery, Data Recovery Tools, Data Recovery Procedures
9
and Ethics, Preserve and safely handle original media, Document a “Chain of
Custody”, Complete time line analysis of computer files based on file creation,
file modification and file access, Recover Internet Usage Data, Recover Swap
Files/Temporary Files/Cache Files, Introduction to Encase Forensic Edition,
Forensic Tool Kit (FTK) etc, Use computer forensics software tools to cross
validate findings in computer evidence-related cases, Dump Analysis, Browser
forensics, Multimedia forensics, Taking RAM dump and Volatile Memory
Analysis
Unit 3:

Software Security: Memory Layout, Buffer Overflow, Code Injection, Other

Memory Exploits, Format String Vulnerabilities, Defenses against low-level
exploits: Memory Safety, Type Safety, Avoiding Exploitation, Return Oriented
3
Programming, Control Flow Integrity, Secure Coding; Web Security: Basics, 10
SQL Injection, Countermeasures, Session Hijacking, Cross Site Scripting,
Program Analysis

Image Analysis: Using software to analyze an image, Searching image for

evidence, File carving
Unit 4:
4 Hardware Security: Digital System Specification, Watermarking, Good
Watermarks, Fingerprinting, Hardware metering, Physical Attacks and 8
Countermeasures, Modular Exponentiation (ME) Basics, ME in Cryptography,
ME Implementation and Vulnerability, Montgomery Reduction
Unit 5:

Analysis and Validation: Types of Investigation Software, Validating

Forensics Data, Data Hiding Techniques, Performing Remote Acquisition,
Network Forensics, Email Investigations, Cell Phone and Mobile Devices 8
5
Forensics, Virtual Machin Forensics, Cloud forensics, Live forensics

Case Studies: Blackmailing, Credit-Card fraud, Hosting Obscene Profiles,

Illegal money transfer, Fake Travel Agent
Total 44

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Bill Nelson, Amelia Phillips, Frank Enfinger, Christopher 6th 2020
Steuart, ―”Computer Forensics and Investigations”,
Cengage Learning, India Edition,
2 MarjieT.Britz, “Computer Forensics and Cyber Crime”: An 3rd 2013
Introduction”, 3rd Edition, Prentice Hall
Reference Books
1. Kenneth C.Brancik ―”Insider Computer Fraud Auerbach 1st 2019
Publications Taylor”; Francis Group
2 “CEH official Certfied Ethical Hacking Review Guide”, 1st 2015
Wiley India Edition,

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

Cloud Infrastructure
1. Subject Code: TCS 761 Course Title: Services

2. Contact Hours: L: T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DE

8. Pre-requisite: TCS604, TCS651

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Demonstrate basics of cloud infrastructure.
CO2: Distinguish the insight of cloud infrastructure.

CO3: Distinguish different components of service oriented architecture.

CO4: Evaluate the insight of the cloud storage.

CO5: Evaluate the case study of the cloud infrastructure services.

CO6: Design and deploy the cloud infrastructure services.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.
10. Details of the Course:
SL. Contact
Contents
NO. Hours
Unit - I
Introduction to Cloud Infrastructure
1 Cloud Evolution, Cloud Services, Cloud Deployment Types, Main
9
Challenges of Cloud Infrastructure, Cloud Reference Model, Cloud
Management, Cloud Structure, Infrastructure Components, Cloud
Layers, Cloud Relations, Cloud Dynamics, Data Types
Unit - II
2
Exploring Cloud Infrastructures
Managing the Cloud - Administrating the Clouds , Management
responsibilities , Lifecycle management , Cloud Management Products 9
,Emerging Cloud Management Standards, DMTF cloud management
standards, Cloud Commons and SMI ,Infrastructure Security : Network
Level , Host Level , Application Level
Unit – III
Understanding Services Oriented Architecture
3 SOA : Introduction , Event driven SOA , SOA 2.0 , Enterprise Service
10
Bus , Service catalogues, Defining SOA Communications , Managing
& Monitoring SOA , SOA Security , Relating SOA & Cloud
Computing
Unit – IV
Exploring Cloud Infrastructure Services Overview of cloud Infrastructure
Services, Measuring the Digital Universe: Cloud storage in the Digital
4 Universe, Cloud storage definition, Provisioning Cloud Storage: Unmanaged
cloud storage, 9
Managed cloud storage, creating cloud storage systems, Virtual storage containers,
Exploring Cloud Backup Solutions: Backup types, Cloud backup features, Cloud
attached backup, Cloud Storage Interoperability: Cloud Data Management
Interface (CDMI), Open Cloud Computing Interface (OCCI).
Unit – V 8
5 Case Study: AWS Cloud Infrastructure Services AWS networking and databases:
Virtual private clouds, Cloud models, Private DNS servers (Route 53)), Relational
database service – DynamoDB, ElastiCache, Redshift. Case Study: AZURE Cloud
Infrastructure Services Azure Virtual Machines, Azure Kubernetes Service (AKS),
Azure Red Hat OpenShift, Azure Arc, Azure Stack HCI, Azure Stack Edge, Azure
Stack Hub, Azure IoT
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Editi Year of
No. on Publication /
Reprint
Textbooks
1. Barrie Sisisky,“Cloud Computing Bible”, Published by Wiley 1st 2011
Publishing, Inc.
2 Berners Lee, Godel and Turing, “Thinking on the Web” - Wiley 1st 2008.
inter science,
3 Peter Mika, “Social Networks and the Semantic Web”, 2nd 2007.
Springer,
4 Thomas ,“Cloud Computing: Concepts, Technology & 1st 2013
Architecture” ,Erl Published May
5 David S. Linthicum ,“Cloud Computing and SOA Convergence 2nd 2009
in your Enterprise, a step by step guide

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TIT 721 Course Title: Business Intelligence

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DE

8. Pre-requisite: TCS 509,TCS 571, TCS 671

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the frameworks of Business Intelligence
CO2: Categorize the structured, semi structured and unstructured data
CO3: Create the schemas for data warehouse
CO4: Perform the multi-dimensional data modeling
CO5: Use of different visualization techniques
CO6: Use of Business Intelligence for ERP
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Business view of Information Technology Application
Business Enterprise Organization, its functions, and core business process,
Baldrige Business Excellence Framework: - Leadership, Strategic Planning,
1 Customer Focus, Measurement, Analysis and Knowledge Management 9
Workforce Focus, Process Management,
Key Purpose of using IT in Business, Enterprise Application (ERP/CRM etc)
and Bespoke IT Application.
Unit 2: Types of Digital Data, getting to know structured data, characteristics of
structured data, were does structured data come from? Hassle free Retrieval
Getting to know unstructured data, were does unstructured data comes from?
How to manage unstructured data? How to store unstructured data? Solutions to
storage challenges of unstructured data, how to extract information from stored
2 9
unstructured data? UIMA: A possible solution for unstructured data
Getting to know semi structured data, where does semi structured data come
from? How to manage semi structured data, modeling semi structured data
(OEM), How to extract information from semi structured data, XML: A solution
for semi structured data Management.
Unit 3: Introduction to OLTP and OLAP
OLTP:- Queries that an OLTP system can process, Advantage of an OLTP
system, Challenges of an OLTP system, The queries that OLTP cannot answer.
3 OLAP:-one dimension data, two-dimension data, three-dimension data, should 9
we go beyond the third dimension, queries that an OLAP system can process,
Advantage of an OLAP system Different OLAP Architecture: -MOLAP,
ROLAP, HOLAP Data Models for OLTP and OLAP, Role of OLAP tools in the
BI Architecture, OLAP operations on multidimensional data.
Unit 4: BI component framework: - Business layer, Administration and
operational layer, Implementation layer.
Who is BI for? - BI for Management, Operational BI, BI for process
4 Improvement, BI to improve customer experience. 9
Business Intelligence Application: -Technology Solutions, Business solutions.
BI roles and Responsibility: -BI program team roles, BI project team roles, Best
practice in BI/DW, Popular BI tools.
Need for Data Warehouse, what is a Data Mart, Goals of a Data Warehouse.
Multidimensional data modeling: - Data Modeling Basics, Types of Data model,
Data Modeling Techniques, Fact table, Dimension table, Dimensional modeling
life cycle.
Unit 5: Measure, Metrics, KPIs, and Performance Management Understanding
Measure and performance, Measurement system terminology, Fact based
Decision Making and KPIS, KPI usage in companies.
5 Basics of Enterprise Reporting: - Report standardization and presentation 9
practices, Enterprise reporting characteristics in OLAP world, Balance score
cards, Dashboards, how do you create Dashboards, Scorecards Vs Dashboards.
BI and Cloud Computing, Business Intelligence for ERP systems
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Fundamentals of Business Analytics by R.N. Prasad and 2nd 2016
Seema Acharya, Wiley India

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII

Name of Department: - Computer Science and Engineering

Human Computer
1. Subject Code: TCS 756 Course Title: Interaction
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DE

8. Pre-requisite: Fundamentals of Computer architecture

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Explain the capabilities of both humans and computers from the
viewpoint of human information processing.
CO2: Describe typical human–computer interaction (HCI) models, styles, and
various historic HCI paradigms.
CO3: Apply an interactive design process and universal design principles to
designing HCI systems.
CO4: Describe and use HCI design principles, standards, and guidelines.
CO5: Analyze and identify user models, user support, socio-
organizational issues, and stakeholder requirements of HCI
systems.
CO6: Discuss tasks and dialogs of relevant HCI systems based on task analysis and
dialog design.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Introduction: Importance of user Interface – definition, importance of good
design. Benefits of good design. A brief history of Screen design. The graphical
1 8
user interface – popularity of graphics, the concept of direct manipulation,
graphical system, Characteristics, Web user – Interface
popularity, characteristics- Principles of user interface
Unit 2:
Design process – Human interaction with computers, importance of human
2 8
characteristics human consideration, Human interaction speeds, understanding
business junctions
Unit 3:
Screen Designing: Design goals – Screen planning and purpose, organizing
screen elements, ordering of screen data and content – screen navigation and
3 flow – Visually pleasing composition – amount of information 9
– focus and emphasis – presentation information simply and meaningfully.
information retrieval on web – statistical graphics – Technological consideration
in interface design
Unit 4:
Windows – New and Navigation schemes selection of window, selection of
4 devicesbased and screen-based controls. Components – text and messages, 9
Icons and increases – Multimedia, colors, uses problems, choosing colors.

Unit 5:
Software tools – Specification methods, interface – Building Tools.
5 Interaction Devices – Keyboard and function keys – pointing devices – speech 8
recognition digitization and generation – image and video displays.
– drivers
Total 41
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. “The essential guide to user interface design”, Wilbert O 2nd 2016
Galitz, Wiley DreamaTech.
2. “Designing the user interface”. 3rd Edition Ben 3rd 2009
Shneidermann , Pearson Education Asia.

Reference Books
1. “Human – Computer Interaction”. ALAN DIX, JANET 3rd 2003
FINCAY, GRE GORYD, ABOWD, RUSSELL BEALG,
PEARSON.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII

Name of Department: - Computer Science and Engineering

Data Warehousing and
1. Subject Code: TCS 722 Course Title: Data Mining

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VII

7. Category of Course: DE

8. Pre-requisite: Excellent knowledge of Database Management Systems

9. Course After completion of the course the students will be able to:
Outcome: CO1: Describe the fundamental concepts, benefits and problem areas associated
with data warehousing.
CO2: Understand the various architectures and main components of a data
warehouse.
CO3: Identify the issues that arise when implementing a data warehouse.
CO4: Examine the techniques applied in data mining.
CO5: Compare and contrast OLAP and data mining as techniques for extracting
knowledge from a data warehouse.
CO6: Develop the association rules for mining
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Data Warehousing: Overview, Definition, Delivery Process, Difference between
Database System and Data Warehouse, Multi Dimensional Data Model, Data
1 9
Cubes, Stars, Snow Flakes, Fact Constellations, Concept hierarchy, Process
Architecture, 3 Tier Architecture, Data Marting

Unit 2: Aggregation, Historical information, Query Facility, OLAP function and

Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface,
2 Security, Backup and Recovery, Tuning Data Warehouse, Testing Data 8
Warehouse

Unit 3: Overview, Motivation (for Data Mining), Data Mining-Definition &

Functionalities, Data Processing, Form of Data Preprocessing, Data Cleaning:
Missing Values, Noisy Data,(Binning, Clustering, Regression, Computer and
Human inspection), Inconsistent Data, Data Integration and Transformation. Data
3 9
Reduction:-Data Cube Aggregation, Dimensionality reduction, Data
Compression, Numerosity Reduction, Clustering, Discretization and Concept
hierarchy generation

Unit 4: Concept Description: - Definition, Data Generalization, Analytical

Characterization, Analysis of attribute relevance, Mining Class comparisons,
Statistical measures in large Databases. Measuring Central Tendency, Measuring
Dispersion of Data, Graph Displays of Basic Statistical class Description, Mining
4 Association Rules in Large Databases, Association rule mining, mining Single- 8
Dimensional Boolean Association rules from Transactional Databases– Apriori
Algorithm, Mining Multilevel Association rules from Transaction Databases and
Mining Multi-Dimensional Association rules from Relational Databases

5 Unit 5: Classification & Prediction, Issues regarding Classification and 9

prediction, Decision tree, Bayesian Classification, Classification by Back
propagation, Multilayer feed-forward Neural Network, Back propagation
Algorithm, Classification methods K-nearest neighbor classifiers, Genetic
Algorithm. Cluster Analysis: Data types in cluster analysis, Categories of
clustering methods, Partitioning methods. Hierarchical Clustering- CURE and
Chameleon, Density Based Methods-DBSCAN, OPTICS, Grid Based
Methods- STING, CLIQUE, Model Based Method –Statistical Approach,
Neural Network approach, Outlier Analysis
Total 43
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Jiawei Han, Micheline Kamber, Jian Pei, “Data Mining: 3rd 2011
Concepts and Techniques”, Morgan Kaufmann

2. M.H Dunham, “Data Mining: Introductory and Advanced 1st 2006

Topics”, Pearson Education
Reference Books
1. A. Berson, S.Smith, “Data Warehousing, Data Mining, and 1st 1997
OLAP, McGraw Hill Education

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 723 Course Title: Distributed Systems

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VII

7. Category of Course: DE

8. Pre-requisite: TCS 604

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Characterize Distributed Systems and understand the Theoretical
Foundations for Distributed Systems
CO2: Evaluate various distributed mutual exclusion algorithms
CO3: Demonstrate knowledge of deploying different distributed deadlock
algorithms in various models of distributed systems.
CO4: Determine the appropriate use of different Agreement protocols
CO5: Identify the state of a distributed system to apply the appropriate context
of commit protocols CO6: Utilize real life DFS (NFS4 and GFS) to
examine work of distributed file systems

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Conta
SL.
Contents ct
NO.
Hours
Unit 1:
Characterization of Distributed Systems: Introduction, Examples of distributed
Systems, Resource sharing
Theoretical Foundation for Distributed System: Limitation of Distributed system, 9
1 absence of global clock, shared memory, Logical clocks, Lamport’s& vector logical
clocks, Causal ordering of messages, Birman-Schiper-Stephenson protocol, Global
State: Chandy-Lamport algorithm, Termination detection: Huang’s Algorithm
Unit 2:
Distributed Mutual Exclusion: Classification of distributed mutual exclusion,
2 Requirements of mutual exclusion algorithms, Performance metric for distributed
mutual exclusion algorithms. Non-Token Based Algorithms: Lamport, Ricart-
Agrawala, Rouicarol-Carvalho; Quorum Based Algorithms: Maekawa; Token- 10
Based Algorithms: Suzuki-Kasami
Leader Election in a Ring: LeLann& Chang-Robert’s Algorithm, Hirshberg-Sinclair
Algorithm
Unit 3:
Distributed Deadlock Detection: system model, Wait for Graphs, Deadlock handling
strategies, Centralized dead lock detection, Path pushing algorithms, Chandy’s et all
3 edge chasing algorithm.
Agreement Protocols: Introduction, System models, classification of Agreement 8
Problem, Byzantine agreement problem, Consensus problem, Interactive
consistency Problem, Byzantine Agreement problem, Application of Agreement
problem
Unit 4:
4 Commit Protocols: Distributed Transactions, Transaction System Architecture,
System Failure modes, Two Phase commit protocol, Handling of Failures: Site 9
failure, Coordinator failure, Network Partition, Recovery and Concurrency Control,
Three Phase Commit protocol
Self Stabilization: Definition, Randomized Self Stabilization, Probabilistic Self
stabilization, Issues in design of self-stabilization algorithms, Dijkstra’s self-
stabilizing token ring

Unit 5:
Distributed file systems: Design Goals, DFS architecture, Naming Schemes,
Mounting Remote Directories, Caching to improve performance, Design issues of
cache, cache location, Cache update policies, Cache consistency, Sharing semantics
5 in DFS, Stateless vs Stateful service NFS, Basic NFS architecture, Caching in NFS3,
NFS v4 improvements, NFSv4 details: Compounding, Open/Close, Locking, 10
Caching, Open Delegation, Recalling Delegation, Replication and Security
Case Study: Google File System(GFS): Design constraints, Architectural Design,
GFS Architecture, Single Master Design, Chunk Size, Metadata, System
Interactions, Write process, Consistency Model, Master Operations, Locking
Operations, Replica Placements, Garbage collection, Fault Tolerance and Diagnosis
Total 46
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Singhal & Shivaratri, "Advanced Concept in Operating 1st 2007
Systems", McGraw Hill
2 Coulouris, Dollimore, Kindberg, "Distributed System: 4th 2008
Concepts and Design”, Pearson Ed.
3 Gerald Tel, "Distributed Algorithms", Cambridge 2nd 2000
University Press
LaxmiPublicationa (P) Ltd., New Delhi.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam

GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN

SEMESTER VII

Name of Department: - Computer Science and Engineering

Software verification
1. Subject Code: TCS 799 Course Title: validation and testing

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DE
8. Pre-requisite: TIT 501

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Demonstrate the application of verification and validation tasks and their
outcomes during the software life cycle.
CO2: Apply various verification and validation techniques based on various
characteristics of the system/software (safety, security, risk, etc).
CO3: Differentiate between the overall role of verification and validation and
the specific role of software/system testing.
CO4: Compare and Contrast the theoretical and practical limitations to software
verification and validation analysis.

CO5: Apply appropriate planning and scoping to a verification and validation

effort based on the needs of the software system being developed.

CO6: Develop a software verification and validation plan that reflects an

understanding of verification and validation objectives, and appropriate
problem/risk identification and tracking.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:

Introduction: What is software testing and why it is so hard?, Error, Fault,

11
1 Failure, Incident, TestCases, Testing Process, Limitations of Testing, No
absolute proof of correctness, Overview of Graph Theory.

2 Unit 2:
Functional Testing: Boundary Value Analysis, Equivalence Class Testing,
Decision Table Based Testing, Cause Effect Graphing Technique. 12
Structural Testing: Path testing, DD-Paths, Cyclomatic Complexity, Graph
Metrics, Data Flow Testing, Mutation testing.
Unit 3:
3 Reducing the number of test cases: 12
Prioritization guidelines, Priority category, Scheme, Risk Analysis, Regression
Testing, Slice based testing
Unit 4:
Testing Activities: Unit Testing, Levels of Testing, Integration Testing, System
4 10
Testing, Debugging, Domain Testing.

Unit 5:
Object Oriented Testing: Issues in Object Oriented Testing, Class Testing,
5 GUI Testing, Object Oriented Integration and System Testing.Testing Tools:
Static Testing Tools, Dynamic Testing Tools, Characteristics of Modern Tools.

Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. William Perry, “Effective Methods for Software Testing”, 3rd 2006
John Wiley & Sons, New York,.
2 CemKaner, Jack Falk, Nguyen Quoc, “Testing Computer 2nd 1993
Software”, Second Edition, Van Nostrand Reinhold, New
York,.
3 Boris Beizer, “Software Testing Techniques”, Second 2nd 1990.
Volume, Second Edition, Van Nostrand Reinhold, New
York,
4 Louise Tamres, “Software Testing”, Pearson Education 1st 2002
Asia,
Reference Books
1. Roger S. Pressman, “Software Engineering – A 8th 2019
Practitioner’s Approach”, Fifth Edition, McGraw-Hill
International Edition, New Delhi,
2 Boris Beizer, “Black-Box Testing – Techniques for 1st 1995
Functional Testing of Software and Systems”, John Wiley
& Sons Inc., New York,.

3 K.K. Aggarwal & Yogesh Singh, “Software Engineering”, 1st 2003

New Age International Publishers, New Delhi,.

4 Marc Roper, “Software Testing”, McGraw-Hill Book Co., 2nd 1994

London,.

5 Gordon Schulmeyer, “Zero Defect Software”, McGraw- 3rd 1990

Hill, New York,.

6 Watts Humphrey, “Managing the Software Process”, 1st 1989

Addison Wesley Pub. Co. Inc., Massachusetts,.

7 Boris Beizer, “Software System Testing and Quality 1st 1984

Assurance”, Van Nostrand Reinhold, New York, .

8 Glenford Myers, “The Art of Software Testing”, John 1st 1979

Wiley & Sons Inc., New York,.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS781 Course Title: Deep Learning

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DE

8. Pre-requisite: Fundamental of Machine Learning (TCS333)

Python Programming (TCS434)

9. Course After completion of the course the students will be able to:
Outcome**: CO1: To understand the fundamental concepts and principles of deep learning.
CO2: To evaluate and use the most important concepts and the methods in the
area ML and deep learning.
CO3: Examine modern practical deep networks.
CO4: Know deep Learning Research Areas.
CO5: Use software libraries of deep learning
CO6: Use deep learning models.
. ** Describe the specific knowledge, skills or competencies the students are
expected to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction to deep learning: basics of Machine Learning, Machine
1 Learning vs Deep Learning, deep learning process, neural network, 8

Unit 2: Modern practical deep networks: Deep Feed forward Networks,

Regularization for Deep Learning, Optimization for Training Deep Models,
2 9
Convolutional Networks, Variants of CNN: DenseNet, PixelNet

Unit 3: Popular CNN Architectures: ResNet, AlexNet, Sequence Modeling:

3 Recurrent and Recursive Nets, Practical Methodology, Applications, Transfer 9
learning Techniques,
Unit 4: Deep Learning Research: Linear Factor Models, Auto-encoders,
4 Representation Learning, Structured Probabilistic Models for Deep Learning, 8
Monte Carlo Methods, Confronting the Partition Function, Approximate
Inference Deep Generative Models.
Unit 5: Deep Learning Platforms and Software Libraries: What is a Deep
Learning Platform? H2O.ai, Data GraphLab, Deep Learning Libraries,
5 Theano, Caffe, TensorFlow 10

Total 44

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Ellis Horowitz and Sartaj Sahni, Fundamentals of Data 1st 2014
Structures in C, Universities Press
2. Josh Patterson, Adam Gibson "Deep Learning: A 1st 2017
Practitioner's Approach", O'Reilly Media, 2017.
3. Umberto Michelucci “Applied Deep Learning. A Case- 1st 2018
based Approach to Understanding Deep Neural Networks”
Apress, 2018.
4. Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy 2nd 2017
"Deep Learning with TensorFlow: Explore neural networks
with Python", Packt Publisher, 2017.
Reference Books
1. Deep Learning A Practitioner’s Approach Josh Patterson and 2nd 2017
Adam Gibson O’Reilly Media, Inc.2017

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering Robotic Process Automation

TCS-734 Design and Development
1. Subject Code: Course Title:

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DE

8. Pre-requisite: Design and Analysis of Algorithms (TCS-409),

Programming for Problem Solving (TCS-201)

9. Course After completion of the course the students will be able to:
Outcome**: CO1: To Understand the basic concepts of RPA
CO2: To Describe various components and platforms of RPA
CO3: To Describe the different types of variables, control flow and data
manipulation techniques
CO4: To Understand various control techniques and OCR in RPA
CO5: To Describe various types and strategies to handle exceptions
CO6: To Discuss the benefits of RPA

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate
10. Details of the Course:
Sl. Contact
Contents
No. Hours
Unit 1:
RPA Foundations- What is RPA – Flavors of RPA- History of RPA- The
Benefits of RPA- The downsides of RPA- RPA Compared to BPO, BPM and
1 BPA – Consumer Willingness for Automation- The Workforce of the Future- 10
RPA Skills-On-Premise Vs. the Cloud- Web Technology- Programming
Languages and Low Code- OCR-Databases-APIs- AI-Cognitive Automation-
Agile, Scrum, Kanban and Waterfall0 DevOps- Flowcharts.
2 Unit 2: 8
RPA Platforms- Components of RPA- RPA Platforms-About Ui Path- About
UiPath - The future of automation - Record and Play - Downloading and
installing UiPath Studio -Learning Ui Path Studio- - Task recorder - Step-by-
step examples using the recorder.
Unit 3:
Sequence, Flowchart, and Control Flow-Sequencing the workflow-Activities-
Control flow, various types of loops, and decision making-Step-by-step
example using Sequence and Flowchart-Step-by-step example using Sequence
3 9
and Control flow-Data Manipulation-Variables and Scope-
CollectionsArguments – Purpose and use-Data table usage with examples-
Clipboard management-File operation with step-by-step example-CSV/Excel
to data table and vice versa (with a step-by-step example).
Unit 4:
Taking Control of the Controls- Finding and attaching windows- Finding the
control- Techniques for waiting for a control- Act on controls – mouse and
4 9
keyboard activities- Working with UiExplorerHandling events- Revisit
recorder- Screen Scraping- When to use OCR- Types of OCR available- How
to use OCR- Avoiding typical failure points.
Unit 5:
Exception Handling, Debugging, and Logging- Exception handling- Common
5 exceptions and ways to handle them- Logging and taking screensHOT- 9
Debugging techniques- Collecting crash dumps- Error reporting- Future of
RPA
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Tom Taulli , The Robotic Process Automation Handbook : 1st 2020
A Guide to Implementing RPA Systems, Publisher : Apress

2. Alok Mani Tripathi, Learning Robotic Process Automation, 1st 2018

Publisher: Packt Publishing Release
Reference Books
1. Frank Casale, Rebecca Dilla, Heidi Jaynes, Lauren 1st 2015
Livingston, “Introduction to Robotic Process Automation: a
Primer”, Institute of Robotic Process Automation.

2. Richard Murdoch, Robotic Process Automation: Guide To 1st 2018

Building Software Robots, Automate Repetitive Tasks &
Become An RPA Consultant

3. Srikanth Merianda,Robotic Process Automation Tools, 1st 2018

Process Automation and their benefits: Understanding RPA
and Intelligent Automation, Consulting Opportunity
Holdings Llc

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

Cryptography and
1. Subject Code: TCS 795 Course Title: Network Security

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VII

7. Category of Course: DE

8. Pre-requisite: Subject Name with Code

9. After completion of the course the students will be able to:

Course CO1: Classify security vulnerabilities involved in data communication over Internet and
Outcome make use of classical algorithms to address the vulnerabilities.
CO2: Make use of modern block ciphers to secure data transmission and storage
**:
CO3: Analyze challenges involved in key distribution and select approache that can be
adopted

CO4: Analyze strengths of public key algorithms and explore applications in exchange,
authentication and hashing of messages.

CO5: Appreciate application of algorithms for ensuring access control, authentication,

secured transmission of data at different layers.

CO6: Appraiserisks related to wireless, web, cloud security and measures to be adopted to
secure organizational network.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Introduction to security attacks, services and mechanism, introduction to
1 cryptography.
8
Conventional Encryption: Conventional encryption model, classical encryption
techniques- substitution ciphers and transposition ciphers, cryptanalysis,
stenography, stream and block ciphers.
Unit 2:
2 Modern Block Ciphers: Block ciphers principals, Shannon's theory of confusion
and diffusion, Modes of operations of block ciphers: ECB, CBC, OFB, CFB,
Advanced Encryption Standard (AES) 10
Traffic confidentiality, Key distribution, random numbers, Pseudo random
number generation using Linear Congruential and Blum BlumShub algorithms

Unit 3:
Prime and relative prime numbers, modular arithmetic, Primality testing,
Euclid's Algorithm for GCD and Extended Euclid's Algorithm for
3 Multiplicative inverse
Principals of public key crypto systems, RSA algorithm, security of RSA, key 8
management, Diffle-Hellman key exchange algorithm
Message Authentication: Requirements, Message Authentication Functions
Cryptographic Hash Functions:Applications of Cryptographic Hash Functions,
Secure Hash Algorithm (SHA)-512
Unit 4:
Authentication Applications: Kerberos and X.509 directory authentication
service, electronic mail security-S /MIME 9
4 IP Security: Architecture, Authentication header, Encapsulating security
payloads, combining security associations, key management.

Unit 5:
Wireless Network Security: Wireless Network Threats, Wireless Security
Measures, Mobile Device Security, Security Threats and Security Strategy,
IEEE 802.11 Wireless LAN Overview, The Wi-Fi Alliance, IEEE 802
Protocol Architecture, IEEE 802.11 Network Components and Architectural
5 Model, IEEE 802.11 Services.Concept of Wireless LAN security and brief
of phases of operation 10
Web and Cloud Security: Web Security Considerations, Transport Layer
Security, HTTPS,Cloud Security risks and Countermeasures; Data protection in
cloud.
System Security: The Need for Firewalls, Firewall Characteristics, Types of
Firewalls
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. William Stallings, “Cryptography and Network Security: 7th 2017
Principals and Practice”, 7th Edition, Pearson,
2 William Stallings, “Network Security Essentials – 4th 2011
Applications and Standards”, 4th edition, Pearson
Education,
Reference Books
1. Behrouz A Forouzan, Debdeep Mukhopadhyay, 3rd 2015
“Cryptography and Network Security”Mc-GrawHill, 3rd
Edition,
2 Johannes A. Buchmann, “Introduction to Cryptography”, 2nd 2012
Springer-Verlag,

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII
Name of Department: - Computer Science and Engineering
1. Subject Code: Course Title: Artificial Intelligence
TCS 706
2. Contact Hours: L:
3 T: 0 P: 0
3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3
6. Semester: VII
7. Category of Course: DE
8. Pre-requisite: Basics of mathematics and database are required

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the basics of the theory and practice of Artificial Intelligence.

CO2: Learn the basics of Artificial Intelligence programming.

CO3: Understand various searching techniques use to solve the AI problems.

CO4: Apply knowledge representation techniques and problem-solving strategies to

common AI applications.
CO5: Build self-learning and research skills to tackle a topic of interest on his/her own
or as part of a team.

CO6: Apply the knowledge of AI and agents in developing multidisciplinary real world
projects.

** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.
10. Details of the Course:
SL. Contact
Contents
No. Hours
Unit 1:
IntroductionIntroduction to Artificial Intelligence, Simulation of sophisticated &
Intelligent Behavior indifferent area, problem solving in games, natural language, 10
1
automated reasoning visualperception, heuristic algorithm versus solution guaranteed
algorithms.
2 Unit 2:
Understanding Natural Languages Parsing techniques, context free and
transformational grammars, transition nets, augmentedtransition nets, 9
Fillmore’s grammars, Shanks Conceptual Dependency, grammar free
analyzers, sentence generation, and translation.

3 Unit 3:
10
Knowledge Representation
First order predicate calculus, Horn Clauses, Introduction to PROLOG,
Semantic NetsPartitioned Nets, Minskey frames, Case Grammar Theory,
Production Rules KnowledgeBase, The Inference System, Forward & Backward
Deduction
Unit 4:
Expert System
4 Existing Systems (DENDRAL, MYCIN), domain exploration, Meta 9
Knowledge, Expertise Transfer, Self Explaining System
Unit 5:
Pattern Recognition
Introduction to pattern Recognition, Structured Description, Symbolic
5 Description, Machineperception, Line Finding, Interception, Semantic, & 8
Model, Object Identification, SpeechRecognition.
Programming Language: Introduction to programming Language, LISP,
PROLOG
Total 46
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint
Textbooks
1. Charnick “Introduction to Artificial Intelligence.” 2nd 2010
Addision Wesley.
2. Rich & Knight, “Artificial Intelligence”.TMH 3rd 2017

3. Winston, “LISP”, Addison Wesley. 1st 1989

4. Marcellous, “Expert Systems Programming”, PHI. 1st 1989

Reference Books
1. Charnick “Introduction to Artificial Intelligence.” Addision 1st 2010
Wesley.
2. Rich & Knight, “Artificial Intelligence”.TMH 3rd 2017

3. Winston, “LISP”, Addison Wesley. 2nd 1989

4. Marcellous, “Expert Systems Programming”, PHI. 1st 1989

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS-762 Course Title: NoSQL Database

ProfessionaCommuni
2. Contact Hours: L: 3 T: 0 P: 0 cation
3. Examination Duration (Hrs): Theory 3 Practical 0
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DC

8. Pre-requisite: TCS-462, TCS-663

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Demonstrate an understanding of the detailed architecture of
Column Oriented NoSQL databases, Document databases, Graph
databases.
CO2: Make use of the concepts pertaining to all the types of databases
CO3: Apply performance tuning on Column-oriented NoSQL databases
and Document-oriented NoSQL Databases.
CO4: Analyze the structural Models of NoSQL.
CO5: Evaluate several applications for location based service and
recommendation services. Devise an application using the components
of NoSQL.
CO6: Develop various applications using NoSQL databases.
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Why NoSQL? The Value of Relational Databases, Getting at Persistent
Data, Concurrency, Integration, A (Mostly) Standard Model, Impedance
Mismatch, Application and Integration Databases, Attack of the Clusters,
1 The Emergence of NoSQL. 10
Aggregate Data Models: Aggregates, Example of Relations and
Aggregates, Consequences of Aggregate Orientation, Key-Value and
Document Data Models, Column-Family Stores, Summarizing
AggregateOriented Databases.
More Details on Data Models: Relationships, Graph Databases,
Schemaless Databases, Materialized Views, Modeling for Data Access

Unit 2:
Distribution Models: Single Server, Sharding, Master-Slave Replication,
Peer-to-Peer Replication, Combining Sharding and Replication
Consistency, Update Consistency, Read Consistency, Relaxing
2 8
Consistency, The CAP Theorem, Relaxing Durability, Quorums. Version
Stamps, Business and System Transactions, Version Stamps on Multiple
Nodes

Unit 3:
Map-Reduce: Basic Map-Reduce, Partitioning and Combining,
Composing Map-Reduce Calculations, A Two Stage Map-Reduce
Example, Incremental Map-Reduce Key-Value Databases: What Is a
Key-Value Store, Key-Value Store Features, Consistency, Transactions,
3 9
Query Features, Structure of Data, Scaling, Suitable Use Cases, Storing
Session Information, User Profiles, Preference, Shopping Cart Data,
When Not to Use, Relationships among Data, Multioperation
Transactions, Query by Data, Operations by Sets

Unit 4:
Document Databases: What Is a Document Database?, Features,
Consistency, Transactions, Availability, Query Features, Scaling,
Suitable Use Cases, Event Logging, Content Management Systems,
4 9
Blogging Platforms, Web Analytics or Real-Time Analytics, E- Commerce
Applications, When Not to Use, Complex Transactions Spanning Different
Operations, Queries against Varying Aggregate Structure

Unit 5:
Graph Databases: What Is a Graph Database?, Features, Consistency,
Transactions, Availability, Query Features, Scaling, Suitable Use Cases,
5 Connected Data, Routing, Dispatch, and Location-Based Services, 9
Recommendation Engines, When Not to Use

Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Editio Year of
No. n Publication /
Reprint
Textbooks
1. Sadalage, P. & Fowler, NoSQL Distilled: A Brief Guide to 1st 2012
the Emerging World of Polyglot Persistence, Pearson
Addision Wesley
Reference Books
1. Dan Sullivan, "NoSQL For Mere Mortals", Pearson 1st 2015
Education India
2. Dan McCreary and Ann Kelly, "Making Sense of 1st 2013
NoSQL: A guide for Managers and the Rest of us",
Manning Publication/Dreamtech Press
3. Kristina Chodorow, "Mongodb: The Definitive Guide- 2nd 2013
Powerful and Scalable Data Storage", O'Reilly
Publications

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 763 Course Title: Social and Web Analytics

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DE

8. Pre-requisite: TCS 341, TCS 462

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Be able to understand social media, web and social media
analytics, and their potential impact.
CO2: Be able to identify key performance indicators for a given goal,
identify data relating to the metrics and key performance indicators.
CO3: Be able to design and analyze understand usability metrics,
web, and social media metrics.
CO4: Be able to use ready-made web analytics tools (Google
Analytics)
CO5: Be able to understand a statistical programming language (R)
and use its graphical development environment (Deduce) for data
exploration and analysis.
CO6: Be able to create web analytics solutions for Real World
Problems
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Web and social media (Web sites, web apps, mobile apps and
social media), Usability, user experience, customer experience,
customer sentiments, web marketing, conversion rates, ROI, brand
1 reputation, competitive advantages , Web analytics and a Web analytics 8
2.0 framework (clickstream, multiple outcomes analysis,
experimentation and testing, voice of customer, competitive intelligence,
Insights)
2 Unit 2: 10
Data (Structured data, unstructured data, metadata, Big Data and Linked
Data), Lab testing and experiment design (selecting participants, within-
subjects or between subjects’ study, counter balancing, independent
and dependent variable; A/B testing,
multivariate testing, controlled experiments)
Data analysis basics (types of data, metrics and data, descriptive
statistics, comparing means, correlations, nonparametric tests,
presenting data graphically)

Unit 3:
Measuring user experience - Usability metrics (performance metrics,
issues-based metrics, self-reported metrics), Planning and performing a
usability study (study goals, user goals, metrics and
3 evaluation methods, participants, data collection, data analysis), Typical 9
types of usability studies and their corresponding metrics (comparing
alternative designs, comparing with competition, completing a task or
transaction, evaluating the
impact of subtle changes)
Unit 4:
Web metrics and web analytics- PULSE metrics (Page views, Uptime,
Latency, Seven-day active users) on business and
4 technical issues; HEART metrics (Happiness, Engagement, Adoption, 9
Retention, and Task success) on user behavior issues; On-site web
analytics, off-site web analytics, the goal-signal-metric process.

Unit 5:
Social media analytics – Introduction, Social media KPIs (reach and
engagement), Performing social media analytics (business goal, KPIs,
data gathering, analysis, measure and feedback), Data analysis
5 language and tools: Ready-made tools for Web and social media 10
analytics (Key Google Analytics metrics, Dashboard, social reports)
Statistical programming language (R), its graphical development
environment (Deducer) or data exploration and analysis, and its social
media analysis packages
Total 49
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Avinash Kaushik, Web Analytics 2.0: The Art of 1st 2009
Online Accountability and Science of Customer
Centricity, Sybex
2 Matthew Ganis, Avinash Kohirkar ,Social Media 1st 2015
Analytics: Techniques and Insights for Extracting
Business Value Out of Social Media, IBM Press

Reference Books
1. Marshall Sponder, Social Media Analytics: Effective 1st 2014
Tools for Building, Interpreting, and Using Metrics,
McGraw Hill

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 797 Course Title: Deep Learning

DeeDeepProfessiona
2. Contact Hours: L: 3 T: 0 P: 0 Communication
3. Examination Duration (Hrs): Theory 3 Practical 0
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DC

8. Pre-requisite: Fundamental of Machine Learning (TCS333), Python

Programming (TCS434)

9. Course After completion of the course the students will be able to:
Outcome**: CO1: To understand the fundamental concepts and principles of deep
learning.
CO2: To evaluate and use the most important concepts and the methods
in the area ML and deep learning.
CO3: Examine modern practical deep networks.
CO4: Know deep Learning Research Areas.
CO5: Use software libraries of deep learning
CO6: Use deep learning models.
. ** Describe the specific knowledge, skills or competencies the students are
expected to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction to deep learning: basics of Machine Learning, Machine
1 Learning vs Deep Learning, deep learning process, neural network, 8

Unit 2: Modern practical deep networks: Deep Feed forward Networks,

Regularization for Deep Learning, Optimization for Training Deep Models,
2 9
Convolutional Networks, Variants of CNN: DenseNet, PixelNet

Unit 3: Popular CNN Architectures: ResNet, AlexNet, Sequence Modeling:

3 Recurrent and Recursive Nets, Practical Methodology, Applications, Transfer 9
learning Techniques,
Unit 4: Deep Learning Research: Linear Factor Models, Auto-encoders,
4 Representation Learning, Structured Probabilistic Models for Deep Learning, 8
Monte Carlo Methods, Confronting the Partition Function, Approximate
Inference Deep Generative Models.
Unit 5: Deep Learning Platforms and Software Libraries: What is a Deep
Learning Platform? H2O.ai, Data GraphLab, Deep Learning Libraries, Theano,
5 Caffe, TensorFlow 10

Total 44
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Ellis Horowitz and Sartaj Sahni, Fundamentals of Data 2014
Structures in C, Universities Press
2. Josh Patterson, Adam Gibson "Deep Learning: A 2017
Practitioner's Approach", O'Reilly Media, 2017.
3. Umberto Michelucci “Applied Deep Learning. A Case- 2018
based Approach to Understanding Deep Neural Networks”
Apress, 2018.
4. Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy 2017
"Deep Learning with TensorFlow: Explore neural networks
with Python", Packt Publisher, 2017.
Reference Books
1. Deep Learning A Practitioner’s Approach Josh Patterson and 2017
Adam Gibson O’Reilly Media, Inc.2017

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 743 Course Title: Evolutionary

Computation
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7

7. Category of Course: DE

8. Pre-requisite: TCS 302 Data Structures with C

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the relations between the most important evolutionary
algorithms.
CO2: Understand the implementation issues of evolutionary algorithms.
CO3: Determine the appropriate parameter settings to make different
evolutionary algorithms work well.
CO4: Design new evolutionary operators, representations, and fitness
functions
. ** Describe the specific knowledge, skills or competencies the students are
expected to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Introduction to Models and Concept of Computational Intelligence, Social
Behavior as Optimization: Discrete and Continuous Optimization Problems,
Classification of Optimization Algorithms, Optimization background and
1 terminology: Gradient optimization methods, sampling methods, linear 10
programming, combinatorial optimization. Evolutionary Biology background:
Genotype and phenotype, unit of selection, genes and traits, chromosomes,
alleles, diploid and haploid, fitness, mutation and recombination.

Selection, variation and landscapes. The strengths and weaknesses of the

evolutionary model. Inductive bias. The No free lunch theorem. Genetic
2 Algorithms: Representation, operators, and standard algorithm. Evolutionary 8
strategies: Evolution in continuous variables. Transformations. Genetic
Programming.
Building blocks and architecture-altering operators. Libraries and Trees.
Selection mechanisms: Fitness proportionate, rank, tournament, Stochastic
Universal Sampling and Boltzman selection methods. Niching methods. Spatial
3 methods. Artificial landscapes and test functions: The Two-armed bandit 8
problem. Gene Expression Programming, Multi-modal and deceptive
functions. Royal roads. N-k landscapes.

Hierarchical and fractal functions. Pareto evolution. Co-evolution: Multiple

populations and single-population co-evolution, Multiobjective evolutionary
algorithms: Plasticity and life-time learning. Lamarckian learning, The Baldwin
4 effect. Symbiosis as a source of evolutionary innovation. Macro-mutations, 8
Tabu Search: Tabu Tenure, Cycle Detection & Aspiration Criterion, Reactive
Tabu Search.

Swarm Intelligence Techniques: Particle Swarm Optimization, Ant Colony

Optimization, Artificial Bees and Firefly Algorithm, Hybridization and
5 Comparisons of Swarm Techniques, Application of Swarm Techniques in 8
Different Domains and Real-World Problems

Total
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Melanie Mitchell, “An introduction to genetic algorithms”, 1st 1998
MIT Press.
2 A.P. Engelbrecht, “Computational Intelligence: An 2nd 2007
Introduction”, Wiley.
Reference Books
1. D. E. Goldberg , “Genetic Algorithm in Search 1st 2008
Optimization and Machine Learning”, Pearson Education

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 742 Course Title: Deep Learning

DeeDeepProfessiona
2. Contact Hours: L: 3 T: 0 P: 0 Communication
3. Examination Duration (Hrs): Theory 3 Practical 0
4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7th

7. Category of Course: DC

8. Pre-requisite: Fundamental of Machine Learning (TCS333)

Python Programming (TCS434)

9. Course After completion of the course the students will be able to:
Outcome**: CO1: To understand the fundamental concepts and principles of deep
learning.
CO2: To evaluate and use the most important concepts and the methods
in the area ML and deep learning.
CO3: Examine modern practical deep networks.
CO4: Know deep Learning Research Areas.
CO5: Use software libraries of deep learning
CO6: Use deep learning models.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction to deep learning: basics of Machine Learning, Machine
1 Learning vs Deep Learning, deep learning process, neural network, 8

Unit 2: Modern practical deep networks: Deep Feed forward Networks,

Regularization for Deep Learning, Optimization for Training Deep Models,
2 9
Convolutional Networks, Variants of CNN: DenseNet, PixelNet

Unit 3: Popular CNN Architectures: ResNet, AlexNet, Sequence Modeling:

3 Recurrent and Recursive Nets, Practical Methodology, Applications, Transfer 9
learning Techniques,
Unit 4: Deep Learning Research: Linear Factor Models, Auto-encoders,
4 Representation Learning, Structured Probabilistic Models for Deep Learning, 8
Monte Carlo Methods, Confronting the Partition Function, Approximate Inference
Deep Generative Models.
Unit 5: Deep Learning Platforms and Software Libraries: What is a Deep Learning
Platform? H2O.ai, Data GraphLab, Deep Learning Libraries, Theano, Caffe,
5 TensorFlow 10

Total 44
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
2. Josh Patterson, Adam Gibson "Deep Learning: A 1st 2017
Practitioner's Approach", O'Reilly Media, 2017.
3. Umberto Michelucci “Applied Deep Learning. A Case- 1st 2018
based Approach to Understanding Deep Neural Networks”
Apress, 2018.
4. Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy 1st 2017
"Deep Learning with TensorFlow: Explore neural networks
with Python", Packt Publisher, 2017.
Reference Books
1. Deep Learning A Practitioner’s Approach Josh Patterson and 1st 2017
Adam Gibson O’Reilly Media, Inc.2017

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 791 Course Title: Internet Security

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE SEE 50
25
5. Credits: 3

6. Semester: 7

7. Category of Course: DC

8.Pre-requisite: Computer Organization TCS 404, Java Programming Language

TCS 408

9. Course After completion of the course, the students will be able to:
Outcome**:
CO1: Explain the architecture of the Internet.

CO2: Know the working of Internet security mechanisms.

CO3: Use cryptography to secure various applications.

CO4: Analyze various network security mechanisms.

CO5: Apply security mechanisms to protect online systems.

CO6: Develop Internet security protocols.

** Describe the specific knowledge, skills, or competencies the students are

expected to acquire or demonstrate.

10.Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Introduction and Overview:
1 5
Internet Architecture, How the Internet Works (high-level overview), IP
address.
Unit 2:
2 Internet SecurityMechanism: 10
Denial-of-Service, Traceback, DoS Defence, Network Intrusion Detection
Systems, Fundamental NIDS Issues,NIDS Evaluation, Scanning (NMAP, Nessus,
NetTools, Smart Whois), Anonymity Tor browser
Unit 3:
Cryptography Basics and Applications:
3 10
Secret Key encryption, DES, AES, One-way Hash functions, MD5, SHA-1 and
SHA-2, collision attacks, Diffie-Hellman Key Exchange, Public-Key Encryption
(RSA), Digital Signatures, Public-key Infrastructure (PKI).
Unit 4:
Network Security Mechanisms:
4 Ip Tunneling and SSH Tunneling, Virtual Private Networks, Firewalls, 9
Bypassing Firewalls, Transport Layer Security (TLS/SSL), TLS
Programming, Packet Sniffer (Wireshark), Man in the middle attack
Unit 5:
5 Monitoring systems over network. 8
Malware attacks, Virus, Worms, Trojans horse, ransomware, keylogger, spyware,
bot, botnet, botnet detection, and intrusion detection techniques.
Total 42
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
1 Cryptography and Network Security - Principles and 7th 2016
Practice, Seventh Edition, by William Stallings.
2. Firewalls and Internet Security: Repelling the Wily 2nd 2003
Hacker (Addison-Wesley Professional Computing
Series) by William Cheswick, Steven Bellovin, Aviel
Rubin.
Reference Books
1. Network Security Essentials: Applications and 4th 2011
Standards, 4/Ed, by William Stallings.

12. Mode of Evaluation Test / Quiz / Assignment / Mid-Term Exam / End-Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

Information Security and
1. Subject Code: TCS-788 Course Title: Audit Monitoring

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7th

7. Category of Course: DE

8. Pre-requisite: TCS 492 Fundamental of Cyber Security

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Explain information security audit.
CO2: Know the working of information security audit and monitoring.
CO3: Analyze the various mechanisms of information security audit.
CO4: Use information security audit and monitoring to prevent the information
securityattacks.
CO5: Apply information security audit and monitoring in various applications.
CO6: Develop strategies for information security audit and monitoring.
** Describe the specific knowledge, skills, or competencies the students are
expected to acquire or demonstrate.

10.Details of the Course:

SN Contents Hours
Unit-1
Overview
What is information security (IS), evaluation of information security, CIA triad,
1 Components of IS, control in IT environment, components of information 10
security management system (ISMS), framework for the development of ISMS,
need of information security, threats to information security, risk to information
systems, cybercrimes and attacks, information security policy, security life
cycle
Unit-2
Risk management and analysis
2 Overview of risk management, risk identification, identifying the assets, threats 8
and vulnerabilities, risk control strategies, selection of a risk control strategy,
planning for risk analysis, performing risk analysis and assessment
Unit-3
Security principles, types of information security policies, structure and
framework of compressive security policy, policy infrastructure, policy design
3 life cycle and design processes, PDCA model, security policy standards and 12
practices - BS7799, ISO/IEC 17799, ISO 27001. Auditing tools such as ISO
27001 ISMS TOOL KIT, NGS AUDITOR, Windows password auditor, ISO IES
27002 2005 IS AUDIT TOOL

Unit-4
Domains of IT security
4 Authentication and access control, physical access, Internet access, e-mail, digital 8
signature, outsourcing, software development and acquisition, hardware
acquisition, security organization structure.
Unit-5
Auditing and controls
Auditing concepts, information security audit (ISA) need, concept, standards,
5 performance, steps, techniques, methodologies, around and through computer, 8
controls-concept objectives, types, risk, input, process,validation, output, logical
access, physical access database, network, environment, BCP, evidence
collection, evaluation and reporting methodologies
Total 46
11. Suggested Books:
S Name of Authors/Books/Publishers Edition Year of
N Publication
Reprint
Textbooks
1. 2nd 2007
Michael E. Whitman and Herbert J. Mattord, Principles of
Information Security, (2e), Thomson Learning, 2007
2. 2nd 2018
Angel R. Otero, "Information Technology Control and Audit",
2018, CRC Press
Reference Books
1. 6th 2016
William Stallings, “Network Security Essentials:
Applications and Standards”, Prentice Hall.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 793 Course Title: Cloud Security

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 7th

7. Category of Course: DE

8. Pre-requisite: TCS351 Fundamental of Cloud Computing and

Bigdata

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understanding the need of cloud security, cloud security reference models
and standards.
CO2: Understand security & privacy concepts and various cloud security issues.
CO3: Identify threat model and attacks in cloud environment.
CO4: Understand advanced security concepts.
CO5: Understand and analyze intrusion detection techniques.
CO6: Implement some intrusion detection tools.
** Describe the specific knowledge, skills, or competencies the students are
expected to acquire or demonstrate.

10.Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Introduction to Cloud Security
What is Cloud Security?, Motivation for Cloud Security, Reference models and 08
Standards for Security Management, Information Technology Infrastructure
1 Library (ITIL), ISO 27001/27002, CSA Cloud Reference Model with security
boundaries, NIST Standards and Guidelines for Cloud Security, NIST Cloud
Computing Security Reference Architecture.
Unit 2:
2 Cloud Security & Privacy: Concepts and Issues
Security Concepts: Confidentiality, Integrity, Authentication, NonRepudiation, 08
Availability, Access control, Defense in depth, Least privilege, Authorization,
Cryptography, Auditing, Accountability) , Privacy : What is Privacy ,Key
privacy concerns in Cloud ,Security Management in Cloud , Security aspects at
different layers
Cloud Security Issues: A Brief Discussion - Application-level, Network-level,
Virtualization-level (i.e. Multi-Tenancy), Data Storage-level, Hardware-level,
Identity Access Management level, Auditing, Governance and Regulatory
Compliance, Cloud and CSP Migration, SLA and Trust level issues etc.)
Unit 3: 10
Threat Model and Virtualization System-Specific Attacks
Threat Model and Virtualization System-Specific Attacks Threat Model and
Attack Taxonomy, Virtualization-specific Attacks: VM Escape, Cross-VM Side
3 Channel Attack, Guest hopping, Guest DoS, VM Malware Injection, VM
migration attack, VMM DoS, VMM Hyperjacking, VMM Malware Injection,
VMM Backdoor

Unit 4:
Advanced Security Concepts
Securing the Cloud , The security boundary ,Security service boundary Security 10
4 mapping , Securing Data , Brokered cloud storage access , Establishing Identity
and Presence ,Identity protocol standards , Windows Azure identity standards ,
Identity and Access Management: Why IAM, IAM Challenges, Definitions,
Architecture &Practice
Unit 5:
Cloud Security Defensive Approaches Evolution of Cloud-Intrusion 08
5 Detection System (IDS), Deployment of IDS in Cloud, Intrusion Detection
Techniques in Cloud, Brief Discussion on Virtual Machine Introspection and
Hypervisor Introspection Techniques
Total 44
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Barrie Sisisky, “Cloud Computing Bible” Published by Wiley 1st 2011
Publishing, Inc. Cloud

2 “Cloud Security and Privacy” by Tim Mather, Subra, Shahed Latif 1st 2009
(Publ. Orielly Media),

3 “Mastering Cloud Computing” by Raj Kumar 2nd 2013

Buyya,Vecchiola&Selvi (Published by Mc Graw Hill Education
Pvt. Ltd) –
4 “Securing the Cloud “ By Vic (J.R.) Winkler 1st edition , 1st 2011

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

1. Subject Code: TDM 881 Course Title: Disaster Management

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE SEE
25 SEE 50
5. Credits: 3

6. Semester: VIII

7. Category of Course: DE

8. Pre-requisite: NIL

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understanding foundations of hazards, disasters and associated
natural/social phenomena of India
CO2: Study the various natural disasters.
CO3: Study the various manmade disasters.

CO4: Understand the disaster management principles.

CO5: Study the modern techniques used in disaster mitigation and management.
CO6: Formulate Technological innovations in Disaster Risk Reduction
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Introduction, Definitions and Classification:
Concepts and definitions - Disaster, Hazard, Vulnerability, Resilience, Risks
Natural disasters : Cloud bursts, earth quakes, Tsunami, snow, avalanches,
landslides, forest fires,diversion of river routes (ex. Kosi river), Floods,
1 Droughts Cyclones, volcanic hazards/ disasters(Mud volcanoes): causes and
distribution, hazardous effects and environmental impacts of naturaldisasters, 9
mitigation measures, natural disaster prune areas in India, major natural disasters
in
India with special reference to Uttarakhand.Man-induced disasters: water
logging, subsidence, ground water depletion, soil erosion,, release
of toxic gases and hazardous chemicals into environment , nuclear explosions
Unit 2:
Inter-relationship between Disasters and Development
Factors affecting vulnerabilities, differential impacts, impacts of development
projects such asdams, embankments, changes in land use etc. climate change
2 adaption, relevance of indigenous knowledge, appropriate technology and local 8
resources, sustainable development and its role in disaster mitigation, roles and
responsibilities of community, panchayat raj institutions/urban local bodies, state,
centre and other stake holders in disaster mitigation.

Unit 3:
Disaster Management (Pre-disasterstage, Emergency stage and Post
Disaster Stage)
1. Pre-disaster stage (preparedness): Preparing hazard zonation maps,
predictably/forecastingand warning, preparing disaster preparedness plans, land
3 use zoning, preparedness through information, education and communication
(IEC), disaster resistant house construction, population reduction in vulnerable 9
areas, awareness2. Emergency Stage: Rescue training for search & operation at
national & regional level,immediate relief, assessment surveys
3. Post Disaster stage: Rehabilitation and reconstruction of disaster affected
areas; urban disaster mitigation: Political and administrative aspects, social
aspects, economic aspects, environmental aspects.

Unit 4:
Disaster Management Laws and Policies in India Environmental legislations
related to disaster management in India: Disaster Management Act,2005;
Environmental policies & programs in India- Institutions &nationalcentres for
4 natural disaster mitigation: National Disaster Management Authority 8
(NDMA):structure and functional responsibilities, National Disaster Response
Force (NDRF): Rule andresponsibilities, National Institute Of Disaster
Management (NlDM): Rule and responsibilities.

Total 34

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. M MSulphey,” Disaster Management”, PHI, 1st 2016

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

Advanced Computer
1. Subject Code: TCS 881 Course Title: Vision

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8th

7. Category of Course: DE

8. Pre-requisite: TCS 301, Any Programming Language

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the principals the computer vision and image procesing used
to describe features of images.
CO2: Understand the mathematical foundations for digital manipulation of
images
CO3: Design, code and test computer applications using MATLAB/OpenCV.
CO4: Analyze a wide range of problems and provide solutions related to the
design of computer applications through suitable algorithms, structures,
diagrams, and other appropriate methods.
CO5: Plan and undertake a major individual computer applications.
CO6: Write programs in MATLAB/OpenCV.for digital manipulation of images;
image acquisition; preprocessing; segmentation.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Image Processing Techniques: Image Enhancement: Histogram
Processing, Smoothing filters, Sharpening filters; Image restoration, Image
1 Compression– coding Redundancy, spatial and temporal redundancy, 9
Compression models Segmentation: Segmentation concepts, point, line and
Edge detection, Thresholding, region based segmentation.
Unit 2: Edge detection, Thresholding, Region growing, Fuzzy clustering,
Watershed algorithm, Active contour models, Texture feature based
2 segmentation, Graph based segmentation, Applications of image segmentation. 9
Background Subtraction in Videos, Morphological processing,
Unit 3: Image content Analysis: Feature mapping using the scale-invariant
feature transform (SIFT) algorithm, Image registration, Image classification,
3 11
Object Localization, Object Recognition, Object Detection, Object recognition,
shape from shading, foreground-background separation.
Unit 4: Motion Estimation: Optical flow, general methodologies, pixel based
motion estimation, Block matching algorithm, Mesh based motion Estimation,
4 global Motion Estimation, Region based motion estimation, multi resolution 9
motion estimation. Waveform based coding, Block based transform coding,
predictive coding, Application of motion estimation in video coding.
Unit 5: Introduction to deep learning, deep learning techniques, CNN:
Architectures, Convolution, Pooling Layers, Transfer Learning, Applications:
5 Image classification using Convolutional Neural Networks (CNNs), Anomaly 10
detection using Convolutional Neural Networks (CNNs), medical image-fusion
using deep learning method, Image Captioning.
Total 48

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Digital Image Processing, by R. C. Gonzalez, R. E. Woods 4th 2017
and S. L. Eddins , Publisher: Pearson. Edition
2. 2nd 2017
Digital Image Processing using Matlab, by R. C. Gonzalez,
R. E. Woods and S. L. Eddins , Publisher: Pearson.
3. 1st 2018
Deep Learning for Computer Vision, by Rajalingappaa
Shanmugamani, Publisher: O Reilly
Reference Books
1. 1st 2017
Deep Learning with Keras by Antonio Gulli, Sujit Pal,
Publisher: O Reilly
2. Programming Computer Vision with Python", Jan Salem, 1st 2012
Publisher: O Reilly

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII
Name of Department: - Computer Science and Engineering Services Oriented Cloud architecture
1. Subject Code: TCS 859 Course Title:
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE SEE 50
25
5. Credits: 3
6. Semester: 8
7. Category of Course: DE
8. Pre-requisite: : TCS 602

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Identify the concept of service-oriented cloud architecture.

CO2: Demonstrate microservices architecture and services-oriented cloud architecture

protocols with security aspects.

CO3: Analyze the service-oriented cloud architecture for business technology and
policy management.

CO4: Apply digital age technologies in services-oriented cloud architecture-based

cloud applications.

CO5: Evaluate the performance of services-oriented cloud architecture governance

framework and their legacy evolution.

CO6: Design a serverless cloud-based web application and deploy as a Microservice

on OpenShift and as static files on Cloud Object Storage.
** Describe the specific knowledge, skills or competencies the students are expected to
acquire or demonstrate.
10. Details of the Course:
SL. Contact
Contents
NO. Hours
Unit - I
Services oriented cloud architecture: Defining concepts, Principles, Patterns,
Importance, components, working, limitations, Enterprise Service Bus (ESB)
1 9
in service-oriented cloud architecture, Benefits of ESB, Interfaces and
namespaces, SOLID principles, IoC containers, Case study of service oriented
cloud architectures.
Unit – II
2 Microservices architecture, Services oriented cloud architecture protocols, 9
Security, Full stack cloud applications, Distributed applications and Web
services. Technologies and frameworks for distributed and server side
application development, Service-oriented cloud architecture communications,
Case study of service oriented cloud architecture protocols and
communications.
Unit – III
Services oriented cloud architecture for Business Technology, Cloud service
accessibility, cloud service visibility, cloud service extensibility, Cloud service
3 9
SLAs, cloud service deployment using contract-management techniques,
Policy management techniques, Case study of service oriented cloud
architecture for business policy management.
Unit – IV
Services oriented cloud architecture in the Digital Age, Designing domain
specific cloud services, Service-oriented distributed applications, Semantic
web and web services, RESTful, AJAX, JSON, Web API, Web Socket
4 9
application in cloud services, Open Standards Landscape around Architecture,
Develop an application on Cloud leveraging cloud services like AI-powered
APIs and NoSQL databases, Case study of service oriented cloud architecture
in digital age applications.
Unit – V
Services oriented cloud architecture governance framework, Legacy evolution,
Services oriented cloud reference architecture, Windows communication
foundation services (WCF), Hosting and consuming WCF services, evaluating
5 9
performance of WCF services in cloud platform, Serverless computing, Create
a serverless cloud based web application and deploy as a Microservice on
OpenShift and as static files on Cloud Object Storage, Case study of service
oriented cloud architecture for business governance.
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication /
No. Reprint
Textbooks
1. Barrie Sisisky, “Cloud Computing Bible” Published by Wiley 2nd 2010
Publishing, Inc.

2 Berners Lee, Godel and Turing, “Thinking on the Web”, Wiley 1st 2008
inter science,.
3 Peter Mika, “Social Networks and the Semantic Web”, Springer,. 1st 2007

Reference Books
1. Thomas Erl ,“Cloud Computing: Concepts, Technology & 1st 2013
Architecture” Published May

2 David S. Linthicum ,“Cloud Computing and SOA Convergence in 1st 2009

your Enterprise, a step by step guide”

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 801 Course Title: Mobile Computing

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite:Communication models and Protocols (TCS 53),

Computer Networks (TCS 604), Network Programming and Wireless Technologies
(TCS 631)

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Exemplify the concepts, techniques, protocols and architecture employed
in wireless local area networks, cellular networks, and Adhoc Networks
based on the standards
CO2: Describe and analyze the network infrastructure requirements to support
mobile devices and users.
CO3: Design and implement mobile applications to realize location-aware
computing
CO4: Asses the important issues and concerns on security and Data management
CO5: Development of various scenarios for mobile computing systems.
CO6: Evaluate the concepts of mobile agents and mobile Adhoc algorithms with
the help of NS2.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
No. Hours
Unit 1:
Introduction, issues in mobile computing, overview of wireless telephony:
1 cellular concept, GSM: air-interface, channel structure, location management: 9
HLR-VLR, hierarchical, handoffs, channel allocation in cellular systems,
CDMA, GPRS
Unit 2:
2 Wireless Networking, Wireless LAN Overview: MAC issues, IEEE 802.11,
Blue Tooth, Wireless multiple access protocols, TCP over wireless, Wireless
applications, data broadcasting, Mobile IP, WAP: Architecture, protocol stack,
8
application environment, applications.

3 Unit 3:
Data management issues, data replication for mobile computers, adaptive 9
clustering for mobile wireless networks, File system, Disconnected operations
Unit 4:
Mobile Agents computing, security and fault tolerance, transaction processing
4 8
in mobile computing environment.

Unit 5:
Ad Hoc networks, localization, MAC issues, Routing protocols, global state
5 routing (GSR), Destination sequenced distance vector routing (DSDV), 9
Dynamic source routing (DSR), Ad Hoc on demand distance vector routing
(AODV), Temporary ordered routing algorithm (TORA), QoS in Ad Hoc
Networks, applications
Total 45
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. J. Schiller,” Mobile Communications”, Addison Wesley. 2nd 2008

2 A. Mehrotra ,“GSM System Engineering”. 1997

3 M. V. D. Heijden, M. Taylor, “Understanding WAP”, 2000

Artech House
Reference Books
1. J. Schiller,” Mobile Communications”, Addison Wesley. 2nd 2008
2 A. Mehrotra ,“GSM System Engineering”. 1997

3 M. V. D. Heijden, M. Taylor, “Understanding WAP”, 2000

Artech House

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

Mobile Applications
1. Subject Code: TCS 822 Course Title: Development

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: VIII

7. Category of Course: DE

8. Pre-requisite: TCS 693

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand and apply the key technological principles and methods for
delivering and maintaining mobile applications,

CO2: Evaluate and contrast requirements for mobile platforms to establish

appropriate strategies for development and deployment,

CO3: Develop and apply current standard-compliant scripting/programming

techniques for the successful deployment of mobile applications targeting
a variety of platforms,

CO4: Carry out appropriate formative and summative evaluation and

testingutilising a range of mobile platforms,

CO5: Interpret a scenario, plan, design and develop a prototype hybrid and
native mobile application,

CO6: investigate the leading edge developments in mobile application

development and use these to inform the design process.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
1 Unit 1:
Getting started with Mobility 9
Mobility landscape, Mobile platforms, Mobile apps development, Overview of
Android platform, setting up the mobile app development environment along with
an emulator, a case study on Mobile app development

Unit 2:
Building blocks of mobile apps
2 App user interface designing – mobile UI resources (Layout, UI elements, Draw-
able, Menu), Activity- states and life cycle, interaction amongst activities. 8
App functionality beyond user interface - Threads, Async task, Services – states
and life cycle, Notifications, Broadcast receivers, Telephony and SMS APIs
Native data handling – on-device file I/O, shared preferences, mobile databases
such as SQLite, and enterprise data access (via Internet/Intranet)
Unit 3:
Sprucing up mobile apps
Graphics and animation – custom views, canvas, animation APIs, multimedia –
3
audio/video playback and record, location awareness, and native hardware access 9
(sensors such as accelerometer and gyroscope)

Unit 4:
4 Testing mobile apps
Debugging mobile apps, White box testing, Black box testing, and test automation 9
of mobile apps, JUnit for Android, Robotium, MonkeyTalk

Unit 5:
Taking apps to Market
5 Versioning, signing and packaging mobile apps, distributing apps on mobile 8
market place

Total 43

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Jeff McWherter, Scott Gowell, “Professional Mobile 1st 2012
Application Development”,Wrox Publication.
2 “Mobile Application Development “Black 2nd 2014
Book, Dreamtech Press

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 821 Course Title: Soft Computing

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite: Good knowledge of Artificial Intelligence

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Summarize about soft computing techniques and their applications
CO2: Analyze various neural network architectures
CO3: Designperceptrons and counter propagation networks.
CO4: Classify the fuzzy systems
CO5: Analyze the genetic algorithms and their applications.
CO6: Compose the fuzzy rules.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Contact
S.NO. Contents
Hours
Unit 1:
Fundamentals of ANN: The Biological Neural Network, Artificial Neural
1 Networks -Building Blocks of ANN and ANN terminologies: architecture, setting 9
of weights,activation functions - McCulloch-pitts Neuron Model, Hebbian
Learning rule, Perceptionlearning rule, Delta learning rule.
2 Unit 2:
Models of ANN: Single layer perception, Architecture, Algorithm, application
procedure- Feedback Networks: Hopfield Net and BAM - Feed Forward Networks: 8
BackPropogation Network (BPN) and Radial Basis Function Network (RBFN) –
SelfOrganizing Feature Maps: SOM and LVQ

Unit 3:
3 Fuzzy Sets, properties and operations - Fuzzy relations, cardinality, operations 9
andproperties of fuzzy relations, fuzzy composition.
Unit 4:
Fuzzy variables - Types of membership functions - fuzzy rules: Takagi and
4 9
Mamdani –fuzzy inference systems: fuzzification, inference, rulebase,
defuzzification.
Unit 5:
Genetic Algorithm (GA): Biological terminology – elements of GA: encoding,
5 types ofselection, types of crossover, mutation, reinsertion – a simple genetic 9
algorithm –Theoretical foundation: schema, fundamental theorem of GA, building
block hypothesis.
Total 44

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. S. N. Sivanandam, S. Sumathi, S.N. Deepa, “Introduction 1st 2017
to Neural Networks using MATLAB 6.0 “, Tata McGraw-
Hill, New Delhi, 2006
2 S. N. Sivanandam, S.N. Deepa, “Principles of Soft 1st 2008
Computing”, Wiley-India, 2008.
3 D.E. Goldberg, “Genetic algorithms, optimization and 1st 2008
machine learning”

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

Multimedia Systems and
1. Subject Code: TCS 823 Course Title: Data Compression
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite: TCS 308

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Demonstrate the basic concept of multimedia information representation.
Delve into the requirement of multimedia communication in today’s digital
world.
CO2: Compare circuit mode and packet mode.Explain QoS and its applications.
CO3: Summarize the various multimedia information representations
CO4: Compute Arithmetic, Huffman, Lempel –Ziv and Lempel–Ziv Welsh
coding. Summarize Joint Photographic Expert Group (JPEG).
CO5: Differentiate between the audio compression techniques: PCM, DPCM,
ADPCM, LPC, CELPC and MPEG. Differentiate MPEG1, MPEG2 and
MPEG4.
CO6: Construct Haptic Interfaces and Virtual reality Systems

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
No. Hours
Unit. I
Introduction to Multimedia Presentation and Production, Multisensory
Perception,
Digital Representation of Data: Why it is required, Analog to Digital Conversion
1 and Digital to Analog Conversion, Nyquist’s Theorem, Relation between 10
Sampling Rate and Bit Depth, Quantization Error, Fourier Representation, Pulse
Modulation
Describing Multimedia Presentations: SMIL
Text: Typeface, Fonts; Tracking, Kerning, Spacing; Optical Character
Recognition; Unicode Standard; Text to Voice
Unit. II
Data Compression: Approaches to compression, Basic Techniques: Run-Length
2 Encoding ; Statistical Methods: Information Theory Concepts, Variable-Size
9
codes, Shanon-Fano coding, Huffman coding, Adaptive Huffman Coding,
Arithmetic Coding; Dictionary Methods: LZ77(Sliding Window), LZ78, LZW;
Various LZ Applications, Deflate: zip and Gzip, LZMA and 7-zip.
Unit. III
Image types, how we see color, Vector and Bitmap, Color Models: RGB, CMYK,
Lab, HSL, HSB/HSV, YUV, conversion between different color models; Basic
steps of image processing, Scanner, Digital Camera, Gamma Correction, General
3 9
Study of the following image formats: BMP,TIF,PNG,GIF,SVG
Image Compression: Approaches, Image Transforms, The Discrete Cosine
Transform, Detailed study of JPEG,JPEG-LS, Progressive image compression,
JBIG
Unit – IV

Acoustics and the Nature of Sound Waves, Fundamental Characteristics of

Sound, Musical Note, Pitch, Beat, Rhythm, Melody, Harmony and Tempo;
Elements of Audio Systems, General study of Microphone, Amplifier,
Loudspeaker, Mixer; Digital Audio, Synthesizers, MIDI, MIDI Connections,
MIDI messages, Staff Notation, Sound Card, Audio Codecs: AIFF, WAV, Apple
4 Lossless, Dolby TrueHD, DTS-HD Master Audio, FLAC, WMA, Audio Playing 9
Software, Audio Recording using Dolby, Dolby Digital and Dolby Digital
Surround EX, Voice Recognition
Video: Analog Video, Transmission of Video Signals, Chroma Sub sampling,
Composite and Components Video, NTSC, PAL and SECAM, Digital Video,
High Definition TV, Video Recording Formats; Video Compression, MPEG,
MPEG-4; General Study of the following formats and codecs: avi, flv, m4v
Unit – V
Multimedia Messaging Service(MMS): MMS standard, MMS Architecture, An
Engineering perspective on How a MMS is created, sent and retrieved
5 8
Introduction to Virtual Reality: Components of a VR System, Haptic Interfaces,
Virtual Reality Programming, Impact of Virtual Reality, Case study of Second
Life
Total 45

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Ranjan Parekh, “Principles of Multimedia”, McGraw Hill, 2nd 2006

Reference Books
1. David Salomon, “Data Compression: The Complete 1st 2003
Reference”, Fourth Edition, Springer Books
2. GrigoreBurdea, Philippe Coiffet, “Virtual reality 2nd 2003
technology, Volume 1”, Wiley,

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

Unix Systems
1. Subject Code: TCS 826 Course Title: Programming

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite: TCS-302, TCS-502 DSA, Operating Systems

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Experiment with various system calls
CO2: Compare between ANSI C AND C++ AND POSIX standards
CO3: Mapping the relationship between UNIX Kernel support for files
CO4: Use Kernel support for process creation and termination and memory
allocation
CO5: Analyze Process Accounting process UID ,Terminal logins, network logins
CO6: Analyze process control,Deamon characteristics, coding rules and error
logging
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
No. Hours
Unit 1:
Introduction to System Programming, File I/O, Difference between Buffered
and Unbuffered I/O, I/O system calls: open(), close(), read(), write(), Effect of
1 I/O buffering in stdio and the kernel; synchronized I/O, Seeking to a file offset: 9
lseek(), File control: fcntl(), Locking, Open file status flags, Open files and file
descriptors, Duplicating file descriptors with dup, dup2 and fcntl. A brief recap
of Buffered I/O, Forays into Advanced I/O
2 Unit 2:
Processes: Process ID and Parent process ID, Memory layout, Running and
Terminating a process, Waiting for Terminated child processes (fork, the exec
family, wait, waitpid), copy on write, Advanced Process Management: Process 10
Priorities, nice(), Setting the scheduling policy
Unit 3:
3 Processes and Inter-Process Communication: Introduction, pipes, FIFOs, XSI 9
IPC: Message Queues, Semaphores, Shared Memory
Unit 4:
Signals: Signal types and default actions, Basic Signal management, signal
4 function, unreliable signals, SIGCLD, Sending signals, Signal sets, Blocking 8
signals (the signal mask), Interruption and restarting of system calls, Designing
signal handlers
Unit 5:
5 Network Programming: Sockets, Operation, Socket types, Client/Server
9
Models, Connection Based Services, Handling Out of Band Data,
Connectionless Services, Design issues of Concurrent and iterative servers,
Socket options
Total 45

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Richard Stevens and Stephen Rago,” Advanced 3rd 2013
Programming in the Unix Environment”, Addison-Wesley
2 Michael Kerrisk,” The Linux Programming Interface”, No 2nd 2010
Starch Press
Reference Books
1. Richard Stevens and Stephen Rago,” Advanced 3rd 2013
Programming in the Unix Environment”, Addison-Wesley
2 Michael Kerrisk,” The Linux Programming Interface”, No 2nd 2010
Starch Press

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 851 Course Title: Storage Networks

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite: Computer Network(TCS-703), TCS-604

9. Course After completion of the course the students will be able to:
Outcome**:
CO1: Understand the different aspects of storage management

CO2: Describe the various applications of RAID

CO3: Compare and contrast the I/O Techniques

CO4: Categorize virtualization on various levels of storage network

CO5: Estimate the various requirements of storage management systems

CO6: Design a complete data center and enhance employability in this field

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Cont
SL. act
Contents
No. Hour
s
Unit 1:
Introduction to Storage Technology

1 Introduction to storage network, Five pillars of IT, parameters related with storage, 10
data proliferation, problem caused by data proliferation, Hierarchical storage
management, Information life cycle management (ILM), Role of ILM, Information
value vs. time mapping, Evolution of storage, Storage infrastructure component, basic
storage management skills and activities, Introduction to Datacenters, Technical &
Physical components for building datacenters
Unit 2:
Technologies for Storage network
2
Server centric IT architecture & its limitations, Storage centric IT architecture &
advantages, replacing a server with storage networks, Disk subsystems, Architecture
of disk subsystem, Hard disks and Internal I/O channel, JBOD, RAID& RAID levels, 9
RAID parity, comparison of RAID levels, Hot sparing, Hot swapping, Caching :
acceleration of hard disk access, Intelligent Disk subsystem architecture
Tape drives: Introduction to tape drives, Tape media, caring for Tape& Tape heads,
Tape drive performance, Linear tape technology, Helical scan tape technology
Unit 3:
I/O techniques
I/O path from CPU to storage systems, SCSI technology – basics & protocol, SCSI
and storage networks, Limitations of SCSI
Fibre channel: Fibre channel, characteristic of fibre channel, serial data transfer vs.
parallel data transfer, Fibre channel protocol stack, Links, ports & topologies, Data
3 transport in fibre channel, 10

Addressing in fibre channel, Designing of FC-SAN, components, Interoperability of

FCSAN, FC products

IP Storage: IP storage standards (iSCSI, iFCP, FCIP, iSNS), IPSAN products,

Security in IP SAN, introduction to InfiniBand, Architecture of InfiniBand
NAS – Evolution, elements & connectivity, NAS architecture
Unit 4:
Storage Virtualization

4 Introduction to storage virtualization, products, definition, core concepts, 9

virtualization on various levels of storage network, advantages and disadvantages,
Symmetric and asymmetric virtualization, performance of San virtualization, Scaling
storage with virtualization
Unit 5:
Management of storage Networks
5 Management of storage network, SNMP protocol, requirements of management 8
systems, Management interfaces, Standardized and proprietary mechanism, In-band&
Out-band management, Backup and Recovery
Total 46

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. "Storage Networks: The Complete Reference", R. Spalding, 1st 2017
McGraw-Hill
2 "Storage Networking Fundamentals: An Introduction to 3rd 2004
Storage Devices, Subsystems,
3 Applications, Management, and Filing Systems", Marc 1st 2005
Farley, Cisco Press.
Reference Books
1. "Storage Networks: The Complete Reference", R. Spalding, 1st 2017
McGraw-Hill
2 "Storage Networking Fundamentals: An Introduction to 3rd 2004
Storage Devices, Subsystems,

3 Applications, Management, and Filing Systems", Marc 2nd 2004

Farley, Cisco Press.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 852 Course Title: Pattern

Recognition
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite: Knowledge of Probability theory, mathematics, and algorithm s

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Explain and compare a variety of pattern classification, structural pattern
recognition, and pattern classifier combination techniques.
CO2. Summarize, analyze, and relate research in the pattern recognition area
verbally and in writing.
CO3. Apply performance evaluation methods for pattern recognition, and
critique comparisons of techniques made in the research literature.
CO4. Apply pattern recognition techniques to real-world problems such as
document analysis and recognition.
CO5. Implement simple pattern classifiers, classifier combinations, and
structural pattern recognizers.
CO6. Describe the various clustering methods
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Introduction: Machine perception, pattern recognition example, pattern
recognition systems, the design cycle, learning and adaptation Bayesian Decision
1 Theory : Introduction, continuous features – two categories classifications, 9
minimum error-rate classification- zero–one loss function, classifiers,
discriminant functions, and decision surfaces
Unit 2: Normal density: Univariate and multivariate density, discriminant
functions for the normal densitydifferent cases, Bayes decision theory – discrete
2 9
features, compound Bayesian decision theory and context Maximum likelihood
and Bayesian parameter estimation : Introduction, maximum likelihood
estimation, Bayesian estimation, Bayesian parameter estimation–Gaussian

Un-supervised learning and clustering: Introduction, mixture densities and

identifiability, maximum likelihood estimates, application to normal mixtures, K-
means clustering. Date description and clustering – similarity measures, criteria
3 8
function for clustering Component analysis: Principal component analysis, non-
linear component analysis; Low dimensional representations and multi-
dimensional scaling

4 Discrete Hidden Markov Models : Introduction, Discrete–time Markov process, 8

extensions to hidden Markov models, three basic problems for HMMs

Continuous hidden Markov models : Observation densities, training and testing

5 8
with continuous HMMs, types of HMMs
Total 48
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Richard O. Duda, Peter E. Hart, David G. Stroke. “Pattern 2nd 2021
Recognition”, Wiley (Indian adaptation)

2 M. Narasimha Murty, V. Susheela Devi, “Pattern 1st 2016

Recognition”, Universities Press

Reference Books
1. Christopher M. Bishop, “Pattern Recognition and Machine 1st 2016
Learning”, Springer

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

Agile Software
1. Subject Code: TCS 855 Course Title: Engineering
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite: Software Engineering(TCS-611)

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Describe two or more agile software development methodologies.
CO2: Identify the benefits and pitfalls of transitioning to agile.
CO3: Compare agile software development to traditional software
development models.
CO4: Apply agile practices such as test-driven development, standup meetings,
and pair programming to their software engineering practices.
CO5: Apply the agile testing
CO6: Describe the agile in current market scenario.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit 1:
Fundamentals of Agile:
The Genesis of Agile, Introduction and background, Agile Manifesto and
Principles, Overview of Agile Methodologies – Scrum methodology, Extreme
1 10
Programming, Feature Driven development, Design and development practices
in an Agile projects, Test Driven Development, Continuous Integration,
Refactoring, Pair Programming, Simple Design, User Stories, Agile Testing,
Agile Tools
2 Unit 2:
Agile Project Management:
Agile Scrum Methodology, Project phases, Agile Estimation, Planning game, 10
Product backlog, Sprint backlog, Iteration planning, User story definition,
Characteristics and content of user stories, Acceptance tests and Verifying
stories, Agile project velocity, Burn down chart, Sprint planning and
retrospective, Daily scrum, Scrum roles – Product Owner, Scrum Master, Scrum
Developer, Scrum case study, Tools for Agile project management
Unit 3:
Agile Software Design and Programming:
Agile Design Principles with UML examples, Single Responsibility Principle,
3 Open Closed Principle, Liskov Substitution Principle, Interface Segregation
9
Principles, Dependency Inversion Principle, Need and significance of
Refactoring, Refactoring Techniques, Continuous Integration, Automated build
tools, Version control, Test-Driven Development (TDD), xUnit framework and
tools for TDD
Unit 4:
Agile Testing:
4 The Agile lifecycle and its impact on testing, Testing user stories - acceptance
9
tests and scenarios, Planning and managing Agile testing, Exploratory testing,
Risk based testing, Regression tests, Test Automation, Tools to support the
Agile tester
Unit 5:
Agile in Market:
Market scenario and adoption of Agile, Roles in an Agile project, Agile
5 applicability, Agile in Distributed teams, Business benefits, Challenges in Agile, 8
Risks and Mitigation, Agile projects on Cloud, Balancing Agility with
Discipline, Agile rapid development technologies

Total 46
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Ken Schawber, Mike Beedle, “Agile Software 2008
Development with Scrum”, Pearson

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 857 Course Title: Game Theory

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite: Excellent knowledge of programming and mathematics

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Identify strategic situations and represent them as games

CO2: Find dominant strategy equilibrium, pure and mixed strategy Nash
equilibrium,

CO3: Solve simple games using various techniques

CO4: Analyze economic situations using game theoretic techniques

CO5: Recommend and prescribe which strategies to implement

CO6: Find the needs of extensive games.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
No. Hours
Unit 1:
Introduction, Strategic Games: What is game theory? The theory of rational
choice; Interacting decision makers.
1 Strategic games; Examples: The prisoner’s dilemma, Bach or Stravinsky, 11
Matching pennies; Nash equilibrium; Examples of Nash equilibrium; Best-
response functions; Dominated actions; Equilibrium in a single population:
symmetric games and symmetric equilibria.
Mixed Strategy Equilibrium: Introduction; Strategic games in which players
may randomize; Mixed strategy Nash equilibrium; Dominated actions; Pure
equilibria when randomization is allowed, Illustration: Expert Diagnosis;
Equilibrium in a single population, Illustration: Reporting a crime; The
formation of players’ beliefs; Extensions; Representing preferences by expected
payoffs
Unit 2:
Extensive Games: Extensive games with perfect information; Strategies and
2 outcomes; Nash equilibrium; Subgame perfect equilibrium; Finding subgame
perfect equilibria of finite horizon games: Backward induction. Illustrations: The
ultimatum game, Stackelberg’s model of duopoly, Buying votes.
10

Extensive games: Extensions and Discussions: Extensions: Allowing for

simultaneous moves, Illustrations: Entry in to a monopolized industry, Electoral
competition with strategic voters, Committee decision making, Exit from a
declining industry; Allowing for exogenous uncertainty, Discussion: subgame
perfect equilibrium and backward induction
Unit 3:
Bayesian Games, Extensive Games with Imperfect Information:
Motivational examples; General definitions; Two examples concerning
information; Illustrations: Cournot’s duopoly game with imperfect information,
Providing a public good, Auctions; Auctions with an arbitrary distribution of
valuations.
Extensive games with imperfect information; Strategies; Nash equilibrium;
Beliefs and sequential equilibrium; Signaling games; Illustration: Strategic
information transmission.
3 10

Strictly Competitive Games, Evolutionary Equilibrium: Strictly competitive

games and maximization; Maximization and Nash equilibrium; Strictly
competitive games; Maximization and Nash equilibrium in strictly competitive
games.
Evolutionary Equilibrium: Monomorphic pure strategy equilibrium; Mixed
strategies and polymorphic equilibrium; Asymmetric contests; Variations on
themes: Sibling behavior, Nesting behavior of wasps, The evolution of sex ratio
Unit 4:
Iterated Games: Repeated games: The main idea; Preferences; Repeated
4 games; Finitely and infinitely repeated Prisoner’s dilemma; Strategies in an
8
infinitely repeated Prisoner’s dilemma; Some Nash equilibria of an infinitely
repeated Prisoner’s dilemma, Nash equilibrium payoffs of an infinitely repeated
Prisoner’s dilemma
Unit 5:
5 Coalitional Games and Bargaining: Coalitional games. The Core.
8
Illustrations: Ownership and distribution of wealth, Exchanging homogeneous
items, Exchanging heterogeneous items, Voting, Matching. Bargaining as an
extensive game; Illustration of trade in a market; Nash's axiomatic model of
bargaining
Total 47

11. Suggested Books:

SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Martin Osborne: “An Introduction to Game Theory”, Oxford 2nd 2004
University Press, Indian Edition,.

Reference Books
1. Roger B. Myerson: “Game Theory: Analysis of Conflict”, 1st 1997
Harvard University Press,

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

Quantum Computing
1. Subject Code: TCS- 841 Course Title:

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8th

7. Category of Course: DE

8. Pre-requisite: Probability and Linear Algebra

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the principles of quantum computation.
CO2: Learn the circuit and gates involved in quantum computing.
CO3: Understand the algorithms used in quantum computing.
CO4: Study the information theory aspects of quantum computing.
CO5: Use and build Quantum algorithms for solving various problems.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Introduction: Overview of classical computing and its limitations; Introduction to
1 quantum mechanics and quantum information theory; Quantum bits; Bloch sphere
10
representation of a qubit; multiple qubits; Dirac notation; Hilbert space;
Probabilities and measurements; Density operators and correlation; Quantum
Parallelism; Entanglement and Superposition.
Unit 2:
Quantum Gates and Circuits: Quantum circuit representation; Quantum Logic
2 Gates; Unitary Transformation; Hadamard Gate; Controlled Quantum Gates; 8
Universal Quantum Gates; Special 2-Qubit Gates (CSIGN; SWAPα; iSWAP;
Berkeley B); Quantum measurements and observables
Unit 3:
Quantum Algorithms: Relationship between classical and quantum algorithms;
3 Classical computation on quantum computers; Relationship between quantum and
10
classical complexity classes; Deutsch’s algorithm, Deutsch’s-Jozsa algorithm; Shor
factorization algorithm; Grover search algorithm; Quantum Approximate
optimization algorithm (QAOA).
Unit 4:
Quantum Information Theory: Entropy and information; Shannon entropy; Basic
properties of entropy; Von Neumann entropy; Physical realisation; Harmonic
4 oscillator quantum computer; Quantum teleportation and superdense coding; 12
Quantum noise and quantum operations; Marcov process; Data compression;
Quantum error corrections; 3 Qubit phase flip code; Fault tolerant quantum
computation; Quantum cryptography.
Unit 5:
Qiskit Programming: Introduction and IBM Quantum Perspective; Qiskit
5 software; Quantum gates and operations in Qiskit, Quantum measurement and 8
simulation in qiskit; Quantum teleportation with qiskit; Implementation of Grover
algorithm qiskit.
Total 48
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. 10th 2010
Nielsen, M., & Chuang, I. In Quantum Computation and
Quantum Information. Cambridge University Press
2. John Gribbin (2014), Computing with Quantum Cats: From 1st 2014
Colossus to Qubits, Prometheus Books.
3. 1st 2011
"Quantum Computing: A Gentle Introduction" by Eleanor G.
Rieffel and Wolfgang H. Polak. The MIT Press
4. 1st 2013
Quantum information Theory, Mark M. Wilde, Cambridge
University Press.
Reference Books
1. 1st 2013
Quantum error correction, D. A. Lidar & T. A. Brun,
Cambridge University Press.

2. Quantum Computing: From Linear Algebra to Physical 1st 2008

Realizations" by Mikio Nakahara and Tetsuo Ohmi.

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 842 Course Title: Recent trends

in AI
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 ESE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite: TCS 542

9. Course After completion of the course the students will be able to:
Outcome**: CO1: learn about the current trends in AI in chatbots and virtual assistants
CO2: understand the research progress being made in AI-driven cyber-
security and ITOps
CO3: learn current applications of AI in healthcare
CO4: learn about the applications of AI in in IoT and quantum computing
CO5: understand and create an AI application from recent trends
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
AI-powered Chatbots & Virtual Assistants: AI-driven chatbots such as ChatGPT,
virtual assistants, question answering systems, Hyper-automation: automation
tools, such as AI, ML, RPA, and NLP, to streamline intricate workflows, utilizing
1 the newest advances in AI app trends to provide effective and cost-efficient 10
solutions

AI-driven cybersecurity: safety against cyber threats and overall cybersecurity

defense, utilizing the technology of artificial intelligence and machine learning
2 technology 9
Artificial intelligence for IT operations: machine learning and other AI techniques
to automate and optimize IT operations and management.

3 AI in healthcare: leveraging artificial intelligence and machine learning to create 9

efficient delivery, maximize patient outcomes, and minimize expenses

Integration of AI and IoT: leverage machine learning algorithms to interpret the

tremendous data generated from IoT-enabled devices

AI and quantum computing are increasingly converging as AI technologies

4 progress, with quantum computing playing an essential role in refining the 8
precision and practicality of AI algorithms.

Apply the knowledge gained from the recent trends in AI to develop and
demonstrate an application that demonstrates their learning.
5 6

Total 42
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
There is no textbook, recent research papers and material
available online will be discussed
Reference Books

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

1. Subject Code: TCS 862 Course Title: FATE in AI

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8

7. Category of Course: DE

8. Pre-requisite: TCS 562

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Apply Fairness in designing AI algorithms.
CO2: Understand and Examine Accountability in AI
CO3: Understand and Examine pitfalls of various contemporary AI
applications.
CO4: Analyze the ethics for designing future AI systems.
CO5: Evaluate fairness and transparency of AI systems.
CO6: Examine Real World Cases from policy perspectives
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1: Data: Protection rational & genesis, data protection in India
(judicial developments on right to privacy, legislative developments);
Territorial and Personal Scope; Personal data; Sensitive personal data;
1 8
Processing of data; Processing of sensitive data; Rights: Introduction,
right to object to processing, right to be forget; Case studies.

Unit 2: Fairness: Introduction, sources of unfairness, definitions; Metrics

for fairness, fair data; pre-processing methods; In-processing methods;
post-processing methods; Model auditing for fairness; ML models and
2 10
privacy; ML models and security; Fair product design & development;
Laws for ML; Compliance tools: Anonymization, Privacy by design.

3 Unit 3: 8
Accountability & Ethics: Introduction, Guidelines in AI ethics; AI in
practice; Advances in AI ethics;
Unit 4: Transparency (Explainability): Importance of explainability in AI
systems, Case studies; Accuracy-interpretability tradeoff in machine
4 learning; Different types of interpretability approaches: Rule-based, 10
Prototype-based, Feature importance-based, post-hoc explanations.

Unit 5:
FATE incorporation in AI designing systems, Issues, Effectiveness,
5 12
Responsible AI, Algorithm Inclusivity and Accessibility, Real World use
case examination.
Total 48
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of
No. Publication /
Reprint
Textbooks
1. Aileen Nielsen, Practical Fairness: Achieving Fair and 1st 2021
Secure Data Models, O'Reilly Media, Inc
2. Solon Barocas, Moritz Hardt, Arvind Narayanan, 1st 2019
Fairness And Machine Learning Limitations and
Opportunities

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering Privacy and Security in

1. Subject Code: TCS 893 Course Title: Online social media

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

Architecture
4. Relative Weight: CIE 25 MSE 25 SEE 50
5. Credits: 3

6. Semester: 8th

7. Category of Course: DE

8. Pre-requisite: TCS 693 Full Stack Web Development

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Understand the security and privacy concerns in Online social media.
CO2: Develop Secure Web Applications
CO3: Understand the Architecture of Web and working with social media
APIs.
CO4: Perform social media analysis and visualization using various
tools and techniques.
CO5: Know the various cases and data protection laws.
CO6: Analyze the security parameters in social media.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10.Details of the Course:

SL. Contact
Contents
NO. Hours
Unit - I

1 12

Unit – II
2 10
Social Media Attacks-Phishing, Reconnaissance, Fake Profiles, Social
Engineering, Fake News, Profile Compromise

Unit – III

3 10

Unit – IV

4 10

Unit – V
5 8

Total 50
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Social Media Security: Leveraging Social Networking While 1st 2017
Mitigating Risk 1st Edition by Michael Cross

Reference Books

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering E-Privacy: Privacy and

1. Subject Code: TCS 894 Course Title: Trust in Electronic
Society
2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50
Architecture
5. Credits: 3

6. Semester: 8th

7. Category of Course: DE

8. Pre-requisite: TCS 332 Fundamentals of Information Security and Block

Chain

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Explain e-privacy and trust.

CO2: Know the working of e-privacy mechanisms.

CO3: Analyze the various mechanisms of e-privacy.

CO4: Use e-privacy and trust to maintain the privacy of an information

system.

CO5: Apply e-privacy in various applications.

CO6: Develop strategies of e-privacy.

** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

SL. Contact
Contents
NO. Hours
Unit - I
Overview
1 e-privacy, information security, CIA triad, vulnerability, threats, attacks, 12
trust, computation of trust, privacy- an issue of global concern,
technology and rising concern over privacy, a new theory of privacy
Unit – II
2 8
Privacy theories and reconstructing
Methods of conceptualizing, conceptions of privacy, feasibility of privacy
conceptualizing, privacy- method, generality, variability and focus
Unit – III
Value of privacy
3 8
Virtues and vices of privacy, theories of valuation of privacy, social value
of privacy, privacy’s pluralistic value
Unit – IV
Taxonomy of privacy
4 8
Information collection, information processing, information dissemination,
invasion.
Unit – V
Privacy benefits and future
5 10
Nature of privacy problems, privacy and society, privacy and cultural
difference, benefits of a pluralistic conception of privacy, future of privacy
Total 46
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Daniel J. Solove, "Understanding Privacy", Harvard university 1st 2008
press..

2 Peter Carey, Eduardo Ustaran, “E-privacy and Online Data 1st 2002
Protection”, Tottel publishing.
3 Michael E. Whitman and Herbert J. Mattord, Principles of 2nd 2007
Information Security, (2e), Thomson Learning, 2007

Reference Books
2 Ronald Leenes, Rosamunde van Brakel, Serge Gutwirth, 1st 2017
Paul de Hert, "Data Protection and Privacy: The Age of
Intelligent Machines", Hart Publishing, 2017
3

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam
GRAPHIC ERA (DEEMED TO BE UNIVERSITY), DEHRADUN
SEMESTER VIII

Name of Department: - Computer Science and Engineering

Crypto assets and crypto
1. Subject Code: TCS 895 Course Title: economy.

2. Contact Hours: L: 3 T: 0 P: 0

3. Examination Duration (Hrs): Theory 3 Practical 0

4. Relative Weight: CIE 25 MSE 25 SEE 50

5. Credits: 3

6. Semester: 8th

7. Category of Course: DE

8. Pre-requisite: TCS 332 Fundamentals of Information Security and Block Chain

9. Course After completion of the course the students will be able to:
Outcome**: CO1: Explain fundamentals of crypto assets and crypto.
CO2: Know the working mechanism of crypto assets.
CO3: Analyze the different mechanism of crypto assets and crypto economy.
CO4: Use blockchain mechanism to maintain crypto assets.
CO5: Apply crypto assets in crypto economy applications.
CO6: Develop policies of crypto economy.
.
** Describe the specific knowledge, skills or competencies the students are expected
to acquire or demonstrate.

10. Details of the Course:

Sl. Contact
Contents
No. Hours
Unit 1:
Overview of distributed ledger technology:
1 6
Details of blockchain, distributed ledger technology, its working mechanism and
usability, security of crypto assets
Unit 2:
Overview of crypto assets:
Different types of crypto assets and their use, cryptocurrencies and non- currency
assets-tokens and utility tokens, characteristics of cryptocurrencies- borderless,
censorship-free, greater financial control, greater security, lower costs, greater
2 8
accessibility

Working mechanism of crypto assets:

Mechanism of bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), ripple (XRP), zcash
(ZEC), bitcoin cash, Ethereum classic, stellar lumen (XLM)
Unit 3:
Overview of crypto economy:
3 Introduction to crypto economy, past, present, and future of crypto economy, the 10
institutional economics of blockchain, the universal turing institution, the micro
foundations of ledgers.
Unit 4:
Advanced topics of crypto economy:
4 10
Money, dequity, and the barter economy of the future, supply chains and identity,
the V-form organization, and the future of the firm.
Unit 5:
Ethics and issues:
5 4
Ethics and issues in crypto economy, capitalism, policy in blockchain era,
capitalism after Satoshi.
Total 46
11. Suggested Books:
SL. Name of Authors/Books/Publishers Edition Year of Publication
No. / Reprint
Textbooks
1. Chris Berg, Sinclair Davidson, and Jason Potts, 1st 2019
Understanding the Blockchain Economy- An Introduction
to Institutional Crypto economics, Edward Elgar
Publishing, (1e) 2019.
2. 2nd
Imran Bashir,” Mastering Blockchain”: Distributed Ledger
Technology, decentralization, and smart contracts
explained”, 2nd edition, Packt publishing
Reference Books
1. 1st 2020
George Icahn, “the complete guide to understand the
blockchain technology”, 2020

12. Mode of Evaluation Test / Quiz / Assignment / Mid Term Exam / End Term Exam