Indian Institute of Information Technology Bhopal

Department of Computer Science & Engineering

st
1 Sem

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- First Year- First
Course Name Engineering Mathematics-I (Calculus)
Course Code CSE-1001
Compulsory/Elective Compulsory
/Open Elective
Prerequisites

N/A
Course Learning Objectives
1. To understand the approximations and effectively utilize the approximation tools in various
mathematical and scientific contexts.
2. To incorporate the knowledge of calculus in engineering applications.
3. To apply the principles of integrals to solve problems related to areas of bounded regions and
lengths of curves.
4. To form and solve ordinary differential equations with engineering applications.
Course Content

Module 1. Single variable calculus: Linear and Quadratic approximations, Error estimates,
Taylor's Theorem, Infinite series, Tests of convergence, Absolute and Conditional convergence,
Taylor and Maclaurin series.

Module 2. Multivariable calculus: Partial derivatives, Chain rules, Implicit differentiation,

Gradient, Directional derivatives, Total differential, Tangent planes and Normal, Mean value
theorem, Rolle’s Theorem, Lagrange Mean Value Theorem, Maxima and Minima, Lagrange
Method of Undetermined Multipliers, Curve sketching.

Module 3. Integral Calculus with Applications: Simple definite integrals, Fundamental

theorems of calculus, Multiple Integrals with Two and Three Variables, Applications of Integral
to find areas and volumes, Change of variables, Beta and Gamma Function, Reduction formulae,
Area of bounded regions, length of the curves.

Module 4. Ordinary Differential Equation: Concept of Order and Degree of Ordinary

Differential Equation, Formation and Solution of Differential Equation of First Order and First
Degree,
Module 5. Linear Differential Equation of Higher Order: with Constant Coefficients,
Solution of Simultaneous Differential Equation, Solution of Differential Equation of Second
Order with Variable Coefficients, Method of Parameter Variation with Applications.

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Course Outcomes
At the end of this course, the students will be able to:
CO 1: Apply the concept of approximations and distinguish between absolute and conditional
convergence.
CO 2: Identify extreme values of functions and interpret the engineering problems.
CO 3: Apply integration principles to solve problems involving functions of multiple variables
and analyze various physical and mathematical phenomena.
CO 4: Model simple physical problems as differential equations analyze and interpret the
solutions.
List of Text Books

1. Erwin Kreyszig, Advanced Engineering Mathematics, 10th Edition, USA, John Wiley &
Sons, INC, 2011.
2. B. S. Grewal, Higher Engineering Mathematics, 43rd Edition. New Delhi, Khanna
Publishers, 2015.
3. H. K. Dass, Advanced Engineering Mathematics, New Delhi, S Chand Publishers, 2019
4. B V Ramana, Higher Engineering mathematics, Noida, Mcgraw Hill Education, 2017.
List of Reference Books
1. Richard Bronson and Gabriel Costa, Schaum's Outline of Differential Equations (Schaum's
Outlines), 4th Edition McGraw Hill Education, 2014
2. James Stewart, Multivariable Calculus, Seventh Edition, Brooks/Cole, Cengage Learning,
USA, 2012

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- First Year- First
Course Name Engineering Physics
Course Code CSE-1002
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To impart knowledge in basic concepts of Physics relevant to engineering applications.
2. To introduce the idea of Einstein's special relativity.
3. To familiarize with the working principle of Laser and optical fiber and their applications.
Course Content
Module 1. Wave Optics: Introduction to Interference, Interference in thin films (due to
reflected and transmitted light), Newton’s ring, Michelson’s Interferometer, Diffraction,
Fraunhofer’s Diffraction due to single slit, Double slit, Missing order, N-slit (Diffraction
Grating).
Module 2. Solid State and Semiconductor Physics: Free electron theory, Band theory of
solids, Effective mass, Fermi energy and Fermi level in intrinsic and extrinsic semiconductors,
P- N junction diode, Photodiode, Solar cell, Hall effect and its applications, Transistor,
Transistor parameters. Introduction to superconductors, Meissner effect, Type-I & Type-II
superconductors and applications.
Module 3. Quantum Physics: Introduction to quantum mechanics, De-Broglie hypothesis,
Properties of matter waves, Concept of wave packet, Heisenberg’s uncertainty principle, Wave
function and its properties, Schrodinger's equation (Time dependent & Time independent),
Particle in a box, Potential barrier and Quantum tunneling.
Module 4. Nuclear Physics: Nuclear properties, Mass defect, Binding energy, Fission and
Fusion, Particle accelerators: Linear accelerator, Cyclotron, Betatron, Geiger-Muller (GM)
counter.
Module 5. Laser and Fiber Optics: Introduction to Laser, Absorption and emission process,
Einstein’s A & B coefficient, Pumping schemes, Characteristics of Laser, Ruby Laser, He-Ne
Laser and applications, Fundamental idea of optical fiber, Acceptance angle & cone, Types of
Fiber, Numerical aperture, V-Number, Losses in optical Fiber, Attenuation and applications.

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Department of Computer Science & Engineering
Module 6. Theory of Relativity: Introduction, Frame of references, Michelson-Morley
Experiment, Postulates of theory of relativity, Galilean transformation, Lorentz transformation,
Length contraction, Time dilation, Variation of mass with velocity, Mass-Energy equivalence
relation.
Course Outcomes
At the end of this course, students will be able to:
CO 1 : Apply an understanding of these concepts to various systems and devices.
CO 2 : Interpret the microscopic behavior of matter with quantum mechanics.
CO 3 : Implement optical phenomena like Interference and Diffraction in various fields.
CO 4 : Illustrate the concept of Superconductors and formation of energy bands also
classifies solids on its basis.
List of Text Books
1. David J. Griffiths, Darrell F. Schroeter, “Introduction to Quantum Mechanics”, Third
edition, 2018.
2. H. K Malik, A. K. Singh, “Engineering Physics”, Second edition, McGraw Hill, 2018.
3. S. Chandra, M. K. Sharma, “A Textbook of Optics” Ane Books, 2018.
4. N. Subrahmanyam, Brijlal Subramanyam “A Textbook of Optics” Revised edition, S.
Chand 2017.
5. M.N. Avadhanulu, P.G.Kshirsagar, “A Textbook of Engineering Physics”, Third edition, S.
Chand, 2014.
List of Reference Books
1. K.S. Krane, “Introductory Nuclear Physics”, Third edition, Wiley, 2022.
2. D.A. Cardwell, D.C.Larbalestier, A. Braginski “Superconductivity” Second edition, CRC
Press 2022.
3. C. Kittel, “Introduction to Solid State Physics”, Eighth edition, Wiley, 2019.
4. S.O. Pillai, “Solid State Physics” Tenth edition, New age international,2022.
5. A. Ghatak, “Optics”, Seventh edition, McGraw Hill, 2020.
6. J.H. Smith, “Introduction to Special Theory of Relativity” Revised edition, Dover
Publications, 2016.
7. J. Lilley, “Nuclear Physics: Principles and applications”, Wiley, 2016.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- First Year- First
Subject Name Fundamentals of Computer Programming
Course Code CSE-1003
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To understand the need to learn new languages to solve complex problems in different
domains.
2. To provide fundamentals of computers and understand the use of software, compiler, and
programming language.
3. To understand the basic concepts of input, output, control statements, arrays, strings,
functions, pointers, and structures for problem-solving using programming.
Course Content
Module 1. Fundamentals of Programming: Brief History of Computing and Computers,
Basic Organization of Computer, Representing Information as Bit Patterns, Number System,
Basics of Computer Languages, Generation of Programming Languages, Compilers, Interpreter,
Programming Environments and Debugging, Types of Errors and Debugging Techniques,
Problem-Solving Aspects, Introduction to Algorithms, Flowcharts, Pseudocode.
Module 2. Basic Programming and Control Statements: Structure of C Program, Life Cycle
of Program from Source Code to Executable, Keywords, Identifiers, Primitive Data Types in C,
Variables, Constants, Input/Output Statements in C, Operators, Type Conversion and Type
Casting, Conditional Branching Statements, Iterative Statements, Nested Loops, Break and
Continue Statements.
Module 3. Modular Programming and Recursion: Functions, Declaration, Definition, Call
and Return, Call by Value, Call by Reference, Showcase Stack Usage with help of Debugger,
Scope of Variables, Storage Classes, Recursive Functions, Recursion vs Iteration.
Module 4. Array-based Programming: Arrays, One-dimensional, Two Dimensional, and
Multidimensional Arrays, Operations on Array, Traversal, Insertion, Deletion, Merging and
Searching, Inter-Function Communication via Arrays, Passing a Row, Passing the Entire Array,
Matrices, Strings, Read Operation, Writing and Manipulating Strings.

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Department of Computer Science & Engineering
Module 5. C Pointers, DMA and Structure: Pointers, Understanding Computer Memory,
Accessing via Pointers, Pointers to Arrays, Drawback of Pointers, Dynamic Memory
Allocation, Structures, Unions.
Module 6. File in C Programming: File Handling, File Redirection, File Pointers,
Preprocessor, Library Functions, Low-Level Programming.
Course Outcomes
At the end of this course, the students will be able to:
CO 1: Understand the basic terminology and program structures used in computer
programming to solve real-world problems.
CO 2: Apply the process of representing problems and writing, compiling, and debugging
programs.
CO 3: Develop programming skills in using different data types, decision structures, loop
functions, pointers, data files, and dynamic memory allocation/deallocation.
List of Textbooks
1. Byron S Gottfried, Programming with C, 4th Edition, (Schaum's Outlines) Paperback,
McGraw Hill Education, 2018.
2. Herbert Schildt, C: The Complete Reference, 4th edition, McGraw Hill Education (India)
Private Limited, Noida, Uttar Pradesh, 2017.
3. E. Balaguruswamy, Programming in ANSI C, Eighth edition, McGraw Hill Education
(India) Private Limited, Noida, Uttar Pradesh, 2019.
List of Reference Books
1. Kernighan, B.W. and D. M. Ritchie, The C Programming Language, 2nd ed., Pearson
Education India, 2015.
2. Yashavant Kanetkar, Let Us C: Authentic Guide to C Programming Language (18th
Edition), BPB Publications, India, 2021.
3. King K. N, C Programming: A Modern Approach, 2nd ed., W. W. Norton & Company,
2008.

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
Name of Program B. Tech. Semester- First Year- First
Course Name Fundamentals of Electrical and Electronics

Course Code CSE-1004

Compulsory/Elective/ Compulsory
Open Elective

Prerequisites
N/A
Course Learning Objectives
1. To understand the fundamental components, their connections and their usefulness for
electrical and electronics circuits.

2. To introduce the basics of D. C. circuits, A.C. circuits, and transformers in circuit analysis.

3. To provide the fundamentals of semiconductors, semiconductor component physics and

digital logic for electronic circuit design.

Course Content
Module 1. D.C. Circuits: Voltage and Current Sources, KCL, KVL, Loop and Nodal
Equations, Network Theorems. Star Delta Transformations, Simple Series and Parallel Circuits.
Module 2. A.C. Circuits: Alternating Quantities, RMS and Average Value, Phase, Phase
Difference, Power and Power Factor, Series and Parallel AC Circuits, Resonance, Faraday’s
Law of Electromagnetic Induction.
Module 3. Transformers: Construction, Principle of Operation, Phasor Diagrams, Equivalent
Circuit, Tests, Losses and Efficiency.
Module 4. Semiconductor Devices and Applications: Characteristics of PN Junction Diode,
Zener Effect, Zener Diode and its Characteristics, Half Wave and Full Wave Rectifiers, Ripple
Factor, Conversion Efficiency. Bipolar Junction Transistor: Principle of Operation,
Input/Output and Transfer Characteristics of BJT in CB, CE, and CC Configurations.
Module 5. Introduction to Digital Logic: Number systems, Boolean Algebra, Boolean
Theorems, Logic Gates, Introduction to Combinational and Sequential Circuits.

Course Outcomes
At the end of this course the students will be able to:
CO 1: Understand circuit designing by using basic components and sources and their analysis
by using network theorems.

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CO 2: Learn DC circuits, AC circuits, transformers and their relevant fundamental
terminologies and importance for real world applications.
CO 3: Explain the physics of p-n junction, the function and transport characteristics of the p-n
diode and the bipolar transistor.
CO 4: Describe digital logic, boolean algebra, logic gates and digital system design.
List of Text Books

1. Boylestad Robert, and Nashelsky Louis, Electronic Devices and Circuit Theory,United
Kingdom, Pearson, 2013.
2. Rauf, S. Bobby, Electrical Engineering Fundamentals, United States, CRC Press, 2020.
3. Albert Malvino, Electronic Principles,7th Edition, McGraw-Hill Education (India) Pvt
Limited, 2007.
List of Reference Books
1. Nagsarkar, T. K., and Sukhija, M. S., Basic Electrical Engineering, India, Oxford University
Press, 2017.
2. Nahvi, Mahmood, and Edminister, Joseph, Schaum's Outline of Electric Circuits, 7th Edition.
United Kingdom, McGraw-Hill Education, 2017.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- First Year- First
Course Name Professional Communication
Course Code CSE-1005
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To develop communication competence in prospective engineers.
2. To provide a learning environment to practice listening, speaking, reading and writing
skills.
3. To develop the ability to comprehend text in various contexts.
Course Content
Module 1. Communication Skill: Introduction to Communication Skills, Concept of
communication, Types of Communication, Communication cycle, Barriers to effective
communication, Verbal v/s nonverbal communication, 7 C’s of Communication.
Module 2. Interpersonal and Technology Based Skill: Interpersonal skills, Time
management, Team building, Leadership skills, Emotional Intelligence, Self Development and
Self Assessment, Technology based Communication, Effective email messages, slide
presentations, editing skills using software, Modern day research and study skills, Search
engines, Repositories, Forums.
Module 3. Written Communication: Concept of word formation, Introduction to colloquial
language, Common Errors in Writing, Writing Practices: Reading and comprehension,
Summary Writing, Business Letter Writing (Inquiry, Complaint), Critical thinking and analysis,
Technical writing (definition and description), Job Application - resume and cover letter.
Module. 4 Listening and Speaking Skill: Listening process, Importance, Barriers and tips to
effective listening, Intensive listening for specific information, To answer, and to understand.
Pronunciation intonation stress and Rhythm, Public speaking, Non-verbal aspects of speaking,
Accent, Characteristics, Types, Paralanguage (voice, tone, volume, speed, pitch, effective
pause), Audience analysis.
Module. 5 Essentials of Group Discussions /Presentation: Differences between Group
discussion and debate, questioning and clarifying, GD strategies, Activities to improve GD
skills, Mode of Presentation, Purpose, Content, Body language in effective presentation, Time

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Department of Computer Science & Engineering
dimension. Interviews and its types, Interview etiquette, Dress code, Attending the interview,
Interview process.
Course Outcomes
At the end of this course students will be able to:
CO 1 : Communicate effectively in public with an increase in their confidence.
CO 2 : Implement interpersonal skills in real life situations, including active listening, verbal
and non-verbal skills.
CO 3 : Learn skills used in real-world situations (Presentations, Interviews, Group
discussions, Leadership and Teamwork).
List of Text Books
1. N. Konar, “Communication Skills for Professionals”, Third edition, PHI learning,2022
2. R.S. Salaria, K.B.Kumar, “Effective Communication Skills” Khanna publishing house,
2016
3. K.C. Verma “The art of Communication” Kalpaz publications, 2013
4. A.K. Jain, P.S.R. Bhatia, A.M. Sheikh, “Professional Communication Skill” Chand, 2008
List of Reference Books
1. D. Fine “The Fine Art of Small Talk” Hachette Books,2023
2. I. Tuhovsky “Communication Skills Training” Create Space Independent Publishing
Platform,2015
3. Ajay Singh, “Proficiency in reading Comprehension”, Arihant Publication, 2018
4. R. Almonte, “A practical guide to soft skills communication psychology and ethics for your
professional life” First edition Taylor & Francis Ltd.,2021
5. Barun K. Mitra, “Personality Development and Soft Skills”, Oxford University Press, 2016.
6. W.Sanborn and T.V.S Padmaja, “Technical Communication: A Practical Approach”, Sixth
edition, Delhi: Pearson, 2007

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- First Year- First
Course Name Health & Yoga
Course Code CSE-1006
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To impart the students with basic concepts of Yoga, different asana, Pranayama and its
benefits.
2. To facilitate the students with the importance of yoga for health and sports.
3. To expose the students to a variety of physical and yogic activities.
4. To understand the importance of nutrition and a balanced diet in maintaining a healthy
lifestyle.
Course Content
Module 1. Introduction to Yoga: Origin of Yoga, History of yoga in Indian context, Definition,
Characteristics, Objectives of Yoga, Misconceptions, Importance and development,
Classification of yoga, Karma Yoga, Bhakthi yoga, Raja yoga/Ashtanga yoga etc., Asanas and
Pranayam, Suryanamsakar, Methods and benefits.
Module 2. Yoga for Health: Brief introduction of Yoga for health, Concepts of health and
fitness, Holistic approach of yoga towards the health and diseases, Balanced diet and its
importance in Yog sadhana, Meditation, Basic concepts of immunity, Effect of yoga on
Physiological System, Circulatory, Skeletal, Digestive, Nervous, Respiratory, Excretory
system.
Module 3. Sports and Fitness: Concepts of sports and fitness, Importance, Difference
between games and sports, History of sports, Application of Yoga for specific types of sports
(Target Sports/Sports using one side of body/ Endurance Sports/Strength and Balance
Sports/Team Sports), Role of yoga in Psychological preparation of athlete, Mental wellbeing,
Anxiety, Depression concentration, Self actualization.
Module 4. Safety Measures and Precautions: Resistance training for muscular strength and
Endurance, Principles of resistance training, Safety techniques (Spotting, proper body
alignment, Lifting techniques, Spatial, Awareness and Proper breathing techniques).
Course Outcomes
At the end of this course, students will be able to:

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Department of Computer Science & Engineering
CO 1 : Understand the foundation and background of Yoga.
CO 2 : Describe the role of sports and fitness in personality development.
CO 3 : Understand the benefits and effects of Kriyas, Bandhas, and Pranayama.
CO 4 : Continue professional courses and research in yoga.
List of Text Books
1. Pamela Seelig, “Threads of Yoga: Themes, Reflections, and Meditations to Weave into
Your Practice”, Shambhala, 2021.
2. A. Swanson, “Science of Yoga: Understand the Anatomy and Physiology to Perfect your
Practice” DK ,2019
3. A. K. Malhotra “An Introduction to Yoga Philosophy”, Taylor & Francis,2017
4. Ashwani Kumar,“Yoga: A way of life”, New Delhi: Khel Sahitya Kendra ,2015.
List of Reference Books
1. S. Vivekananda “Meditation-And-Its-Methods by Swami Vivekananda: Meditation and Its
Methods - Exploring the Practice of Meditation” Kindle edition, Prabhat Prakashan,2021
2. N. Bachman, The Path of the Yoga Sutras, JAICO Publishing House, 2016
3. S.Sachidananda,“The yoga sutras of Patanjali” Integral yoga Publications 2012

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Department of Computer Science & Engineering

2nd Sem

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Second Year- First
Course Name Engineering Mathematics-II (Linear Algebra and Differential
Equations)
Course Code CSE-2001
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Engineering Mathematics-I(CSE-1001)
Course Learning Objectives
1. To apply fundamental concepts of linear algebra to solve systems of linear equations and
perform matrix operations efficiently.
2. To comprehend the functions of complex variables to evaluate integrals over singularities.
3. To understand the physical interpretations of vectors and their significance in various
contexts.
4. To solve various types of partial differential equations.
Course Content
Module 1. Linear Algebra: Review of Matrices, Rank, Row-reduced Echelon form, Test of
consistency and Solution of system of linear equations using matrices, Eigenvalues and
Eigenvectors, Diagonalization of a matrix,
Module 2. Algebra of Vector spaces: Linearly dependent and independent vectors, Subspaces,
basis, Orthogonal basis, Gram-Schmidt Orthogonalization, Linear Operators, Matrix
representation of Vector spaces.
Module 3. Complex Analysis: Functions of a Complex Variable, Analytical functions, Cauchy
Reimann equations, Elementary functions, Contour integrals, Cauchy's Theorem, Residue
Theorem, Power series, Taylor and Laurent series, zeros, poles, essential singularities,
evaluation of integrals.
Module 4. Vector Calculus: Vector fields, Divergence and Curl, Line Integrals, Green's
Theorem, Surface Integrals, Divergence Theorem, Stoke's Theorem and applications.
Module 5. Partial Differential Equation: Linear & Non-Linear Partial Differential Equation
of First Order, Homogeneous & Non-Homogeneous Linear Partial Differential Equation with
constant coefficient of Higher Order, Separation of Variables.

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Department of Computer Science & Engineering
Course Outcomes
At the end of this course, the students will be able to:
CO 1: Use matrices to represent and solve systems of linear equations and understand the
relationship between linear transformations and matrix operations.
CO 2: Understand functions of complex variables and their applicability in different
engineering fields.
CO 3: Apply vector calculus in practical applications and problem-solving.
CO 4: Apply the acquired knowledge to model and analyze real-world problems involving
partial differential equations.
List of Text Books
1. K. Hoffman, R. A. Kunze, Linear Algebra, 2nd Edition, PHI Learning, 2018.
2. Erwin Kreyszig, Advanced Engineering Mathematics, 10th Edition, USA, John Wiley &
Sons, INC, 2011.
3. B. S. Grewal, Higher Engineering Mathematics, 43rd Edition. New Delhi, Khanna
Publishers, 2015.
4. H. K. Dass, Advanced Engineering Mathematics, New Delhi, S Chand Publishers, 2019
5. B V Ramana, Higher Engineering mathematics, Noida, Mcgraw Hilal Education, 2017.
List of Reference Books
1. Seymour Lipschutz and Marc Lipson, Schaum's Outline of Linear Algebra, 3rd Indian
Edition, McGraw Hill Education, pp. 432, 2017.
2. M. R. Spiegel, S. Lipschutz, J. J. Schiller and D. Spellman, Schaum’s Outline of Complex
Variables, 2nd Edition, McGraw Hill Education, pp. 384, 2009.
3. Peter V. O' Neil, Advanced Engineering Mathematics, 7th Edition, Cengage Learning
Book/Cole, pp.913, 2012.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Second Year- First
Course Name Discrete Structure
Course Code CSE-2002
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Engineering Mathematics – I (Calculus) (CSE-1001)
Course Learning Objectives
1. To develop critical thinking and problem-solving skills through various exercises and
assignments.
2. To apply discrete mathematics concepts in other fields, such as computer science,
engineering, and data science.
Course Content
Module 1: Sets: Methods for describing a set, e.g., listing elements, set builder notation, Venn
diagrams, Union, intersection, set difference, complement, Cartesian product, Power sets,
Cardinality of finite sets, Inclusion-exclusion principle, Proof Technique: Mathematical
Induction.

Module 2. Relations: Reflexivity, Symmetry, Antisymmetry, Transitivity, Equivalence

relations, Equivalence class. Functions: Domain, Target, Range/Image of a function,
Surjections, Injections, Bijections, Inverses, Composition, Partial Ordering sets, Linear
Ordering, Hasse Diagrams, Maximum and Minimum elements, Lattices, The pigeonhole
principle, Algebraic Structure: Group, Ring, Field.

Module 3. Basic of Logic: Propositional logic, Logical connectives, Truth tables, Tautology
and Contradiction, Disjunctive normal form, Conjunctive normal form, Validity of a well-
formed formula, Propositional inference rules (e.g., modus ponens, modus tollens), Universal
and existential quantifiers and their negations.

Module 4. Trees and Graphs: Properties of tree, Traversal strategies. Introduction to graphs
and their basic properties: Degree, Path, Cycle, Subgraph, Isomorphism, Eulerian and
Hamiltonian walk, Planar graph. Undirected graphs, Directed graphs, Weighted graphs,
Spanning tree, Spanning tree algorithms, Forests, Graph isomorphism. Graph coloring,
Covering, and Partitioning. Bipartite graph, Chromatic number, Chromatic Partitioning,
Chromatic Polynomial, Matching, Covering, Four color problem.

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Department of Computer Science & Engineering
Module 5. Recurrence Relations: Definition of recurrence relations, Formulating recurrence
relations, Linear homogeneous recurrence relations with constant coefficients, Solving linear
homogeneous recurrence relations with constant coefficients of degree two when characteristic
equation has distinct roots and only one root, Particular solutions of nonlinear homogeneous
recurrence relation, Solution of recurrence relation by the method of generation functions.

Course Outcomes
At the end of this course the students will be able to:
CO 1: Describe the basic principles of sets and operations in sets.
CO 2: Determine the properties of relations and functions.
CO 3: Define basic notions in graph theory and chromatic graph theory
CO 4: Demonstrate different traversal methods for trees and graphs.
List of Text Books
1. Rosen, Kenneth H., Krithivasan, Kamala. Discrete Mathematics and Its Applications.
Singapore: McGraw-Hill, 2013.
2. Richard Johnsonbaugh, Discrete Mathematics, 8th Edition. DePaul University, Chicago,
Pearson, 2017.
3. C. L. Liu, Elements of Discrete Mathematics (SIE), 3rd Edition. Tata McGraw Hill India,
2008.
List of Reference Books
1. Oscar Levin, Discrete Mathematics: An Open Introduction. Independently Published, 2018.
2. Norman Biggs, Discrete Mathematics, Illustrated Reprint Edition. OUP Oxford, 2002.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Second Year- First
Subject Name Digital Logic and Design
Course Code CSE-2003
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Fundamentals of Electrical and Electronics (CSE-1004)
Course Learning Objectives
1. To discuss the basic knowledge of gates, number systems and their Arithmetic, Computer
Codes.
2. To analyze the principles of combinational logic circuits, including their design and its
operation.
3. To describe sequential logic circuits, encompassing flip-flops, state transitions, and timing
analysis.
4. To evaluate the performance and characteristics of various logic families in practical circuit
designs.
Course Content
Module 1. Number System and Codes: Number system & Boolean algebra, number systems:
Binary, Arithmetic, Octal, Hexadecimal & radix conversion. Binary codes: BCD, excess three,
Gray display ASCII, EBCDIC, Parity check codes, Code conversion. Boolean algebra:
theorems, Introduction to logic gates, NAND, NOR realization, Boolean laws & theorems.
Simplification of Boolean expression, Sum of product & Product of sum forms, concept of
minterms & maxterms, minimization techniques, Karnaugh’s MAP method, Tabulation method.
Module 2. Combinational logic circuits: Combinational circuits, Half adder, Full adder,
Subtractor, BCD adder, Multiplexer & demultiplexer, Encoder & Decoder circuits.
Module 3. Sequential logic circuits with their applications: Storage elements: latches & Flip
Flops, Characteristic Equations of Flip Flops, Flip Flop Conversion, Shift Registers, Ripple
Counters, Synchronous Counters, Other Counters: Johnson & Ring Counter.
Module 4. Synchronous & Asynchronous Sequential Circuits: Analysis of clocked
sequential circuits with state machine designing, State reduction and assignments, Design
procedure, Analysis procedure of Asynchronous sequential circuits, Circuit with latches.
Module 5. Digital Logic Families with circuit implementation: Introduction to logic families,
RTL, DTL, TTL, ECL, NMOS, NCMOS, logic, etc.

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Course Outcomes
At the end of this course the students will be able to:
CO 1. Apply concepts of Digital Binary System and implementation of Gates.
CO 2. Describe principles of combinational logic circuits, including logic gates, truth tables,
and design techniques.
CO 3. Explain sequential logic circuits, encompassing flip-flops, state machines, and design
techniques.
CO 4. Discuss synchronous and asynchronous sequential circuits, including their design,
operation, and practical applications.
CO 5. Demonstrate the ability to synthesize and evaluate digital logic components.
List of Textbooks
1. Tocci, Ronald J., Widmer, Neal S., Moss, Gregory L., Digital Systems: Principles and
Applications. Germany: Prentice Hall, 2017.
2. M.Morris Meno, Digital Logic and Computer Design, Pearson Education India, 2017.
List of Reference Book
1. A.K.Maini, Digital Electronics Principles and Integrated Circuits, Wiley India,2019.
2. Floyd and Jain, Digital Fundamentals, Pearson Education, 2013.
3. RP Jain, Modern Digital Electronics, Publisher, McGraw-Hill Education (India), 2010.

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Name of Program B. Tech. Semester- Second Year- First

Course Name Data Structure and Algorithms
Course Code CSE-2004
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Fundamentals of Computer Programming (CSE-1003)
Course Learning Objectives
1. To explain how to analyze the time and space complexity of an algorithm and how to choose
the most efficient algorithm for a given problem.
2. To develop an understanding of basic data structures, algorithms, and their underlying
principles.
Course Content
Module 1. Introduction: Abstract data types, data representation, elementary data types, basic
concepts of Data Structures, algorithm analysis and asymptotic notations, function, Recursion-
linear, binary, and multiple recursions.
Module 2. Primitive and non primitive data types: Arrays, types of Arrays, Sparse Matrices,
Structure, Pointers, Stacks: representation of Stacks and operations, applications of Stacks,
Prefix, Postfix and Infix notations and conversion, Recursion, Towers of Hanoi. Queues: Types
of Queue and its application. Linked lists: Types of Linked list, implementation of Stack and
Queue using Linked list.
Module 3. Tree: Binary Trees, Binary search trees, Tree traversal, Expression manipulation,
Symbol table construction, Height balanced trees, Red-black trees.
Module 4. Graphs: Graphs representations, Depth first and Breadth first search algorithms,
Minimum spanning trees, Shortest path algorithms, Application of Graphs, Directed acyclic
graphs.
Module 5. Searching and Shorting: Sequential Search, Binary Search, and Hashing. Sorting:
External and Internal Sort, Selection Sort, Bubble Sort, Insertion Sort, Radix Sort, and Bucket
Sort.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Analyze the efficiency of an algorithm based on space and time complexity.

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Department of Computer Science & Engineering
CO 2: Able to choose appropriate data structures to represent data items.
CO 3: Understand different techniques for solving problems like sorting and searching.
List of Textbooks
1. Cormen, Thomas H.,Leiserson, Charles E.,Rivest, Ronald L.,Stein Clifford. Introduction to
Algorithms, 4th Edition. United States: MIT Press, 2022.
List of Reference Books
1. Reema Thareja, Data Structures using C,3rd Edition, Oxford University Press, 2023.
2. Ch. Rajaramesh C.V. Sastry, Rakesh Nayak, Data Structures and Algorithms, Dreamtech
Press, 2019.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Second Year- First
Course Name Object Oriented Programming
Course Code CSE-2005
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Fundamentals of Computer Programming (CSE-1003)

Course Learning Objectives

1. To learn about the data and its various instructions.
2. To learn about the parameters to decide access level modifiers to achieve appropriate level
of encapsulation
3. To learn the designing and implementation of programs where two or more classes interact.
Course Content
Module 1: Introduction to Object-oriented programming and its properties, Object-oriented
vs. Procedural programming, Pillars of OOPs: Encapsulation, Abstraction, Inheritance, and
Polymorphism, Data Types, Variables, Arrays, Operators, Control Statements, Programming
Structures
Module 2: OOPS concepts: Classes and Objects, creating classes and objects, accessing
members of class, accessing object properties and methods, constructors and destructors,
Default Constructor, Parameterized Constructor, Copy Constructor, The Default Copy
Constructor, Objects as Function Arguments, Returning Objects from Functions, Structures and
Classes, Memory allocation for Objects, Static members, Member functions defined outside the
class.
Module 3: Inheritance: Inheriting from a class, overriding methods, Fundamental of operator
overloading, Restriction on operator overloading, abstract classes, Polymorphism: Virtual
methods, Dynamic binding, virtual functions, Overloading unary and binary operator, Data
Conversion (basic to basic, basic to user-defined, user-defined to basic, user-defined to user-
defined)
Module 4: C++ Program, Program Features, Comments, Output Operators, I/O stream File,
Namespace, Return Type of main (), C++ Statements, Variable, Input Operator, Cascading I/O
Operator, Example with Class, Structure of C++, Creating Source File, Compiling and Linking.
Course Outcomes

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At the end of this course the students will be able to:
CO 1 : To understand how to use functions and pointers in C++ programs.
CO 2 : Understand the use of tokens, expressions, and control structures.
CO 3 : Understand the use arrays and strings and create programs using them.
List of Text Books
1. Deitel, Paul., Deitel, Harvey. C++20 for Programmers: An Objects-Natural Approach.
United Kingdom: Pearson Education, 2022.
2. Blokdyk, Gerard. Object-oriented Analysis and Design: A Comprehensive Primer. N.p.:
CreateSpace Independent Publishing Platform, 2018.
List of Reference Books
1. Stanley Lippman, Josée Lajoie , Barbara Moo, C++ Primer, 5th Edition,2012
2. Robert Lafore, Object Oriented Programming in C++, 4th edition, ,2001.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Second Year- First
Course Name Ethics and Human Values for Engineers
Course Code CSE-2006
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To give basic insights and inputs to the student to inculcate human values to grow as
responsible human beings with a proper personality.
2. To instill moral and social values and loyalty.
3. To develop a set of beliefs, attitudes and habits that engineers should display concerning
morality.
Course Content
Module 1. Basic Ethics: Introduction, Basic ethical principles, Oral developments, Essence
of ethics, Determinants and consequences of ethics, Ethical theories, Work ethic, Service
learning, Civic virtue, Respect for others, Living peacefully, Non violence, Valuing time,
Justice and responsibility, Living in harmony with self, Family, Society and nature, Empathy,
Self-confidence, Character, Spirituality.
Module 2. Human Values: Classification of values, Extrinsic values, Universal and
situational values, Environmental, Economic, Social, Aesthetic, Moral and religious values.
Human Dignity, Human Rights, Fundamental duties, The problem of hierarchy of values and
their choice.
Module 3. Engineering Ethics and Social Experimentation: History of Ethics, Need of
Engineering Ethics, Senses of Engineering Ethics, Profession and Professionalism, Types of
inquiry, Moral dilemmas, Moral Autonomy, Kohlberg’s theory, Gilligan’s theory, Consensus
and Controversy, Models of professional roles, Theories about right action, Engineering as
social experimentation, Framing the problem, Determining the facts, Codes of Ethics,
Clarifying Concepts, Application issues, Common ground, General principles, Case study.
Module 4. Safety, Responsibilities and Rights: Safety and risk, Assessment of safety and
risk, Risk benefit analysis and reducing risk, Respect for authority, Collective bargaining,
Confidentiality, Conflicts of interest, Occupational crime, Professional rights, Employee
Rights, Intellectual property Rights (IPR), Discrimination.
Module 5. Global Issues: Multinational corporations, Environmental ethics, Business ethics,
Media ethics, Computer ethics and research ethics.

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Course Outcomes
At the end of this course students will be able to:
CO 1 : Apply ethics in society, discuss the ethical issues related to engineering.
CO 2 : Realize the responsibilities and rights in society.
CO 3 : Develop knowledge about global issues.
CO 4 : Learn about the different professional roles.

List of Text Books

1. R.S. Naagarazan “A Textbook on Professional Ethics and Human Values” Third edition,
New Age International Pvt. Ltd, 2022.
2. K. A. Hite, J. L. Seitz “Global Issues: An Introduction”, Wiley-Blackwell, Sixth
edition,2021.
3. K. S. Verma “Human Values and Ethics for Engineers and Professional” Axiom Nutrifit,
2018.
4. C. Debangshu, “Human Values and Ethics” Himalaya Publishing House, 2016.
5. M. Govindarajan, S. Natarajan, V. S. Senthil Kumar, “Engineering Ethics”, Prentice Hall
of India, 2011.
List of Reference Books
1. B. G. Blundell “Ethics in Computing, Science, and Engineering” Springer International
Publishing, 2020.
2. Byars, Stephen M., Stanberry, Kurt “Business Ethics” United States: OpenStax, 2018.
3. P. A. Vesilind, A. S. Gunn “The Engineer's Responsibility to Society” Cengage Learning,
2015.

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Department of Computer Science & Engineering

3rd Sem

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Third Year- Second
Course Name Design and Analysis of Algorithms
Course Code CSE-3001
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Data Structure and Algorithms (CSE-2004)
Course Learning Objectives
1. To explain the importance of algorithm analysis and its role in designing efficient algorithms.
2. To explain the selection of appropriate data structures based on the requirements of a given
algorithm.
3. To explain the concepts of P, NP, NP-hard and NP-complete problems.
Course Content
Module 1. Introduction: Algorithm design paradigms, analysis of an algorithms, Asymptotic
Notations, Growth rate, average and worst case analysis, Recurrence Relations- substitution,
change of variables, master’s method
Module 2. Divide and Conquer Approach:Sets and disjoint sets, Union and Find algorithms,
Quick sort, Finding the maximum and minimum, Merge sort, Heap and Heap sort.
Module 3. Greedy Algorithms: Optimal storage on tapes, Knapsack problem, Job sequencing
with deadlines, Minimum Spanning trees: Prim’s algorithm and Kruskal’s algorithm, Huffman
codes.
Module 4. Dynamic Programming: Matrix chain multiplication, Traveling Salesman Problem,
longest Common sequence, 0/1 knapsack. Backtracking: 8-Queen Problem, Sum of subsets,
Graph Coloring, Hamiltonian cycles.
Module 5. Graph Algorithms: Representation of graphs, BFS, DFS, Topological sort, strongly
connected components, single source shortest paths: Bellmen-Ford algorithm, Dijkstra’s
algorithm, All pairs shortest path: The Warshall’s algorithm.
Module 6. Branch and Bound: LC searching Bounding, FIFO branch and bound, LC branch
and bound application: 0/1 Knapsack problem, Traveling Salesman Problem.
Computational Complexity: Complexity measures, Polynomial vs. non-polynomial time
complexity; NP-hard and NP-complete classes, examples.
Course Outcomes

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Department of Computer Science & Engineering
At the end of this course the students will be able to
CO 1: Understand how to analyze the performance of an algorithm.
CO 2: Understand and apply various problem-solving techniques.
CO 3: Know the concepts of P, NP, NP-hard, and NP-complete problems.
List of Text Books
1. Michael T. Goodrich, Roberto Tamassia,Design and Analysis of Algorithms, An Indian
Adaptation,Wiley Publisher, 2021
2. Soltys, Michael. An Introduction to the Analysis of Algorithms. Japan: World
Scientific, 2018.
List of Reference Books
1. Alsuwaiyel, M. H.. Algorithms: Design Techniques and Analysis. Singapore: World
Scientific, 2016.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Third Year- Second
Subject Name Database Management System
Course Code CSE-3002
Compulsory/Elective Compulsory
/Open Elective
Prerequisites
Discrete Structure (CSE-2002)
Course Learning Objectives
1. To understand fundamentals of data models and to conceptualize a database system for user
requirements.
2. To study the fundamentals of database query language, like SQL, relational algebra, and
concept of normalization in database design.
3. To learn fundamental concepts of transaction processing, concurrency control techniques
and database recovery procedure.
4. To understand the professional, ethical, security issues and responsibilities in database
design.
Course Content
Module 1. Introduction: DBMS Historical Perspective, File Versus a DBMS, Advantages of
DBMS, Architecture of DBMS, Data Independence, Database Languages & Interfaces, DDL,
DML, DCL, Database Administrator, Database Users, Different Data Models, Comparison of
Various Database Models, Protection, Security.
Module 2. Entity Relationship Model: Data Modeling using ER Model, Features of ER model,
Entities, Attributes and Relationships, Constraints, Entity Sets, Attributes Sets, Conceptual
Design using ER model, Design for Large Enterprises, Extended ER Model, Translating ER
Model into Tables.
Module 3. Relational Data Model and Query Language: RDBMS Concepts, Characteristics,
Schema, Constraints, Relational Algebra, Relational Calculus, Domain and Tuple Calculus, A
Relational Database Language, SQL, Creation and Basic Query Structure, Basic Operations,
Aggregate, Grouping, Having Clause, Exist, Set, Join, Division Operation, SQL Completeness,
Nested Subqueries, Query Optimization, Views and Triggers.
Module 4. Relational Database Design: Database Design Concept, Functional Dependencies,
Dependency Preserving, Decomposition, Lossless Join, Problems with Null Valued, Dangling
Tuples, Normalization for Relational Databases, Database Normalization: 1NF, 2NF, 3NF,
BCNF, Multivalued Dependency, 4NF, Join Dependency, 5NF.
Module 5. Concurrent Operations on Databases: Concepts of Transaction Processing,
Schedule, Transaction Failure, Recovery, Concurrency Control, Locking Based Protocols.

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Department of Computer Science & Engineering
Module 6. File Organizations and Indexing: File Organizations Techniques, Primary
Secondary Index Structures, Various Index Structures, Hash-based Indexing, Dynamic Hashing
Techniques, Multi-level Indexes, B+ Trees.

Course Outcomes:
At the end of this course the students will be able to:
CO 1: Demonstrate various database models and tools.
CO 2: Compare and contrast logical design methods of DBMS.
CO 3: Develop sophisticated queries to extract information from large datasets.
CO 4: Understand and evaluate the role of database management systems in software.
List of Text Books
1. Silberschatz, A., Korth, H. F., Sudarshan, S., Database System Concepts, United
Kingdom: McGraw-Hill Education, 2011.
2. Elmasri, R., Navathe, S., Fundamentals of Database Systems, United Kingdom: Pearson,
2016.
List of Reference Books
1. Feuerstein, S., Pribyl, B., Oracle PL/SQL Programming. Germany: O'Reilly, 2002.
2. McFadden, F. R., Hoffer, J. A., Prescott, M. B., Modern Database Management. Singapore:
Addison-Wesley, 1998.
3. Bayross I., SQL, PL/SQL The Programming Language of Oracle, BPB Publications (2009)
4th ed.
4. Hoffer J., Venkataraman, R. and Topi, H., Modern Database Management, Pearson (2016)
12th ed.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Third Year- Second
Course Name Computer Organization and Architecture
Course Code CSE-3003
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Digital Logic and Design (CSE-2003)
Course Learning Objectives
1. To understand the basics of computer hardware and how software interacts with computer
hardware.
2. To provide an overview of the design principles of digital computing systems
3. To gain a better understanding of how data is represented and manipulated by machines.
4. To understand how computations are performed at the level of the machine.
Course Content
Module-1. Overview of Computer Architecture and Organization: Contrast between
computer architecture and organization, Fundamentals of computer architecture, Organization
of von Neumann machine. Computers Classification: Micro, Mini, Mainframe and Super
Computer. System Bus and Interconnection, Structure of IAS. Fundamental Concepts of
Fetching and storing a word in Memory, Register Transfer.
Module-2. Computer Arithmetic and Machine Instruction: Control word, Stack
Organization, Register Stack, Memory Stack, Instruction Format: Three Address, Two Address,
One Address and Zero Address Instruction, Addressing Modes: Types of Addressing modes,
Numerical Examples, Program Relocation, Compaction.
Module-3. Data Transfer & Manipulation: Data transfer, Data Manipulation, Arithmetic,
Logical & Bit Manipulation Instruction, Program Control: Conditional Branch Instruction,
Subroutine, Program Interrupt, Types of Interrupt, I-Cycle, Interrupt and Class of Interrupts.
RISC & CISC Characteristic. Control Unit Design: Instruction sequencing, Instruction
interpretation, control memory, Hardwired Control, Micro programmed Control, Micro
programmed Computers.
Module-4. I/O organization: Bus control, Serial I/O, Asynchronous and synchronous modes,
Program controlled: Asynchronous, synchronous & Interrupt driven modes, DMA mode,
interrupt controller and DMA controller. Memory System Organization and Architecture:
Memory system hierarchy, main memory organization, cache memory, virtual memory.

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Module-5. Intel 8085: Functional Block Diagram, Pin Configuration, Description of each
Block: Registers, Flag, Data and Address Bus including Bidirectional Address/Data Bus,
Timing and Control Unit, Interrupts, Instructions: Op-Code and Operands Addressing Modes,
Instructions and Data Flow, Basic Assembly Language Programming using 8085 Instruction
Sets Addition, Subtraction, Multiplication and Division, Simple Sequence Programs.
Course Outcomes
At the end of this course the students will be able to:
CO 1: List the different types of memory and distinguish them.
CO 2: Analyse the abstraction of various components of a computer.
CO 3: Discriminate the various functional units of CPU and illustrate functioning of I/O
devices.
CO 4: Explain latest processor technologies and evaluate systems for one’s own requirements.
List of Textbooks
1. D. A. Pattersen and J. L. Hennesy, Computer Computer Architecture: A Quantitative
Approach, 6th Edition, Morgan Kaufman, 2019.
2. William Stalling, Computer Organization & Architecture, 11th Edition. Pearson Education,
2022.
3. Mano M. Morris, Computer System Architecture, 3rd Edition. Pearson Education, 2017.
List of Reference Books
1. D. A. Pattersen and J. L. Hennesy, Computer Organisation and Design MIPS Edition: The
Hardware/ Software Interface, 6th Edition, Morgan Kaufman, 2020.
2. Ramesh Gaonkar, Microprocessor Architecture, Programming, and Applications with the
8085, 6th Edition, Penram International Publishing, 2013.
3. V. P. Heuring and H. F. Jordan, Computer System Design and Architecture, Prentice Hall,
2003.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Third Year- Second
Subject Name Formal Language and Automata Theory
Course Code CSE-3004
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Discrete Structure (CSE-2002)
Course Learning Objectives
1. To understand formal languages, grammars and computation theory.
2. To demonstrate the concepts of automata theory and turing machines.
3. To compare and contrast solvable and unsolvable problems.
Course Content
Module 1. Introduction to Theory of Computation: Overview of Formal Languages,
Alphabets, Automata and Their Significance, Historical Development and Key Contributors in
the field.
Module 2. Finite Automata and Regular Languages: Definitions and types of Finite State
Machine, Transition Graphs, Regular Grammar, Convert Regular Expression to NDFA, Convert
NDFA to DFA, Minimization of DFA, Moore machine and Mealy Machine, Conversion of
Moore Machine to Mealy Machine & Vice-Versa, Conversion of DFA to Regular Expression,
Pumping Lemma, Properties and Limitations of Finite State Machine, Application of Finite
Automata.
Module 3. Context-Free Grammars and Pushdown Automata: Derivation Tree and
Ambiguity, Unambiguous CFG for algebraic expressions, Chomsky and Greibach Normal form,
Properties of Context Free Grammar, Application of Context Free Grammars, CKY Algorithm,
Decidable Properties of Context Free Grammar, Pumping Lemma for Context free Grammar,
Pushdown Automata, Design of Deterministic and Non-Deterministic Push-Down Automata,
PDA to CFG and Vice Versa.
Module 4. Turing Machines and Computability: Definitions of Turing Machines,
Computable Languages and Functions, Techniques for Turing Machine Construction, Multi
Head and Multi Tape Turing Machines, The Halting Problem, Partial Solvability, Problems in
Turing Machines, Chomsky Hierarchy of Languages.
Module 5. Unsolvable Problems: Computable Functions, Recursive and Recursively
Enumerable Languages, Universal Turing Machine, Measuring and Classifying Complexity,

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Department of Computer Science & Engineering
Tractable and Intractable Problems, Tractable and Possibly Intractable Problems, P and NP
Completeness, Polynomial Time Reductions, NP-Complete Problems from Other Domains,
Graphs, Clique, Vertex Cover, Independent Sets, Hamiltonian Cycle, Number Problem, Set
Cover, Cook’s Theorem.
Course Outcomes:
At the end of this course the students will be able to:
CO 1. Understand the basic models of computation, such as finite automata, pushdown
automata, and Turing machines.
CO 2. Use regular expressions to describe patterns and recognize regular languages.
CO 3. Identify undecidable problems and use techniques such as diagonalization to prove
undecidability.
List of Text Books
1. Hopcroft, J. E., Introduction to Automata Theory, Languages, and Computation, India:
Pearson Education, 2008.
2. 1. Martin, J. C., Introduction to Languages and the Theory of Computation, Colombia,
McGraw-Hill, 2003.
3. Mishra, K. L. P., CHANDRASEKARAN, N., Theory of Computer Science: Automata,
Languages and Computation. India: PHI Learning, 2007.
List of Reference Books
1. Harry R Lewis and Christos H Papadimitriou, Elements of the Theory of Computation,
Prentice Hall of India, Pearson Education, New Delhi, 2003.
2. Krithivasan, K., Introduction to Formal Languages, Automata Theory and
Computation. India: Pearson Education, 2009.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Third Year- Second
Subject Name Signal and Communication Systems
Course Code CSE-3005
Compulsory/Elective/Open Elective Compulsory

Prerequisites
N/A
Course Learning Objectives

1. To discuss concepts of signals, systems, and transforms.

2. To apply fundamental concepts of communication systems, signal types, transmission media, and
analog modulation techniques (AM, FM, PM) while understanding key parameters.
3. To explain the principles of noise, channel capacity, pulse modulation, and digital modulation
techniques.
4. To illustrate wireless communication, cellular concepts, and multiple access techniques.
Course Content
Module 1. Signals, Systems, and Transforms: Introduction to Signals and Systems, Linear Time
Invariant (LTI) System Overview, Laplace Transform and its properties, Fourier Series and its
properties, Fourier Transform for Continuous-Time Signals.
Module 2. Fundamentals of Communication Systems: Basic concepts and components of
communication systems, Types of signals: analog and digital, Transmission media and channel
impairments.
Module 3. Analog Modulation and Demodulation: Amplitude Modulation (AM) and
Demodulation, Frequency Modulation (FM) and Demodulation, Phase Modulation (PM) and
Demodulation, Modulation index, bandwidth, and modulation efficiency
Module 4. Noise and Channel Capacity, Pulse Modulation Systems: Sources of noise in
communication systems, Signal to Noise Ratio (SNR) and Eb/N0, Shannon's theorem and channel
capacity, Capacity limits of communication channels. Sampling theorem, Pulse modulation
schemes – PAM, PPM and PWM generation and detection-Pulse code modulation.
Module 5. Digital Modulation and Transmission: Digital modulation techniques: ASK, FSK,
PSK, Constellations and signal space diagrams, Error probability and bit error rate (BER) analysis.
Module 6. Wireless Communication and Multiple Access: Cellular concepts and frequency
reuse, Multiple access techniques: FDMA, TDMA, CDMA, OFDMA and SDMA.
Course Outcomes
At the end of this course the students will be able to:
CO 1. Evaluate and apply knowledge of signals, systems, and transforms, including Laplace
Transform, Fourier Series, and Fourier Transform for continuous-time signals
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CO 2. Analyze communication systems, signal types, transmission media, and analog

modulation techniques, including AM, FM, and PM, while comprehending key parameters.
CO 3. Discuss the knowledge of noise, channel capacity, pulse modulation, and digital
modulation techniques.
CO 4. Assess knowledge of wireless communication principles, cellular concepts, and
multiple access techniques .
List of Textbooks
1. A. V. Oppenheim, A. S. Willsky, and S. H. Nawab, Signals and Systems, 2nd Edition, Prentice
Hall, 2003.
2. Simon Haykin, Communication Systems, 4th Edition, India: Wiley India Pvt. Limited, 2018.
3. Roddy and Coolen, Electronic Communication, 4th Edition, Pearson Education, 2014.
4. Carlson, Crilly, Communication Systems, 5th McGraw Hill Education, 2017.
List of Reference Books
1. S. Haykin and B. V. Veen, Signals and Systems, 2nd Edition, Wiley, 2007.
2. Hwei Hsu and Debjani Mitra, Analog and Digital Communication: Schaum’s Outline Series, 3rd
Edition, McGraw Hill Education, New Delhi, India, 2017.
3. Herbert Taub and Donald Schilling, Principles of Communication Systems, 4th edition, Mc Graw
Hill, 2017.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Third Year- Second
Subject Name Advanced Programming Lab – 1(Python)
Course Code CSE-3006
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Fundamentals of Computer Programming (CSE-1003)
Course Learning Objectives
1. To introduce the Python programming language and its features.
2. To familiarize with basic data structures and algorithms.
3. To enhance ability for clear, organized and efficient coding.
Syllabus

Module 1: Basic Python Programming: Understand Python syntax and its role in
programming, Write simple Python programs using variables, Loops, and Conditionals, Apply
Python coding conventions for readability and maintainability.
Module 2: Functions and Modular Programming: Define functions with parameters and
return values, utilize functions to encapsulate code and enhance reusability, and implement
modular programming to organize and structure code.
Module 3: Data Manipulation and Structures: Manipulate strings, lists, tuples, and
dictionaries effectively, perform operations on data structures to solve practical problems, and
apply built-in functions for data transformation and manipulation.
Module 4: Algorithms and Problem Solving: Develop algorithmic thinking and problem-
solving skills, analyze problem statements and design algorithmic solutions, Implement
algorithms for searching, sorting, and other tasks.
Module 5: Application Development and Libraries: Develop small-scale applications using
Python, utilize Python libraries and modules to add functionalities, integrate external libraries
for tasks like data visualization.
Module 6: Project and Practical Application: Apply concepts learned throughout the course
to design and implement a Python project, develop a complete Python program that solves a
real-world problem or simulates a scenario, Present and demonstrate the project, showcasing
proficiency in Python programming.

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Department of Computer Science & Engineering

Course Outcomes:
At the end of this course the students will be able to:
CO 1: Learn problem-solving skills for working on programming tasks and challenges.
CO 2: Demonstrate proficiency in writing, debugging, and optimizing code.
CO 3: Use python libraries and frameworks for different applications.
List of Textbooks:
1. John Zelle, Franklin, “Python Programming: An Introduction to Computer Science”, Beedle
& Associates Inc., 3rd Edition, 2016.
2. Mark Lutz, “Learning Python”, O'Reilly Media, 5th Edition, 2013.
List of Reference Books
1. Wes McKinney, “Python for Data Analysis", O'Reilly Media, 2nd Edition, 2017.

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Department of Computer Science & Engineering

4th Sem

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fourth Year- Second
Course Name Engineering Mathematics- III (Numerical Methods and Statistics)
Course Code CSE-4001
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Engineering Mathematics-I (CSE-1001), Engineering Mathematics-II(CSE-2001)
Course Learning Objectives
1. To develop a strong understanding of numerical methods and their applications in solving
mathematical and engineering problems.
2. To understand finite difference operators and their applications.
3. To understand statistical analysis techniques and their applications in various fields.
4. To demonstrate a deep understanding of the fundamental concepts, principles, and
mathematical techniques related to probability theory.
Course Content

Module 1. Numerical Methods: Solution of algebraic and transcendental equations, Solution

of linear Simultaneous Equations.
Module 2. Finite Differences: Finite Difference Operators, Interpolation formula for equal and
unequal intervals, Central Difference formula, Inverse Interpolation, Numerical Differentiation.
Numerical Integration, Numerical solution of Ordinary Differential Equations.
Module 3. Statistics: Introduction, Measure of central tendency, Measures of Dispersion, Curve
fitting by the numerical methods, Correlation and Regression Analysis.
Module 4. Probability: Introduction of Probability, Addition and multiplication theorem of
Probability, Conditional Probability, Bayes’ theorem.
Module 5. Random Variables: Discrete and Continuous Random Variables, Probability
Density Functions. Theoretical Distributions, Binomial, Poisson & Normal Distribution.
Course Outcomes

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Department of Computer Science & Engineering
By the end of this course, students will be able to
CO 1: Apply numerical methods to approximate solutions for various mathematical equations
and problems.
CO 2: Demonstrate proficiency in using finite difference methods to approximate derivatives,
integrals, and solutions to ordinary differential equations.
CO 3: Apply correlation and regression analysis to investigate relationships between variables
and make predictions based on data.
CO 4: Apply Bayes' theorem to solve real-world problems involving conditional probability
and uncertain decision-making.
List of Text Books
1. Richard Hamming, Numerical Methods for Scientists and Engineers, 2nd Revised Edition,
Dover Publication, 2012.
2. B. S. Grewal, Numerical Methods in Engineering & Science, 11th Edition, New Delhi,
Khanna Publishers, 2015.
3. S.S. Sastry, Introductory methods of numerical analysis, 5th Edition, UK, Prentice Hall India
Learning Private Limited, 2012.
4. M. Ray, Har Swarup Sharma, S.S Chaudhary, Mathematical Statistics, 12th Edition, Agra,
Ram Prasad Publication, 2022.
List of Reference Books
1. M.K. Jain, S.R. lyengar and R.K. Jain, Numerical methods for scientific and Engineering,
7th Edition, New Age International Private Ltd., 2019.
2. Erwin Kreyszig, Advanced Engineering Mathematics, 8th Edition, Johan Wiley & Sons,
Wiley student Edition, 2011.
3. Steren C. Chapra and Raymond P. Canale, Numerical methods for Engineers with
programming and software applications, 7th Edition, New York, Tata McGraw Hill
Education, 2015.
4. Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers and Keying E. Ye, Probability
and Statistics for Engineers and Scientists, 9th Edition, USA, 2010.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fourth Year- Second
Course Name Computer Networks and Communication
Course Code CSE-4002
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Signal and Communication Systems (CSE-3005)
Course Learning Objectives
1. To explore the functions and responsibilities of each OSI layer in network communication.
2. To establish connections between devices to facilitate seamless data transmission and
effective communication.
3. To acquire knowledge about key concepts such as Flow Control, Error Detection &
Correction, and Transmission Media.
4. To understand the fundamental principles governing routing and addressing in networking.

Course Content
Module-1: Network hardware, Network software, OSI, TCP/IP Reference Models, Example
Networks: ARPANET, Internet. Physical Layer: Guided Transmission Media: Twisted Pairs,
Coaxial Cable, Fibre Optics, Wireless Transmission.
Module-2: Data-Link Layer: Design Issues, Framing, Error Detection and Correction, FEC
Vs Retransmission, Encoding and Decoding Techniques, Error Detection and Correction,
Elementary data link protocols: Noisy and Noiseless Channels, Medium Access sublayer:
Channel Allocation Problem, Multiple Access Protocols: ALOHA, Carrier Sense Multiple
Access Protocols, Collision Free Protocols. Wireless LANs, Data Link Layer Switching.
Module-3: Network Layer: Design Issues, IP Addressing: Subnetting and Supernetting,
Routing Algorithms: Shortest Path Routing, Flooding, Hierarchical Routing, Broadcast,
Multicast, Distance Vector Routing, Congestion Control Algorithms, Quality of Service,
Internetworking.
Module-4: Transport Layer: Transport Services, Elements of Transport Protocols, Connection
Management, TCP and UDP Protocols. Multiplexing, Flow Control and Retransmission,
Window Management, TCP Congestion Control, Quality of service.
Module-5: Application Layer –Domain Name System, SNMP, Electronic Mail; the World
Wide Web, HTTP, Streaming Audio and Video. File Transfer Protocol, Remote Login, Network
Management, Data Compression, Cryptography: Fundamentals.

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Department of Computer Science & Engineering
Course Outcomes
At the end of the course students will be able to:
CO 1: Recognise the essential concepts, the OSI reference model, the TCP/IP protocol,
services, networks, transmission mediums, and analogue and digital data transfer.

CO 2: Work with network layer's features, such as the routing algorithm, logical addressing,
and subnetting.

CO 3: Analyze the functions offered by session and presentation layer and their
Implementation.

CO 4: Simulate the different protocols used at application layer i.e. HTTP, SNMP, SMTP,
FTP, TELNET and VPN.
List of Textbooks
1. Andrew S. Tanenbaum, Nick Feamster, David J. Wetherall, Computer Networks, 6th
edition, Pearson Education, April 2021.
List of Reference Books
1. James, Kurose & Keith W. Ross, “Computer Networking: A Top-Down Approach
Featuring the Internet”, 7th Edition, Pearson Education, 2017
2. W.A. Shay, “Understanding communications and Networks”, 3rd Edition, B.S. publications,
2008.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester – Fourth Year- Second
Course Name Operating System
Course Code CSE-4003
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Computer Organization and Architecture (CSE-3003)
Course Learning Objectives
1. To understand the services and design of an operating system.
2. To understand the structure and organization of the file system.
3. To understand the process states and various related concepts such as scheduling and
synchronization.
4. To understand different memory management approaches.
Course Content
Module 1. Evolution of OS: The Evolution of Operating Systems (OS), Fundamental goals of
operating systems overview of essential features of OS operation. Overview of OS:
multiprogramming, Batch, interactive, time sharing, distributed and real-time operating
systems; Concurrency and parallelism.
Module 2. Process management and scheduling: Concept of process and process
synchronization, process states, process state transitions, the process control block, operations
on processes, suspend and resume, interrupt processing, mutual exclusion, the
producer/consumer problem, the critical section problem, semaphores, classical problems in
concurrency, inter-process communication; Issues in user service and system performance.
Module 3. Process Synchronization and Deadlocks: Synchronization primitives and
problems, deadlocks (essential topics: Peterson's algorithm, monitors), detection and prevention
of deadlocks, dynamic resource allocation.
Module 4. Memory Management: Memory fragmentation and techniques for memory reuse
paging, virtual memory management using paging, Segmentation, Distributed and
Multiprocessor Systems.
Module 5. File Management: File systems, implementation of file Operations. Protection of
files.
Course Outcomes

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At the end of the course, the student will be able to:
CO 1: Describe functional architecture of operating system.
CO 2: Describe process concept and its implementation, compare process scheduling
algorithms.
CO 3: Classify memory management schemes and compare them on the basis of related
advantages and disadvantages.
CO 4: Describe Process synchronization mechanisms used to solve synchronization problems,
describe mechanisms for handling Deadlock problems.
List of Text Books
1. Silberschatz, Abraham., Galvin, Peter B., Gagne, Greg. Operating System Concepts. 10th
Edition, United Kingdom: Wiley, 2021.
2. Tanenbaum, Andrew S.., Bos, Herbert. Modern Operating Systems, 5th Edition. United
Kingdom: Pearson Education, 2023.
List of Reference Books
1. Stallings, William. Operating Systems: Internals and Design Principles, 9th Edition. United
Kingdom: Pearson Education, 2018.
2. Comer, Douglas. Operating System Design: The Xinu Approach, Second Edition. United
States: CRC Press, 2015.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fourth Year- Second
Course Name Data Mining and Warehousing
Course Code CSE- 4004
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Database Management System (CSE-3002)
Course Learning Objectives
1. To uncover valuable knowledge and insights from large datasets through data mining
techniques.
2. To facilitate Decision-Making: Support effective decision-making by providing timely and
relevant information stored in data warehouses.
3. To enhance Predictive Analysis: Develop models for predicting future trends and behaviors
based on historical data stored in data warehouses.
4. To optimize Query Performance: Improve the efficiency of data retrieval and analysis
through well-designed data warehousing structures.
Course Content
Module 1. Introduction: Data Mining Concepts, Input, Instances, Attributes and Output,
Knowledge Representation. Data Objects and Attribute Types, Basic Statistical Descriptions of
Data, Data Visualization, Measuring Data Similarity and Dissimilarity. Data Preparation: Data
Cleaning, Data Integration & Transformation, Data Reduction. Data Discretization and Concept
hierarchy generation,
Module 2. Mining Association Rules: Associations, Maximal Frequent & Closed Frequent
item sets, Covering Algorithms & Association Rules, Linear Models & Instance-Based
Learning, Mining Association Rules from Transactional databases, Mining Association Rules
from Relational databases & Warehouses, Correlation analysis & Constraint based Association
Mining.
Module 3. Classification and Prediction: Basic Concepts, Supervised Learning Framework,
concepts & hypothesis, Training & Learning. Issues regarding Classification & Prediction,
Classification by Decision Tree induction, Bayesian classification, Classification by Back
Propagation, k-Nearest Neighbor Classifiers, Genetic algorithms, Rough Set & Fuzzy Set
approaches.

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Module 4. Cluster Analysis: Types of data in Clustering Analysis, Categorization of Major
Clustering methods, Hierarchical methods, Density-based methods, Grid-based methods,
Model-based Clustering methods.
Module 5. Data Warehouse Basic Concepts: Data Cube and OLAP, Typical OLAP
Operations, Data Warehouse Design and Usage: OLAP Servers, ROLAP, MOLAP, HOLAP,
Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data
Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications,
Web Mining, Spatial Mining and Temporal Mining.
Course Outcomes
At the end of the course, the student will be able to:
CO 1: Extract meaningful patterns and knowledge from large datasets for informed decision-
making.
CO 2: Identify hidden relationships and trends within data to support business intelligence.
CO 3: Enhance predictive modeling by discovering valuable insights and patterns in historical
data.
CO 4: Improve marketing strategies by analyzing customer behavior and preferences through
data mining.
List of Text Books
1. Han, Jiawei., Pei, Jian., Tong, Hanghang. Data Mining: Concepts and Techniques.
Netherlands: Elsevier Science, 2022.
2. Berson, Alex, and Stephen J. Smith. Data warehousing, data mining, and OLAP. McGraw-
Hill, Inc., 2017.
List of Reference Books
1. Humphries, Mark. Data warehousing: architecture and implementation. Pearson Education
India, 1999.
2. Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and
Techniques with Java implementations, Morgan Kaufmann Publishers, San Fransisco, CA
2000.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fourth Year- Second
Subject Name Principles of Compiler Design
Course Code CSE-4005
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Formal Language and Automata Theory (CSE-3004)
Course Learning Objectives
1. To understand the theoretical foundations of compilers and their role in software
development.
2. To learn the various phases of the compilation process and the interactions between them.
3. To develop proficiency in designing and implementing lexical analyzers, parsers, and
semantic analyzers.
Course Content
Module 1. Introduction to Compilers: Role of compilers in software development, Structure
of a compiler and its phases, Overview of compilation process, Lexical analysis and
tokenization, Lexical analyzer generator tools, Lex.
Module 2. Syntax Analysis: Context-free grammars, top-down parsing- bottom-up parsing, LL
(1) and LR(1) grammars, Parse tables and parsing algorithms, LL, SLR, LALR, LR, Parser
generator tools, Yacc.
Module 3. Semantic Analysis: Semantic errors and error recovery, Symbol tables and their
organization, Type checking and type inference, Attribute grammars and semantic actions,
syntax directed definition, bottom‐up evaluation of S-attributed definitions, Intermediate
representation.
Module 4. Code Generation: Overview of code generation, Target machine and runtime,
environment, Three-address code and quadruples, Code generation for expressions, statements,
and control structures, Activation records and runtime stack.
Module 5. Code Optimization: Principles of code optimization, Local optimizations, constant
folding, common subexpression elimination, Global optimizations, data flow analysis, register
allocation, Loop optimizations and control flow optimizations, Code optimization tools and
techniques.

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Department of Computer Science & Engineering
Module 6. Project Work: Students work on a practical project related to compiler design,
Implementation of a simple compiler for a subset of a programming language, Project involves
multiple phases of compilation.
Course Outcomes:
At the end of this course the students will be able to:
CO 1: Explain the principles and functions of compiler design.
CO 2: Apply Syntax Analysis techniques to comply with and transform input according to the
grammar of the language.
CO 3: Analyze different optimization techniques for code optimization.
CO 4: Design and Develop code generator and mini compiler for a language.
List of Textbooks:
1. Keith D. Cooper and Linda Torczon, Engineering a Compiler, Morgan Kaufmann, 2nd
Edition 2011.
2. Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman, Compilers: Principles,
Techniques, and Tools, Pearson, 2nd Edition, 2006.
List of Reference Books
1. Andrew W. Appel, Modern Compiler Implementation in C/Java/ML, Cambridge University
Press, 2nd Edition, 2002.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fourth Year- Second
Course Name Advance Programming Lab-II (Web Tech)
Course Code CSE- 4006
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Fundamentals of Computer Programming (CSE-1003)
Course Learning Objectives
1. To identify and explain the basic syntax and structure of HTML, CSS, JavaScript, and
MySQL.
2. To combine front-end and back-end components to create a seamless user experience.
3. To develop static web pages and create interactive and responsive user interfaces using
JavaScript and CSS.
Course Content
Module 1. Web Basics: Protocol, Internet, TCP/IP Protocol, DNS, HTTP, Client Server
Technology, Working of Website, List of Web servers, Types of Languages, Compiler vs
Interpreter, Meaning of Full Stack Developer, Skills Required for Website Development, List
of Famous Software’s for Web Development.
Module 2. Front End Web Development: HTML: Introduction to HTML5, Tag, Element and
Attribute, Creating Tables, Embedding Contents, Working with Forms, Meta Tags, iFrames,
List of all HTML Tags in Single Page. CSS: Introduction to CSS3, Inline CSS, CSS Comments,
Internal CSS, External CSS, Linking CSS, How to Debug CSS Code, CSS Box, CSS Floating
Columns, Positioning Elements, Display Inline or Block, Use Google Fonts in your Website,
HTML5 + CSS3 Projects.
Module 3. JAVA Script: Introduction to JavaScript, JavaScript Terminology, How to Debug
JavaScript Code, JavaScript Language Syntax, Projects with JavaScript.
BOOTSTRAP: Overview of Bootstrap, how to use Bootstrap, Project 1: Starter Website with
Bootstrap, Project 2: Personal Portfolio Page,
Module 4. Back End Web Development: PHP: Setting up the PHP Environment, PHP
Language Basics, Variable and Constants, Expressions and Operators, Data Types,
Namespaces, Control Structures, Strings, Arrays, Functions, Handling Exceptions, Files, Date
and Time

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Department of Computer Science & Engineering
Module 5. Introduction to XML: Complete understanding of JSON, Introduction to rest and
API and PROJECTS: Complete Website with PHP, Complete Website with WordPress,
MYSQL, WORDPRESS, PHP OOP.

Course Outcomes
After completion of the course, students will be able to
CO 1: Demonstrate proficiency in creating and designing web pages using HTML, CSS, and
JavaScript.
CO 2: Build interactive and dynamic web applications that communicate with a back-end
server.
CO 3: Exhibit competence in utilizing MySQL to manage and manipulate data within a
database.
CO 4: Integrate front-end and back-end components to develop full-stack web applications.
List of Text Books
1. Jon Duckett, HTML and CSS: Design and Build Websites. Wiley, 2011.
2. Marijn Haverbeke, Eloquent JavaScript: A Modern Introduction to Programming, 3rd
Edition. No Starch Press, 2018.
3. Jennifer Niederst Robbins, Learning Web Design: A Beginner's Guide to HTML, CSS,
JavaScript, and Web Graphics, 5th Edition. O'Reilly Media, 2018.
List of Reference Books
1. Eric Freeman and Elisabeth Robson, Head First HTML and CSS: A Learner's Guide to
Creating Standards-Based Web Pages, 2nd Edition, O'Reilly Media, 2012.
2. Paul DuBois, MySQL Cookbook: Solutions for Database Developers and Administrators,
3rd Edition. O'Reilly Media, 2014.
3. Nicholas C. Zakas, Professional JavaScript for Web Developers, 4th Edition. Wrox, 2019.

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Department of Computer Science & Engineering

5th Sem

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fifth Year- Third
Course Name Cryptography and Cyber Security
Course Code CSE-5001
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Discrete Structure (CSE-2002)
Course Learning Objectives
1. To learn the emerging concepts of cryptography and algorithms.
2. To explain how to secure a message over an insecure channel by various means.
3. To understand various protocols for network security to protect against the threats in the
networks.
Course Content
Module 1. Introduction: Security Components, OSI Security Architecture, aspects of security,
Passive Attacks, Active Attacks, Security Services (X.800), model for Network Security, model
for Network Access Security, Symmetric Cipher Model, Cryptography Classification,
Cryptanalysis, Substitution: Other forms, Poly-alphabetic Substitution Ciphers, One-Time Pad,
Transposition (Permutation) Ciphers, Product Ciphers.
Module 2. Number Theory and Prime Numbers: Groups, Rings, and Fields, Modular
Arithmetic, Euclid’s Algorithm, Finite Fields of the Form GF(p), Polynomial Arithmetic, Finite
Fields of the Form GF(2n ). Generation of large prime numbers, Prime factorization, Euler
Totient Function ø(n), Euler's Theorem, Primality Test- Fermat's Little Theorem, Baillie-PSW,
Solovay-Strassen, Miller Rabin Algorithm, AKS Algorithm, Cyclotomic primality test, Elliptic
Curve Primality Test, Prime Distribution, Chinese Remainder Theorem, Primitive Roots,
Discrete Logarithms.
Module 3. Cryptographic Techniques: Perfect security, Feistel Cipher Structure, Block
Cipher- DES, differential and linear Cryptanalysis, Avalanche Effect, Double-DES, Triple-
DES, Cipher modes of operations: block and stream mode, AES, International Data Encryption
Algorithm (IDEA), Blowfish Algorithm; RC4; Pseudo number generation.
Module 4. Public-Key Cryptography and Message Authentication: The Key Distribution
Problem, Public-Key Cryptosystems, The RSA Algorithm, The Key Management riddle, The
Diffie-Hellman Key Exchange, Elliptic Curve Cryptography, Message Authentication,
requirements and functions, Message Authentication Codes, Hash Functions, Birthday Problem,
SHA-X, SHA-512, Authentication, Access control policies, The Message Digest (MD5)
Algorithm, HMAC fundamentals, Digital Signature basics, Authentication Protocols, The
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Department of Computer Science & Engineering
Digital Signature Standard, Kerberos Authentication scheme, The X.509 Directory
Authentication scheme.
Module 5. Security Protocols: Secure User Authentication, Mail security, PGP, database
security, file system security, program security, memory security, session security, SSH, Web
security, Replay Attacks, Needham Schroeder Protocol, IPSec, SSL, IEEE 802.11, Wired
Equivalent Privacy (WEP).
Course Outcomes
At the end of course the students will be able to:
CO 1: Analyze the cryptographic algorithms for Information Security.
CO 2: Identify and investigate network security threats.
CO 3: Identify the requirements for secure communication and challenges related to the
secure web services.
List of Text Books
1. William Stallings, Cryptography and Network Security: Principles and Practice. United
Kingdom: Pearson Education, 2023.
2. Sarhan M. Musa, Network Security and Cryptography,Mercury Learning and
Information,2nd Edition, 2022
3. Douglas Robert Stinson, Maura Paterson, Cryptography: Theory and Practice, CRC Press,
2018
List of Reference Books
1. Dr.S.Bose, Dr.P.Vijaykumar, Cryptography and Network Security, Pearson, 2017
2. Musa, Sarhan M. Network Security and Cryptography, Germany: Mercury Learning and
Information, 2018.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester-Fifth Year-Third

Course Name Parallel and Distributed System

Course Code CSE-5002

Compulsory/Elective/ Compulsory
Open Elective

Prerequisites
Computer Networks and Communication (CSE-4002), Operating System (CSE-4003)
Course Learning Objectives
1. To apply knowledge of parallel and distributed computing techniques and methodologies.
2. To gain experience in the design, development, and performance analysis of parallel and
distributed applications.
3. To gain experience in the application of fundamental computer science methods and
algorithms in the development of parallel applications.
Course Content
Module 1. Basic Concepts: Introduction to parallel processing, terminologies, Bernstein’s
conditions levels of parallelism in programs. program flow-control flow, data flow, distributed
systems and tightly-coupled loosely-coupled systems. hardware and software requirements,
design issues.
Module 2. Parallel processing: Structure & organization, taxonomy of parallel processes:
granularity, basic architectures, multiprocessors, vector processors, types of pipelines, pipeline
hazards and their solution, reservation table, scheduling.
Module 3. Distributed Systems: Introduction and types of distributed systems, architecture of
ds, types of processes, models of computation, communication in distributed systems - remote
procedure calls and message-oriented communications and implementation, different forms of
computing: minicomputer model, workstation model, workstation server model, processor pool
model, cluster: - definitions, reasons for its popularity cluster computer system architecture.
Module 4. Clock Synchronization: Clock Synchronization, distributed mutual exclusion,
group based mutual exclusion leader election, deadlock detection, termination detection,
distributed databases, implementations over a simple distributed system and case studies of
distributed databases and systems, distributed file systems: scalable performance, load
balancing, and availability.

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Department of Computer Science & Engineering
Module 5. Parallel & Distributed Programming: Parallel Programming environments,
models, synchronous asynchronous programming, modulla-2, FORTRAN, DAP FORTRAN,
C-linda, Actus, data flow programming, VAL etc., MPI, Open MP.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Understand the design principles and architecture for parallel and distributed systems.
CO 2: Understand the requirements for programming parallel systems and how they can be
used to facilitate the programming of concurrent systems.
CO 3: Design, develop, and performance analysis of parallel and distributed applications.
List of Text Books
1. Alberto Ros, Parallel and Distributed Computing, 2nd Edition, IN-TECH, 2010.
2. Ajay D. Kshemkalyani and MukeshSinghal,Distributed Computing: Principles,
Algorithms and Systems, Cambridge University Press, 2011.
3. Andrew S. Tanenbaum, Nick Feamster, David J. Wetherall,Computer Networks, 6th
edition, Pearson, 2020.
List of Reference Books
1. Garg VK, Elements of distributed computing, John Wiley & Sons, 2002.
2. George Coulouris, Jean Dollimore, Tim Kindberg and Gordon Blair, Distributed Systems:
Concepts and Design, Fifth Edition, Pearson Education, 2017.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fifth Year- Third
Course Name Artificial Intelligence and Machine Learning
Course Code CSE-5003
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Data Mining and Warehousing (CSE-4004)

Course Learning Objectives

1. To avail knowledge representation, reasoning methods, and various knowledge structures in
artificial intelligence.
2. To explore various machine learning techniques for classification, regression, and clustering
and apply them in practical scenarios.
3. To develop problem-solving skills through artificial intelligence.
Course Content
Module-1. Introduction: Artificial Intelligence, Production Systems: types, characteristics,
study and comparison search techniques: BSF, DSF, Hill Climbing, Best First Search, A*
algorithm, AO* algorithm etc., types of control strategies.
Module-2. Knowledge representation: Problems faced, Propositional and Predicate logic,
Resolution and Refutation, Deduction, Theorem Proving. Reasoning: Introduction, reasoning
methods, Bayes’ Theorem, Bayesian Network, Fuzzy Logic. Slot and filler structures: Semantic
Networks, Frames, Conceptual dependency, scripts etc.
Module-3: Learning and its Techniques: Neural Networks and its applications, Expert
systems. Supervised Learning-Feature Selection, Cross Validation, Bootstrapping,
Normalization Classification: Naïve Bayes, Bayesian Network, C4.5, ID3, Support Vector
Machine, Extreme Learning Machine, Neural Network, VC Dimension, Regularization,
Regression: Linear, Polynomial, Multiple Linear Regression, Support Vector Regression.
Committee Machines/ Ensemble Learning: Bagging, Boosting.
Module-4: Unsupervised Learning: Clustering: K-Nearest Neighbor, K-Means, Fuzzy K-
Means, Hierarchical Clustering, Single Linkage, Complete Linkage, Average Linkage, Non-
Spherical Clustering Algorithms. Statistical Testing Methods, Probabilistic Inference, Machine
Learning Applications: Text Classification, Disease Diagnosis, Biometric Systems, Real Valued
Classification.
Module 5. Constraint Satisfaction Problems: Game Theory: Local search for constraint
satisfaction problems. Adversarial Search, Games, Optimal Decisions & strategies in games,

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the minimax search procedure, Alpha-Beta Pruning, Additional Refinements, Iterative
Deepening.
Course Outcomes
At the end of the course, students will be able to:
CO1. Comprehensive understanding of AI and ML fundamentals
CO2. Proficiency in knowledge representation and reasoning
CO3. Problem-solving with constraint satisfaction and game theory.
CO4. Identify the challenges in representing knowledge in AI systems.
List of Text Books
1. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall, 3rd
Edition, 2021.
List of Reference Books
1. Elaine Rich, Kevin Knight, & Shivashankar B Nair, “Artificial Intelligence”, McGraw Hill,
3rd Edition,2017.
2. Patterson, Dan. Introduction to artificial intelligence and expert systems. Prentice-Hall, Inc.,
2015.
3. Kaushik, Saroj. Logic and prolog programming. New Age International, 2007.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fifth Year- Third
Course Name Software Engineering and Project Management
Course Code CSE-5004
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To discuss the evolution, impact and emergence of the software engineering discipline.
2. To use of different software life cycle models for real life industrial applications.
3. To discuss different aspects of software project management, risk management and
configuration management
4. To explain various requirement elicitation, analysis and specification techniques.
Course Content
Module 1. Introduction to Software Engineering: The Evolving Role of Software, Software
Engineering importance, Emergence, Phases of software development, Feasibility study,
Requirement Analysis, Design, Implementation, Testing, and Maintenance phases. Software
Myths. A generic view of Process: Software Engineering-A Layered Technology, A process
framework,
Module 2. Software Life Cycle Models: Classical waterfall, Iterative, Incremental Process
Models, The RAD model, Evolutionary Process models: Prototyping, Spiral, The Concurrent
Development model, and Agile model, Compare Life cycle models. An Agile view of Process:
What is Agility, Agile Process models: XP, ASD, DSDM, Scrum, Crystal, FDD, AM.
Module 3. Requirements Analysis and Design: Requirement Analysis – Analysis process,
Requirements specification, Desirable characteristics of an SRS, Structure of an SRS document,
Data Flow Diagrams, Role of Software Architecture and Architecture, Views, Planning for a
Software Project. Software design concepts,Function Oriented Design and its Complexity
Metrics, Object Oriented Design and its Complexity Metrics, Detailed Design.
Module 4. Software Implementation and Testing: Software Coding, Programming principles
and coding guidelines, Method of incrementally developing code, Managing the evolving code.
Testing: Unit testing and Code Inspection, Testing concepts and testing process, Design of Test
case and Test plan, Black-box testing, White box testing.

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Module 5. Software Project Management: Software Project Management Framework,
Methods to estimate project time and cost, Resource Management, Identification, Analysis,
Mitigation, and monitoring of Project Risks, Ensuring Project quality and quality management,
Configuration Management, Change management, CMMI, Different levels and need of
accreditation.
Course Outcomes
At the end of the course, students will be able to:
CO 1: Choose a proper life cycle model for different real life industrial applications, design
software using function-oriented approach (DFDs) and object-oriented approach (UML
diagrams).
CO 2: Understand the concepts of computer aided software engineering (CASE) and use
different CASE tools in the development, maintenance and reuse of software systems.
CO 3: Know the emerging concepts like cloud computing, middleware, SOA etc., their
functioning and their applications in real life problems.
List of Text Books
1. Pressman, Roger S., Maxim, Bruce R.. Software Engineering: A Practitioner's Approach.
United Kingdom: McGraw-Hill Education, 2020.
2. R. Mall, Fundamentals of Software Engineering, Prentice Hall of India, 2018.
List of Reference Books
1. Sommerville, Ian. Software Engineering, 9/e. India: Dorling Kindersley, 2011..
2. Jalote, Pankaj. An Integrated Approach to Software Engineering. Germany: Springer New
York, 2013.

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Department of Computer Science & Engineering
Name of Program B.Tech. Semester-Fifth Year-Third
Subject Name Project Design
Course Code CSE-5005
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Description:
This course is part of a large project divided into three parts namely project design in fifth
semester, project implementation in sixth semester and project dissertation in seventh.
Course Objective: Students are given the responsibility of conducting a survey or actively
engaging with society to identify real-life problems. They are expected to propose logical and
well-thought-out steps while utilizing appropriate technologies to address these identified
problems effectively. Afterward, the students are required to design system components or
processes that aim to provide viable solutions to the real-life problems. The primary focus of
this task involves problem identification and validation, the formulation and evaluation of needs,
generation and assessment of potential solutions, and the final selection of the most appropriate
ones. Furthermore, students will engage in the development and evaluation of prototypes as part
of the comprehensive process.
Project Group and Supervisor: A project group refers to a collaborative effort where students,
under the guidance of a faculty member, work together on a specific problem, subtask of a larger
problem, or a problem set. In such instances, it is essential to establish clear deliverables for
each student within the group.
Duration: The project design requires a minimum duration of approximately 12 to 16 weeks,
with an expected completion by the final week of the semester in the relevant academic year.
The project work allows for three possible approaches:
1. Pursuing novel and innovative ideas.
2. Extending previous research efforts.
3. Tackling abstract or proof-of-concept problems.
Deliverables: The expected deliverables will consist of one or more of the following:
1. Software/hardware-based product addressing a real-world problem.
2. Embedded systems (software-hardware combined).

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Department of Computer Science & Engineering
3. Research work presented in the form of conference/journal papers.
4. Comparative studies of products, methods, or designs.
5. Results from system design and simulation studies.
6. Theoretical modeling and technical studies.
In cases where certain projects do not neatly fit into the mentioned categories, the respective
supervisor will identify specific deliverables for those projects. This process will require a
comprehensive report and approval from higher authorities.
Expectations from the student: During the project tenure, students are expected to adhere to
the following guidelines:
1. Completing the assigned project work provided by the supervisor and meeting all identified
milestones promptly.
2. Following the work-plan established by their respective supervisor, which includes adhering
to reporting procedures and complying with designated working hours throughout the
project duration.
3. Submitting the project report in the prescribed format to the project coordinator only after
obtaining approval from the supervisor, well before the due date.
4. Taking full responsibility for avoiding plagiarism and respecting copyright issues.
Project Outcomes:
The outcome of the project should be in the form of as followings:
1. Research Paper/Patent/Copyright.
2. Any winning position in hackathon/national or international competitions.
Report: The mid-semester report must strictly adhere to specific guidelines, limiting its length
to 10 pages in a single-column format, using 12-point Times New Roman font. It is essential to
submit the report before the mid-semester examination of the academic year. As for the final
report, it should be a comprehensive summary of the student's work, meeting the minimum
length requirement of 40 pages in a single-column format, with a font size of 12 points and
Times New Roman. The minimum page limit of 40 pages will be strictly enforced, and students
should ensure that their report encompasses all relevant aspects of their work. To maintain
academic integrity, all reports will undergo a plagiarism check using Turnitin or a similar anti-
plagiarism software. Any reports with a similarity of more than 5% to a single source and a
cumulative similarity of over 20% will not be accepted and will not be forwarded to the
evaluation committee supervisor/mentor. It is important to note that once submitted, subsequent
revisions of the report will not be allowed under any circumstances. Therefore, students must
exercise caution and precision when preparing their reports to meet the specified requirements.
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Department of Computer Science & Engineering
General Instructions:
1. Students need to form a group of 3 to 5 students, and consult with the faculties (based on
area of expertise) to work on a project. The same group will be continued for project design,
project implementation and project dissertation. Any changes in group or supervisor(s) at
any stage need proper justification and approval from higher authorities.
2. Students need to submit the project group details to the project coordinator by 2nd week of
starting the semester. Project group details include project title, abstract, details group
member, name of supervisor(s) and approval sign of concern supervisor(s).
3. Evaluation will be carried out as following:
Continuous Evaluation End Term Evaluation
(last week of each month) (at the time of end term exam) Total

Viva-Voce Marks
E1 E2 E3 E4 Report Outcomes
Presentation
10 10 10 10 20 20 20 100

4. Students must report the progress of the project work to the respective supervisor(s) at least
twice in week as given schedule in the class time table.
5. Continuous Evaluation (E1/E2/E3/E4) will be carried out by the respective supervisor(s) and
marks will be submitted to the project coordinator by the end of every month.
6. End Term Evaluation will be carried out by both respective supervisor(s) and project
coordinator.

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Department of Computer Science & Engineering

6th Sem

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Subject Name Digital Image Processing
Course Code CSE-6001
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Signal and Communication Systems (CSE-3005)
Course Learning Objectives
1. To understand the fundamental concepts of image processing with various components and
steps involved.
2. To implement the various image enhancement and filtering techniques.
3. To understand and analyze various image processing techniques in real life applications.
Course Content

Module 1. Introduction to Image Processing Systems: Origin and examples, Basic image
processing steps and components, Elements of visual perception, Image sensing and
acquisition, Sampling and quantization, Relationship between pixels, Image operations like
arithmetic, logical, and geometrical operation.

Module 2. Image Enhancement in Spatial Domain: Basic intensity transformation functions,

Point processing, Neighborhood processing, Histogram equalization and specification, Spatial
filtering, Smoothing filtering, Sharpening filtering.

Module 3. Image Enhancement in Frequency Domain: Image Transforms, Low pass

frequency domain filter, High pass frequency domain filters, Homomorphic filtering, Fourier
Transform with its limitations, Wavelets with its properties, Discrete Wavelet Transform,
Advanced Wavelet Transforms.

Module 4. Image Segmentation: Detection of discontinuation by point detection, line

detection, edge detection, Edge linking and boundary detection, Local analysis, global by
graph, Theoretic techniques, Thresh-holding.
Module 5. Miscellaneous: Morphological image processing, Basic morphological algorithms,
dilation, erosion. Image restoration. Image degradation models. Image Compression. Color
image processing.
Course Outcomes

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Department of Computer Science & Engineering
At the end of this course the students will be able to:
CO 1: To perform and apply digital image processing in real-life applications.
CO 2: Understand various image processing techniques in spatial and frequency domain.
CO 3: Understand and apply image processing techniques to solve various real time problems.
List of Text Books
1. Rafael C. Gonzalez & Richard E. Woods, Digital Image Processing, 4th edition, Pearson
Education, New York, 2018.
2. Tian-Xiao He, Wavelet Analysis and Multiresolution Methods (Lecture Notes in Pure and
Applied Mathematics Book 212), CRC Press; 1st edition, 2021.
3. Anil K. Jain, Fundamentals of Digital Image Processing,1st edition, Pearson India, 2015.
List of Reference Books
1. Sabrine Arfaoui, Anouar Ben Mabrouk, Carlo Cattani, Wavelet Analysis Basic Concepts
and Applications, CRC Press, 2021.
2. R.J. Schalkoff, Digital Image Processing and Computer Vision, John Wiley and Sons, NY,
1989.
3. William K. Pratt, Digital Image Processing, John Wiley and Sons, NY, 2007.

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Subject Name Entrepreneurship Development
Course Code CSE- 6002
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To understand the concept of entrepreneurship and identify the key characteristics of successful
entrepreneurs.
2. To recognize the significance of entrepreneurship in driving economic growth, job creation, and
fostering innovation in various industries.
3. To assess the role of entrepreneurs in societal development, including their contributions to society.
4. To identify the obstacles and barriers that entrepreneurs commonly encounter and explore strategies
to overcome them.
Course Content
Module 1: Introduction to Entrepreneurship: Definition, Characteristics and types of entrepreneur
(social, tourism, women), New Generation of entrepreneurship, Importance of entrepreneurship in the
economy, Factors influencing, Role of entrepreneurs in societal development, Barriers to
entrepreneurship.
Module 2: Entrepreneurial Mindset, Opportunity Recognition and Idea Generation: Developing
entrepreneurial thinking, Identifying and evaluating opportunities, Creativity and innovation in
entrepreneurship. Identifying market gaps and unmet needs, Techniques for generating business ideas,
Evaluating the feasibility and potential of business ideas.
Module 3: Business Planning with Legal and Ethical Considerations: Elements of a comprehensive
business plan, Market analysis and competitive assessment, Financial planning and projections, Risk
assessment and mitigation strategies. Legal forms of business organizations, Intellectual property
protection, Ethical challenges. Financing the Venture: Sources of capital for startups, Bootstrapping and
self-funding, Venture capital and angel investors, Crowd funding and alternative funding options.
Module 4: Marketing Strategies for Start-ups: Identifying target markets and customer segments,
Developing a unique value proposition, Marketing mix: product, price, place, promotion.
Module 5: Operations and Growth Strategies, Managing Entrepreneurial Risks: Managing
resources and operations, Scaling the business and managing growth, Strategic partnerships and
collaborations. Identifying and managing risks in startups, Contingency planning and risk mitigation,
Failure and learning from failure.
Module 6: Social Entrepreneurship and Sustainable Business Models: Concepts and principles of
social entrepreneurship, Creating businesses with a social impact Sustainable business practice.

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Department of Computer Science & Engineering
Course Outcomes
At the end of the course, the student will be able to:
CO 1: Develop a mind-set conducive to entrepreneurship by fostering creative thinking, problem-
solving skills, and a proactive attitude towards opportunities.
CO 2: Understand the various forms of business organizations and ethical challenges faced by
entrepreneurs in their business operations.
CO 3: Identify strategic operations for efficient and sustainable growth of enterprises.
CO 4: Understand the concepts and principles of social entrepreneurship for sustainable business
practices.
List of Text Books
1. Haidi, Patricia and Brush, Teaching Entrepreneurship, USA, Edward Elgar, 2014.
2. Bruce R. Barringer and R. Duane Ireland, Entrepreneurship: Successfully Launching New Ventures,
5th Edition, Pearson, India, 2015.
3. Bill Aulet, Disciplined Entrepreneurship: 24 Steps to a Successful Startup, Ist Edition, Wiley, UK,
2013.
4. Stuart Read, Saras Sarasvathy, Nick Dew, Robert Wiltbank and Anne-Valérie Ohlsson, Effectual
Entrepreneurship, 1st Edition, Routledge; India, 2010.
List of Reference Books
1. Eric Ries, The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create
Radically Successful Businesses, Currency, India, 2011.
2. Steve Blank and Bob Dorf, The Startup Owner's Manual: The Step-By-Step Guide for Building a
Great Company, Wiley, 1st Edition, 2020.
3. Brad Feld and Jason Mendelson, Venture Deals: Be Smarter Than Your Lawyer and Venture
Capitalist, Wiley, 4th Edition, USA, 2015.

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Subject Name Project Implementation
Course Code CSE-6003
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Project Design (CSE-5005)
Course Description:
This course is part of a large project divided into three parts namely project design in fifth
semester, project implementation in sixth semester and project dissertation in seventh.
Course Objective: Students are expected to take the designs and prototypes they developed
during the project design subject in the previous semester and translate them into practical
product implementations with real-life applications in society or industry. This demonstrates
their ability to convert the developed prototypes or working models into viable solutions that
have a positive impact on society or industry. Throughout the product development phase,
students must adhere to applicable codes, regulations, standards, and models, and they are also
required to document their progress through clear and concise technical reports or research
articles. The assignment places particular emphasis on providing a well-defined problem
specification and establishing clear milestones to be achieved in order to effectively address the
identified problem.
Project Group and Supervisor: A project group refers to a collaborative effort where students,
under the guidance of a faculty member, work together on a specific problem, subtask of a larger
problem, or a problem set. In such instances, it is essential to establish clear deliverables for
each student within the group.
Duration: The project Implementation requires a minimum duration of approximately 12 to 16
weeks, with an expected completion by the final week of the semester in the relevant academic
year.
The project work allows for three possible approaches:
1. Pursuing novel and innovative ideas.
2. Extending previous research efforts.
3. Tackling abstract or proof-of-concept problems.
Deliverables: The expected deliverables will consist of one or more of the following:
1. Software/hardware-based product addressing a real-world problem.

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Department of Computer Science & Engineering
2. Embedded systems (software-hardware combined).
3. Research work presented in the form of conference/journal papers.
4. Comparative studies of products, methods, or designs.
5. Results from system design and simulation studies.
6. Theoretical modeling and technical studies.
In cases where certain projects do not neatly fit into the mentioned categories, the respective
supervisor will identify specific deliverables for those projects. This process will require a
comprehensive report and approval from higher authorities.
Expectations from the student: During the project tenure, students are expected to adhere to
the following guidelines:
1. Completing the assigned project work provided by the supervisor and meeting all identified
milestones promptly.
2. Following the work-plan established by their respective supervisor, which includes adhering
to reporting procedures and complying with designated working hours throughout the
project duration.
3. Submitting the project report in the prescribed format to the project coordinator only after
obtaining approval from the supervisor, well before the due date.
4. Taking full responsibility for avoiding plagiarism and respecting copyright issues.
Project Outcomes:
The outcome of the project should be in the form of as followings:
1. Research Paper/Patent/Copyright.
2. Any winning position in hackathon/national or international competitions.
Report: The mid-semester report must strictly adhere to specific guidelines, limiting its length
to 10 pages in a single-column format, using 12-point Times New Roman font. It is essential to
submit the report before the mid-semester examination of the academic year. As for the final
report, it should be a comprehensive summary of the student's work, meeting the minimum
length requirement of 40 pages in a single-column format, with a font size of 12 points and
Times New Roman. The minimum page limit of 40 pages will be strictly enforced, and students
should ensure that their report encompasses all relevant aspects of their work. To maintain
academic integrity, all reports will undergo a plagiarism check using Turnitin or a similar anti-
plagiarism software. Any reports with a similarity of more than 5% to a single source and a
cumulative similarity of over 20% will not be accepted and will not be forwarded to the
evaluation committee supervisor/mentor. It is important to note that once submitted, subsequent

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Department of Computer Science & Engineering
revisions of the report will not be allowed under any circumstances. Therefore, students must
exercise caution and precision when preparing their reports to meet the specified requirements.
General Instructions:
1. Students need to form a group of 3 to 5 students, and consult with the faculties (based on
area of expertise) to work on a project. The same group will be continued for project design,
project implementation and project dissertation. Any changes in group or supervisor(s) at
any stage need proper justification and approval from higher authorities.
2. Students need to submit the project group details to the project coordinator by 2nd week
of starting the semester. Project group details include project title, abstract, details group
member, name of supervisor(s) and approval sign of concern supervisor(s).
3. Evaluation will be carried out as following:
Continuous Evaluation End Term Evaluation
(last week of each month) (at the time of end term exam) Total

Viva-Voce Marks
E1 E2 E3 E4 Report Outcomes
Presentation
10 10 10 10 20 20 20 100

4. Students must report the progress of the project work to the respective supervisor(s) at
least twice in week as given schedule in the class time table.
5. Continuous Evaluation (E1/E2/E3/E4) will be carried out by the respective supervisor(s)
and marks will be submitted to the project coordinator by the end of every month.
6. End Term Evaluation will be carried out by both respective supervisor(s) and project
coordinator.

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Department of Computer Science & Engineering

7th Sem

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
Name of Program B. Tech. Semester-Seventh Year-Fourth
Course Name Soft Computing
Course Code CSE-7001
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)
Course Learning Objectives
1. To learn soft computing techniques and their applications.
2. To understand the concept of fuzziness involved in various systems.
3. To design an algorithm for the optimization problem.
Course Content
Module 1. Introduction: Introduction of soft computing, soft computing vs. hard computing,
various types of soft computing techniques, applications of soft computing, Neuron-Nerve
structure and synapse, Artificial Neuron and its model, activation functions, Neural network
architecture, single layer and multilayer feed-forward networks, McCullochPitts neuron model,
perceptron model, MLP-back propagation learning methods, effect of learning rule coefficient.
Module 2. Architecture: Counter propagation network, architecture, functioning &
characteristics of counter, Propagation network - Hopfield/Recurrent network, configuration,
stability constraints, associative memory, characteristics, limitations and applications, Hopfield
v/s Boltzman machine, Adaptive Resonance Theory – Architecture, classifications,
Implementation and training, Associative Memory.
Module 3. Fuzzy Systems: Different faces of imprecision, inexactness, Ambiguity,
Undecidability, Fuzziness and certainty, Fuzzy sets and crisp sets, Intersections of Fuzzy sets,
Union of Fuzzy sets, the complement of Fuzzy sets, Fuzzy reasoning, Linguistic variables, Fuzzy
propositions, Fuzzy compositional rules of inference, Methods of decompositions and
defuzzification.
Module 4. Optimization Algorithm: Basic concept of Genetic algorithm and detail algorithmic
steps, adjustment of free Parameters, Solution of typical control problems using genetic
algorithm, Concept of some other search techniques like tabu search and ant colony, search
techniques for solving optimization problems.
Module 5. MATLAB Tool Box for Fuzzy Logic and Neural Network: GA application to
optimization problems, Case studies: Identification and control of linear and nonlinear dynamic
systems using MATLAB, Neural Network toolbox, Stability analysis of Neural Network

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Department of Computer Science & Engineering
interconnection systems, Implementation of fuzzy logic controller using MATLAB fuzzy logic
toolbox, Stability analysis of fuzzy control systems.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Understand soft computing techniques and their role in problem solving.
CO 2: Apply basics of Fuzzy logic and neural networks
CO 3: Design and develop Machine Learning techniques with the assistance of MATLAB.
List of Text Books
1. Timothy J. Ross, Fuzzy Logic with Engineering Applications, Third Edition, Wiley India,
2012.
2. Zimmermann H. J., Fuzzy Set Theory and its Applications, Springer International Edition,
2011.
List of Reference Books
1. David E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning,
Pearson Education, 2009.
2. Amit Konar Artificial Intelligence and Soft Computing - Behavioural and Cognitive
Modeling of the Human Brain, CRC Press.
3. Attaway, Dorothy C., Attaway, Stormy. Matlab: A Practical Introduction to Programming
and Problem Solving. Elsevier Science, 2013.

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Industrial Training Seminar
Course Code CSE-7002
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To expose students to the 'real' working environment and get acquainted with the
organization structure, business operations and administrative functions.
2. To have hands-on experience in the student’s related field so that they can relate and
reinforce what has been taught at the Institute.
3. To set the stage for future recruitment by potential employers.
Course Content
Module 1. Guidelines for the industrial training: The four-to-six-week industrial training that
is completed by students during the semester break after the sixth semester will be assessed
following the commencement of the seventh semester. On the basis of the report and the seminar,
students will receive a grade based on their performance. In addition, students are required to
submit to the department a completed training certificate from the organization in which they
participated, in the prescribed form. Without this certificate, the student will be unable to receive
an assessment.
Module 2. Procedures:
a. The Training and Placement (T&P) Cell of the institution shall make the list of
organizations with seats available for students of different branches with a stipend and/or
without a stipend available to the members of the institution. Seats in those industries should
be allocated according to merit/inter-se-merit among applicants/students of different
branches.
b. Students can also apply to appear in several various tests organized by different industries /
organizations of repute for selection of students for industrial training with stipend. The
concerned course coordinator/faculty coordinator will provide all necessary support and
issue a certificate to enable the student to participate in the training program at industry
during the semester break.

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Department of Computer Science & Engineering
c. T & P Cell shall issue a letter of recommendation to any student who desires to engage in
industrial training at an organization/industry of his/her choice. This letter will include
details of the requirements and necessary guidelines for the company/organization.
Module 3. Assessment of Performance: The training is graded based on:
Presentation: 25%, Student’s reports: 40%, Viva voce: 25%, Duration and attendance of the
training: 10%.
Course Outcomes
At the end of this course the students will be able to develop:
CO 1: An ability to work in actual working environment.
CO 2: An ability to write technical documents and give oral presentations related to the work
completed.
CO 3: An ability to promote cooperation and to develop synergetic collaboration between
industry and institute in promoting a knowledgeable society.

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
Name of Program B. Tech. Semester-Seventh Year- Forth
Subject Name Project Dissertation
Course Code CSE-7003
Compulsory/Elective/
Compulsory
Open Elective
Prerequisites
Project Implementation (CSE-6003)
Course Description:
This course is part of a large project divided into three parts namely project design in fifth
semester, project implementation in sixth semester and project dissertation in seventh.
Course Objective: Students are expected to take the designs prototypes and implementation
they developed during the project design and project implementation subject in the previous
semesters and utilize them into develop a functional product while documenting the outcomes
in a well-structured and articulate thesis. Students are required to produce a comprehensive
dissertation that encompasses the entire problem, including an in-depth survey of existing
literature, implementation details, and the diverse results obtained. This dissertation should also
include a description of the proposed solutions to the identified problem. Furthermore, students
are expected to present a demonstration of the solution and explore potential future work on the
same problem. The major focus is on developing and evaluating a functional project while
documenting the outcomes in a clear and well-structured thesis.
Project Group and Supervisor: A project group refers to a collaborative effort where students,
under the guidance of a faculty member, work together on a specific problem, subtask of a larger
problem, or a problem set. In such instances, it is essential to establish clear deliverables for
each student within the group.
Duration: The project dissertation requires a minimum duration of approximately 12 to 16
weeks, with an expected completion by the final week of the semester in the relevant academic
year.
The project work allows for three possible approaches:
1. Pursuing novel and innovative ideas.
2. Extending previous research efforts.
3. Tackling abstract or proof-of-concept problems.
Deliverables: The expected deliverables will consist of one or more of the following:
1. Software/hardware-based product addressing a real-world problem.

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Department of Computer Science & Engineering
2. Embedded systems (software-hardware combined).
3. Research work presented in the form of conference/journal papers.
4. Comparative studies of products, methods, or designs.
5. Results from system design and simulation studies.
6. Theoretical modeling and technical studies.
In cases where certain projects do not neatly fit into the mentioned categories, the respective
supervisor will identify specific deliverables for those projects. This process will require a
comprehensive report and approval from higher authorities.
Expectations from the student: During the project tenure, students are expected to adhere to
the following guidelines:
1. Completing the assigned project work provided by the supervisor and meeting all identified
milestones promptly.
2. Following the work-plan established by their respective supervisor, which includes
adhering to reporting procedures and complying with designated working hours
throughout the project duration.
3. Submitting the project report in the prescribed format to the project coordinator only after
obtaining approval from the supervisor, well before the due date.
4. Taking full responsibility for avoiding plagiarism and respecting copyright issues.
Project Outcomes:
The outcome of the project should be in the form of as followings:
1. Research Paper/Patent/Copyright.
2. Any winning position in hackathon/national or international competitions.
Report: The mid-semester report must strictly adhere to specific guidelines, limiting its length
to 10 pages in a single-column format, using 12-point Times New Roman font. It is essential to
submit the report before the mid-semester examination of the academic year. As for the final
report, it should be a comprehensive summary of the student's work, meeting the minimum
length requirement of 40 pages in a single-column format, with a font size of 12 points and
Times New Roman. The minimum page limit of 40 pages will be strictly enforced, and students
should ensure that their report encompasses all relevant aspects of their work. To maintain
academic integrity, all reports will undergo a plagiarism check using Turnitin or a similar anti-
plagiarism software. Any reports with a similarity of more than 5% to a single source and a
cumulative similarity of over 20% will not be accepted and will not be forwarded to the
evaluation committee supervisor/mentor. It is important to note that once submitted, subsequent

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
revisions of the report will not be allowed under any circumstances. Therefore, students must
exercise caution and precision when preparing their reports to meet the specified requirements.
General Instructions:
1. Students need to form a group of 3 to 5 students, and consult with the faculties (based
on area of expertise) to work on a project. The same group will be continued for project
design, project implementation and project dissertation. Any changes in group or
supervisor(s) at any stage need proper justification and approval from higher authorities.
2. Students need to submit the project group details to the project coordinator by 2nd week
of starting the semester. Project group details include project title, abstract, details group
member, name of supervisor(s) and approval sign of concern supervisor(s).
3. Evaluation will be carried out as following:
Continuous Evaluation End Term Evaluation
(last week of each month) (at the time of end term exam) Total

Viva-Voce Marks
E1 E2 E3 E4 Report Outcomes
Presentation
10 10 10 10 20 20 20 100

4. Students must report the progress of the project work to the respective supervisor(s) at
least twice in week as given schedule in the class time table.
5. Continuous Evaluation (E1/E2/E3/E4) will be carried out by the respective supervisor(s)
and marks will be submitted to the project coordinator by the end of every month.
6. End Term Evaluation will be carried out by both respective supervisor(s) and project
coordinator.

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Department of Computer Science & Engineering

8th Sem

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Eighth Year- Fourth
Course Name Industrial Internship / R&D Project
Course Code CSE-8001
Compulsory/Elective/ Compulsory
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To give the opportunity to apply the knowledge and skills they have acquired on campus in
a real-life work situation.
2. To provide practical, hands-on learning from practitioners in the student’s areas of
specialization.
3. To expose a work environment, common practices, employment opportunities and work
ethics in their relevant field.
Course Content
Industrial Internship: As per the regulations, the student should undergo industrial internship
for a period of six months after 7th semester. Before proceeding on Industrial Internship, students
must seek instructions from the Training & Placement officer or the Faculty coordinator, who
is the in-charge of Industrial Training.
Student: The student is responsible to ensure that all matters relating to the Industrial Training
Programme are conducted in an ethical, conscientious, trustworthy and committed manner.
(A) Before Industrial Training
a. To apply for a suitable Industrial Training, submit an application form (available on
website) through the Training and Placement Officer and faculty Coordinator.
b. Submit one copy of the offer letter for the Industrial Training with application form to
the faculty coordinator (Industrial Training). Students are not allowed to change their
Industrial Training after obtaining the approval and confirmation from the industry.
c. To complete the Industrial Training placement process within the specified time based
on the Industrial Training Programme schedule.
d. To ensure that the Industrial Training is not performed in a family-owned company so
as to avoid conflict of interest.
(B) During Industrial Training
a. Once the student has reached the training place, he / she must send a mail to the Faculty
coordinator Industrial Training / course coordinator and Training and Placement Officer

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that he / she has joined the training from in the industry (Name) __________________
and forward his / her contact nos., E-mail ID and the contact nos. of the company
representative.
b. During the training, students will be given 3-4 practical problems by the industry in
which they are undergoing training. In case the industry does not give them the problems,
the students will themselves formulate minimum three problems and maximum four
problems and carry out detailed study on them and recommend the optimum solution
based on their theory knowledge.
c. To maintain discipline and abide by all rules and regulations enforced by the
organization and to ensure FULL attendance during the Industrial Training duration.
d. To maintain confidentiality and to not disseminate / share any information related to the
organization to third parties.
e. To be responsible for maintaining the security of properties belonging to the
organization.
(C) Assessment components: Assessment within the Industrial Training context aims to
evaluate the student’s work quality and appropriateness to the field of study with
reference to the learning outcomes of the Internship Programmed. Students have to
register for NPTEL courses to earn the same number of theory credits equivalent to
regular courses of the institute and at the end of semester they will have to submit a
course completion certificate. Students should be evaluated by the Training and
Placement Officer, Faculty coordinator. Evaluation methods used may consist of the
following: industrial training report, presentation by the student and viva-voce.
(D) Disciplinary procedures during industrial training programme: Within the training
period, the student is wholly responsible to the organization where he or she has been
placed. This means that the student must observe specified office hours, and must adhere
to all rules and regulations of the organization, just like the other staff within the
organization, during the entire training period.
(E) Departmental report: When the training of the student in an industry is completed, he
/ she should write a report. Report should include a description of the company, the
processes and procedures followed in it. The report should also contain entire studies &
discussions carried out by the students in addition to what he / she has observed during
his / her day-to-day work. The report should be signed by the student and also by his
officer-in-charge of that company.
Format of industrial training report
The following titles must be incorporated in the final industrial training report:
1. Preface/Acknowledgement
2. Certificate with Signatures and Seal of the Industry Person

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3. Contents/Index
4. Introduction about the Industry
5. Training Schedule
6. Work Done / Observations
7. Specific Assignment / Project Handled
8. Learning after Training
9. Summary
Evaluation through seminar presentation : The students will present his/her report through a
seminar, which will be held by an expert committee constituted by the concerned department as
per norms of the institute. The evaluation through seminar presentation will be based on the
following criteria.
a) Quality of material presented.
b) Effectiveness of presentation.
c) Depth of knowledge and skills.
R&D Project: The primary objective of this Research and Development (R&D) project is to
foster innovative thinking, enhance technical skills, and contribute to the advancement of
knowledge in the field of Computer Science and Engineering. Through this project, the aim is
to address a specific challenge, explore new possibilities, or improve existing solutions within
the chosen domain. The project intends to achieve the following objectives:
a. Problem Identification and Analysis: Identify and define a specific problem, challenge, or
opportunity within the Computer Science and Engineering domain. Conduct a thorough
literature review to understand the existing solutions and research gaps.
b. Research and Exploration: Develop a comprehensive understanding of the theoretical
foundations and practical aspects related to the identified problem. Explore various
methodologies, techniques, and approaches that can be applied to address the problem.
c. Innovation and Solution Development: Propose innovative ideas, concepts, or solutions
that have the potential to overcome the identified challenge. Design and develop a novel
approach, system, product, or process based on the chosen solution.
d. Implementation and Experimentation: Implement the proposed solution in a controlled
environment or through simulations. Conduct experiments, tests, or simulations to evaluate
the effectiveness, efficiency, and feasibility of the developed solution.

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e. Data Collection and Analysis: Collect relevant data from experiments, simulations, or real-
world scenarios. Analyse the collected data to quantify the performance of the proposed
solution and draw meaningful conclusions.
f. Comparison and Evaluation: Compare the results of the developed solution with existing
methods or benchmarks. Evaluate the strengths, limitations, and practical applicability of
the proposed solution.
g. Documentation and Presentation: Document the entire research process, including
problem formulation, methodology, implementation details, results, and analysis.Create a
comprehensive report/paper and presentation materials to effectively communicate the
project's findings and contributions.
Project Group and Supervisor:A project group refers to a collaborative effort where students,
under the guidance of a faculty member, work together on a specific problem, subtask of a larger
problem, or a problem set. In such instances, it is essential to establish clear deliverables for
each student within the group.
Duration: The project dissertation requires a minimum duration of approximately 12 to 16
weeks, with an expected completion by the final week of the semester in the relevant academic
year.
The project work allows for three possible approaches:
a. Pursuing novel and innovative ideas.
b. Extending previous research efforts.
c. Tackling abstract or proof-of-concept problems.
Deliverables: The expected deliverables will consist of one or more of the following:
a. Software/hardware-based product addressing a real-world problem.
b. Embedded systems (software-hardware combined).
c. Research work presented in the form of conference/journal papers.
d. Comparative studies of products, methods, or designs.
e. Results from system design and simulation studies.
f. Theoretical modeling and technical studies.
In cases where certain projects do not neatly fit into the mentioned categories, the respective
supervisor will identify specific deliverables for those projects. This process will require a
comprehensive report and approval from higher authorities.
Expectations from the student:During the project tenure, students are expected to adhere to
the following guidelines:
a. Completing the assigned project work provided by the supervisor and meeting all
identified milestones promptly.

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b. Following the work-plan established by their respective supervisor, which includes
adhering to reporting procedures and complying with designated working hours
throughout the project duration.
c. Submitting the project report in the prescribed format to the project coordinator only
after obtaining approval from the supervisor, well before the due date.
d. Taking full responsibility for avoiding plagiarism and respecting copyright issues.
Project Outcomes:
The outcome of the project should be in the form of as followings:
1. Research Paper/Patent/Copyright.
2. Any winning position in hackathon/national or international competitions.
General Instructions:
1. Students need to consult with the faculties (based on area of expertise) to work on a R&D
project. Student may work on this project either as a single member or form a group of
3 to 5 students.Any changes in group or supervisor(s) at any stage need proper
justification and approval from higher authorities.
2. Students need to submit the project group details to the project coordinator by 2nd week
of starting the semester. Project group details include project title, abstract, details group
member, name of supervisor(s) and approval sign of concern supervisor(s).
3. Evaluation will be carried out as following:
Continuous Evaluation End Term Evaluation
(last week of each month) (at the time of end term exam) Total

Viva-Voce Marks
E1 E2 E3 E4 Report Outcomes
Presentation
10 10 10 10 20 20 20 100

4. Students must report the progress of the project work to the respective supervisor(s)
every week as given schedule in the class time table.
5. Continuous Evaluation (E1/E2/E3/E4) will be carried out by the respective supervisor(s)
and marks will be submitted to the project coordinator by the end of every month.
6. End Term Evaluation will be carried out by both respective supervisor(s) and project
coordinator.
Course Outcomes
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Upon completion of this course, the students will be able to:
CO 1: Improve their knowledge and skills relevant to their areas of specialization.
CO 2: Relate, apply and adapt relevant knowledge, concepts and theories within an industrial
organization, practice and ethics.
CO 3: Acquire knowledge and skills to compete in the job market with this experience and
exposure.
CO 4: To grab the opportunities of job in the organizations in which they undergo their
Industrial Training.

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th
Elective 5
Semester

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fifth Year- Third
Course Name Advanced Computer Architecture
Course Code CSE - 5101
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Computer Organization and Architecture (CSE-3003)
Course Learning Objectives
1. To identify the requirement for improving the performance of computing systems.
2. To identify the characteristics and performance of interconnection networks.
3. To design and develop computing systems for different applications.
Course Content
Module 1. Fundamentals of Computer Design: Trends in technology, power, integrated
circuits, cost. Dependability, measuring the performance, Hierarchical memory system, main,
cache and auxiliary memory, I/O subsystem, Average and worst-case access time, multi-level
cache memory, Split Cache, Cache Consistency.
Module 2. Classification of computer architecture: SIMD, MIMD, SISD and MISD
Processing unit design: Data path implementation, Microprogrammed execution, pipelining:
Pipelining fundamentals, Linear and Nonlinear Pipeline Processors, Arithmetic and instruction
pipelining, Pipeline hazards, Techniques for overcoming or reducing the effects of various
hazards.
Module 3. Instruction level parallelism (ILP): Concept and Challenges of ILP, Basic compiler
techniques, reducing branch costs, overcoming data hazards, multiple issue and static
scheduling, dynamic scheduling, speculation, Limitations of instruction level parallelism,
Multithreading and thread level parallelism, VLIW and Vector processors.
Module 4. Multiprocessors Architectures: Introduction, Inter processor Communication
Mechanisms, System Deadlock and Protection, Multiprocessor Scheduling Strategies,
Taxonomy of parallel architecture, Centralized shared-memory architecture: synchronization,
memory consistency, interconnection networks.
Module 5. Recent advances in computer architecture: Introduction to clusters, Grids, Cloud,
Fog and Mist Computers, Interconnection Networks: Topology, Different interconnection
networks, Routing mechanism.
Course Outcomes
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At the end of the course, the student will be able to:
CO 1: To know about the classes of computers, and new trends and developments in
computer architecture.
CO 2: Understanding the key requirement of computer system architecture.
CO 3: Designing parallel algorithms for different architectures.
List of Text Books
1. John L. Hennessy and David A. Patterson, Computer Architecture: A Quantitative Approach
5th Edition, Elsevier, 2012.
2. Kai Hwang, Advanced Computer Architecture, 2nd Edition, Tata McGraw Hill, 2008.
List of Reference Books
1. Rajaraman, Vaidyeswaran, and RAM MURTHY C. SIVA. Parallel Computers Architecture
and Programming, PHI Learning Pvt. Ltd., 2016.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fifth Year- Third
Subject Name Advances in Database Management Systems
Course Code CSE-5102
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Database Management System (CSE-3002)
Course Learning Objectives
1. To understand the emerging database systems and the current issues.
2. To understand the principles of distributed database systems.
3. To study advanced techniques for concurrency control in multi-user database
environments.
Syllabus
Module 1: Advanced Database Concepts: Recap of fundamental Database Concepts,
Advanced ER Modeling, Advanced Relational Database Design, Cost-based Query
Optimization and Execution Plans, Data Storage and File Structures for efficient I/O, Buffer
Management and Caching Strategies.
Module 2: Distributed Databases and Replication: Concepts of Distributed Databases and
Distributed Data Storage, Distributed Database Architecture and Design, Replication
Strategies and Distributed Query Processing.
Module 3: Transaction Management and Concurrency Control: ACID properties of
Transactions, Isolation levels and Transaction Scheduling, Concurrency Control Techniques:
Locking, Timestamps, and Multi-Version Concurrency Control (MVCC).
Module 4: Emerging Database Technologies: Introduction to NoSQL Databases and their
Categories, NewSQL Databases: Characteristics and Use Cases, In-memory Databases and
Columnar Storage.
Module 5: Data Warehousing and Online Analytical Processing (OLAP): Data
Warehousing Architecture and ETL Processes, Star and Snowflake Schemas, Data Cubes, and
Aggregation, OLAP Operations: roll-up, drill-down, slice-and-dice.
Module 6: Big Data Management and NoSQL Databases: Introduction to Big Data and
Challenges, Hadoop Ecosystem: HDFS, MapReduce, Hive, Spark, NoSQL Databases for Big
Data: MongoDB, Cassandra.

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Project: Students work on a comprehensive database design and optimization project, Project
presentation and demonstration.

Course Outcomes
Upon successful completion of this course, students will be able to:
CO 1. Implement and manage transactions for ensuring data integrity and consistency.
CO 2. Learn emerging database technologies and their engineering applications.
CO 3. Analyze, design, and manage distributed databases and data warehousing solutions.
CO 4. Identify and address performance bottlenecks in real-world database applications.
List of Text Books
1. Hoffer, J. A., Venkataraman, R., Topi, H. (2019). Modern Database Management, Global
Edition. United Kingdom: Pearson Education.
2. Ray, C. (2020). Advanced Database System. (n.p.): Independently Published.
3. Bharat K. Bhargava, Nabil R. Adam. (2014). Advanced Database Systems. United
States: Springer.
List of Reference Books
1. McFadden, F. R., Hoffer, J. A., Prescott, M. B. (1998). Modern Database Management.
Singapore: Addison-Wesley.
2. Guoliang Li, Joao Gama, Juggapong Natwichai, Jun Yang, Yongxin Tong, (2019).
Database Systems for Advanced Applications: 24th International Conference, DASFAA
2019, Chiang Mai, Thailand, Proceedings, Part II. (2019).

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fifth Year- Third
Course Name Unix and Shell Programming
Course Code CSE-5103
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Operating System (CSE-4003)
Course Learning Objectives
1. To understand the UNIX operating system and its memory management, input/output
processing, internal and external commands.
2. To learn the file systems and Process Management of UNIX.
3. To apply and explore the use of operating system utilities such as text editors.
4. To understand Shell Scripting and Shell Programming.
Course Content
Module 1. Overview of UNIX: Architecture, Kernel & Shell, Installation Process, Features,
Internal And External Commands, Basic Commands: cal, date, echo, bc, script, passwd, PATH,
who, uname, pwd, cd, mkdir, rmdir etc. Command Structure, Shell Script & Shell
Programming.
Module 2. File System: Definition of File System, Boot Block, Super Block, Inode. File
creations and its related commands cat, cp, rm, mv, more, file, ls, wc, pg, cmp, comm, diff.
Zipping & unzipping files, gzip, tar, zip, df, du, mount, umount, etc. The vi editor. File
Permissions with chgrp & chmod. Process Control: Viewing a Process, Command to display
Process, Process Attributes, Process States, Process Fields, ps Commands options, Handling
Jobs, Foreground & Background Jobs.
Module 3. Redirection & Pipes: Standard I/O Streams, Redirection & Pipes, Command
Execution, Command-Line Editing, Quotes. Filters: Filters, Concatenating, Beginning and End
of files, Cut and Paste, Sorting, Translating Characters, Ordering a File. Regular Expressions:
Atoms, operators, grep, sed, awk etc.
Module 4. Shell Scripting: Introduction to Shell, Types of Shell, C shell features, writing first
script writing script, Executing & Debugging script. Shell Programming: Shell variables,
Output, Input, exit Status of a Command, Branching Control Structures, Loop-Control
Structure, and Continue and break Statements, Expressions, Command Substitution, Command
Line Arguments and Functions.
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Module 5. Unix system administration: Adding and removing users. User accounting.
Adding and removing hardware. Performing backups and restore. Disk space management.
Unix system administration: Configuring the kernel. Network management in UNIX.
Performance analysis. Unix Desktop. Installation of Unix/Linux system – Unix/Linux
Installation requirement, complete Procedure steps, Partitioning the Hard drive, System startup
and shut-down process, init and run levels. File system mounting, lpstat, backup strategy,
installing software on Unix/Linux.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Identify and use UNIX utilities to create and manage simple file processing operations,
Organize directory structures with appropriate security.
CO 2: Utilize the UNIX operating system to enhance productivity and streamline system
management
CO 3: Monitor system performance metrics and acquire proficiency in creating and executing
shell scripts.
CO 4: Use the shell scripts in designing a program for engineering problems.
List of Text Books
1. B.A. Forouzan, Unix & Shell Programming, Cengage Learning,2004.
2. V. Murthy, Introduction to Unix &Shell, Pearson Edu,2005.
3. S. Das,Unix Concept & Application, TMH,4th Edition,2017.

List of Reference Books

1. Venkateswarlu, N. B. Linux programming tools unveiled. BS Publications, 2007.

2. Kanetkar, Yashavant P. UNIX shell programming. BPB publications, 2003.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Fifth Year- Third
Course Name TCP IP Protocol Suite
Course Code CSE-5104
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Computer Networks and Communication (CSE-4002)

Course Learning Objectives

1. To build an understanding among students about the fundamental concepts of computer
networking, protocols, architectures, and applications.
2. To understand design issues of TCP/IP layered architecture.
3. To learn how to use a protocol analyzer and common IP software tools to document and troubleshoot
a TCP/IP network, including basic addressing and setup.
Course Content
Module 1. Introduction to Network Models: Layered Tasks, The OSI Model, Layers in OSI
Model, TCP/IP Protocol suite, Connecting devices at different layers, Principals of physical
layer: Media, Bandwidth, Data rate and Modulations.
Module 2. Network Layer: Introduction to Network Layer, IPv4 Addresses, Delivery and
Forwarding of IP Packets, Internet Protocol Version 4 (IPv4), Address Resolution Protocol
(ARP), Internet Control Message Protocol Version 4 (ICMPv4), Mobile IP, Unicast Routing
Protocols (RIP, OSPF, and BGP), Multicasting and Multicast Routing Protocols. Mobile
Network Layer: Entities and Terminology, IP Packet Delivery, Agents, Addressing, Agent
Discovery, Registration, Tunneling and Encapsulation, Inefficiency in Mobile IP.
Module 3. Transport Layer: Introduction to the Transport Layer, User Datagram Protocol
(UDP), Transmission Control Protocol (TCP), Stream Control Transmission Protocol (SCTP),
Flow Control, Error Control, Congestion Control. Congestion and Quality of Service: Data
Traffic, Congestion, Congestion Control, Congestion Control in TCP, Congestion Control in
Frame Relay, Source Based Congestion Avoidance, DEC Bit Scheme, Quality of Service,
Techniques to Improve QOS: Scheduling, Traffic Shaping, Admission Control, Resource
Reservation, Integrated Services and Differentiated Services. Mobile Transport Layer:
Classical TCP Improvements, Indirect TCP, Snooping TCP, Mobile TCP, Fast Retransmit/Fast
Recovery, Transmission, Timeout Freezing, Selective Retransmission, Transaction Oriented
TCP.

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Module 4. Application Layer: Introduction to the Application Layer, Host Configuration:
DHCP, Domain Name System (DNS), Remote Login: TELNET and SSH, File Transfer: FTP
and TFTP, World Wide Web and HTTP, Electronic Mail: SMTP, POP, IMAP, and MIME,
Network Management: SNMP, Multimedia.
Module 5. Introduction of Next Generation Networks: IPv6 Addressing, IPv6 Protocol,
ICMPv6, Security, Cryptography and Network Security, Internet Security.
Course Outcomes
At the end of this course the student will be able to:
CO 1: Interpret the different building blocks of the Communication network and its
architecture.
CO 2: Understand the formats of a frame, a packet and a segment.
CO 3: Understand the fundamentals of network traffic and collision avoiding techniques.
CO 4: Classify routing, congestion and IPV4/IPV6 addressing scheme.
CO 5: Design subnetting and analyze the performance of network layer protocols.
List of Text Books
1. Forouzan, Behrouz A. TCP/IP protocol suite. McGraw-Hill Higher Education, 2017.
2. Forouzan, B. A., & Fegan, S. C. Data Communications and networking. Tata McGraw-Hill
Education, 2007.
List of Reference Books
1. Hassan, M., & Jain, R., High performance TCP/IP networking: Concepts, Issues, and
Solutions. Prentice Hall, 2004.
2. Comer, D., & Stevens, D. L., Internetworking with TCP/IP, Addison-Wesley Professional,
6th edition, 2015.
3. Bonaventure, O., Computer networking: Principles, Protocols and Practice, 2016.
4. Schiller. Mobile Communications 2/e, 2008.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester-Fifth Year- Third
Subject Name Microprocessors and Microcontrollers
Course Code CSE-5105
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Computer Organization and Architecture (CSE-3003)
Course Learning Objectives

1. To analyze 8085 microprocessors pin configuration and architecture.

2. To comprehend, and apply 8086 microprocessors pin configuration and architecture.
3. To examine memory interfacing concepts.
4. To apply 8051 microcontroller fundamentals, including architecture, I/O configuration. set,
and practical applications.
Course Content

Module 1. Introduction & Architecture of 8085: Introduction to Microprocessor, its

historical background and Microprocessor applications. INTEL 8085: Microprocessor
Architecture and its operations, 8085 MPU and its architecture, 8085 instruction cycle and
timing diagram, Memory read and Memory Write operations, Instructions for 8085: Data
movement, Arithmetic and logic; and branch control instructions. RISC v/s CISC processors.

Module 2. Introduction & Architecture of 8086: INTEL 8086: Introduction, 8086

Architecture, real and Protected mode, Memory Addressing, Memory Paging, Addressing
Modes. Pin diagram of 8086.

Module 3. Memory Interfacing: Basic Interfacing devices: Memory interfacing, 8255,

8253, 8259, 8257, 8251, Interfacing A/D and D/A converters, Case studies of microprocessor
based systems. Salient features of advanced microprocessors: 80286,386,486, Pentium.
Module 4. Introduction to 8051 microcontrollers: Introduction to 8051 microcontrollers,
its architecture, pin description, I/O configuration, interrupts, addressing modes, an overview of
8051 instruction set, Microcontroller applications.
Course Outcomes:
At the end of this course the students will be able to:

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CO 1: Apply knowledge of 8085 microprocessor fundamentals and architecture.
CO 2: Illustrate 8086 microprocessor principles, encompassing architecture, operating modes.
CO 3: Determine memory interfacing techniques, interfacing devices.
CO 4: Classify 8051 microcontrollers, encompassing architecture, pin configuration,
addressing modes, and instruction set.
List of Textbooks

1. Gaonkar, Ramesh S., Microprocessor Architecture, Programming, and Applications with the
8085. United Kingdom: Prentice Hall, 2013.
2. D. V. Hall, Microprocessor and Interfacing, McGraw Hill, 2021.
List of Reference Books

1. Kamal, Raj. Microcontrollers: Architecture, Programming, Interfacing and System Design.

India: Pearson Education, 2011.
2. Brey, Barry B. The Intel Microprocessors: India: Pearson Prentice Hall, 2011.

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th
Elective 6
Semester

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester-Sixth Year-Third
Course Name Pattern Recognition
Course Code CSE-6101
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Engineering Mathematics -III (Numerical Methods and Statistics) (CSE-4001)
Course Learning Objectives
1. To learn the designing of automated systems that improve their own performance through
experience.
2. To learn the pattern based designing principle.
3. To learn to apply the pattern based analysis and design to the software to be developed.
Course Content
Module 1 Introduction: Definition of Pattern Recognition, Feature Detection, Feature selection ,
Classification Review of Probability Theory, Conditional Probability, Regular Pattern, Irregular Pattern,
Approaches to Pattern Recognition, Parametric, Non-Parametric Approaches, Search methods, Pattern
Recognition Applications.

Module 2 Discriminant functions: Decision surfaces, Classification algorithms: Naive Bayes, Random
Tree, Decision Trees, Random Forest, Classification using SVM, Classification Review of Probability
Theory, Classifier Ensembles, Linear Regression, Types of Clustering, K-Mean Clustering, Iso-data
Clustering, Clustering Metrics, Clustering applications, Clustering tendency.

Module 3 Fuzzy K-Mean: Classifier Ensembles, Linear Regression, Semi Supervised learning, Fuzzy
variants of classification, clustering algorithms, Neural networks fundamentals, Clustering, Vector
Quantization.

Module 4 Genetic Algorithms: Genetic based approaches for Pattern recognition, Self-organizing
maps, Advantages and Disadvantages of Neural based approaches for Pattern Recognition, Genetic
Algorithms Combination for Multiple Classifiers.

Module 5 Optimization Techniques: Fisher Discriminant Sufficient Statistics, Linear

Discriminant/Perceptron Learning, Optimization by Gradient Descent, Density Estimation, Parzen
Estimation, Unsupervised Learning, Multi-layer Perceptron, Reinforcement Learning with Human
Interaction.
Course Outcomes
At the end of this course the student will be able to:
CO 1: Learn to design patterns as a solution, and they can solve many problems that can be encountered
in the future.
CO 2: Learns to analyze the success of a feature recognition system and understands the basic structure
of pattern recognition systems.
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CO 3: Learns to Select suitable pattern recognition techniques and effectively apply it to solve real-
world problems.

List of Text Books

1. Robert B. Macy, Abhijit S. Pandya, Pattern Recognition with Neural Networks in C++ , CRC Press
, 2020.
2. Earl Gose, Richard Johnson Baugh, Steve Jost, Pattern recognition and image processing,First
Edition, Pearson, 2015.
3. William Gibson, Pattern Recognition, Penguin Books Limited, 2004.
4. Richard Duda, Peter Hart, David Stork, Pattern Classification, Second edition John Wiley & Sons,
2000.
List of Reference Books
1. Jianxin Wu, Essentials of Pattern Recognition-An Accessible Approach, Cambridge University
Press, 2020.
2. Sergios Theodoridis, Konstantinos Koutroumbas, Pattern Recognition, Third Edition , Academic
Press, 2006.
3. Christopher M Bishop, Pattern Recognition and Machine Learning, Springer , 2006.

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Department of Computer Science & Engineering
Name of Program B. Tech Semester- Sixth Year-Third
Course Name Real Time System
Course Code CSE-6102
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Computer Organization and Architecture (CSE-3003), Operating System (CSE-4003)
Course Learning Objectives
1. To learn various techniques to handle multiple processes at one time, ensuring that these
processes respond to events within a predictable time limit.
2. To understand the basics of tasks , scheduling,clock synchronization and real time
communication.
Course Content
Module 1: Introduction: Real Time System Characteristics, Issues in Real Time Computing,
Structure of a Real Time System, Task classes, Performance Measures for Real time Systems,
Task Assignment and Scheduling, Cyclic Scheduler, Event – Driven Scheduling, Rate
Monotonic Scheduler, RMA Scheduling, Classical uniprocessor scheduling algorithms.
Module 2: RM algorithm with different cases-Priority ceiling precedence constraints- using
of primary and alternative tasks, Deadline Monotonic Scheduling and Other Issues, Resource
Sharing Among Real-Time Tasks.
Module 3: Real Time Task Scheduling on Multiprocessors and Distributed Systems, Clock
Synchronization in Distributed Real-Time Systems, A Few Basic Issues in Real-Time Operating
Systems.
Module 4: Real Time Databases: Real time Vs General purpose databases, Main Memory
Databases, Transaction priorities, Transaction Aborts, Concurrency control issues, Disk
Scheduling Algorithms, two phase Approach to improve Predictability, Maintaining
Serialization Consistency, Databases for Hard Real Time System.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Develop real-time algorithm for task scheduling.
CO 2: Learned the working of real-time operating systems and real-time databases.
CO 3: Work on design and development of protocols related to real-time communication.

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List of Text Books
1. Phillip A. Laplante, Real-Time Systems Design and Analysis, 4th edition, Wiley, 2011.
2. Albert M. K. Cheng, Real-Time Systems: Scheduling, Analysis, and Verification, 1st edition
Wiley-Inter science, 2002.
List of Reference Books
1. Andrews Tanenbaum, Herbert Bos, Modern Operating Systems, 4th edition, Pearson, 2008.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Mobile Computing
Course Code CSE-6103
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Computer Networks and Communication (CSE-4002)
Course Learning Objectives
1. To explain the mobile computing nomenclature to describe and analyze existng mobile computing
frameworks and architectures.
2. To understand wireless LAN media access methods for effective network communication..
3. To define mobile technologies in terms of hardware, software, and communications..
4. To evaluate the effectiveness of different mobile computing frameworks.
Course Content
Module 1. Introduction to mobile computing: Definitions, Principles, Classification &
Overview of Devices, Operating Systems, Adaptability Issues (transparency, Environmental
Constraints, application aware adaptation), Mechanisms for Adaptation and Incorporating
Adaptations. Mobility Management: Mobility Management, Location Management Principle
and Techniques, PCS Location Management Scheme.
Module 2. Wireless transmission: Introduction of wireless and mobile systems (wireless
LANs, Cellular Systems, Sensor Networks, etc.), Multipath Propagation, Hidden & Exposed
Terminals. Medium Access Control & Protocols: SDMA, FDMA, TDMA, DAMA, FAMA,
PRMA, Reservation TDMA, polling, CSMA/CA, CDMA etc.
Module 3. Mobile Network Layer: Mobile IP, IP Packet delivery, Dynamic Host
Configuration Protocol (DHCP), Database systems in mobile environments, World Wide Web
and Mobility. Mobile Transport Layer: Traditional TCP, Indirect TCP, Snooping TCP, Mobile
TCP, Fast retransmit/fast recovery, Transmission/time-out freezing, Selective retransmission,
Transaction oriented TCP.
Module 4. Mobile System Development & Support: File Systems; World Wide Web, HTTP;
HTML; System Architectures; WAP; Architecture, Wireless Datagram Protocol, Wireless
Transport Layer Security, Wireless Transaction Protocol, Wireless Session Protocol, Wireless
Application Environment. Introduction to mobile middleware. Middleware for application

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development: adaptation, Mobile agents. Service Discovery Middleware: Service Discovery &
standardization Methods.
Module 5. Mobile Ad hoc Networks (MANETs): Properties of a MANET, spectrum of
MANET applications, localization, MAC issues, Routing protocols, global state routing (GSR),
Destination sequenced distance vector routing (DSDV), security in MANETs. Protocols and
Tools: Wireless Application Protocol-WAP. Bluetooth and J2ME.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Explore mobile computing fundamentals, including adaptation and mobility
management principles.
CO 2: Examine wireless transmission, MAC protocols, and network access mechanisms.
CO 3: Learn about mobile network and transport layer technologies and enhancements.
CO 4: Understand mobile system development, middleware, and mobile ad hoc networks.
List of Text Books
1. Frank Adelstein, Sandeep K.S. Gupta, Golden Richard III, Loren Schwiebert,
Fundamentals of Mobile and Pervasive Computing, McGraw Hill, 2005.
2. Uwe Hansmann, Lothar Merk, Martin S. Nicklous, Thomas Stober, Principles of mobile
computing, Springer, 2006.
3. Matthew S. Gast, 802.11 Wireless Networks: The Definitive Guide, O’Reilly, 2nd
Edition, 2005.
4. William Stallings, Wireless Communications and Networks, Pearson, 2nd Edition, 2009.
List of Reference Books
1. J. H. Schiller, Mobile Communications, Pearson Education India, 2nd Edition, 2008.
2. T. S. Rappaport, Wireless Communication: Principles and Practice, Pearson Education
India, 2nd Edition, 2009.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Malware Analysis
Course Code CSE-6104
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Cryptography and Cyber Security (CSE-5001)
Course Learning Objectives
1. To understand malware taxonomy and life cycle.
2. To analyze malware samples using static, dynamic analysis, and reverse engineering
techniques.
3. To detect and analyse obfuscation and anti-malware techniques.
Course Content
Module 1. Introduction to Malware: Malware Taxonomy, Types of malware analysis,
Malware attack life cycle, The Combat Teams, Anti-malware Products, Reverse Engineering
for Windows and Linux systems.
Module 2. Static and Dynamic Malware Analysis: Determining the file types, Fingerprinting
the Malware, Header analysis, Extracting Strings, Classifying Malware using Tools, Inspecting
PE header Comparing and classifying the Malware using YARA. Dynamic Malware Analysis,
Behavior Events Analysis using ProcMon and Autoruns, Detecting Code Injection, Automated
dynamic analysis, Sandboxing: Tools and Techniques, Virus Total.
Module 3. Debugging the Malware: Debugging concepts, launching, processing, and
executing processes using Low-Level Language: Registers, Memory addressing, Opcode bytes
- Builder and debugger: IDA Pro, Ollydebug -Windows API libraries - Packing and Encryption.
Module 4: File Obfuscation - Binary Obfuscation Techniques - Assembly of data - Encrypted
data identification - Decrypting with x86dbg - Control flow flattening obfuscation - Garbage
code insertion - Dynamic library loading.
Module 5: Memory Forensics and Volatility: Memory Dumping, Windows Memory Toolkit,
Accessing VM Memory Files, Investigating Processes in Memory Dumps, Code Injection and
Extraction, Detecting and Capturing Suspicious Loaded DLLs, Finding Artifacts in Process
Memory, Identifying Injected Code with Malfind and YARA. Researching and Mapping Source
Domains/IPs: Using WHOIS to Research Domains, DNS Hostname Resolution, Querying

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Passive DNS, Checking DNS Records, Reverse IP Search New Course Form, Creating Static
Maps, Creating Interactive Maps.

Course Outcomes

At the end of this course, the students will be able to:

CO 1: Apply the static and dynamic malware analysis on emerging samples.
CO 2: Analyze the executable file and malware classification.
CO 3: Explore the anti-malware analysis techniques.

List of Text Books

1. Monnappa K A, Learning Malware Analysis, Packet publishing, O’Reilly, 2018.
List of Reference Books
1. Abhijit Mohanta, Anoop Saldanha, Malware Analysis and Detection Engineering a
Comprehensive Approach to Detect and Analyze Modern Malware, 1st edition, Apress,
2020.
2. Michael Sikorski, Andrew Honig, Practical Malware Analysis: The Hands-On Guide to
Dissecting Malicious Software, No Starch Press, 2012.
3. Dang, Gazet, Bachaalany, Practical Reverse Engineering, Wiley, 2014.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year-Third
Course Name Deep Learning and its Applications
Course Code CSE-6105
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)
Course Learning Objectives
1. To introduce deep learning basics like neural networks, activation functions, and layers.
2. To demonstrate hyperparameters optimization operations to improve the performance of deep
learning models.
3. To learn deep learning modules for their application in emerging research domains.
Course Content
Module 1. Introduction to Deep Learning: Basics of Deep Learning, Performance
Measures, Decision Surfaces, Bayesian Learning, Linear Classifiers and Machines, Hinge Loss,
Optimization Techniques, Gradient Descent, Batch Optimization.
Module 2. Basics of Neural networks: Introduction to Neural Network, Overview and
representation of Neural Network, Activation Function, Perceptrons and their limitations,
Multilayer network, Feed-forward Neural Networks, Back Propagation learning, Recurrent
Neural Networks, Artificial Neural Networks.
Module 3. Convolution neural networks: Convolutional Neural Network, Building blocks of
CNN, Unsupervised Learning with Deep Network, Parameter Initialization, Regularization to
CNN, Optimizer selection, Recent advancement in CNN Architectures, Applications areas of
CNNs, Transfer Learning.
Module 4. Recent Trends in Deep Learning: AlexNet, VGGNet, GoogLeNet, ResNet,
DenseNet, Image Denoising, Classical Supervised Tasks with Deep Learning, Semantic
Segmentation, Object Detection.
Module 5. Enhanced Deep Learning models: Variational Autoencoder, Generative
Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam.
Course Outcomes

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At the end of the course the student will be able to:
CO 1. Explain and differentiate between the basic principles behind neural network and deep
learning and compare the modelling aspects of neural network architectures.
CO 2. Implement and apply deep learning modules in real-datasets.
CO 3. Learn the applications of deep learning algorithms and their incorporation in the society.
CO 4. Understand and critically analyze the research work that applies the concepts of deep
learning in every possible domain.
List of Text Books
1. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2023.
2. Liu, Yuxi (Hayden), and Mehta, Saransh. Hands-On Deep Learning Architectures with
Python: Create Deep Neural Networks to Solve Computational Problems Using TensorFlow
and Keras. India, Packt Publishing, 2019.
List of Reference Books
1. Kelleher, John D, Deep Learning, United Kingdom, MIT Press, 2019.
2. Nikhil Ketkar, Deep Learning with Python: A Hands-on Introduction, Apress, 2017.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Fundamentals of Big Data
Course Code CSE - 6106
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Database Management System (CSE-3002)
Course Learning Objectives
1. To understand terminologies and the core concepts behind big data problems, applications,
systems and the techniques.
2. To be familiar with the specific features of big data, such as big data analytics and big data
applications.
3. To investigate various case studies involving recent developments in Hadoop, Spark, and
Map Reduce programming.
Course Content
Module 1. Introduction to Big Data: Big Data & Why is it Important, Characteristics of Big
Data, Source of Big Data, Challenges and applications of Big Data, Data in warehouse, Big data
importance, Big data application, Big Data in Businesses, and types of Big Data analytics.
Module 2. Hadoop Framework: Requirement of Hadoop Framework, Design principle of
Hadoop, Core components of the Hadoop ecosystem, NoSQL database, Storing Data in Hadoop,
Hadoop Distributed File System (HDFS), HDFS Architecture, HBASE, HBase Architecture,
Map Reduce YARN, HBase, Hive, Pig, Sqoop, Zookeeper, Flume, Oozie etc.
Module 3. Introduction to MapReduce: Understanding MapReduce functions - Scaling out -
Anatomy of a MapReduce Job Run - Failures - Shuffle and sort - MapReduce types and formats
- features - counters - sorting - MapReduce Applications – Configuring and setting the
environment - Unit test with MR unit - local test, Map Reduce Programming Model.
Module 4. Spark: Installing spark, Spark applications, Jobs, Stages and Tasks, Spark Model of
Parallel Computing, RDD, Types of RDDs, Spark Job Scheduling, Dataframe API, Data
Representation in Data Frames, Introduction to Big Data applications (Graph Processing),
Introduction to Pregel.
Course Outcomes
At the end of this course the students will be able to:

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CO 1: Understand the attributes of big data and the principles underlying the Hadoop
environment.
CO 2: Apply the MapReduce programming methodology to massive data processing.
CO 3: Study Spark's applications for processing massive data.
CO 4: Design software for Hadoop-based big data applications.
List of Text Books
1. Holden Karau et al., Learning Spark: Lightning-Fast Big Data Analysis, O'Reilly Media,
2015.
2. Bart Baesens, Analytics in a Big Data World: The Essential Guide to Data Science and
its Applications, Wiley Publishers, 2015.
3. David Loshin, Big Data Analytics: From Strategic Planning to Enterprise Integration
with Tools, Techniques, NoSQL, and Graph, Morgan Kaufmann/Elsevier Publishers,
2013.
4. Chris Eaton, Dirk deroos et al., Understanding Big data, McGraw Hill, 2012.
List of Reference Books
1. Simon Walkowiak, Big Data Analytics with R, PackT Publishers, 2016.
2. EMC Education Services, Data Science and Big Data Analytics: Discovering,
Analysing, Visualizing and Presenting Data, Wiley publishers, 2015.
3. Dietmar Jannach and Markus Zanker, Recommender Systems: An Introduction,
Cambridge University Press, 2010.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester – Sixth Year- Third
Course Name Ad-hoc Networks
Course Code CSE-6107
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Computer Networks and Communication (CSE-4002)
Course Learning Objectives
1. To understand the concepts of Ad-hoc and Wireless networks.
2. To understand the protocols and standards for Ad-hoc networks.
3. To understand the node, network architecture, security challenges, and energy
management in Ad-hoc networks
Course Content

Module 1. Introduction: Wired and Wireless Networks, Ad-hoc Networks, Issues in Ad-hoc
Wireless Networks, MAC Protocols for Ad-hoc Wireless Networks: Introduction, Issues in
Designing a MAC Protocol, Design Goals of MAC Protocols, Classification of MAC protocols,
Contention-Based Protocols, Contention-Based Protocols with Reservation Mechanisms,
Contention-Based Protocols with Scheduling Mechanisms, MAC Protocols that use Directional
Antennas.

Module 2. Routing protocols: Routing Protocol for Ad-Hoc Networks, Classifications of

Routing Protocols, Power Aware Routing Protocols, Multicast routing in Ad Hoc Wireless
Networks: Issues in Designing a Multicast Routing Protocol, Classifications of Multicast
Routing Protocols, Energy Efficient Multicasting, Multicasting with Quality of Service
Guarantees.

Module 3. Security challenges: Application Dependent Multicast Routing Security Protocols

for Ad Hoc Wireless Networks, Network Security Requirements. Issues and Challenges in
Security Provisioning, Network, Security Attacks, Key Management, Secure Routing in Ad Hoc
Wireless Networks.

Module 4. Energy Model and Energy Management in Ad-hoc Wireless Networks:

Classification of Energy Management Schemes, Transmission Power Management Schemes,
System Power Management Schemes.

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Module 5. Open research issues: Introduction to Clustering and Localization in Ad-hoc and
wireless networks, Evaluation methods.

Course Outcomes
At the end of the course, the student will be able to
CO 1: Understand MAC protocols and network design for ad-hoc networks.
CO 2: Explore routing, multicast, and security in wireless ad-hoc systems.
CO 3: Master energy management schemes for efficient network operation.
CO 4: Investigate open research topics, including clustering and localization in wireless
networks.
List of Text Books
1. Shashikant v. Athawale, Ad-hoc and Wireless Sensor Network, 1st Edition, Pearson
Education, 2021.
2. C. Siva Ram Murthy & B. S. Manoj: Ad-hoc Wireless Networks, 2nd Edition, Pearson
Education, 2011.
List of Reference Books
1. Sudip Misra, Isaac Woungang, Subhas Chandra Misra, Guide to Wireless Ad Hoc
Networks, Springer Science & Business Media, 2009.
2. B. Tavli and W. Heinzelman, Mobile Ad Hoc Networks: Energy Efficient Real Time
Communication, Springer, 1st Edition, 2006.
3. G. Anastasi, E. Ancillotti, R. Bernasconi, and E. S. Biagioni, Multi-Hop Ad Hoc Networks
from Theory to Reality, Nova Science Publishers, 2008.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Cyber Crime and Information Warfare
Course Code CSE-6108
Compulsory/Elective/Open Elective
Elective
Prerequisites:
Cryptography and Cyber Security (CSE-5001)
Course Learning Objectives :
1. To understand the various forms of cyber threats and Identify common vulnerabilities in
information systems.
2. To explore the principles and practices of cybercrime investigations, including digital
forensics techniques.
3. To examine the concept of information warfare and its role in modern conflicts.
Course Content
Module 1. Introduction of Cybercrime: The evolution of Cybercrime, Challenges of Cyber-
Crime, Categorizing Cybercrime, Cyber terrorism, Virtual crimes, and Perception of Cyber
criminals, their motives, type, and organization.
Module 2. Cyber Crime Cases: Money laundering, bank fraud, Avance fee fraud, Malicious
agents, Stock robot manipulation, Identity theft, Digital piracy, Intellectual property crime,
Internet gambling, Tools used to implement attacks, System protection against attacks.
Module 3. Perception of Cyber Criminals: Hackers, insurgents and extremist groups,
interception of data, surveillance and protection, criminal copyright infringement, cyberstalking,
hiding crimes in cyberspace and methods of concealment.
Module 4. Privacy in CyberSpace: Web defacements and semantic attacks, DNS attacks, code
injection attacks, challenges of fighting cybercrime: opportunities, general challenges, legal
challenges.
Module 5. Information Warfare Concept: Information as an intelligence weapon, attacks and
retaliation, attack and defense, information warfare strategies and tactics from a military
perspective, information warfare strategies and tactics from a corporate perspective, strategies
and tactics from a terrorist and criminal perspective. an I-war risk analysis model, implication
of I-war for information managers, perceptual intelligence and I-war, handling cyber terrorism
and information warfare, jurisdiction.
Course Outcomes

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At the end of the course, the student will be able to
CO 1: Analyze how criminals utilize the Internet as a medium of attacks in cyber warfare to
penetrate automated networks and seize control of critical infrastructure by case studies.
CO 2: Understand the concepts of information warfare and ways to deal with the related
problems.
CO 3: Safeguarding critical information infrastructure by detecting and preventing cyber
threats.
CO 4: Enhancing national security through the protection of sensitive digital assets from cyber
attacks.
CO 5: Ensuring the privacy and confidentiality of individuals and organizations in cyberspace.
List of Text Books
1. Jonathan Clough, Principles of Cybercrime, 2nd Edition, Cambridge University Press, 2015.
2. W. Singer and Allan Friedma, Cyber Security and Cyber War, Oxford University Press
India; Illustrated edition 2014.
List of Reference Books
1. Hutchinson, William., Warren, Mat. Information Warfare. United Kingdom: Taylor &
Francis Group, 2017.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Natural Language Processing
Course Code CSE-6109
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)
Course Learning Objectives
1. To understand the core concepts, challenges, and applications of NLP.
2. To apply linguistic theories to analyze and process text data effectively.
3. To perform syntactic and semantic analysis of sentences and texts.
4. To apply NLP techniques to tasks like sentiment analysis and machine translation.
Course Content
Module 1: Introduction to NLP: Definition and Scope of NLP, Historical Developments and
Milestones, Language Understanding and Generation, Challenges in NLP, Text Preprocessing,
Tokenization, Stemming, Lemmatization, Stop Word Removal.
Module 2: Linguistic Concepts and Language Modeling: Basics of Morphology, Regular
Expressions, Their Limitations, Finite-State Automata, N-grams and Language Modeling,
Probability Distribution and Smoothing Techniques, Part-of-Speech Tagging, Hidden Markov
Models, Viterbi Algorithm.
Module 3: Syntax Analysis and Parsing: Context-Free Grammars and Syntax Rules, Parsing
Techniques: Top-Down, Bottom-Up, Efficient Parsing for Context-Free Grammars (CFGs),
Statistical Parsing and Probabilistic CFGs (PCFGs).
Module 4: Semantics Analysis and Sentiment Analysis: Semantic Roles and Predicate-
Argument Structures, Sentiment Analysis: Lexicon-Based and Machine Learning Approaches,
Handling Sarcasm and Irony,
Module 5: Advanced Topics in NLP: Basics of Neural Networks and Backpropagation,
Recurrent Neural Networks (RNNs), LSTMs, Word Embeddings: Word2Vec, Rule-Based and
Statistical Machine Translation, Text Generation Techniques, Named Entity Recognition
(NER), Discourse Analysis.
Module 6: Ethical Considerations in NLP: Bias and Fairness in NLP Models, Privacy
Concerns And Data Anonymization, Social Implications Of AI And NLP Technologies, Future

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Directions And Research Areas, Students Choose An NLP Topic of Interest, Conduct Literature
Review and Analysis, Present Findings and Implications.
Course Outcomes
At the end of this course the students will be able to:
CO 1. Comprehend the concepts of word-form using morphology analysis.
CO 2. Demonstrates n-gram models and POS tagging in english language.
CO 3. Acquire the knowledge of syntax and semantics related to natural languages.
CO 4. Acquire knowledge of machine learning techniques used in NLP.
List of Text Books
1. Eisenstein, Jacob. Introduction to Natural Language Processing. United Kingdom: MIT
Press, 2019.
2. Dale R., Moisl H. and Somers H., Handbook of Natural Language Processing, CRC Press,
2nd Edition, 2010.
List of Reference Books
1. Bird S., Klein E. and Loper E., Natural Language Processing with Python, Oreilly
Publication, 2nd Edition, 2009.
2. D. Manning, Christopher., Schütze, Hinrich. Foundations of Statistical Natural Language
Processing. N.p.: CreateSpace Independent Publishing Platform, 2016.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester-Sixth Year-Fourth
Course Name Data Science and Analytics
Course Code CSE-6110
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Object Oriented Programming (CSE-2005), Engineering Mathematics -III (Numerical Methods
and Statistics) (CSE-4001).

Course Learning Objectives

1. To learn and understand the proficiency in acquisition and organization of data.
2. To understand various techniques to demonstrate intermediate proficiency in the
visualization of data to communicate information and patterns that exist in the data.
3. To understand computing theory, languages, and algorithms, as well as mathematical and
statistical models, and the principles of optimization to appropriately formulate and use data
analyses
Course Content

Module 1 Introduction: Overview of data science, exploratory data analysis, Introduction to

machine learning, Linear regression and regularization, Model selection and evaluation,
Classification: KNN, decision trees. Types of Big Data, Design goals of Big Data platforms,
and where in the systems landscape these platforms fall.

Module 2 Data Definitions and Analysis Techniques-Elements, Variables, and Data

categorization, Levels of Measurement, Data management and indexing, Introduction to
statistical learning and R-Programming

Module 3 Descriptive Statistics-Measures of central tendency, Measures of location of

dispersions, Practice and analysis with R. Basic Analysis Techniques-Statistical hypothesis
generation and testing, Chi-Square test t-Test, Analysis of variance, Correlation analysis,
Maximum likelihood test, Practice and analysis with R.

Module 4 Data analysis techniques-Regression analysis, Classification techniques, Clustering,

Association rules analysis, Practice and analysis with R.

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Module 5 Case studies and projects: Understanding business scenarios, Feature engineering
and visualization, Scalable and parallel computing with Hadoop and Map-Reduce, Sensitivity
Analysis

Course Outcomes
At the end of this course the students will be able to:
CO 1: Become proficient in the statistical analysis of data and the use of computation tools
for data analysis.
CO 2: Apply statistical and computational tools to applied problems, and clearly
communicate the results in both written reports and oral presentations.
CO 3: Understand the importance of proper data management, documentation of work to
allow reproducibility of results.
List of Text Books
1. James, Gareth., Witten, Daniela., Hastie, Trevor., Tibshirani, Robert., Taylor, Jonathan. An
Introduction to Statistical Learning: With Applications in Python. Germany: Springer
International Publishing, 2023.
2. Han, Jiawei., Pei, Jian., Tong, Hanghang. Data Mining: Concepts and Techniques.
Netherlands: Elsevier Science, 2022.
List of Reference Books
1. Hastie, Trevor., Tibshirani, Robert., Friedman, Jerome. The Elements of Statistical
Learning: Data Mining, Inference, and Prediction. Germany: Springer New York, 2013.
2. Kevin P. Murphy, Machine learning: A probabilistic perspective, MIT Press,2012

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Cloud Computing
Course Code CSE-6111
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Computer Networks and Communication (CSE-4002)
Course Learning Objectives
1. To provide a comprehensive knowledge of Cloud Computing, fundamental issues,
technologies, applications, and implementations.
2. To expose the students to the frontier areas of Cloud Computing.
3. To shed light on the security issues in Cloud Computing.
Course Content
Module 1. History of Centralized and Distributed Computing: Overview of Distributed
Computing, Cluster computing, Grid computing. Technologies for Network based systems-
System models for Distributed and cloud computing- Software environments for distributed
systems and clouds.
Module 2. Introduction to Cloud Computing: Cloud issues and challenges - Properties -
Characteristics - Service models, Deployment models. Cloud resources: Network and API -
Virtual and Physical computational resources - Data-storage. Virtualization concepts - Types of
Virtualizations- Introduction to Various Hypervisors - High Availability (HA)/Disaster
Recovery (DR) using Virtualization, Moving VMs.
Module 3. Service models: Infrastructure as a Service (IaaS), Resource Virtualization, Server,
Storage, Network Case studies, Platform as a Service (PaaS), Cloud platform & Management,
Computation, Storage, Case studies. Software as a Service (SaaS), Web services, Web 2.0, Web
OS, Case studies, Anything as a service (XaaS).
Module 4. Cloud Programming and Software Environments: Parallel and Distributed
Programming paradigms, Programming on Amazon AWS and Microsoft Azure, Programming
support of Google App Engine, Emerging Cloud software Environment.
Module 5. Cloud Access: authentication, authorization and accounting - Cloud Provenance and
meta-data, Cloud Reliability and fault-tolerance, Cloud Security, privacy, policy and
compliance- Cloud federation, interoperability and standards.

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Module 6. Use Cases: Azure features use cases, Google cloud services (GCP) Features Use
cases, AWS features use cases.
Course Outcomes
At the end of this course, the students will be able to:
CO 1: Articulate the main concepts, key technologies, strengths, and limitations of cloud
computing and the possible applications for state-of-the-art cloud computing.
CO 2: Identify the architecture and infrastructure of cloud computing, including SaaS, PaaS,
IaaS, public cloud, private cloud, hybrid cloud, etc.
CO 3: Understand the security needs for cloud service.
List of Text Books
1. Erl, Thomas., Monroy, Eric. Cloud Computing: Concepts, Technology, Security, and
Architecture. United States: Pearson Education, 2023.
2. Toby Velte, Anthony Velte, Robert Elsenpeter, Cloud Computing a Practical Approach,
McGraw Hill Education, 2017.
List of Reference Books
1. Wu, Caesar., Buyya, Rajkumar. Cloud Data Centers and Cost Modeling: A Complete Guide
To Planning, Designing and Building a Cloud Data Center. Netherlands: Elsevier Science,
2015.
2. Ronald L. Krutz, Russell Dean Vines, Cloud Security: A Comprehensive Guide to Secure
Cloud Computing, 1st Edition, Wiley, 2010.
3. Kai Hwang, Geoffrey C. Fox and Jack J. Dongarra, “Distributed and Cloud Computing
from Parallel Processing to the Internet of Things”, Morgan Kaufmann, Elsevier, 2012.
4. James Broberg, Andrzej M. Goscinski, Cloud Computing: Principles and Paradigms,
Wiley, 2011.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester – Sixth Year- Third
Course Name Cyber Forensic
Course Code CSE-6112
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Cryptography and Cyber Security (CSE-5001)
Course Learning Objectives
1. To gain knowledge and skills required to understand and recreate the criminal terminology
and Cyber Forensics investigation process.
2. To gain knowledge of cyber forensic tools.
Course Content
Module 1. Introduction to Cybercrime: Jurisdictional Issues, Quantifying Cybercrime,
differentiating crimes that uses the internet and depend on the internet, Categorizing
Cybercrime, Computer hacking and malicious software, Prioritizing Cybercrime Enforcement,
digital privacy, Reasons for Cybercrimes, Cyber forensics fundamentals and types, Benefits of
forensics

Module 2. Understanding Investigation process: Procedure for corporate High-Tech

investigations, Understanding data recovery work station and software, securing investigation,
and policy violation, documenting and reporting evidence.

Module 3: Data acquisition- Understanding storage formats and digital evidence, determining
the best acquisition method, acquisition tools, validating data acquisitions, performing RAID
data acquisitions, remote network acquisition tools, other forensics acquisition tools.

Module 4: Collecting and preserving evidence: Processing crimes and incident scenes,
securing a computer incident or crime, seizing digital evidence at scene, storing digital evidence,
obtaining digital hash, reviewing case.

Module 5: Computer forensics tools- software, hardware tools, validating and testing forensic
software, addressing data-hiding techniques, performing remote acquisitions, E-Mail

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investigations- investigating email crime and violations, understanding E-Mail servers,
specialized E-Mail forensics tool.

Course Outcomes
At the end of this course, the students will be able to:
CO 1: Analyse and demonstrate the crime scene and criminology.
CO 2: Understand different crime scenes using the digital investigation process.
CO 3: Know techniques for recovery of evidence and creating documents for judicial
proceedings.
List of Text Books
1.Thomas J. Holt, Adam M. Bossler, Kathryn C. Seigfried-Spellar, Cybercrime and Digital
Forensics an Introduction, Routledge, 2022.

List of Reference Books

1. Shinder L. D., Cross M., Scene of the Cybercrime, Syngress, 2nd Edition, 2008.
2. Marcella J. A. and Guillossou F., Cyber Forensics: From Data to Digital Evidence, Wiley,
2012.
3. Nina Godbole, Sunit Belapure, Cyber Security, Wiley, 2011.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Web Search and Mining
Course Code CSE-6113
Compulsory/Elective/Open Elective
Elective
Prerequisites
Data Mining and Warehousing (CSE-4004)
Course Learning Objectives
1. To describe web mining and understand the need for web mining.
2. To differentiate between web mining and data mining.
Course Content
Module 1. Information retrieval model: Basic Information Retrieval model, Boolean and
vector-space retrieval models, Ranked retrieval, Text-similarity metrics, TF-IDF (term
frequency/inverse document frequency) Weighting, Cosine similarity. Document
Representation, Simple tokenizing, Stop-word removal, Stemming.
Module 2. Performance metrics: Recall, Precision, and F-measure; Evaluations on benchmark
text collections. Query expansion, Query languages and query operation, Web Search, Web
crawling, Link analysis, Ontology, Domain specific search.
Module 3. Web search basics: Background and history, Web characteristics, The web graph,
Spam, Advertising as the economic model, The search user experience, User query needs, Index
size and estimation, Near-duplicates and shingling.
Module 4. Web crawling and indexes: Overview, Features a crawler must and should provide,
Crawling, Crawler architecture, DNS resolution, The URL frontier, Distributing indexes,
Connectivity servers.
Module 5. Link analysis: Web as a graph, Anchor text and the web graph, PageRank, Markov
chains, The PageRank computation, Topic-specific Page Rank, Hubs and Authorities, Choosing
the subset of the Web.
Course Outcomes
At the end of this course, the students will be able to:
CO 1: Understand the different application areas for web mining.
CO 2: Understand the different methods to introduce structure to web-based data.

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CO 3: Apply the methods of Web usage mining.
List of Text Books
1. Manning, Christopher D, Raghavan, Prabhakar., Schütze, Hinrich. Introduction to
Information Retrieval. India: Cambridge University Press, 2017.
2. Bruce Croft, Donald Metzler, Trevor Strohman, Search Engines: Information Retrieval in
Practice, Pearson Education, 2011.
List of Reference Books
1. Web Usage Mining Techniques and Applications Across Industries. United States: IGI
Global, 2016.
2. Bing Liu, Web Data Mining. Springer-Verlag Berlin Heidelberg 2011.
3. Soumen Chakrabarti, Mining the Web: Discovering Knowledge from Hypertext Data,
Morgan Kaufmann; First Edition 2002.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Internet of Things
Course Code CSE-6114
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Computer Networks and Communication (CSE-4002)
Course Learning Objectives
1. To introduce different architectures used for connected smart devices.

2. To study various protocols used in the Internet of Things environment.

3. To explain Internet of Things systems using Arduino and Raspberry Pi.

4. To understand data analytics and cloud in IoT.
Course Content
Module 1. Overview: Building architecture, Main design principles and needed capabilities,
An IoT architecture, devices and gateways, local and wide area networking, Data Management,
Business processes in IoT, Everything as a Service (XaaS), M2M and IoT Analytics, Knowledge
Management.
Module 2. Reference Architecture: IoT Architecture, IoT Reference Model, Functional View,
Information View, Deployment and Operational View, other relevant architectural views, Real-
World Design Constraints- Introduction, technical design constraints,data representation and
visualization, interaction and remote control.
Module 3. Data Link Layer & Network Layer Protocols: PHY/MAC Layer (3GPP MTC,
IEEE 802.11, IEEE 802.15), Wireless HART, Z-Wave, Bluetooth Low Energy, Zigbee Smart
Energy, DASH7 - Network Layer-IPv4, IPv6, 6LoWPAN, 6TiSCH, ND, DHCP, ICMP, RPL,
CORPL, CARP.
Module 4. Transport & Session Layer Protocols: Transport Layer (TCP, MPTCP, UDP,
DCCP, SCTP)- (TLS, DTLS) Session Layer-HTTP, CoAP, XMPP, AMQP, MQTT.
Module 5. Service Layer Protocols & Security: Service Layer -oneM2M, ETSI M2M, OMA,
BBF, Security in IoT Protocols – MAC 802.15.4, 6LoWPAN, RPL, Application Layer.
Course Outcomes

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At the end of this course the students will be able to:
CO 1: Understand the basic architecture of Internet of Things based devices.
CO 2: Analyze light weight protocols implemented for connected devices.
CO 3: Design and develop Smart Devices using IoT.
List of Text Books

1. Samuel Greengard, Internet of Things, 2nd Edition,MIT press, 2021.

2. Andy King, Programming the Internet of Things: An Introduction to Building Integrated,
Device-to-Cloud IoT Solutions, Shroff/O'Reilly, 2021.
List of Reference Books
1. Raj Kamal, Internet of Things Architecture and Design Principles, 2nd Edition,McGraw Hill
Education Private Lim., 2022.
2. Anupama C. Raman (Author), Pethuru Raj (Author), The Internet of Things: Enabling
Technologies, Platforms, and Use Cases, 1st Edition, CRC Press, 2022.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Software Testing and Quality
Course Code CSE - 6115
Compulsory/Elective Elective
/Open Elective
Prerequisites
Software Engineering and Project Management (CSE-5004)
Course Learning Objectives
1. To gain knowledge and learn about software quality and software testing.
2. To design test cases using black-box and white-box testing techniques.
3. To understand basic concepts of regression testing and types of regression testing
techniques.
4. To learn about various quality assurance models and understand the audit and assessment
procedures to achieve quality.
Course Content
Module 1. Introduction to software quality, Software quality factors, Quality Frameworks and
ISO-9126, Quality assurance, QA Activities in Software Processes, Components of SQA
system, Verification and Validation Perspectives of QA, Introduction to software testing, Black
box testing: Boundary value testing, Equivalence class testing, State Table based Testing,
Decision Table Based Testing, Cause-Effect Graph based Testing.
Module 2. White box techniques like, structural testing, control flow based - block, branch,
predicate, MCDC, path testing, data flow based- p-use, d-use, all-use, and others, mutation
testing, coverage criteria and code coverage, examples and case studies.
Module 3. Levels of Testing, Debugging, Regression Testing, Prioritizing the Test-cases,
Domain Testing, Object Oriented Testing, Testing Web Applications, Agile Testing, Scrum
Testing, Mobile Application Testing.
Module 4. Code reviews and inspections, Static code analysis, SCA tools like Find bugs, and
others, Other specialized Testing like performance testing, load testing, security testing, GUI
testing, Regression testing.
Module 5. Quality Engineering Activities and Process, Quality Planning Goal Setting and
Strategy Formation, Quality Assessment and Improvement, Quality Assurance beyond testing,
Defect Prevention and Process Improvement, Fault Tolerance, Failure Containment, Comparing
Quality Assurance Techniques and Activities.
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Course Outcomes
At the end of this course the students will be able to:
CO 1: Understand the evolution of software testing techniques, their goals and learn the various
models of software testing.
CO 2: Generate test cases for software systems using black-box and white-box testing
techniques.
CO 3: Carry out regression testing of software systems.
CO 4: To understand how to detect, classify, prevent and remove defects.
CO 5: To identify risks for quality improvement.
List of Text Books
1. Jorgensen PC., Software testing: a craftsman's approach, Auerbach Publications, CRC
Press, 2013.
2. S. Desikan, G. Ramesh, Software testing: principles and practice, Pearson Education India,
2006.
3. Jeff Tian, Software Quality Engineering: Testing, Quality Assurance, and Quantifiable
Improvement, Wiley-IEEE Computer Society Press, 1st Edition, 2005.
List of Reference Books
1. Boris Beizer, Software Testing Techniques, Dreamtech Press, 2nd Edition, 2014.
2. K Naik, P Tripathi, Software Testing and Quality Assurance Theory and practice, John
Wiley & Sons, 1st Edition, 2008.
3. William E. Perry, Effective Methods for Software Testing, John Wiley & Sons, 3nd Edition,
2006.
4. Daniel Galin, Software Quality Assurance from theory to implementation, Pearson
Education, 1st Edition, 2005.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Digital Forensics and Cyber Law
Course Code CSE-6116
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Cryptography and Cyber Security (CSE-5001)
Course Learning Objectives
1. To understand the basics of digital forensics technology, systems, and services.
2. To learn about data recovery, data seizure, digital evidence controls, and forensics analysis.
3. To learn and develop different tools for digital forensic acquisition and analysis.
Course Content
Module 1. Introduction to Digital Forensics: Digital forensics fundamentals, Use of
Computer Forensics, Benefits of Professional Forensics Methodology, Steps Taken by
Computer Forensics Specialists, Case Studies, Types of Computer Forensics Technology,
Military, Law Enforcement, Business, Specialized Forensics Techniques, Protecting Data from
Being Compromised, Internet Tracing Methods.
Module 2. Digital Forensics Systems and Services: Types of Computer Forensics Systems:
Firewall and IDS Security Systems, Storage Area Network Security Systems, Instant Messaging
(IM) Security Systems, Biometric Security Systems, Computer Forensics Services, Cyber
Detectives, Fighting Cyber Crime with Risk Management Techniques, Computer Forensics
Investigative Services, Forensic Process Improvement.
Module 3. Digital Forensics Evidence and Capture: Data Recovery, Data Recovery Solution,
Hiding and Recovering Hidden Data, Evidence Collection and Data Seizure, Types of Evidence,
The Rules of Evidence, Volatile Evidence
Module 4. Data Preservation and Forensics Analysis: Duplication and Preservation of Digital
Evidence, Preserving the Digital Crime Scene, Computer Evidence Processing Steps, Legal
Aspects of Collecting and Preserving Evidence, Computer Image Verification and
Authentication,, Computer Forensics Analysis, Discovery of Electronic Evidence, Identification
of Data, Reconstructing Past Events, Disk and file system analysis.
Module 5. Network, Operating System Forensic: Network forensics, Investigation on virtual
network and Email, Email Tracing Internet Fraud, Internet Artifacts - Damaging Computer

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Department of Computer Science & Engineering
Evidence - System Testing - Operating System Artifacts: Windows System Artifacts, Linux
System Artifacts.
Module 6. Need for a Cyber Act: Information Technology Act, Scope and further
Development, Information Technology Act (Amendment), coverage of Cyber Security and
Cyber Crime Indian cyber-Laws vs. cyber laws of U.S.A, similarities, scope and coverage,
Effectiveness.
Course Outcomes

At the end of this course the students will be able to:

CO 1: Learn the fundamentals of digital forensics technology along with different systems and
services.
CO 2: Recover and seize data from a crime scene without damage, using legal procedures and
standards.
CO 3: Exhibit knowledge in forensic data acquisition and analysis and investigate artifacts in
different operating systems.
List of Text Books
1. Thomas J. Holt, Adam M. Bossler, Kathryn C. Seigfried-Spellar, Cybercrime and Digital
Forensics: An Introduction, Routledge 2022.
2. Marjie T Britz, Computer Forensics and Cyber Crime: An Introduction, 4th Edition, Pearson
Education India, 2022.
3. Gerard Johansen, Digital Forensics and Incident Response: Incident response techniques
and procedures to respond to modern cyber threats), Packt Publishing Limited, 2nd Edition,
2020.
List of Reference Books
1. B. Nelson, A. Phillips, F. Enfinger, and C. Steuart, Guide to Computer Forensics and
Investigations, 6th Edition, 2019.
2. Niranjan Reddy, Practical Cyber Forensics, 1st Edition, Apress, 2019.

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Department of Computer Science & Engineering

th
Elective 7
Semester

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Information Retrieval and Semantic Web
Course Code CSE-7101
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Data Mining and Warehousing (CSE-4004)
Course Learning Objectives
1. To apply Information Retrieval (IR) concepts and Semantic Web technologies to design
efficient search systems and enhance web-based information organization.
2. To summarize the process of information retrieval, compare and contrast different
information retrieval models.
3. To interpret RDF (Resource Description Framework) and OWL (Web Ontology Language)
concepts.
Course Content
Module 1. Boolean retrieval: An example information retrieval problem, A first take at
building an inverted index, Processing Boolean queries, The extended Boolean model versus
ranked retrieval. The term vocabulary and postings list, Document delineation and character
sequence decoding, Tokenization, Stemming and lemmatization, Positional postings and phrase
queries.
Module 2. Dictionaries and tolerant retrieval: Search structures for dictionaries, Wildcard
queries, k-gram indexes for wildcard queries, Spelling correction, Index construction, Index
compression,
Module 3. Scoring, term weighting and the vector space model: Parametric and zone
indexes, Inverse document frequency, Tf-idf weighting, The vector space model for scoring,
Variant Tf-idf functions, Computing scores in a complete search system, Components of an
information retrieval system.
Module 4. Evaluation in information retrieval: Standard test collections, Evaluation of
unranked retrieval sets, Evaluation of ranked retrieval results, A broader perspective: System
quality and user utility, XML retrieval. The Probability Ranking Principle, Language models
for information retrieval, The query likelihood model, Language modeling versus other
approaches in IR, Extended language modeling approaches.

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Module 5. Semantic Web Vision: Todays’ web, Examples of the semantic web from today’s
web, Semantic web technologies, layered approach. Structured web documents in XML: The
XML language, Structuring, Namespaces, Querying and Addressing XML documents,
Processing.
Module 6. Describing Web Resources: Introduction, RDF, RDF Schema syntax and language,
Direct Inference System, Querying RQL. Web Ontology Language: Introduction, OWL
language, Examples, OWL in OWL, Future extensions.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Construct information retrieval models and techniques for effective information
extraction and ranking.
CO 2: Design and evaluate search algorithms to enhance the efficiency of information retrieval
systems.
CO 3: Implement RDF and OWL constructs to build and query semantic data models.
CO 4: Apply knowledge of the course to real-world scenarios, fostering innovation in search
and web-based information management.
List of Text Books
1. Manning, Christopher D. An introduction to information retrieval. Cambridge university
press, 2015.
2. Grigoris Antoniou, Paul Groth, Frank van Harmelen, Rinke Hoekstra, A Semantic Web
Primer, 3rd edition The MIT Press Cambridge, Massachusetts London, England, 2012.
3. Bruce Croft, Donald Metzler, Trevor Strohman, Search Engines: Information Retrieval in
Practice, Pearson Education, 2011.
List of Reference Books
1. Bing Liu, Web Data Mining. Springer-Verlag Berlin Heidelberg 2011.
2. Soumen Chakrabarti, Mining the Web: Discovering Knowledge from Hypertext Data,
Morgan Kaufmann; First Edition 2002.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Neural Networks Architectures for Data Analysis
Course Code CSE-7102
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)
Course Learning Objectives
1. To understand the biological neural network and to model equivalent neuron models.
2. To understand the architecture, learning algorithms and issues of various feed forward and
feedback neural networks.
3. To introduces the methods for data preparation and data understanding.
Course Content
Module 1. Introduction: A Neural Network, Human Brain, Models of a Neuron, Network
Architectures, Knowledge Representation, Artificial Intelligence and Neural Networks,
Learning Process: Error Correction Learning, Memory Based Learning, Hebbian Learning,
Competitive, Boltzmann Learning.
Module 2. Single Layer Perceptron: Unconstrained optimization, LMS algorithm, learning
curves, perceptron, convergence theorem, limitations of single-layer perceptron Multilayer
Perceptron: Back-propagation algorithm, XOR problem, feature detection, accelerated
convergence of back-propagation algorithm, limitations
Module 3. Back Propagation: Back Propagation and Differentiation, Hessian Matrix,
Generalization, Cross Validation, Network Pruning Techniques, Virtues, and Limitations of
Back Propagation Learning, Accelerated Convergence, Supervised Learning.
Module 4. Self-Organizing Maps (SOM): Two Basic Feature Mapping Models, Self-
Organization Map, SOM Algorithm, Properties of Feature Map, Learning Vector Quantization,
Adaptive Pattern Classification. Introduction, Principal Component Analysis (PCA), Kernel
PCA, Canonical Correlation Analysis, Factor Analysis, Multidimensional scaling,
Correspondence Analysis
Module 5. Case Study: Using Feed forward Neural Networks for Handwritten Digit
Recognition.
Course Outcomes

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At the end of this course the student will be able to:
CO1. Examine different Neural Network Architectures and Learning Rules.
CO2. Apply Back Propagation and solve different Neural Network Problems.
CO3. Apply Self Organizing Maps in solving different pattern classification tasks.
CO4. Choose appropriate feature selection and dimensionality reduction.
List of Text Books
1. Haykin, Simon. Neural networks: a comprehensive foundation. Prentice Hall PTR, 1999.
2. Gurney, Kevin. “An introduction to neural networks,” CRC press, 2018.
3. Sivanandam, S. N., Sai Sumathi, and S. N. Deepa. "Introduction to neural networks using
Matlab 6.0” McGraw Hill Education(india) Private Limited, 2006.
List of Reference Books
1. Yoshua Benjio, Aaron Courville, “Deep Learning- Ian Goodfelllow,” The MIT Press.
2. K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press.
3. Michael Jambu, “Exploratory and multivariate data analysis”, Academic Press Inc. 1990.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Wireless Sensor Network
Course Code CSE-7103
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Computer Networks and Communication (CSE-4002)
Course Learning Objectives
1. To understand the fundamentals of wireless sensor networks and its application to critical
real time scenarios.
2. To study the various protocols at various layers and its differences with traditional protocols.
3. To understand the issues pertaining to sensor networks and the challenges involved in managing a
sensor network.
Course Content

Module 1. Introduction of Wireless Communication: Fundamentals of wireless

communication technology, the electromagnetic spectrum radio propagation, characteristics of
wireless channels, modulation techniques, multiple access techniques, wireless LANs, PANs,
WANs, and MANs, Wireless Internet.
Module 2. Adhoc/Sensor Networks: Key definitions of adhoc/ sensor networks, unique
constraints and challenges, advantages of ad-hoc/sensor network, driving applications, issues in
adhoc wireless networks, issues in design of sensor network, sensor network architecture, data
dissemination and gathering.
Module 3. MAC Protocols: Issues in designing MAC protocols for adhoc wireless networks,
design goals, classification of MAC protocols, MAC protocols for sensor network, location
discovery, quality, other issues, S-MAC, IEEE 802.15.4.
Module 4. Routing Protocols: Issues in designing a routing protocol, classification of routing
protocols, table-driven, on-demand, hybrid, flooding, hierarchical, and power aware routing
protocols.
Module 5. QoS and Energy Management: Issues and Challenges in providing QoS,
classifications, MAC, network layer solutions, QoS frameworks, need for energy management,
classification, battery, transmission power, and system power management schemes.
Course Outcomes

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At the end of this course the student will be able to:
CO 1: Build a Wireless Sensor Network.
CO 2: Analysis of various critical parameters in deploying a WSN.
CO 3: Evaluate QoS and Energy Management.
List of Text Books
1. Wireless Sensor Networks: Technology Protocols and Applications by Kazem Sohraby,
Wiley India Pvt. Ltd, January 2011.
2. C. Siva Ram Murthy, and B. S. Manoj, "AdHoc Wireless networks ", Pearson Education,
2008.
List of Reference Books
1. Feng Zhao and Leonides Guibas, "Wireless sensor networks ", Elsevier publication, 2004.
2. Jochen Schiller, "Mobile Communications", Pearson Education, 2nd Edition, 2003.
3. William Stallings, "Wireless Communications and Networks ", Pearson Education, 2004.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester-Seventh Year-Fourth
Course Name Ethical Hacking
Course Code CSE-7104
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Computer Networks and Communication (CSE-4002)
Course Learning Objectives
1. To evaluate the security of and identify vulnerabilities in target systems , networks or
system infrastructure.
2. To learn how hackers work for gaining fame by bringing down a computer.
3. To learn about security testing methodologies.
Course Content

Module 1. Introduction: Overview of Ethical Hacking, Hacking concept, Need of Ethical

hacking, Types of Hacking, Building the foundation for Ethical hacking, Hacking Phases, Role
of Ethical Hacker, Types of Hackers, Roles and Responsibilities, Scope & limitations of
hacking, Advantages & scope for hacking, Drawbacks & Limitation of hacking.

Module 2. Cyber Threats: Threats & its categories, Hacking tools and techniques, Common
Hacking Tools, Hacking Techniques & Approaches, Policies and Controls Information Security
policies, Risk Management & Incident Management, Information Security controls, Data
Management, Concept of Penetration testing, Types of Penetration testing, Phases of
Penetration testing.

Module3. Viruses: Introduction to Virus ,Worms & Trojan, Types of Virus, Worms & Trojan,
Fake Antiviruses, Working of Antivirus, Malware Analysis, Malware Detection Method.

Module 4.Foot printing: Sniffing, Social Engineering, Footprinting through Search Engines,
Web Services, Information Gathering Using Google Advanced Search and Image Search,
Footprinting through Google Hacking Database, Scanning Tools and Techniques, Scanning
PenTesting, Port Scanning & Countermeasures, Sniffing Network, Sniffing Concepts &
Techniques, WireShark installing & concept, Sniffing Detection Techniques, Social
Engineering, Social Engineering Concepts, Social Engineering Techniques.

Module 5.Case study: Study of Hacking in India & across the globe, Principles of Ethical
hacking, Basic Principle, Commandments of Ethical Hacking, Hacking Methodologies.

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Department of Computer Science & Engineering
Course Outcomes
At the end of course the students will be able to:
CO 1: Execute a penetration test using standard hacking tools in an ethical manner.
CO 2: Identify legal and ethical issues related to vulnerability and penetration testing.
CO 3: Report on the strengths and vulnerabilities of the tested network.
List of Text Books
1. Daniel Graham , Ethical Hacking-A Hands-on Introduction to Breaking In, No Starch
Press ,2021.
2. P. Engebretson, The Basics of Hacking and Penetration Testing, Syngress; 2nd edition,
2013.
3. D.Stuttard, The Web Application Hacker’s Handbook, 2nd Edition, Wiley, 2011.
List of Reference Books
1. G.Weidman, Penetration Testing,1st Edition, No Starch Press, 2014.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Biometrics
Course Code CSE-7105
Compulsory/Elective/Open Elective
Elective
Prerequisites
Digital Image Processing (CSE-6001)
Course Learning Objectives
1. To familiarize with the concepts of biometrics like definition, scope, and the role it plays in
authentication and identification..
2. To introduce the concepts of biometric systems like data acquisition, feature extraction,
classification, and matching.
3. To examine the utilization of biometrics in areas of criminal identification, forensics, and
border security.
Course Content
Module 1. Basics and Fundamentals of Biometrics and Image Processing: Biometric traits,
Image processing/ pattern recognition/ statistics, Image processing related operations:
acquisition, type, geometric transformations, point operations, linear interpolation, brightness
correction, histogram, convolution, linear/non-linear filtering, Guassian, median, min, gray
level reduction.
Module 2. Geometric Techniques: Enhancement filter, Laplacian, unsharp masking, high
boot filtering, sharpening special filtering, Edge detection, First and second derivative, steps in
edge detection, smoothening, enhancement, Thresholding, localization, zero crossing, Canny
edge detection, Fourier Series, DFT, inverse of DFT.
Module 3. Feature Detection, description, matching and model fitting: Biometric system,
authentication, properties of biometric system, application area, PCA, Eigen vectors and
values, 2D-PCA, generalization to p-dim, covariance and correlation, algebra of PCA,
projection of data, Identification/verification, threshold, score distribution, FAR/ FRR, System
design issues, Positive/negative identification, Biometric system security, Authentication
protocols.
Module 4. Error Analysis and its application in Biometrics: Null and alternative hypothesis
h0, h1, Error type I/II, Matching score distribution, Interpretation of curves, comparing two
systems using ROC curve, Error anal, cost function, biometric myths and misrepresentations,
negative authentication, trade-offs b/w security and convenience. Selection of suitable
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biometric, Biometric attributes, Zephyr charts, types of multi biometrics. Verification on multi-
model system, normalization strategy, Fusion methods, multi-model identification.
Module 5. Applications of Biometrics: Signature recognition system, cropping,
enhancement, signature parameters, matching and decision recognition, Face detection, feature
template, matching, Fingerprint recognition, Enhancement, Thinning, minutiae, CN number,
matching, Ear and Iris recognition.
Course Outcomes
At the end of the course, student will be able to:
CO 1: Gain an understanding of the core principles and methodologies employed in research
domains like pattern recognition and image processing, and explore their utilization
within the realm of Biometrics.
CO 2: Comprehend and analyze Biometric systems, and then improve them to strengthen
traditional security measures.
CO 3: Evaluate the performance of Biometric systems and apply them to real-world security
problems.
List of Text Books
1. Derbel, Nabil, and Olfa Kanoun, Advanced Methods for Human Biometrics, Springer, 2021.
2. Ashbourn, Julian. Biometrics: advanced identity verification: the complete guide.
Springer, 2020.
List of Reference Books

1. Derbel N, Kanoun O, editors. Advanced Methods for Human Biometrics. Springer; 2021.
2. I. Hayashi, L.C. Jain, S.B. Lee, Shigeyoshi Tsutsui, U. Halici, Intelligent Biometric
Techniques in Fingerprint and Face Recognition, CRC Press, 2022.
3. S.Y. Kung, S.H. Lin, M.W. Mak, Machine Learning and Biometrics. United Kingdom, Intech
Open, 2018..

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester – Seventh Year- Fourth
Course Name Reinforcement Learning
Course Code CSE-7106
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)
Course Learning Objectives
1. To explore recent advances in reinforcement learning.
2. To provide exposure to cutting-edge RL developments.
3. To facilitate comprehensive understanding of advanced RL concepts.
Course Content
Module 1: Introduction and Bandit Algorithms: Overview of Reinforcement Learning,
Introduction to Bandit Algorithms, Exploration vs. Exploitation, Upper Confidence Bound
(UCB) Algorithm, Probably Approximately Correct (PAC) Bandit Algorithms.
Module 2: Markov Decision Processes and Dynamic Programming: Transitioning to Full
Reinforcement Learning, Elements of Markov Decision Processes (MDPs), Bellman Optimality
Equation and Principle, Dynamic Programming: Value Iteration and Policy Iteration.
Module 3: Temporal Difference Learning and Function Approximation: Temporal
Difference Methods, Eligibility Traces for Efficient Learning, Function Approximation in RL.
Module 4: Q-Learning, Policy Gradient, and Hierarchical RL: Q-Learning and Fitted Q
Iteration, Policy Gradient Methods, Hierarchical Reinforcement Learning.
Module 5: Advanced Topics: Partial Observability and Applications: Partially Observable
Markov Decision Processes (POMDPs), Real-world Applications of Reinforcement Learning.
Course Outcomes
At the end of the course, the student will be able to:
CO 1: Evaluate recent breakthroughs in reinforcement learning and their practical implications,
showcasing a deep understanding of their significance.
CO 2: Participate actively in detailed discussions on selected advanced topics, demonstrating
critical thinking and analytical skills.
CO 3: Apply acquired knowledge to address real-world challenges, employing advanced
methodologies discussed in the course.
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CO 4: Build upon foundational knowledge from the introductory course, forming a
comprehensive understanding of complex reinforcement learning concepts.
List of Textbooks
1. Sutton, Richard S., Barto, Andrew G. Reinforcement Learning: An Introduction. United
Kingdom: MIT Press, 2018.
2. Szepesvári, Csaba. Algorithms for Reinforcement Learning. Switzerland: Morgan &
Claypool, 2010.
3. Boris Belousov, Hany Abdulsamad, Jan Peters, Pascal Klink, Simone Parisi, Reinforcement
Learning Algorithms: Analysis and Applications. Germany: Springer International
Publishing, 2021.
List of Reference Books
1. Sewak, Mohit. Deep Reinforcement Learning: Frontiers of Artificial
Intelligence. Germany: Springer Singapore, 2019.
2. Gatti, Christopher. Design of Experiments for Reinforcement
Learning. Germany: Springer International Publishing, 2014.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Mining of Massive Datasets
Course Code CSE-7107
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)

Course Learning Objectives

1. To collect, manage, store, query, and analyze various types of big data.
2. To gain hands-on experience on large-scale analytics tools to solve big data problems.
3. To study the impact of big data analysis for societal and business decisions.
Course Content
Module 1. Introduction to Mining of Massive Datasets: Data Mining, Summarization,
Feature Extraction, Statistical Limits on Data Mining, Bonferroni’s Principle, Hash Functions,
Indexes, Secondary Storage, The Base of Natural Logarithms, Power Laws.
Overviews of Big Data, State of the Practice in Analytics, The Data Scientist, Big Data Analytics
in Industry Verticals, Data Analytics Lifecycle Challenges of Conventional Systems, Statistical
Concepts: Sampling Distributions, Re-Sampling, Statistical Inference, Prediction Error,
Regression Modelling, Multivariate Analysis, Bayesian Modelling.
Module 2. Mining Data Streams: Stream Data Model and Architecture, Stream Computing,
Sampling Data in a Stream, Filtering Streams, Counting Distinct Elements in a Stream,
Estimating Moments, Counting Oneness in a Window, Decaying Window, Real time Analytics,
Platform (RTAP) Applications, Case Studies, Real Time Sentiment Analysis, Stock Market
Prediction.
Module 3. Frequent Itemset and Clustering: Mining Frequent Itemsets, Market Based
Analysis: Apriori Algorithm, Handling Large Data Sets in Main Memory, Limited Pass
Algorithm, Counting Frequent Itemsets in a Stream, clustering based Techniques: Hierarchical,
K-Means etc., Clustering High Dimensional Data, CLIQUE And PROCLUS, Frequent Pattern
based Clustering Methods, Clustering in Non-Euclidean Space, Clustering for Streams and
Parallelism.
Module 4. Frameworks and Visualization: Overview of MapReduce, Hadoop, Hive, MapR,
Sharding, NoSQL Databases, S3, HADOOP, Distributed File System (HDFS), Visualizations:
Visual Data Analysis Techniques, Interaction Technique and Applications,

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Module 5. Introduction to MapReduce and the New Software Stack: Distributed File
Systems, Physical Organization of Compute Nodes, MapReduce, The Map Tasks, Grouping by
Key, The Reduce Tasks, Combiners, Details of MapReduce Execution, Coping with Node
Failures, Algorithms Using MapReduce, Matrix-Vector Multiplication by MapReduce,
Relational-Algebra Operations, Computing Selections by MapReduce, Computing Projections
by MapReduce, Extensions to MapReduce.
Course Outcomes
At the end of this course the student will be able to:
CO 1: Recognizing challenges faced by applications dealing with very large data as well as in
proposing scalable solutions.
CO 2: Design efficient algorithms for mining the massive databases.
CO 3: Model a framework for visualization of big data analytics for business users.
CO 4: Understand the significance of Big Data Analysis in business intelligence, scientific
discovery, and day-to-day life.
List of Text Books
1. Anand Rajaraman (Author), Jeffrey David Ullman (Author), Jure Leskovec (Author)
Mining of Massive Datasets, Cambridge University Press; 2nd edition, November 2014.
2. J. Hahn and Micheline Kamber - Data Mining: Concepts and Techniques
3. R.Kimball - DataWarehouse Toolkit (J.Wiley)
4. A.K.Pujari - Data mining (University Press)
List of Reference Books
1. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer, 2007.
2. Rajaraman, J.D. Ullman, Mining of Massive Datasets, Cambridge University Press, 2012.
3. Bill Franks, Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams
with Advanced Analytics, John Wiley & sons, 2012.
4. J. Han, M. Kamber, Data Mining Concepts and Techniques, 2nd Edition, Elsevier, Reprinted
2008.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Embedded System
Course Code CSE-7108
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Parallel and Distributed System (CSE-5002)
Course Learning Objectives
1. To demonstrate the embedded system with pipelining.
2. To learn the programming languages for embedded system design.
3. To develop an understanding of the implementation methodology of Arduino & Rasberry Pi.
Course Content
Module 1. Introduction to Embedded System, Challenges & Design Matrices, Classification of
Embedded System. Intel 8051 Microcontroller: Basic differences between Microprocessors and
Microcontroller. Introduction to Intel 8051 Microcontroller, architecture, registers, Internal and
External Memory. Instruction Set. On Chip Counters / Timers, Serial I/O, Interrupts and their
use. Assembly language programming.
Module 2. Atmel And PIC Microcontrollers: Introduction to Atmel and PIC C6X
microcontrollers, architecture, registers, Internal and External Memory, Instruction Set, On Chip
Counters / Timers, Serial I/O, Interrupts and their use. PWM, Watchdog Timer, ISP, IAP
features. Assembly language programming.
Module 3. ARM7TDMI (Advanced RISC Machines): ARM Architecture, Cortex-M3 Basics,
Exceptions, Instruction Sets, NVIC, Interrupt Behaviour, Cortex-M3/M4 Programming,
Exception Programming, Memory Protection Unit and other Cortex-M3 features,
STM32L15xxx ARM Cortex M3/M4 Microcontroller Memory and Peripherals, Development
& Debugging Tools.
Module 4. Open Source Embedded Development Board (Arduino): Overview of open
source embedded development board (Arduino), block diagram, pins of embedded development
board, features of open source tool used for programming a development board, programming
of embedded development board, Interface Serial Port with embedded development board,
Program Raspberry Pi: a credit-card sized computer, Python programming for Raspberry Pi,
Interacting and configuring the RPi OS, Porting of Linux Kernel and booting RPi.

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Module 5. Case Studies: Design of Embedded Systems using the Microcontroller– 8051/
Amtel/ Arduino/ Raspberry Pi, for applications in the area of Communications, Automotives,
and industry.
Course Outcomes
At the end of this course the student will be able to:
CO 1: Learn the concept of structure of embedded systems.
CO 2: Identify the needs of Arduino/Rasberry Pi in real life applications.
CO 3: Design Embedded System using microcontroller.
List of Text Books
1. Embedded System Design: A Unified Hardware/Software Introduction by Frank Vahid,
Tony D. Givargis
2. Computers as Components: Principles of Embedded Computing System Design by Marilyn
Wolf.
3. Raj Kumar, “Embedded Systems: Architecture, Programming and Design”, Tata McGraw
Hill, 2017.
List of Reference Books
1. John Catsoulis, O’Reilly, “Designing Embedded Hardware”, First Indian Reprint, 2003.
2. David E. Simon, “An Embedded Software Primer”, Pearson Education Asia, Fifth Indian
Reprint, 2002.
3. Michael Barr, O’Reilly, “Programming Embedded Systems in C and C ++”, second edition
2007.
4. J.W. Valvano, “Embedded Microcomputor System: Real Time Interfacing”, Brooks/Cole,
2000.
5. Jack Ganssle, “The Art of Designing Embedded Systems”, Newnes, second edition 2008.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Distributed Ledger Technology
Course Code CSE-7109
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Cryptography and Cyber Security (CSE-5001)

Course Learning Objectives

1. To provide knowledge on Distributed Ledger Technology.
2. To understand the design of Blockchain transaction and security issues.
3. To study about various use Cases in Blockchain.
Course Content
Module 1. Fundamentals of Distributed Ledger Technology: Distributed Ledger
Technology, Blockchain: Importance and features –Layers of Blockchain: application layer,
execution layer, semantic layer, propagation layer, consensus layer – Types of Blockchain –
Blockchain in practical use today – Blockchain governance challenges – Blockchain technical
challenges.
Module 2. Blockchain for Enterprise: Blockchain Components and Concepts - Block Header
and Identifiers -Linking Blocks in the Blockchain - Mining and Consensus: Aggregating
transactions into Blocks - Mining the Block -Validating and Assembling of Blocks, Selecting
Chains of Blocks.
Module 3. Transactions and Bitcoin Network: Transactions: Lifecycle, Structure, Inputs and
Outputs, Standard Transactions - Bitcoin Network: Network discovery for a new node, Block
propagation.
Module 4. Bitcoin Client: Consensus in Bitcoin: Proof of Work (PoW), Mining the Block,
Changing the Consensus Rules - Bitcoin Core: Bitcoin core application programming interface,
running a bitcoin core node, Alternative clients, libraries and toolkits - Bitcoin Addresses:
Implementing Keys and Addresses in Python –Wallets.
Module 5. Security and privacy practices: Security Architecture principles -Technical and
inherent risks of the blockchain technology Attacks on Privacy: Blockchain and non-blockchain
based Attacks - Risks and Limitations of Blockchain – User security best practices: physical
bitcoin storage, hardware wallets, balancing risk, diversifying risk, multi signature and
governance.

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Module 6. Blockchain Architecture and Applications: Design methodology for blockchain
applications: blockchain application templates, blockchain application development – Ethereum
– Solidity - Deploying a sample application: Blockchain and betting –Colored coins –
Counterparty, Blockchain Use Cases.
Course Outcomes
After completion of this course, the student shall be able to:
CO1. Understand the requirements of the fundamentals of Blockchain.
CO2. Identify and apply the concept of Bitcoin.
CO3. Recognize the underlying technology of transactions, blocks and proof-of-work.
CO4. Gain a deep insight into the Bitcoin network, Bitcoin miners and Bitcoin transactions.
CO5. Design and explore the applications of Blockchain.
List of Text Books
1. Bikramaditya Singhal, Gautam Dhameja, Priyanshu Sekhar Panda, Beginning Blockchain,
“A Beginner’s Guide to Building Blockchain Solutions”, 1st edition, Apress, New York,
2018.
2. Joseph J. Bambara, Paul R. Allen, “Blockchain: a practical guide to developing business,
law and technology solutions,” 1st edition, McGraw-Hill publication, New York, 2018.
List of Reference Books
1. Swan Melanie, “Blockchain: Blueprint for a new economy”, 1st edition, O'Reilly Media,
United States, 2015.
2. Josh Thompson, “Blockchain: The Blockchain for Beginnings, Guide to Blockchain
Technology and Blockchain Programming’, Create Space Independent Publishing
Platform, 2017.
3. Draft version of S. Shukla, M. Dhawan, S. Sharma, S. Venkatesan, “Blockchain
Technology: Cryptocurrency and Applications,” Oxford University Press, 2019.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Multimedia Processing
Course Code CSE-7110
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Digital Image Processing (CSE-6001)
Course Learning Objectives
1. To comprehend the fundamentals of multimedia systems, including their components and
functionality..
2. To analyze and design audio encoders and video encoders.
3. To evaluate and create multimedia communication network architectures.
Course Content
Module-1: Introduction to Multimedia Data Representation: Introduction to Multimedia, A
Taste of Multimedia, Graphics and Image Data Representation, Colour in Image and Video,
Fundamental Concepts in Video, Basics of Digital Audio.
Module-2: Multimedia Data Compression: Lossless Compression Algorithms, Lossy
Compression Algorithms, Image Compression Standards, Basic Video Compression
Techniques.
Module-3: MPEG Video Coding Standards: MPEG-1,2,4 And 7 New Video Coding Standards:
H.264 and H.265.
Module-4: Basic Audio Compression Technique, MPEG Audio Compression.
Module-5: Multimedia Communication and Networking: Network Services and Protocols for
multimedia communications, Multimedia Content Distribution.
Course Outcomes
At the end of this course the students will be able to:
CO1. Understand the fundamental concepts of multimedia signals and systems.
CO2. Analyze multimedia systems and develop multimedia systems.
CO3. Design various multimedia systems like JPEG and MPEG encoders and decoders.
CO4. Carry out research and development in the field of multimedia systems and algorithms.

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List of Text Books
1. Li, Ze-Nian, Mark S. Drew, and Jiangchuan Liu. Fundamentals of multimedia. Upper
Saddle River (NJ) Pearson Prentice Hall, 2004.
2. Alan C. Bovik, “The Essential Guide to Video Processing,” Academic Press 1st Edition,
2009.
3. Parekh, Ranjan. Fundamentals of image, audio, and video processing using Matlab: with
applications to pattern recognition. CRC Press, 2021.
List of Reference Books
1. Ashok Banerji and Anand Mohan Ghosh, “Multimedia Technologies”, TMH, 2010.
2. Parekh, Ranjan. Principles of Multimedia. India: Tata McGraw Hill, 2013..

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Applications of Artificial Intelligence in Healthcare
Course Code CSE-7111
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)
Course Learning Objectives
1. To explore the various forms of electronic health care information.
2. To learn the techniques adopted to analyze healthcare data.
3. To understand the predictive models for clinical data.
Course Content
Module 1: AI and Machine Learning, Applications and Foundations Recent trends in Neural
Networks, Applications of Neural Networks, Introduction of Deep Learning, Difference
between Neural Networks and Deep Learning. Introduction, Principal Component Analysis
(PCA), Kernel PCA, Canonical Correlation Analysis, regression, logistic regression.
Module 2: Using AI for Disease Diagnosis and Patient Mentoring- Medical Image- Diagnosis,
Eye Disease and Cancer Diagnosis, Image Segmentation on MRI Images Image Classification
and Class Imbalance.
Module 3: Natural Language Processing and Data Analytics in Health Care -Machine Learning
and Natural Language Processing in Mental Health, Natural Language Processing of Clinical
Notes on Chronic Diseases Extracting social determinants of health from electronic health
records using natural language processing, Deep learning in clinical natural language
processing.
Module 4: Advanced Data Analytics: Advanced Data Analytics for Healthcare– Review of
Clinical Prediction Models- Temporal Data Mining for Healthcare Data- Visual Analytics for
Healthcare- Predictive Models for Integrating Clinical and Genomic Data- Information
Retrieval for Healthcare- Privacy-Preserving Data Publishing Methods in Healthcare.
Module 5: Applications: Applications and Practical Systems for Healthcare– Data Analytics
for Pervasive Health- Fraud Detection in Healthcare- Data Analytics for Pharmaceutical
Discoveries- Clinical Decision Support Systems- Computer-Assisted Medical Image Analysis
Systems- Mobile Imaging and Analytics for Biomedical Data.

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Course Outcomes
At the end of this course the students will be able to:
CO 1: Analyze health care data using appropriate analytical techniques.
CO 2: Apply analytics for decision making in healthcare services.
CO 3: Compare and select relevant AI methods for the development of AI applications in
healthcare.
CO 4: Apply data mining to integrate health data from multiple sources and develop efficient
clinical decision support systems.
List of Text Books
1. Russell, Stuart Jonathan., Norvig, Peter. Artificial Intelligence: A Modern Approach.
United Kingdom: Pearson, 2021.
2. Hopgood, Adrian A. Intelligent systems for engineers and scientists: a practical guide to
artificial intelligence. CRC press, 2021.
3. Bishop, Christopher M. Neural networks for pattern recognition. Oxford university
press, 1995.
4. Hastie, Trevor, et al. The elements of statistical learning: data mining, inference, and
prediction. Vol. 2. New York: springer, 2009.
List of Reference Books
1. Yang, Hui, and Eva K. Lee, eds. Healthcare analytics: from data to knowledge to
healthcare improvement. John Wiley & Sons, 2016.
2. Reddy, Chandan K., and Charu C. Aggarwal, eds. Healthcare data analytics. Vol. 36.
CRC Press, 2015.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Data Clustering
Course Code CSE-7112
Compulsory/Elective/O Elective
pen Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)
Course Learning Objectives
1. To understand the concepts and significance of data clustering.
2. To identify different types of clustering problems and their real-world applications.
3. To utilize programming and data analysis tools to perform clustering on real datasets.
4. To analyze and interpret clustered data to draw meaningful insights.
Course Content
Module-1: Introduction: Introduction to Data Clustering, Overview of unsupervised learning,
Importance and applications of data clustering, Types of clustering problems: hierarchical,
partitioning, density-based.
Module-2: Clustering Algorithms: Comparative study of K-Means clustering, Hierarchical
clustering, DBSCAN, Density-Based Spatial Clustering of Applications with Noise, EM,
Expectation-Maximization, clustering, Internal evaluation metrics, Silhouette coefficient, Davies-
Bouldin index, External evaluation metrics, Rand index, Fowlkes-Mallows index.
Module-3: Feature Selection and Preprocessing for Clustering: Dimensionality reduction
techniques, Handling missing values and outliers, Customer segmentation for marketing, Image
segmentation, Document clustering.
Module-4: Hands-on Clustering Practice: Implementing clustering algorithms using
programming languages (Python/R), Using popular data analysis libraries, scikit-learn, pandas,
performing clustering on real datasets, Ethical and Social Considerations in Clustering, Privacy
concerns in clustering, Bias and fairness in clustering results.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Comprehend the concept of data clustering, its challenges and applications.
CO 2: Acquire the knowledge of syntax and semantics related to natural languages.

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CO 3: Ability to use and analyze various data clustering algorithms.
CO 4: Acquire knowledge of machine learning techniques used in data clustering.
List of Text Books
1. Chandan K. Reddy, Charu C. Aggarwal, (2018). Data Clustering: Algorithms and
Applications. United States: CRC Press.
2. Everitt, B. S., Landau, S., Leese, M., Stahl, D. (2011). Cluster Analysis. Germany: Wiley.
List of Reference Books
1. Han, J., Kamber, M., Pei, J. (2011). Data Mining: Concepts and Techniques. Netherlands:
Elsevier Science.
2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Switzerland: Springer
New York.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name High Performance Computing
Course Code CSE-7113
Compulsory/Elective/ Elective
Open Elective
Prerequisites
N/A

Course Learning Objectives

1. To understand underlying Operating Systems concepts from a programmer's viewpoint,
including process, memory and file system management.
2. To understand parallel processing systems, from a programmer's viewpoint, and develop
parallel programs.
3. To Study various computing and emerging technology architecture.
Course Content

Module 1. Introduction to High Performance Computing & performance measures:

Speedup, efficiency and scalability. Model of parallel computation and basic communication
primitives. Parallel prefix and applications, Parallel sorting, Embedding, Parallel matrix
algorithms.
Communication networks for parallel computers and parallel models: Communication
networks for parallel computers and parallel models of computation, Parallel fast Fourier
transforms.
Module 2. Parallel Programming with MPI: Writing and executing MPI programs, collective
communication, grouping data for communication, communicators and topologies. Parallel
random number generation, Parallel Octrees, Parallel N-body methods, Parallel Bayesian
network construction.
Module 3. Cluster Computing: Introduction to Cluster Computing, Scalable Parallel Computer
Architectures, Cluster Computer and its Architecture, Classifications, Components for Clusters,
Cluster Middleware and Single System Image, Resource Management and Scheduling,
Programming Environments and Tools, Applications, Representative Cluster Systems,
Heterogeneous Clusters, Security, Resource Sharing, Locality, Dependability, Cluster
Architectures, Detecting and Masking Faults, Recovering from Faults, Condor, Evolution of
Meta computing.

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Module 4. Load Sharing and Balancing: Evolution, Job and Resource Management Systems,
State-of-the-Art in RMS and Job, Rigid Jobs with Process Migration, Communication-Based
Scheduling, Batch Scheduling, Fault Tolerance, Scheduling Problem for Network Computing,
Algorithm - ISH, MCP and ETF, Dynamic Load Balancing, Mapping and Scheduling, Task
Granularity and Partitioning, Static and Dynamic Scheduling.

Module 5. Grid Computing: Introduction to Grid Computing, Virtual Organizations,

Architecture, Applications, Computational, Data, Desktop and Enterprise Grids, Data intensive
Applications, High-Performance Commodity Computing, High-Performance Schedulers, Grid
Middleware: Connectivity, Resource and Collective Layer, Globus Toolkit, GSI, GRAM,
LDAP, Grid FTP, GIIS, Heterogeneous Computing Systems, Mapping Heuristics: Immediate
and Batch Mode Duplex, GA, SA, GSA, Tabu and A*, Expected Time to Compute Matrix,
Makespan, Heterogeneity: Consistent, Inconsistent and Partially-Consistent, QoS Guided Min-
Min, Selective Algorithm, Grid Computing Security.
Course Outcomes
At the end of this course the student will be able to:
CO 1: Understand architecture of high-performance computers.
CO 2: Analyzing speed of programs run on high-performance computers.
CO 3: Understand the importance of communication overhead in high performance
computing.
CO 4: Solve different types of problems are best suited for different types of parallel
computers.
List of Text Books
1. Sterling, T., Anderson, M., & Brodowicz, M. High performance computing: Modern
Systems and Practices. Morgan Kaufmann, 2017.
2. Kirk, D. B., & Hwu, W. W. Programming massively parallel processors: A Hands-on
Approach. Newnes, 2012.
List of Reference Books
1. Grama, An Introduction to Parallel Computing: Design and Analysis of Algorithms, 2/e.
Pearson Education India, 2008.
2. Eijkhout, V. Introduction to high performance scientific computing. Lulu.com, 2012.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Public Key Infrastructure and Trust Management
Course Code CSE-7114
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Cryptography and Cyber Security (CSE-5001)
Course Learning Objectives

1. To learn concepts of public key infrastructures.

2. To understand the need of Identity Management Process and its importance in public key
infrastructure.

3. To learn about Trust Management and Integrating a PKI with Applications.

Course Content
Module 1. Introduction and Infrastructure Concepts: Pervasive security services, Building
a comprehensive security model.
Module 2. PKI System Essentials: Public key cryptosystems, Authentication protocols, Key
management techniques. PKI Functions: encryption, decryption, signature, verification
Certification Authority, Certificate repository, Key recovery, Server & User Certificates, PKI &
IPSec, PKI Technologies, PKI Solutions Interoperability.
Module 3. Enterprise-Wide PKI: Internal PKI Architectures, Key Deployment &
Management, Certification Process, Keys & Policies, Password Validation Procedures,
Managing Keys, Key Distribution, Key Backup & Recovery, PKCS standards.
Module 4. PKI Trust Concepts: Generating, using and validating digital signatures, building
a Certification Authority and extending trust through PKI, integrating a PKI with existing
directory systems, Linking PKIs using cross-certification, identifying certificate components,
P2P trust, Web of Trust.
Module 5. Integrating a PKI with Applications: Implementing a PKI solution to support a
selected environment, Advanced topics.
Course Outcomes
At the end of this course the student will be able to:
CO 1: Distinguish between public key technology and a public key infrastructure.

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CO 2: Explain the need for a rigorous identity management process and its role in a public
key infrastructure.
CO 3: Differentiate the necessary components of a certificate policy and practices
statement.
CO 4: Understand implementation of a public key infrastructure, including the technology,
policy, standards, and long-term maintenance considerations.
List of Text Books
1. Klaus schmeh: “Cryptography and public key infrastructure on the internet”, 1st Edition,
Allied Publishers, 2004.
2. A Scholtens, Basics on Public Key Infrastructure (PKI), 2023.
List of Reference Books
1. Wenbo Mao: “Modern Cryptography: theory and practice”, 1st Edition, Pearson Education,
2005.
2. Jan Camenisch (Editor), Costas Lambrinoudakis, Public Key Infrastructures, Services and
Applications: 7th European Workshop, 2010.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Computer Vision with Deep Learning
Course Code CSE-7115
Compulsory/Elective/ Elective
Open Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)

Course Learning Objectives

1. To develop a foundational understanding of computer vision, including its applications and
significance in various domains.
2. To explore the principles and techniques of deep learning as they apply to computer vision
problems..
3. To study the development of algorithms and techniques to analyze and interpret the visible
world around us.
Course Content
Module 1. History of Computer Vision: Image Representation; Linear Filtering, Correlation,
Convolution; Image in Frequency Domain.
Module 2. Edge Detection: From Edges to Blobs and Corners; Scale Space, Image Pyramids
and Filter Bank; SIFT and Variants.
Module 3. Explaining CNNs: Visualization Methods; Early Methods (Visualization of
Kernels; Backport-image/Deconvolution Methods); Class Attribution Map Methods (CAM,
Grad-CAM, Grad CAM++, etc.); Going Beyond Explaining CNNs.
Module 4. CNNs for Object Detection: CNNs for Segmentation; CNNs for Human
Understanding: Faces.
Module 5. Deep Generative Models: An Introduction; Generative Adversarial Networks,
Variational Autoencoders; Combining VAEs and GANs.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Understand the fundamental problems of computer vision.
CO 2: Analyze and evaluate critically the building and integration of computer vision
algorithms.

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CO 3: Demonstrate awareness of the current key research issues in computer vision.
CO 4: Identify the deep learning algorithms for various types of learning tasks in various
domains.
CO 5: Implement deep learning algorithms and solve real-world problems.
List of Text Books
1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, 2016
2. Michael Nielsen, “Neural Networks and Deep Learning”, 2016.
3. Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning” MIT Press.
List of Reference Books
1. Richard Szeliski, “Computer Vision: Algorithms and Applications,” 2010.
2. Simon Prince, “Computer Vision: Models, Learning, and Inference,” 2012.
3. David Forsyth, Jean Ponce, “Computer Vision: A Modern Approach”, 2002.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Introduction to Robotics
Course Code CSE - 7116
Compulsory/Elective/Ope Elective
n Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)
Course Learning Objectives
1. To understand the basic principles of robotics.
2. To elaborate on the different types of robots and their functionalities.
3. To learn about the components, modeling and basic operation of robots.
4. To identify robots and its peripherals for satisfactory operation and control of robots for
industrial and non-industrial applications.
Course Content
Module 1. Robot-Basic concepts, Need, Law, History, Anatomy, specifications. Robot
configurations-Cartesian, cylinder, polar and articulate. Robot wrist mechanism, Precision and
accuracy of robot.
Module 2. Definition of robotics, Mathematical formulation of robotics Introduction, Mechanical
Systems, Components, Dynamics and Modeling, Control of Actuators in Robotic Mechanisms,
Robotic Sensory Devices.
Module 3. Performance Definition – Accuracy, Repeatability, precision with respect to position
& path, payload, speed, acceleration, cycle time.
Module 4. Challenges of Mobile and other robots- wheeled, tracked, legged, aerial, underwater
robots, surgical robots, rehabilitation robots, humanoid robots.
Module 5. Homogeneous Coordinate Transformation Matrix and its inversion principle, Forward
and Inverse Kinematics Problem, D-H Principle, Trajectory generation.
Course Outcomes
At the end of this course the students will be able to:
CO 1: List and explain the basic elements of robots.
CO 2: Design a small robot for navigation using robot programming.
CO 3: To develop algorithms for putting intelligence into robots.

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CO 4: Classify the various sensors used in robots for better performance.
CO 5: To know about various industrial and non-industrial applications of robots.

List of Text Books

1. Deb.S.R and Sankha Deb, "Robotics Technology and Flexible Automation", Tata McGraw
HillPublishing Company Limited, 2010.
2. Mikell P. Groover, Mitchell Weiss, Roger N Nagel, Nicholas G Odrey, Industrial Robotics
Technology, Programming and Applications, Tata –McGraw Hill Pub. Co., 2008.
3. J. J. Craig, Introduction to robotics, Pearson Educacion, 2006.
List of Reference Books
1. Fu. K. S, Gonzalez R. C. & Lee C. S. G, Robotics control, sensing, vision and intelligence,
Tata- McGraw Hill Pub. Co., 2008.
2. Cubero Sam, Industrial robotics: theory, modelling and control, Pro Literatur Verlag, 2006.
3. P. A, Janaki Raman, Robotics and Image Processing an Introduction, Tata- McGraw Hill
Pub. Co., 1998.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Optimization Techniques
Course Code CSE-7117
Compulsory/Elective/Open Elective
Elective
Prerequisites
Artificial Intelligence and Machine Learning (CSE-5003)
Course Learning Objectives
1. To gain knowledge on theory of optimization and conditions for optimality for unconstraint
and constraint optimization problems.
2. To gain knowledge on theory of optimization and conditions for optimality for unconstraint
and constraint optimization problems.
3. To understand programming techniques and implement different optimization techniques to
solve various models arising from engineering areas.
Course Content
Module 1: Linear Programming (L.P): Revised Simplex Method, Dual simplex Method,
Sensitivity Analysis DYNAMIC PROGRAMMING (D.P): Multistage decision processes.
Concepts of sub optimization, Recursive Relation-calculus method, tabular method, LP as a case
of D.P.
Module 2: Classical Optimization Techniques: Single variable optimization without
constraints, Multi variable optimization without constraints, multivariable optimization with
constraints – method of Lagrange multipliers, Kuhn-Tucker conditions. NUMERICAL
METHODS FOR OPTIMIZATION: Nelder Mead’s Simplex search method, Gradient of a
function, Steepest descent method, Newton’s method.
Module 3: Modern Methods of Optimization: GENETIC ALGORITHM (GA): Differences
and similarities between conventional and evolutionary algorithms, working principle, Genetic
Operators- reproduction, crossover, mutation GENETIC PROGRAMMING (GP): Principles of
genetic programming, terminal sets, functional sets, differences between GA & GP, Random
population generation. Fuzzy Systems: Fuzzy set Theory, Optimization of Fuzzy systems.
Module 4: Integer Programming: Graphical Representation, Gomory’s Cutting Plane Method,
Balas’ Algorithm for Zero–One Programming, Branch-and-Bound Method. APPLICATIONS OF
OPTIMIZATION IN DESIGN AND MANUFACTURING SYSTEMS: Formulation of model-
optimization of path synthesis of a four-bar mechanism, minimization of weight of a cantilever

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beam, general optimization model of a machining process, optimization of arc welding
parameters, and general procedure in optimizing machining operations sequence.
Course Outcomes
At the end of the course students will be able to:
CO 1: Formulate the Engineering Problems as an Optimization Problem.
CO 2: Use classical optimization techniques and numerical methods of optimization.
CO 3: Justify and apply the use of modern heuristic algorithms for solving optimization
problems.
CO 4: Enumerate fundamentals of Integer programming technique and apply different
techniques to solve various optimization problems arising from engineering areas.
List of Reference Books
1. Optimization for Engineering Design by Kalyanmoy Deb, PHI Publishers, 2012.
2. Genetic algorithms in Search, Optimization, and Machine learning – D.E.Goldberg, Addison-
Wesley Publishers.
3. Operations Research by Hillar and Liberman, TMH Publishers.
4. Optimal design – Jasbir Arora, Mc Graw Hill (International) Publishers.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Quantum Computing
Course Code CSE-7118
Compulsory/Elective/Open Elective
Elective
Prerequisites
N/A
Course Learning Objectives

1. To learn the quantum model of computation and the basic principles of quantum
mechanics.
2. To understand quantum protocols such as teleportation and super dense coding.
3. To construct the quantum model related to classical models of deterministic and
probabilistic computation.
Course Content
Module 1. Qubits and quantum states: Introduction to Quantum Computing: Quantum Bits,
Bloch Sphere Representation of a Qubit, Multiple Qubits. Classical & quantum information,
quantum computing laws of physics, quantum information, quantum computers, vector spaces,
postulates of quantum mechanics, linear combinations, basis & dimensions, inner products,
Cauchy-schwart triangle inequalities.
Module 2. Matrices & Operators: Pauli operators, outer products & matrix representation,
Hermitian, unitary & normal operators, eigenvalues and eigen vectors, characteristic equation,
trace of an operator, expectation value of an operator, projection operators.
Module 3. Quantum Gates and Circuits: classical logic gates circuits, one qubit quantum
gates, Hadamard gate, two qubit quantum gates- the CNOT gate, three qubit quantum gates- The
Fredkin gate, The Toffoli gate, quantum circuits, universal quantum gates. Entanglement,
exchange of information using entangled particles, Bell‘s states, Bipartite systems and the Bell
basis.
Module 4. Quantum Algorithms: classical to quantum Turing machines, computational
complexity and entanglement, classes of quantum algorithms, Deutsch‘s algorithm, The
Deutsch-Josza Algorithm, Shor‘s Algorithm, Grover‘s Algorithm, Simon‘s algorithm, quantum
search algorithm.
Module 5. Quantum cryptography: information content in a signal, entropy and Shannon‘s
information theory, deterministic versus probabilistic photon behavior, state description,

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superposition and uncertainty, measurement of superposition states, an augmented probabilistic
model, a photon coincidence experiment, BB84-emergence of quantum cryptography.
Course Outcomes
At the end of this course the student will be able to:
CO 1: Understand the basic principles of Quantum Computing.
CO 2: Analyze conventional computing and quantum computing.
CO 3: Understand the Quantum Computing algorithms and Quantum Cryptography.
CO 4: Solve various problems by Quantum Computers.
List of Text Books
1. Lipton, Richard J.., Regan, Kenneth W.. Introduction to Quantum Algorithms Via Linear
Algebra, Second Edition. United Kingdom: MIT Press, 2021.
2. Nielsen, Michael A.., Chuang, Isaac L.. Quantum Computation and Quantum
Information. India: Cambridge University Press, 2016..
3. Andreas de Vries. Quantum Computation: An Introduction for Engineers and Computer
Scientists, Books on Demand, First Published Edition, 2012.
List of Reference Books
1. David McMahon. Quantum Computing Explained, John Wiley and Sons Inc, Ist Edition
2008.
2. N. David Mermin. Quantum Computer Science – An Introduction, Cambridge
University Press, I Edition 2007.
3. Riley Tipton Perry. Quantum Computing from the Ground Up, World Scientific
Publishing Co. Pte. Ltd, Ist Edition 2012.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester – Seventh Year- Fourth
Course Name Penetration Testing and Vulnerability Analysis
Course Code CSE-7119
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Cryptography and Cyber Security (CSE-5001)
Course Learning Objectives
1. To grasp the fundamentals of penetration testing, ethical hacking, and legal implications.
2. To acquire hands-on skills in exploiting vulnerabilities, utilizing tools like Metasploit and
Wireshark.
3. To learn to assess network security and evaluate web vulnerabilities, including cross-site
scripting.
Course Content
Module 1: Introduction to Penetration Testing and Ethical Hacking: Fundamentals of
Penetration Testing, Concept of Ethical Hacking, Information Gathering Techniques, Legal and
Ethical Considerations.
Module 2: Vulnerability Scanning and Exploitation: Vulnerability Exploitation and Risk
Assessment, Scoping and Engagement Methodology, Port Scanning and OS Fingerprinting,
Man-in-the-Middle, Spoofing, and Sniffing Attacks, Use of Wireshark for Network Analysis.
Module 3: Exploits and Attack Techniques: Understanding Threats, Attacks, and
Vulnerabilities, Exploit Frameworks and Deployment, Network Service Exploitation,
Metasploit for Client-Side Attacks, Social Engineering in Penetration Testing.
Module 4: Network and Web Application Testing: Network Penetration Testing
Methodologies, Web Application Penetration Testing Introduction, Burp Suite Tool and Web
Proxies, Cross-Site Scripting and Request Forgery, Web Authentication and Session
Management.
Module 5: Database and Wireless Penetration Testing: Database Penetration Testing and
SQL Injection, Database Security Assessment, Wireless Penetration Testing Methodology,
Cloud Penetration Testing Basics.
Course Outcomes

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At the end of the course, the student will be able to:
CO 1: Demonstrate penetration testing and security auditing platform with advanced tools to
identify, detect, and exploit any vulnerability uncovered in the target network
environment.
CO 2: Apply appropriate testing methodology with defined business objectives and a
scheduled test plan, which will result in robust network penetration testing.
CO 3: Examine the security of a computer system or network which makes it impermeable
to an attacker.
List of Text Books
1. Hickey, Matthew., Arcuri, Jennifer. Hands on Hacking: Become an Expert at Next Gen
Penetration Testing and Purple Teaming. India: Wiley, 2020.
2. Weidman, Georgia. Penetration Testing: A Hands-On Introduction to Hacking. United
States: No Starch Press, 2014.
3. Kennedy, David., O'Gorman, Jim., Kearns, Devon., Aharoni, Mati. Metasploit: The
Penetration Tester's Guide. United States: No Starch Press, 2011.
List of Reference Books
1. Alcorn, Wade., Frichot, Christian., Orru, Michele. The Browser Hacker's
Handbook. United States: Wiley, 2014.
2. Muniz, Joseph., Lakhani, Aamir. Web Penetration Testing with Kali Linux. United
Kingdom: Packt Publishing, 2013.
3. Stuttard, Dafydd., Pinto, Marcus. The Web Application Hacker's Handbook: Discovering
and Exploiting Security Flaws. Germany: Wiley, 2011.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Biomedical Image Processing
Course Code CSE-7120
Compulsory/Elective/Open Elective
Elective
Prerequisites
Digital Image Processing (CSE-6001)

Course Learning Objectives

1. To learn the principles of Image processing in the biomedical field.
2. To perform statistical analysis on the image and segmentation and apply it in the relevant
areas.
3. To analyze the shape and texture in biomedical Image Processing.

Course Content
Module 1. Introduction of Biomedical Image Processing: Objectives of biomedical image
analysis – Computer aided diagnosis – Nature of medical images: X-ray imaging – Tomography
– Nuclear medicine imaging – SPECT imaging – Positron imaging tomography –
Ultrasonography – Magnetic resonance imaging. Removal of artifacts – Space domain filters –
Frequency domain filters – Optimal filtering – Adaptive filters.
Module 2. Image enhancement: Gray level transforms – Histogram transformation –
Convolution mask operators – Contrast enhancement. Detection of regions of interest –
Thresholding and binarization – Detection of isolated lines and points – Edge detection – Region
growing.
Module 3. Analysis of shape and texture: Representation of shapes and contours – Shape
factors – Models for generation of texture – Statistical analysis of texture – Fractal analysis –
Fourier domain analysis of texture – Segmentation and structural analysis of texture. Pattern
classification and diagnostic decision – Measures of diagnostic accuracy.
Module 4. Applications: Contrast enhancement of mammograms – Detection of calcifications
by region growing – Shape and texture analysis of tumours.
Course Outcomes
At the end of this course the student will be able to:
CO 1: Understand the principles of biomedical imaging modalities such as X-ray, CT,
MRI, Ultrasound, Nuclear Medicine, and Microscopy.

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CO 2: Define and usage of standard biomedical image formats such as DICOM and
TIFF.
CO 3: Design graphics applications with graphical user interfaces (GUI) to implement
image analysis, shape analysis and modeling, volume rendering, 3D surface
reconstruction.
List of Text Books
1. Sinha G. R, Patel, B. C., “Medical Image Processing: Concepts And Applications”,
Prentice Hall, 2014.
2. Gonzalez R C, Woods R E, “Digital Image Processing”, Third Edition, Prentice Hall,
2007.
List of Reference Books
1. Rangayyan R M, “Biomedical Image Analysis”, Fifth Edition, CRC Press, 2005.
2. KayvanNajarian, Robert Splinter, “Biomedical Signal and Image Processing”, Second
Edition, CRC Press, 2014.
3. Deserno T M, “Biomedical Image Processing”, Springer, 2011.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Human Computer Interfacing
Course Code CSE-7121
Compulsory/Elective/Op Elective
en Elective
Prerequisites
Object Oriented Programming (CSE-2005)
Course Learning Objectives
The objectives of the course are to:
1. To provide an overview of Human-Computer Interaction (HCI) and create a base required
for understanding of the user interface design.
2. Explore the psychological and cognitive aspects of user behavior and how they influence
interface design.
3. Gain proficiency in usability evaluation methods and techniques to assess and improve user
interfaces.
Course Content
Module 1. Introduction: Introduction to, Brief history and definition of Interface in Human
Computer Interface, User Interface, Importance of interface, Benefits and features of well-
designed interface.
Module 2. Application of graphics in HCI: A brief history of Screen design. The graphical
user interface – popularity of graphics, Concept of direct manipulation, Graphical system,
Characteristics, Web user – Interface popularity and characteristics.
Module 3. Designing principles of HCI: Direct Manipulation: Overview, Scope, Applications,
Cognitive Framework of HCI, and Perception & Representation.
Module 4. Interface modeling: Attention and Interface Design, Memory in Interface Design,
Knowledge Representation, User Modelling, Interaction with Natural Languages, Next
Generation Interface.
Module 5. Evaluation modules: UI Evaluation, Introduction, Cognitive Walkthrough,
Heuristic Evaluation, Evaluation with Cognitive Models, Evaluation with Users, Model-based
Evaluation.
Course Outcomes

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At the end of the course, student will be able to:
CO 1: Understand and appreciate the importance of design and evaluation methodology that
begins with human-computer interaction while maintaining a focus on the user.
CO 2: Understand the different types of users and how their needs and preferences can be
incorporated into the design of interactive systems.
CO 3: Apply HCI principles to the design of user interfaces, such as graphical user interfaces
(GUIs) and web interfaces.
List of Text Books
1. Lazar, Jonathan, Jinjuan Heidi Feng, and Harry Hochheiser, Research methods in human-
computer interaction, Morgan Kaufmann, 2017.
2. Norman, Kent, and Jurek Kirakowski, eds. The Wiley handbook of human computer
interaction set. John Wiley & Sons, 2017.
3. MacKenzie, I. Scott. Human-Computer Interaction: An Empirical Research Perspective.
Netherlands, Elsevier Science, 2024.
List of Reference Books
1. Shneiderman, Ben, et al, Designing the User Interface: Strategies for Effective Human-
Computer Interaction, Global Edition. Brazil, Pearson Education, 2017.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Social Network Analysis
Course Code CSE-7122
Compulsory/Elective/Open Elective
Elective
Prerequisites
Engineering Mathematics -III (Numerical Methods and Statistics) (CSE-4001), Advance
Programming Lab-1 (Python) (CSE-3006)
Course Learning Objectives
1. To formalize different types of entities and relationships as nodes and edges and represent
this information as relational data.
2. To use advanced network analysis software to generate visualizations and perform
empirical investigations of network data.
3. To collect network data in different ways and from different sources while adhering to legal
standards and ethics standards.
Course Content
Module 1. Introduction to social network analysis and Descriptive network analysis:
Introduction to new science of networks. Network examples. Graph theory basics. Statistical
network properties. Degree distribution, clustering coefficient. Frequent patterns. Network
motifs. Cliques and k-cores.
Module 2. Network structure, Node centralities and ranking on network: Nodes and edges,
network diameter and average path length. Node centrality metrics: degree, closeness and
betweenness centrality. Eigenvector centrality and PageRank. Algorithm HITS
Module 3. Network communities and Affiliation networks: Networks communities. Graph
partitioning and cut metrics. Edge betweenness. Modularity clustering. Affiliation network and
bipartite graphs. 1-mode projections. Recommendation systems.
Module 4. Information and influence propagation on networks and Network visualization:
Social Diffusion. Basic cascade model. Influence maximization. Most influential nodes in the
network. Network visualization and graph layouts. Graph sampling. Low -dimensional
projections.
Module 5. Social media mining and SNA in the real world and Twitter analysis: Natural
language processing and sentiment mining. Properties of large social networks: friends,
connections, likes, re-tweets.

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Course Outcomes
At the end of this course the students will be able to:
CO 1: Compute and interpret metrics that describe individual nodes in a network.
CO 2: Compute and interpret metrics that characterize various qualities of the network as a
whole.
CO 3: Compute and interpret partitioning networks into communities based on different
criteria.
CO 4: Specify, estimate and interpret statistical models of network dynamics and collective
behavior.

List of Text Books

1. Tanmoy Chakraborty “Social Network Analysis”, Wiley, 2021.
2. Wasserman, Stanley, and Katherine Faust. “Social network analysis: Methods and
applications” Cambridge University Press, 1994.
3. Matthew A. Russell. “Mining the Social Web: Data Mining Facebook, Twitter, Linkedin,
Google+, Github, and More”, 2nd Edition, O'Reilly Media, 2013.
List of Reference Books
1. Jennifer Golbeck, “Analyzing the social web”, Morgan Kaufmann, 2013.
2. Aggarwal, Charu C. “An introduction to social network data analytics,” Springer US,
2011.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester – Seventh Year- Fourth
Course Name Privacy and security in IoT
Course Code CSE-7123
Compulsory/Elective/Open Elective
Elective
Prerequisites
Cryptography and Cyber Security (CSE-5001)
Course Learning Objectives
1. To understand the state-of-the-art methodologies in IoT.
2. To explore the privacy preservation and trust models in Internet of Things (IoT).
Course Content
Module 1. Securing the Internet of Things: Vulnerabilities, attacks and countermeasures,
Security concerns in IoT applications, Security architecture in the Internet of Things, Security
Requirements in IoT, Insufficient Authentication/Authorization, Insecure Access Control,
Threats to Access Control, Privacy, and Availability, Attacks Specific to IoT. Vulnerabilities,
Secrecy and Secret, Key Capacity, Authentication/Authorization for Smart Devices, Transport
Encryption, Attack, and Fault trees, secure IoT system implementation lifecycle.
Module 2. Cryptographic fundamentals of IoT: Cryptographic primitives and its role in IoT,
Encryption and Decryption, Hashes, Digital Signatures, Random number generation, Cipher
suites, Key management fundamentals, Cryptographic controls built into IoT messaging and
communication protocols, IoT Node Authentication.
Module 3. Hardware and firmware security in IoT: Attacks to Sensors in IoTs, Attacks to
RFIDs in IoTs, Attacks to Network Functions in IoTs, Attacks to Back-end Systems, Security
in Front-end Sensors and Equipment, Prevent Unauthorized Access to Sensor Data, M2M
Security, RFID Security, Cyber-Physical Object Security, Hardware Security.
Module 4. Privacy Preservation: Data Dissemination, Privacy Preservation for IoT used in
Smart Building, Exploiting mobility social features for location privacy enhancement in Internet
of Vehicles, Lightweight and robust schemes for privacy protection in key personal IoT
applications: Mobile WBSN and Participatory.
Module 5. Cloud services and IoT: Offerings related to IoT from cloud service providers,
Cloud IoT security controls, An enterprise IoT cloud security architecture, New directions in
cloud-enabled IoT computing.

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Course Outcomes
At the end of this course, the student will be able to
CO 1: Assess different Internet of Things technologies and their applications.
CO 2: Acquire understanding on model threats and countermeasures.
CO 3: Solve IoT security problems using lightweight cryptography.
List of Text Books
1. Fei HU, Security and Privacy in Internet of Things (IoTs): Models, Algorithms, and
Implementation, CRC Press, 2016.
2. Brain Russell and Drew Van Duren, “Practical Internet of Things Security”, Packet
Publishing, 2016.
List of Reference Books
1. Brij B. Gupta, Aakanksha Tewari, A Beginner’s Guide to Internet of Things Security:
Attacks, Applications, Authentication, and Fundamentals, CRC Press, 2020.
2. Ollie Whitehouse, “Security of Things: An Implementers’ Guide to Cyber-Security for
Internet of Things Devices and Beyond”, NCC Group, 2014.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Biomedical Signal Processing and Control
Course Code CSE-7124
Compulsory/Elective/Open Elective
Elective
Prerequisites
Digital Image Processing (CSE-6001)
Course Learning Objectives
1. To provide knowledge of various techniques applied for biomedical signal processing and
discuss their mathematical details.
2. To learn physiological signals acquisition mechanisms and extract meaningful information
from them.
3. To identify patterns and trends within acquired signals and use them for analyzing non-
stationary biomedical signals .
Course Content
Module 1. Preliminaries: Properties of biological signals: non-stationary, non-linear, non-
Gaussian Linear shift invariant system, Finite and infinite impulse response, Auto-regressive
and moving average filters, discrete Fourier transform and z-transform, Magnitude and phase
response Poles and zeros stability, Convolution theorem, Linear and circular convolution.
Module 2. Biomedical Signals: Origin and Waveform Characteristics of basic biomedical
signals: Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG),
Phonocardiogram (PCG), Electroneurogram (ENG), Event-Related Potentials (ERPS),
Electrogastrogram (EGG), Computer-Aided Diagnosis, Objectives of Biomedical Signal
Processing, Challenges in Biomedical Signal Processing.
Module 3. Noise and artifacts management: Random noise, Structured Noise, Physiological
Interference, Stationary and Non-stationary Processes, Noises and Artifacts Present in ECG,
Time and Frequency Domain Filtering.
Module 4. Event detection and analysis of important biomedical signals: EEG Signal and
its Characteristics, EEG Analysis, Linear Prediction Theory, Autoregressive Method, Sleep
EEG, Noise cancellation in ECG and EEG Signals using adaptive filters; Detection of P, Q, R,
S and T waves in ECG, EEG Rhythms, Waves and Transients, Detection of Waves and
Transients, Correlation and coherence analysis of EEG Channels.
Module 5. Non-stationary Signals and their analysis: Heart Sounds and Murmurs,
Characterization of non-stationary signals and dynamic systems, Short-Time Fourier
Transform, Considerations in Short-Time Analysis and Adaptive Segmentation.

Course Outcomes
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At the end of the course, the student will be able to:
CO 1: Form a theoretical base of the signal processing techniques used for the analysis of
biomedical signals.
CO 2: Understand filtering methods for noise reduction in biomedical signals to reduce
interference, enhance signal clarity and visualize them using appropriate tools.
CO 3: Acquire strong foundation in the principles of biomedical signal processing and related
events for their application in medical diagnosis, patient care, and healthcare research.
List of Text Books
1. Naik G, Biomedical Signal Processing. Springer Singapore; 2020.
2. Tompkins, Willis J., and Edward M. O'Brien., Biomedical digital signal processing., Annals
of Biomedical Engineering, 2006.
3. Webster, John G, Medical instrumentation: application and design, John Wiley & Sons, 2020.
List of Reference Books
1. Natarajan, R. Ananda. Biomedical Instrumentation and Measurements. India, Prentice Hall
India Pvt., Limited, 2015.
2. Subasi, Abdulhamit. Practical Guide for Biomedical Signals Analysis Using Machine
Learning Techniques: A MATLAB Based Approach. United Kingdom, Elsevier Science,
2019.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Digital Watermarking
Course Code CSE-7125
Compulsory/Elective/Open Elective
Elective
Prerequisites
Digital Image Processing (CSE-6001)
Course Learning Objectives
1. To learn about the watermarking models and message coding.
2. To learn about watermark security and authentication.
3. To learn about steganography Perceptual models.
Course Content
Module1: Information Hiding, Steganography, and Watermarking, History of Watermarking,
History of Steganography, Importance of Digital Watermarking, Importance of Steganography
Applications and Properties.
Module2: Communication-Based Models of Watermarking, Geometric Models of
Watermarking, Modeling Watermark Detection by Correlation, Robust Watermarking
Approaches.
Module3: Security Requirements, Watermark Security and Cryptography, Some Significant
Known Attacks, Content Authentication.
Module4: Information-Theoretic Foundations of Steganography, Steganographic Methods:
Statistics Preserving Steganography, Model-Based Steganography, Masking Embedding as
Natural Processing, Minimizing the Embedding Impact.
Module5: Steganalysis Scenarios, Significant Steganalysis Algorithms.
Course Outcomes
At the end of this course the students will be able to:
CO 1: Learn watermarking techniques and through examples understand the concept.
CO 2: Learn the concept of information hiding.
CO 3: Survey current techniques of steganography and learn how to detect and extract Hidden
Information.
List of Text Books

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Department of Computer Science & Engineering
1. Cox, Ingemar, Matthew Miller, Jeffrey Bloom, Jessica Fridrich, and Ton Kalker. Digital
watermarking and steganography. Morgan Kaufmann, 2007.
2. Michael Arnold, Martin Schmucker, Stephen D. Wolthusen, “Techniques and
Applications of Digital Watermarking and Contest Protection”, Artech House, London,
2003.
List of Reference Books
1. Juergen Seits, “Digital Watermarking for Digital Media”, IDEA Group Publisher, New
York, 2005.
2. Peter Wayner, “Disappearing Cryptography – Information Hiding: Steganography &
Watermarking”, Morgan Kaufmann Publishers, New York, 2002.

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Department of Computer Science & Engineering

Open
th
Elective 6
Semester

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Environmental Science
Course Code CSE-6201
Compulsory/Elective/ Open Elective
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To discuss the complexity of issues and challenges related to energy and environmental
science.
2. To explain the principles of biological air pollution, control technologies and its limitations.
3. To learn about various factors affecting contaminant degradation and types of
biodegradation processes.
4. To get knowledge about the ecosystem and biodiversity.

Course Content
Module 1. Atmosphere and Air Pollution: Composition and structure of atmosphere,
Atmospheric reactions, Types of inversions, Ozone layer, Mechanism of Ozone depletion, Air
pollutants, Sources and sinks, Classification and Effects of Air Pollutants, Air quality index,
Ways of Monitoring, Prevention and control of Air Pollution, Greenhouse effect and Global
warming, Major sources, Effects and remedial measures, Acid rain and their adverse effects,
Indoor Pollution, causes and effects, Case studies of environmental disasters like Bhopal Gas
Tragedy.
Module 2. Ecosystem and Biodiversity: Concept of ecosystem, Structure and function of
ecosystem, Producers, consumers and decomposers, Energy flow in the ecosystem, Ecological
succession, Food chains, Food webs and ecological pyramids, Forest ecosystems, Structure
and functions, Biodiversity, Introduction, Genetics, Species and Ecosystem diversity, Value
of biodiversity, Consumptive use, Productive use, Social, Ethical, Aesthetic and optimal
values. Threats to Biodiversity, Habitat loss, Pollution, Global climate change,
overexploitation, Poaching of wildlife, Wildlife protection act, Forest conservation act, Rare
species.
Module 3. Soil Pollution: Structure of Earth, Crust and its composition, Soil formation by
weathering, Erosion processes, Types and formation of soils, Important physical
characteristics of soil Function and importance of Soil, Sources of soil pollution and
degradation, Effects of soil pollution on environment, Control strategies.

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Module 4. Water Pollution & Plastic Pollution: Characteristics of water: Physical, chemical
and biochemical, Sources of Water pollution, Classification of water pollutants and their
detrimental effects, Organic waste, River/lake/groundwater pollution, Plastics, Types and uses,
Plastic waste sources and production, Impact of Plastics on Marine life, Effect on wildlife,
Human health and environment, Plastic waste management practices, Nuclear hazards, Causes,
Effects and control measures.
Module 5. Society and Environment: Social perspectives of environment, Global and Indian
issues, Sustainable development: Concept, Components and strategies, Environmental
education, Environmental ethics: Issues and possible solutions, Environment protection act,
Air act, Water act. Public awareness, People’s participation in resource conservation and
environmental protection.

Course Outcomes
At the end of this course students will be able to:
CO 1 : Know the environmental pollutants, their health effects, and environmental
remediation and management.
CO 2 : Infer the chemical processes involved in the treatment of water and wastewater.
CO 3 : Understand and convey the ecological, social, and economic impacts of diversity
loss, and apply management principles and tools that are used to conserve diversity.
CO 4 : Describe causes and effects of environmental pollution by various industries and
discuss some remedial strategies.
List of Text Books
1. C.S. Rao, “Environmental Pollution Control Engineering”, Fourth edition, New Age
International Private Limited, 2021.
2. V. Veeraiyan, L. D. Stephen, “Environmental Science & Engineering”, Revised edition,
VRB Publishers, 2019.
3. B. Joseph, “Environmental Science and Engineering”, First edition, McGraw Hill,
Education, 2017.
4. A.V.J. Kumari,J.A. Mohaideen, B. Rajarathinam, M. Gowdhamamoorth, “Environmental
Science and Engineering”, Charulatha Publications, 2017.
List of Reference Books
1. O.P. Gupta “Elements of Land/Soil Pollution” Khanna Book Publishing Company (P)
Limited, 2019.
2. Naik, T. K. Tiwari “Society and Environment” CBS Publishers & Distributors, 2019.
3. Leicht, Alexander, Heiss, Julia, W. J. Byun “Issues and Trends in Education for Sustainable
Development” UNESCO Publishing, 2018.
4. M.N. Rao, R. Sultana, S. H. Kota, “Solid and Hazardous Waste Management” Elsevier
Science, 2016.

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Name of Program B. Tech. Semester- Sixth Year- Third

Course Name Technical Communication
Course Code CSE-6202
Compulsory/Elective/ Open Elective
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To develop student’s awareness towards the value of technical communication and
presentation skills.
2. To enhance the documentation skills of the students with emphasis on formal and informal
writing.
Course Content
Module 1. Basics of Technical Communication: Introduction to technical communication,
Objectives & characteristics, Importance and need for technical communication, Distinction
between general and technical communication, Briefly explain the skills required for technical
communication, Dimensions of communication, Barriers to effective communication.
Module 2. Technical Writing and Speaking: Technical writing, Sentences, Paragraph,
Technical style, Definition, types & methods, Technical essays, Grammar, Identifying
sentence types, Classifying the verb patterns. Interview skills, Group discussion, Objective &
method, Seminar/Conferences presentation skills.
Module 3. Forms of Technical Communication: Technical Report, Importance,
Thesis/Project writing, Structure & importance, Synopsis Writing, Methods, Technical
research paper writing, Methods & style, Seminar & Conference paper writing, Formal letters
CV/Resume.
Module 4. Technical Presentation: Strategies & techniques, Nature and importance of oral
presentation, Defining the purpose, Features of body language, Voice modulation, Analyzing
the audience, Planning and preparing the presentation, Modes of presentation, Handling stage
fright, Confident speaking, Audience analysis & retention of audience interest, Methods of
presentation, Interpersonal, Impersonal, Audience participation, Quizzes & interjections.
Course Outcomes
At the end of this course students will be able to:
CO1 : Utilize the technical writing for the purposes of technical communication and
its exposure in various dimensions.
CO 2 : Create effective technical presentations.
CO3 : Apply various communicative skills in a precise and efficient way in technological
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contexts.
List of Text Books
1. M. Raman and S. Sharma, “Technical Communication” Fourth edition, Oxford University
Press, 2022.
2. J. Tham “Design Thinking in Technical Communication” Taylor & Francis, 2021.
3. M. A. Rizvi, “Effective Technical Communication”, Second edition, McGraw Hill
Education, 2017.
4. P. Anderson “Technical Communication: A Reader-centered Approach”, Wadsworth
Publishing Co Inc, Ninth edition, 2017.
List of Reference Books
1. T. Acharya, “Handbook of Professional, Business & Technical Writing, and Communication
and Journalism” Mantra Records, 2023.
2. D.S. Paul “Advanced Writing Skills”, Goodwill Publishing House, 2022.
3. S. Hundiwala “Group Discussion” Second edition, Arihant Publications,2018.
4. R. Michelle “Report Writing”, United Kingdom: Bloomsbury Academic, 2018.
5. Carnegie, Dale, Esenwein, J. Berg, “The Art of Public Speaking”, United States: Dover
Publications, 2017.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name System Biology
Course Code CSE-6203
Compulsory/Elective/ Open Elective
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To provide insight into quantitative modeling of biological systems.
2. To understand the concept of systems biology and its applications.

Course Content
Module 1. Introduction: Key concepts of systems biology, Dynamic systems, Network, Self-
organization, Emergent properties, Homeostasis, Robustness, Experimental techniques.
Module 2. Standard Models and Approaches: Metabolism, Metabolic networks, Overview
of existing human metabolic networks, Cellular networks, Enzyme kinetics and
thermodynamics, Metabolic control analysis.
Module 3. Flux Balance Analysis: Introduction to Flux balance analysis, Construction of
stoichiometric matrices, Constraint based models, Network basics, Examples of mathematical
reconstruction of transcriptional networks and signal transduction networks.
Module 4. Evolution & Applications: Introduction, Mathematical models, Prediction of
biological systems, Data integration. Systems biology in various fields, Databases and tools,
Modeling tools.
Course Outcomes
At the end of this course students will be able to:
CO 1 : Explain the principles of system biology and experimental techniques.
CO 2 : Interpret the results from commonly used systems biology methods.
CO 3 : Know about the basics of networks and models.
List of Text Books
1. Alon, Uri. “An Introduction to Systems Biology: Design Principles of Biological Circuits”,
CRC Press, 2019.
2. E. Voit “A First Course in Systems Biology”, CRC Press, 2017.

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3. E. Klipp, W. Liebermeister, C. Wierling, A. Kowald, “Systems Biology”, Wiley-Blackwell,
Second edition 2016.

List of Reference Books

1. D. G. Cabrero, G. Bianconi, N. A. Kiani, “Networks of Networks in Biology Concepts,
Tools and Applications”, Cambridge University Press, 2021.
2. D. Costas, Maranas, A. R. Zomorrodi, “Optimization Methods in Metabolic Networks”
Wiley & Sons, 2016.
3. B. Palsson “Systems Biology Constraint-based Reconstruction and Analysis” Cambridge
University Press, 2015.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Sixth Year- Third
Course Name Intellectual Property Right
Course Code CSE-6204
Compulsory/Elective/ Open Elective
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To impart knowledge about the laws governing copyrights, patents, trademarks.
2. To introduce the students about the concepts, practices, methods, management and
valuation of IPRs.
3. To disseminate knowledge on design, Geographical Indication (GI).
Course Content
Module 1. Overview of Intellectual Property Right: Introduction, History, Concept, need,
Importance and characteristics of Intellectual property rights, Types, Patents, Designs, Trade
and copyright. Layout design, Geographical indication, Plant varieties and Traditional
knowledge.
Module 2. Patents: Origin, Meaning of patent, Types, Patent act, Product/Process Patents &
Terminology, Duration of patents, Elements of Patentability, Novelty, Non obviousness
(Inventive Steps), Inventions, Procedure for registration, Term of patent, Rights of patentee,
Basic concept of compulsory license and government use of patent, Infringement of patents,
Remedies & Penalties.
Module 3. Copyrights: Origin & types of copyright, Nature of copyright, Works in which
copyrights subsist, Author & ownership, Rights and protection covered by copyright, Law of
copyrights, Fundamentals of copyright law, Originality of material, Rights of reproduction,
Rights to perform the work publicly, Assignment and license of copyright.
Module 4. Trademarks: Origin & nature of Trademarks, Concept of trademarks, Kind of signs
used as trademarks, Purpose and functions, Registration of trademarks, Infringement of
trademarks, Assignment & transmission, Offenses penalties, Enforcement and remedies,
Trademarks and consumer protection.
Module.5 Other forms of IP: Design, Functions of design, International convention on design,
Layout design protection, Current contour, Geographical indication (GI), Types, GI laws,
Indian GI act.
Course Outcomes
At the end of this course students will be able to:
CO 1 :Understand the concept of intellectual property and its evolution.

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CO 2 : Apply adequate knowledge on patent and copyright for their innovative research work.
CO 3 : Understand the concept of trademarks and its types.
CO 4 : Describe the types of intellectual property protected under IP law.
CO 5 : Describe the registration process and infringement of copyrights.
List of Text Books
1. R. Chintakunta, “Intellectual Property Rights”, Blue Hill Publications, 2022.
2. H.S. Chawla, “Introduction to Intellectual Property Rights”, First edition, Oxford & IBH
Publishing, 2020.
3. R. Shahabadkar and S. S. Satyanarayana Reddy, “Intellectual Property Rights”, First edition,
Notion Press, 2019.
4. K V Nithyananda, “Intellectual Property Rights: Protection and Management” India, IN:
Cengage Learning India Private Limited, 2019.
5. Deborah E. Bouchoux, “Intellectual Property: The Law of Trademarks, Copyrights, Patents,
and Trade Secrets”, Fifth edition, Cengage learning custom publishing, New Delhi, 2017.
6. Prabhuddha Ganguli, “Intellectual Property Rights: Unleashing the Knowledge Economy”,
First edition, Mc-Graw Hill Education, 2017.
7. V.K. Ahuja, “Law relating to Intellectual Property Rights”, Third edition, Lexis Nexis, 2017.
List of Reference Books
1. S. R. Gurnani, “Intellectual Property Rights”, First Edition, C Jamnadas & Co., 2020.
2. B.L. Wadehra, “Law Relating to Patents, Trade Marks”, “Copyright, Designs and
Geographical Indications”, Fifth edition, Universal Law Publishing - An imprint of
LexisNexis, 2016.
3. M.K. Bhandari, “Law relating to Intellectual Property Rights”, Central Law Publications,
2017.
4. Aswani Kumar Bansal, “Law of Trademarks in India”, Thomson Reuters, 2014.

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Department of Computer Science & Engineering

Open
th
Elective 7
Semester

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 Indian Institute of Information Technology Bhopal
Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Principles of Management
Course Code CSE-7201
Compulsory/Elective/ Open Elective
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To understand and explore various principles of Management.
2. To build the managerial and interpersonal skills and characteristics required for successful
managers.
3. To gain the understanding of the functions of management, managerial roles and diverse
nature of modern business organizations.
4. To understand and imply various management practices for sustainable and competitive
businesses.
Course Content
Module 1. Introduction to Management: Definition, Nature & Features of Management,
Functions and Importance of Management, Management as a Process, Management and
Administration. Functional Areas of Management, Managerial Skills, Roles of a Manager,
Levels of Management, Management as a Science, an Art and as a Profession.
Module 2. Historical Perspectives of Management: Early management thought (Classical,
Systematic, Scientific management), Human relations movement, Contemporary management
approaches- Quantitative Management, Organizational Behaviors, Systems Theory,
Contingency Theory.
Module 3. International Business and its Environment: Globalization & WTO, Dynamics of
development Global business environment-. Internal and External analysis. Nature and
Importance of Planning, distinguish between strategic and tactical plans, Management by
Objectives, Styles of management, Decision Making process. Rational decision-making model,
Advantages and disadvantages of group decisions, Case Studies.
Module 4. Organization and Organizational structure: Need and Importance for
Organization, Elements of organization structure, Mechanistic and Organic organizations,
Summarize the effect of strategy, technology, and environment on organization structures.
Contrast the divisional and functional structures. Coordination, Departmentalization, Authority,
Delegation and Decentralization, Case Studies.

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Module 5. Teams and Teamwork: Leader, difference between managers & leaders. Skills,
Styles and theories of Leadership, Motivation at work. Techniques & Theories of motivation.
Control, Different approaches to control, Importance, Qualities of an effective control system.
Concepts of Teamwork, Team Player, Team Building, Collaboration, and Trust between
members of Teams.
Course Outcomes
At the End of this course the students will be able to:
CO 1: Develop proficient managerial and interpersonal skills necessary for effective
leadership and management in various business contexts.
CO 2: Demonstrate the ability to analyze and apply different managerial roles and functions
within a diverse range of modern business organizations.
CO 3: Identify and assess the rewards and challenges associated with pursuing a career in
management, allowing for informed career decision-making.
CO 4: Communicate and collaborate effectively in a team-based managerial environment,
showcasing improved interpersonal skills.
CO 5: Analyze real-world case studies to understand and solve managerial challenges,
applying theoretical knowledge to practical situations.
List of TextBooks
1. Drucker,Peter F., Practice Of Management, 2nd Revised Edition T&F/Routledge, 2014.
2. Stephen P. Robbins, Mary Coulter, and David De Cenzo, Fundamentals of Management,
11th Edition, USA, Pearson Education Limited, 2019.
List of Reference Books
1. Robert N. Lussier, Management Fundamentals: Concepts, Applications, and Skill
Development, 10th Edition, USA, SAGE Publications Inc, 2023.
2. Vasishth Vibhuti, Principles of Management, Taxmann Publications, 2022.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Engineering Economics
Course Code CSE - 7202
Compulsory/Elective/ Open Elective
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To understand the concepts of the time value of money and interest rates.
2. To Analyze cash flow series using present worth, annual equivalent worth and internal rate
of return methods of assessment.
3. To develop cash flow sequences that include the effects of taxes, inflation, depreciation,
loan principal payments and loan interest payments.
4. To assess alternatives and cash flows under risk with varying parameters.
Course Content
Module 1: Introduction to Economics: Introduction to Economics, Time value of money,
Simple and compound interest, Time value equivalence, Compound Interest factors.
Module 2: Cash Flow, Interest and Equivalence: Cash flow diagrams, Calculation of time-
value equivalences. Present worth comparisons, Comparisons of assets with equal, unequal and
infinite lives, Deferred investments, Future worth, payback period comparison.
Module 3: Engineering Costs & Estimation: Fixed, Variable, Marginal, Average, Sunk and
Opportunity Costs, Recurring and Nonrecurring Costs, Incremental Costs, Cash vs Book Costs,
Life-Cycle Costs; Types of Estimates, Estimating Models - Per-Unit and Segmenting Model,
Cost Indexes, Power-Sizing Model, Improvement & Learning Curve, Benefits. Case Study-
Price and Income Elasticity of Demand in the real world.
Module 4: Rate Of Return Analysis: Treatment of Salvage Value, Annual Cash Flow
Analysis, Analysis Periods; Internal Rate Of Return, Calculating Rate of Return, Incremental
Analysis; Choice of Best Alternative, Future Worth, Benefit-Cost Ratio, Sensitivity And
Breakeven Analysis. Economic Analysis in The Public Sector, Quantifying And Valuing
Benefits & drawbacks. Case Study–Tata Motors.
Module 5: Inflation And Price Change – Definition, Effects, Causes, Price Change with
Indexes, Types of Index, Composite vs Commodity Indexes, Use of Price Indexes In

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Engineering Economic Analysis, Cash Flows that inflate at different Rates. Case Study–
Competition in the Advertise Segment in India
Module 6: Present Worth Analysis: End-Of-Year Convention, Viewpoint Of Economic
Analysis Studies, Borrowed Money Viewpoint, Effect Of Inflation & Deflation, Taxes,
Economic Criteria, Applying Present Worth Techniques, Multiple Alternatives.
Course Outcomes
At the End of this course the students will be able to:
CO 1: Understand the principles of economics that govern the operation of any organization
under diverse market conditions.
CO 2: Demonstrates knowledge of professional, societal, and global issues in diverse business
set up.
CO 3: Explain the Inflation & Price Change as well as Present worth Analysis.
CO 4: Apply the principles of economics through various case studies.
List of TextBooks
1. L. Blank and A. Tarquin, Engineering Economy, Seventh Edition Texas & UAE, McGraw-
Hill Education, 2011.
2. S.M. Datar and M. V. Rajan, Horngren’s Cost Accounting, 16th Edition, USA, Pearson
publication, 2017.
List of Reference Books
1. J. A. White, K. E. Case (Author), and D. B. Pratt, Principles of Engineering Economic
Analysis, 6th Edition, USA, Wiley publication, 2012.
2. Managerial Economics: Principles and Worldwide Application, Seventh Edition, USA
Oxford Publisher, 2012.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Organizational Behavior
Course Code CSE-7203
Compulsory/Elective/ Open Elective
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To understand and analyze the individual needs and requirements.
2. To identify and develop effective motivational and leadership skills.
3. To develop skills needed for implementing changes in the organization.
4. To enhance interpersonal and group skills and processes for increasing effectiveness both
within and outside of organizations.
Course Content
Module 1. Introduction of organizational behavior (OB): Learning objectives of OB,
Definition, need and importance of OB, Nature and scope, Frame work, Emerging trends in OB,
The five anchors of OB; Perception: Process & errors, Improving perceptions; Personality
development, Determinants of Personality, Personality traits relevant to OB, Organizational
behavior models, Organization and the environmental factors. Organizational Theory,
Organizational behavior modification.
Module 2. Individual Behavior: Personality - Meaning & Definition, Factors influencing
personality, Personality Traits, Personality & OB. Attitudes-Characteristics, Components,
Formation, Measurement, Values. Perception-Meaning & Definition, Perceptual process,
Importance of Perception, Motivation at work-importance, need, types and its effects on work
behavior. Emotions and Moods in the workplace.
Module 3. Group Behavior: Communication- Importance, Types, Barriers to communication,
Communication as a tool for improving Interpersonal Effectiveness, Groups in organizations,
Influences, Group dynamics, Interpersonal Communication, Team Development, Group
decision making techniques, Conflict- Nature of Conflict, Traditional & Modern view,
Constructive & Destructive conflict, Strategies for encouraging constructive Conflict, Conflict
Resolution.
Module 4. Leadership and Power: Leadership - Meaning, importance, traits, style, Models
and Theories. Leaders Vs Managers, Sources & Consequences of Power, Power centers, Power

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Department of Computer Science & Engineering
and Politics. Organizational Politics, Types of Political Activity, Controlling of Political
Behavior.
Module 5. Dynamics of Organizational Behavior: Organizational culture and climate, Factors
affecting organizational climate, Importance, Introduction to Human Resource Management
(HRM), Selection, Orientation, Training & Development, Performance Appraisal, Incentives.
Organizational change-Importance, Stability Vs Change, Proactive Vs Reactive change, the
change process, Resistance to change, Managing change. Work Stressors -Prevention and
Management of stress, Balancing work and Life. Organizational Development-Characteristics
& objectives. Organizational effectiveness. International OB-An Introduction to Individual &
Interpersonal Behavior in Global Perspectives.
Course Outcomes
At the End of this course the students will be able to:
CO 1: Identify and define organizational behavior concepts.
CO 2: Apply the concepts of OB to improve understanding of work attitudes and behaviors.
CO 3: Apply the concepts of Communication to improve co-operation and co-ordination in
the organization.
CO 4: Apply the concepts of leadership for motivating the workforce of the organization.
List of TextBooks
1. Steven McShane and Van Glinar, Essentials of Organizational Behavior, Pearson, 14th
Edition, India, 2017.
2. Neharika Vohra, Stephen P. Robbins, Timothy A. Judge, Organizational Behavior, 18th
Edition, Pearson Education, India, 2022.
3. Angelo Kinicki and Robert Kreitner, Organizational Behavior: Key Concepts, Skills & Best
Practices, McGraw-Hill Higher Education, 3rd Edition, 2007.
LList of Reference Books
1. R. Agarwal, Organization and Management, McGraw Hill Education, July 2017.
2. John Newstrom and Keith Davis, Organizational Behavior: Human Behavior at Work,
McGraw-Hill Education; 11th Edition, 2001.

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Department of Computer Science & Engineering
Name of Program B. Tech. Semester- Seventh Year- Fourth
Course Name Research Design and Qualitative Methods
Course Code CSE-7204
Compulsory/Elective/ Open Elective
Open Elective
Prerequisites
N/A
Course Learning Objectives
1. To introduce contemporary ideas to design a research project.
2. To explore the various frameworks of Qualitative research.
3. To introduce different kinds of methods for conducting Quantitative research.
4. To conceptualize and design research projects.
5. To use appropriate techniques for analyzing and reporting qualitative and Quantitative
research.
Course Content
Module 1. Introduction of Research and Research Methodology: Definition, Characteristics,
Objectives, Research and Scientific method, Types of Research (Descriptive vs. Analytical
Research, Applied vs. Fundamental Research, Quantitative vs. Qualitative Research,
Conceptual vs. Empirical Research, Conceptual vs. Empirical Research) Research Process,
Basic Overview, Formulating the Research Problem, Defining the Research Problem, Research
Questions, Research Methods vs. Research Methodology.
Module 2. Qualitative research: Theoretical Perspectives in Qualitative Research, Philosophy,
logic & Principles of qualitative research design, Ethical considerations in qualitative research.
Formulating research questions and hypotheses, Sampling strategies & Design in qualitative
research.
Module 3. Interviewing Techniques: Interview Method, interview protocols, Types of
interviews-structured, semi-structured, and unstructured, Questionnaires, Practical exercises in
conducting interviews.
Module 4. Data Collection: Case Study Method, Processing Operations, Statistics in Research,
Types of Analysis, Interpretation of Data, Case Study.
Module 5. Content and Data Analysis: Content analysis as a qualitative research technique.
Different Software, Coding and categorization of textual, visual, or audio data, Validity and
reliability in content analysis, Interpreting and report Writing. Integration of findings from

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different qualitative methods. Strategies for presenting qualitative research to different
audiences. Reflection on the strengths and limitations of qualitative research.
Course Outcomes
At the End of this course the students will be able to:
CO 1: To identify and explain major Qualitative theories and apply them to everyday life.
CO 2: Design a quantitative research project for a specific problem
CO 3: Able to undertake data collection both qualitative and quantitative for a particular
problem.
CO 4: Proficient in writing skills necessary to produce a qualitative research proposal.
CO 5: Able to evaluate and analyze the data and to interpret and document the results.
List of Text Books
1. Maxwell J. A., Qualitative Research Design: An Interactive Approach, Corwin Press Inc;
4th Edition, 2023.
2. Tracy Sarah J., Qualitative Research Methods: Collecting Evidence, Crafting Analysis,
Communicating Impact, Wiley-Blackwell; 2nd Edition, 2019.
3. Creswell, J. W. and Creswell J. David, Research Design: Qualitative, Quantitative, and
Mixed Methods Approaches, SAGE Publications, Inc; 5th Edition, 2018.
4. Floyd J. Fowler, Survey Research Methods (Applied Social Research Methods SAGE
Publications, Inc; 5th Edition, 2013.
List of Reference Books
1. Fowler F J, Survey Research Methods, 5th Edition, Sage, 2014.
2. Harris D F, The Complete Guide to Writing Questionnaires: How to Get Better Information
For Better Decisions, I&M Press; Ist Edition, 2014.
3. Nick Emmel, Sampling and Choosing Cases in Qualitative Research: A Realist Approach,
SAGE Publications Ltd; Ist Edition, 2013.

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