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School of Computing & IT
Department of Computer Science & Engineering
B.Tech Syllabus– 2019 Onwards
EO2001: ECONOMICS [3 0 0 3]
Introduction: Definition, nature and scope of economics, introduction to micro and macro
economics; Microeconomics: Consumer behaviour, cardinal and ordinal approaches of utility, law
of diminishing marginal utility, theory of demand and supply, law of demand, exceptions to the law
of demand, change in demand and change in quantity demanded, elasticity of demand and supply,
Indifference curve, properties, consumer equilibrium, Price and income effect; Production: Law of
production, production function, SR and LR production function, law of returns, Isoquant curve,
characteristics, Isocost, producer’s equilibrium; Cost and revenue analysis: Cost concepts, short
run and long- run cost curves, TR,AR,MR; Various market situations: Characteristics and types,
Break-even analysis; Macro Economics: National Income, Monetary and Fiscal Policies,Inflation,
demand and supply of money, consumption function and business cycle.
References:
1. H.L Ahuja, Macroeconomics Theory and Policy, (20e) S. Chand Publication.
2. Peterson H C et.al., Managerial Economics, (9e), Pearson, 2012
3. P L Mehta, Managerial Economics, Sultan Chand & Sons, New Delhi, 2012.
4. G J Tuesen & H G Tuesen, Engineering Economics, PHI, New Delhi, 2008.
5. J. L. Riggs, D. D. Bedworth, S. U. Randhawa, Engineering Economics, Tata McGraw Hill, 2018.
MA2101: ENGINEERING MATHEMATICS III [2 1 0 3]
Boolean Algebra: Partial ordering relations, Poset, Lattices, Basic Properties of Lattices. Distributive
and complemented lattices, Boolean lattices and Boolean Algebra. Propositional and Predicate
Calculus: Wellformed formula, connectives, quantifications, Inference theory of propositional and
predicate calculus. Elementary configuration: Permutations and Combinations, Generating function,
Principle of inclusion and exclusion Partitions, compositions. Ordering of permutations:
Lexicographical and Fikes. Graph theory: Basic definitions, Degree, regular graphs, Eulerian and
Hamiltonian graphs, Trees and Properties, Center, radius and diameter of a graph, Rooted and binary
trees, Matrices associated with graphs, Algorithms for finding shortest path, Algorithm. Group theory:
Semi groups, Monoids, Groups subgroups, Normal Subgroups, Cosets, Lagrange's Theorem, Cyclic
groups.
References:
1. C. L. Liu, Elements of Discrete Mathematics, (2e), Mc Graw Hill, New Delhi, 2007.
2. J. P. Trembaly and R. Manohar, Discrete Mathematics Structures with application to computer
science, Tata Mc Graw Hill, 2012.
3. E. S. Page and L. B. Wilson, An Introduction to Computational Combinatorics, Cambridge Univ.
Press, 1979.
4. N. Deo, Graph theory with Applications to computer science, PHI, 2012.
CS2101: DATA COMMUNICATIONS [3 1 0 4]
Introduction: Data communications, Networks, Network types, Standards. Protocol Layering:
Protocol, Need for protocol architecture, OSI Model, TCP/IP protocol architecture. Data
Transmission: Concepts and terminology, Analog and digital data transmission, Transmission
impairments, Channel capacity, Transmission Media: Guided transmission media, Wireless
transmission, Wireless propagation, Line-of-Sight transmission. Signal Encoding Techniques:
Analog and digital Signals, Digital-to-digital conversion: Line coding schemes, Block coding,
scrambling, Analog-To-Digital Conversion: Pulse code modulation, Delta modulation. Digital Data
Communication Techniques: asynchronous and synchronous transmission, Types of errors, Error
detection, Error correction, Line configurations. Data Link Control Protocols: Flow control, Error
control, High-level data link control. Multiplexing: Frequency-division multiplexing, Time-division
multiplexing, Code-division multiple access. Space division multiplexing. Multiple Access: Random
access, Aloha, Carrier sense multiple access, Carrier sense multiple access with collision detection,
Carrier sense multiple access with collision avoidance, Code-division multiple access.](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-3-320.jpg)
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References:
1. B. Forouzan, Data Communication & Networking, (5e), McGraw Hill Education, 2013.
2. W. Stallings, Data and Computer Communications, (10e), Pearson Education,2018.
CS2102: COMPUTER SYSTEM ARCHITECTURE [3 1 0 4]
Digital Logic Circuits: Logic Gates, Boolean algebra, Map Simplification, Combinational Circuits, Flip-
Flops, Sequential Circuits. Digital Components: Integrated Circuits, Decoders, Multiplexers,
Registers, Shift Registers, Binary Counters, Memory Unit. Basic Structure of Computers: Computer
Types, Functional Units, Basic Operational Concepts, Software, Performance. Machine Instructions
and Programs: Numbers, Arithmetic Operations and Characters, Memory Locations and Addresses,
Instructions and Instruction Sequencing, Addressing Modes, Assembly Language, Additional
Instructions, Encoding of Machine Instructions. Arithmetic: Addition and Subtraction of Signed
Numbers, Design of Fast Adders, Multiplication of Positive Numbers, Signed Operand Multiplication,
Fast Multiplication, Integer Division, Floating Point Numbers and Operations.
References:
1. M. Morris Mano, Computer System Architecture, (3e), Pearson, 2017.
2. C. Hamacher, Z. Vranesic, S. Zaky, Computer Organization and Embedded Systems, (6e),
McGraw Hill, 2012.
3. J. P. Hayes, Computer Architecture and Organization, (3e), McGraw Hill TMH, 2012.
CS2103: DATA STRUCTURES & ALGORITHMS [3 1 0 4]
Introduction: Data structures classification, time and space complexity, pointers and pointer
applications, Accessing variables through pointers, structures, functions. Array: introduction, Linear
array, representation of an array in memory, multi-dimensional arrays, pointer arrays, matrix
operations. Linked Lists: Introduction, single linked list, representation of a linked list in memory,
Different Operations on a Single linked list, Reversing a single linked list, Advantages and
disadvantages of single linked list, circular linked list, doubly linked list and Header linked list.
Applications: polynomial operations and Josephus problem. Stacks: Basic Stack Operations,
implementation of a Stack using Static Array, Dynamic Array and linked list, Multiple stack
implementation using single array, Stack Applications, Reversing list, Factorial Calculation, Infix to
postfix conversion, evaluation of Arithmetic Expressions and Towers of Hanoi. Queues: Basic Queue
Operations, Representation of a Queue using array and linked list, Implementation of Queue
Operations using Stack, Applications of Queues, Round Robin Algorithm, Circular Queues,
DeQueues, Priority Queues. Trees: Definition of tree, Properties of tree, Binary Tree, Representation
of Binary trees using arrays and linked lists, Operations on a Binary Tree, Threaded binary tree,
Binary Tree Traversals (recursive and using stack), Binary search tree, AVL tree, m-way tree, B-
tree, B+ tree Graphs: Basic concepts, Different representations of Graphs, Graph Traversals (BFS
& DFS), Minimum Spanning Tree (Prims & Kruskal). Searching Techniques: Sequential and binary
search. Hashing: Hash function, Address calculation techniques, and Common hashing functions,
Collision resolution, Linear and Quadratic probing, Double hashing. Sorting Techniques: Basic
concepts, Sorting by: bubble sort, Insertion sort, selection sort, quick sort, heap sort, merge sort,
radix sort.
References:
1. A. S. Tannenbaum, J. Augenstein, Data Structures using C, Pearson India, 2018.
2. E. Horowitz, S. Sahni, Fundamentals of Data Structures in C, (2e), Universities Press, 2008.
3. A. Forouzan, R. F. Gilberg, A Structured Programming Approach Using C, (3e), Cengage
Learning, 2006.
CS2104: OBJECT ORIENTED PROGRAMMING [3 1 0 4]
The History and Evolution of object-oriented technology: benefits of object-oriented programming
(OOP), application of object-oriented programming (OOP), introduction to object-oriented
programming language like Java, C# and C++. Programming Fundamentals: Control flow
statements, operators, datatypes, Type conversion, Wrapper Classes, Arrays. Introduction to
classes: Class fundamentals, declaring objects, Assigning Object reference variables, Introduction](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-4-320.jpg)
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to methods, Constructors, Method Overloading, objects as parameters, argument passing, returning
objects, recursion, access control, final, nested and inner classes. I/O Basics: Reading Console
Input, Writing Console Output, Files handling. Inheritance: base and derived class, multilevel
hierarchy, access modifier in inheritance, method overriding, abstract classes. Exception Handling:
Exception types, creating exception, Try Catch construct, Throw and throws keyword. Multithreaded
programming: Creating and running threads, synchronise methods, inter thread communication,
suspending, resuming and stopping thread.
References:
1. M. Weisfeld, The object-oriented thought process, (4e), Pearson, 2013.
2. H. Schildt, The Complete Reference Java, (10e), Oracle Press, 2018.
3. C. Horstmann, Core Java Volume I—Fundamentals, (10e), Prentice Hall, 2006.
4. H. Schildt, The Complete Reference C++, (4e), Mcgraw Hill, 2003.
CS2130: DATA STRUCTURES & ALGORITHMS LAB [0 0 2 1]
Implementation of array operations: insertion, deletion, linear search and binary search, matrix
operation. Implementation of singly, doubly and circular linked lists: inserting, deleting, and inverting
a linked list, Polynomial addition, subtraction and sparse matrix implementation by linked list,
Josephus problem. Stacks and Queues: adding, deleting elements. Circular Queue: Adding &
deleting elements, conversion of infix to postfix and Evaluation of postfix expressions using stacks
& queues, Implementation of stacks & queues using linked lists. Recursive and Non-recursive
traversal of Trees: Threaded binary tree traversal, BST and AVL tree implementation.
Implementation of sorting and searching algorithms: bubble sort, Insertion sort, selection sort, quick
sort, heap sort, merge sort, radix sort, Hash table implementation.
References:
1. A. S. Tannenbaum, J. Augenstein, Data Structures using C, Pearson India, 2018.
2. E. Horowitz, S. Sahni, Fundamentals of Data Structures in C, (2e), Universities Press, 2008.
3. A. Forouzan, R. F. Gilberg, A Structured Programming Approach Using C, (3e), Cengage
Learning, 2006.
CS2131: OBJECT ORIENTED PROGRAMMING LAB [0 0 2 1]
Introduction to object-oriented programming language: Basic programming construct, flow control,
loops, data type and arrays. Introduction to classes and object: creating class and object, using
object to access class members, declaring method in class, recursion, argument passing and
returning, declaring constructor, constructor overloading and method overloading. Input-output:
Basic technique for input and output, type casting, file handling. Inheritance: creating base class and
derive class, use of different access modifier, overriding base class methods, creating abstract
classes/interfaces. Exception handling: try catch construct, creating own exception, raising
exception. Multi thread programming: creating and running thread, stopping thread, use of wait, inter
thread communication.
References:
1. H. Schildt, The Complete Reference Java, (10e), Oracle Press, 2018.
2. C. Horstmann, Core Java Volume I—Fundamentals, (10e), Prentice Hall, 2006.
3. H. Schildt, The Complete Reference C++, (4e), Mcgraw Hill, 2003.
BB 0025: VALUE, ETHICS & GOVERNANCE [2 0 0 2]
Relevance of Value Education in day-to-day life. Mantra for success - Value, Moral and Ethics.
Determinants of human nature (Three Gunas) and its impact on human life. Relevance of Personality,
Attitude, Behavior, Ego, Character, introspection, Motivation, Leadership and 4 Qs with relevant Case
Studies*.Governance: Understanding of Public and Private sector Governance systems; Courts & CAG.
Public Sector Governance: Need, relevance, stakeholders. Private Sector Governance: Proprietary,
Partnership, Company (Pvt Ltd & Ltd), Company’ Act 2013, Board of Directors; its Roles and
Responsivities. Regulatory bodies; its role in ethical governance. Projects on PPP mode-relevance &
prospects.CSR: Relationship with Society, Philanthropy and Business strategy, CSR Policy, Triple
Bottom Line. Suggestive Case Studies:Uphar Theatre Tragedy- Engineering Ethics, Bhopal Gas](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-5-320.jpg)
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Tragedy- Operational Engineering Ethics, Satyam Case- Financial Reporting Ethics, Enron Case-
Business Ethics, Navin Modi Case- Financial Fraudulence.
References:
1. Professional Module of ICSI.
2. Ghosh B.N., Business Ethics & Corporate Governance, (1e) McGraw Hill, 2011.
3. Mandal S.K., Ethics in Business & Corporate Governance, (2e), McGraw Hill, 2012.
4. Ray C.K., Corporate Governance, Value & Ethics, Vaya Education of India, 2012.
5. Chatterjee Abha, Professional Ethics, (2e) Oxford Publications.
MA2201: ENGINEERING MATHEMATICS IV [2 1 0 3]
Basic Set theory, Axioms of probability, Sample space, conditional probability, total probability theorem,
Baye's theorem. One dimensional and two dimensional random variables, mean and variance,
properties, Chebyschev's inequality, correlation coefficient, Distributions, Binomial, Poisson, Normal
and Chisquare. Functions of random variables: One dimensional and Two dimensional, F & T
distributions , Moment generating functions, Sampling theory, Central limit theorem, Point estimation,
MLE, Interval estimation, Test of Hypothesis : significance level, certain best tests; Chi square test.
References:
1. P. L. Meyer, Introduction to probability and Statistical Applications, (2e), Oxford and IBH
publishing, 1980.
2. Miller, Freund and Johnson, Probability and Statistics for Engineers, (8e), PHI, 2011.
3. Hogg and Craig, Introduction to mathematical statistics, (6e), Pearson education, 2012.
4. S. M. Ross, Introduction to Probability and Statistics for Engineers and Scientists, Elseveir, 2010.
CS2201: OPERATING SYSTEMS [3 1 0 4]
Introduction: Definition of operating systems, Single and multi-processor systems, Operating system
Services, System commands and system calls, Interrupt, System boot. OS Structure: Simple, Layered,
Microkernel, Hybrid, Modules, Types of OS, Multi-user, Multitasking, Embedded, Real-time, Network,
Distributed. Virtualization: Introduction, Hypervisor, Data center, Virtual data center, VMware
virtualization products. Process and Thread: Process concept, Operations on processes, Inter-process
communication, UNIX pipes, Multithreading, Multithreaded models, PThread API. Process Scheduling:
Basic concepts, Scheduling criteria, Scheduling algorithms. Synchronization: Critical section problem,
Peterson solution, Synchronization hardware, Semaphores, Classical problems of synchronization,
Deadlock, Methods for handling deadlock. Memory Management: Swapping, Contiguous memory
allocation, Paging, Structure of Page Table, Segmentation, Demand Paging, Page Replacement
Policies, Allocation of Frames, Thrashing. File System Interface and Implementation: File Concept,
Access Methods, Directory and Disk Structure, File System Mounting, File System Structure, File
System Implementation, Allocation Methods, Free Space Management. Disk Management: Disk
Scheduling Algorithms, Disk Management, Swap Space Management.
References:
1. A. Silberschatz, P. B. Galvin, G. Gagne, Operating System Concepts, (9e), Wiley, 2014.
2. A.S. Tanenbaum, H. Bos, Modern Operating Systems, (4e), Pearson, 2015.
3. W. Stallings, Operating Systems: Internals and Design Principles, (9e), Pearson, 2018.
CS2202: RELATIONAL DATABASE MANAGEMENT SYSTEMS [3 1 0 4]
Introduction: Database systems, RDBMS Definition, data models, 3-schema architecture, challenges in
building RDBMS, different components of a RDBMS. Relational Data Model: Concept of relations and
its characteristics, schema-instance, integrity constraints, E/R Model, Extended E/R Model, converting
the database specification in E/R and Extended E/R notation to the relational schema. Relational Query
Language: Relational Algebra operators- selection, projection, cross product, various types of joins,
division, example queries, tuple relation calculus, domain relational calculus, introduction to SQL, data
definition in SQL, table and different types of constraints definitions, data manipulation in SQL, nested
queries, notion of aggregation. Relational Database Design: functional dependencies and Normal forms,
Armstrong's axioms for FD's, closure of a set of FD's, minimal covers, definitions of 1NF, 2NF, 3NF and
BCNF, decompositions and desirable properties of them, algorithms for 3NF and BCNF normalization,
multi-valued dependencies and 4NF. Transaction Processing: concepts of transaction processing, ACID
properties, concurrency control, locking based protocols, recovery and logging methods. Data Storage](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-6-320.jpg)
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and Indexing: file organizations, primary, secondary index structures, hash-based indexing, dynamic
hashing techniques, multi-level indexes, B-tree and B+ trees.
References:
1. A. Silberschatz, H. F. Korth, S. Sudarshan, Database System Concepts, (6e), McGraw Hill, 2013.
2. R. Elmasri, S. B. Navathe, Fundamentals of Database Systems, (6e), Addison-Wesley, 2010.
3. R. Ramakrishnan, J. Gehrke, Database Management Systems, (3e), McGraw Hill, 2014.
4. I. Bayross, SQL, PL/SQL The Programming Language of Oracle, (4e), BPB Publications, 2010.
5. C. J. Date, An Introduction to Database Systems, (8e), Prentice Hall of India, 2006.
CS2203: COMPUTER ORGANIZATION [3 1 0 4]
Processor Datapath and Control: Logic Design Conventions, Building a Datapath, Implementation
Schemes, Exceptions, Microprogramming. Pipelining: Overview, Pipelined Datapath, Pipelined Control,
Data Hazards and Forwarding, Data Hazards and Stalls, Branch Hazards. Memory Hierarchy: Basics of
Caches, Measuring and Improving Cache Performance, Virtual Memory, Address Translation. Storage
and Other Peripherals: Disk Storage and Dependability, Networks, Connecting I/O Devices to Processor
and Memory, Interfacing I/O Devices to the Memory, Processor, and Operating System, I/O Performance
Measures, Redundant Array of Inexpensive Disks. Multicores, Multiprocessors and Clusters: Shared
Memory Multiprocessors, Clusters and other Message-Passing Multiprocessors, Hardware
Multithreading, SISD, MIMD, SIMD, SPMD and Vector Processors.
References:
1. D. A. Patterson, J. L. Hennessy, Computer Organization and Design: The Hardware and Software
Interface, (5e), Elsevier, 2017.
2. J. L. Hennessy, D. A. Patterson, Computer Architecture: A Quantitative Approach, (6e), Morgan
Kaufmann Publishers, 2019.
3. W. Stallings, Computer Organization and Architecture –Designing for Performance, (9e), Pearson,
2013.
CS2230: OPERATING SYSTEMS LAB [0 0 2 1]
Basic Linux commands: Illustration of shell functions, wild cards, redirection, pipes, sequencing,
grouping, background processing, command substitution, sub shells, Shell programming. System Calls:
File and process, I/O Redirection, IPC using Pipe and Signals. PThread API: Multithreaded programs,
Synchronization programs using PThreads and Semaphores, CPU Scheduling, Deadlock, Memory
Management. Creating a Virtual Machine: Virtual Machine Files and Snapshots, Virtual Machine Cloning
and Exporting.
References:
1. W. R. Stevens , S. A. Rago, Advanced Programming in the UNIX Environment, (3e), Addison-
Wesley, 2013.
2. S. Das, Unix Concepts and Applications, (4e), McGraw Hill, 2006.
3. K. A. Robbins, S. Robbins, Unix Systems Programming: Communication, Concurrency, and
Threads, (2e), Prentice Hall, 2004.
CS2231: RELATIONAL DATABASE MANAGEMENT SYSTEMS LAB [0 0 2 1]
Introduction to SQL and its different command categories i.e. DDL, DML, DQL and DCL, Data Integrity
Constraints and Built-in Functions, Design and implementing the data requirements of a simple DB
application, Experiments on views, indexing, triggers, stored procedures, transaction.
References:
1. I. Bayross, Teach yourself SQL & PL/SQL using Oracle 8i & 9i with SQLJ, BPB Publications, 2010.
2. A. Silberschatz, H. F. Korth, S. Sudarshan, Database System Concepts, (6e), McGraw Hill, 2013.
3. R. Elmasri, S. B. Navathe, Fundamentals of Database Systems, (6e), Addison-Wesley, 2010.
CS2232: WEB TECHNOLOGY LAB [0 0 2 1]
Introduction to WWW: Web Design, Web site design principles, planning the site and navigation. HTML:
The development process, html tags, forms, web site structure. XHTML: XML, move to XHTML, meta](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-7-320.jpg)
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tags, character entities, frames and frame sets, inside browser. Style Sheets: CSS1, CSS2, CSS3.
JavaScript: How to develop javascript, variables, functions, conditions, loops and repetition. Advance
Javascript: Javascript and objects, javascript own objects, the DOM and web browser environments,
forms and validations. DHTML: Combining HTML, CSS and Javascript, events and buttons, controlling
your browser. Ajax: Introduction, advantages, purpose of it, Ajax based web application, alternatives of
Ajax. XML: Introduction to XML, DTD and Schemas, Well formed, using XML with application. XSL:
Introduction to XSL, XML transformed simple example, XSL elements, transforming with XSLT. PHP:
Starting to script on server side, arrays, function and forms, advance PHP. Databases: Connection to
server, creating database, performing data and schema related operations, PHP myadmin and database
bugs. Advanced topics: E-Commerce models and architecture. m-Commerce: WAP and Mobile agents,
search engines and search engine optimization, Introduction to web services and technology.
Introduction, pros and cons of the above technology with advance technology: JQuery, WebRTC, Web
socks, Angularjs, NodeJS, JSON, Bootstrap. All above will be facilitated using Web/Mobile application
projects assigned to the students.
References:
1. R. Connolly, R. Hoar, Fundamentals of Web Development, Pearson Education India, 2015.
2. R. Nixon, Learning PHP, MySQL & JavaScript with jQuery, CSS and HTML5, (5e), O’Reilly
Publications, 2018.
3. L. Welling, L. Thomson, PHP and MySQL Web Development, (5e), Pearson Education, 2017.
4. N. C. Zakas, Professional JavaScript for Web Developers, (3e), Wrox/Wiley India, 2019.
5. D. S. Mcfarland, JavaScript & jQuery: The Missing Manual, (3e), O’Reilly/Shroff Publishers &
Distributors Pvt Ltd, 2014.
6. Z. R. A. Boehm, Murach's HTML5 and CSS3, (4e), Murach's/Shroff Publishers & Distributors Pvt
Ltd, 2018.
CS2080: FUNDAMENTAL OF DATABASES [3 0 0 3]
Introduction: Database-System Applications, Relational Databases, Database Design, Data Storage and
Querying, Transaction Management, Database Architecture. File Management System: Indexing and
Hashing. Relational Algebra: Algebra, Tuple Calculus, Domain Calculus. SQL: Data Definition
Language, Data manipulation language , SQL Data Types and Schemas, Integrity Constraints, Basic
Structure of SQL Queries, Set Operations, Aggregate Functions, Null Values, Nested Sub-queries,
Correlated queries. Join: Inner, Outer, Left, Right and Natural. The Entity-Relationship Model:
Constraints, Entity-Relationship Diagrams, Entity-Relationship Design Issues, Weak Entity Sets,
Extended E-R Features. Normalization: Normal Forms, BCNF.
References:
1. R. Elmasri, S. B. Navathe, Fundamentals of Database Systems, (6e), Addison-Wesley, 2010.
2. R. Ramakrishnan, J. Gehrke, Database Management Systems, (3e), McGraw Hill, 2014.
CS3101: ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING [3 1 0 4]
Fundamental Concepts: Agents, environments, general model, Problem solving techniques. Search
Techniques: Uninformed search, heuristic search, adversarial search and game trees, Solution of
constraint satisfaction problems using search. Knowledge Representation: Propositional and predicate
calculus, semantics for predicate calculus, inference rules, unification, Resolution, semantic networks,
conceptual graphs/Dependency, structured representation. Learning: Inductive learning, decision tree
learning. Natural language processing: introduction, parsing using context free grammars, Chomsky
hierarchy, case grammar. Soft computing: Fuzzy set theory, Fuzzy sets, set-theoretic operations,
membership functions, Union, intersection and complement, fuzzy rules, reasoning and interference.
Neural networks: Perceptron, Back Propagation. Evolutionary techniques: genetic algorithms, Swarm
Algorithm, ant colony optimization.
References:
1. S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach,(3e) PHI, 2011.
2. E. Rich, K. Knight, S. B. Nair, Artificial Intelligence, (3e), Tata McGraw Hill, 2009.
3. G. F. Luger, Artificial Intelligence-Structures and Strategies for Complex Problem Solving, (6e),
Addison-Wesley Pearson Education, 2012.](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-8-320.jpg)
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CS3102: DESIGN & ANALYSIS OF ALGORITHMS [3 1 0 4]
Introduction: Fundamentals of Algorithms, Important Problem Types, Analysis of algorithm efficiency.
Analysis Framework: Asymptotic Notations and Basic Efficiency Classes, Mathematical Analysis of
Nonrecursive and Recursive Algorithms, Brute force Techniques, Divide and Conquer. Decrease and
Conquer: Insertion Sort, Depth First Search, Breadth First Search, Topological Sorting. Transform and
Conquer: Presorting, BST, Heapsort. Space and Time tradeoffs: Input Enhancement in String
Matching. Dynamic Programming: Warshall's and Floyd's Algorithms, The Knapsack Problem. Greedy
Techniques: Prim's, Kruskal's and Dijkstra's Algorithm, Huffman Trees, Coping with limitations of
algorithmic power. Backtracking: nQueens problem, Hamiltonian Circuit Problem, SubsetSum Problem.
BranchandBound: Assignment Problem, Knapsack Problem, TSP. Complexity Clasess: P, NP,and NP-
complete Problems.
References:
1. E. Horowitz, S. Sahni, S. Rajasekaran, Fundamental of Computer Algorithms, (2e), Universities
Press, 2007.
2. T. H. Cormen, C. E. Leiserson, R.L. Rivest and C. Stein, Introduction to Algorithms, (3e), MIT
press, 2009.
CS3103: AUTOMATA THEORY & COMPILER DESIGN [3 1 0 4]
Introduction: Automata Theory, Mathematical Preliminaries and Notation, Review of set theory, function,
relation. Finite Automata: Deterministic and Non Deterministic Finite Automata (FA), Regular languages,
Mealy and Moore machine; Regular Sets and Regular Grammars: Chomsky Hierarchy, Regular
Expressions, Regular Grammar and FA, Pumping Lemma for Regular Languages; Context Free
Languages (CFL) and Grammars: Ambiguity, Methods for Transforming Grammars; Push Down
Automata: Nondeterministic Pushdown Automata (NPDA), Design of NPDA, PDA and CFLs;
Introduction to Turing machine; Introduction to Compiler Design: Structure of a Compiler, Lexical
Analysis, Recognition of Tokens; Introduction to LR Parsing: Simple LR, More Powerful LR Parsers,
Parser Generators; Syntax Directed Translations; Type Checking: Rules for Type Checking; Storage
Organization.
References:
1. P. Linz, An Introduction to Formal Languages and Automata, (6e), Jones and Bartlett Student
Edition, 2016.
2. A. V. Aho, J. Ullman, M. S. Lam, R. Sethi, Compilers: Principles, Techniques and Tools, (2e),
Pearson Education, 2015.
3. M. Sipser, Introduction to the Theory of Computation, (3e), Cengage Learning, 2014.
4. J. C. Martin, Introduction to Languages and the Theory of Computation, (4e), McGraw Hill, 2010.
CS3104: COMPUTER NETWORKS [3 1 0 4]
Network Layer: Network layer design issues, routing algorithms, congestion control algorithms, Quality
of service, MPLS, Classfull addressing, Sub-netting, Classless addressing. Protocols: ARP & DHCP,
Introduction, Packet Format, message types, IPV4 header format, fragmentation, options, checksum.
ICMP: Message format, message types. Dynamic routing protocols: RIP, OSPF & BGP. Multicasting
Protocol: IGMP, Introduction to IPV6. Transport Layer: Transport services, state diagram, Elements of
Transport Protocols, addressing, Connection establishment, connection release, Error control and Flow
Control, Multiplexing. Congestion Control: Bandwidth allocation, regulating the sending rate, UDP, TCP.
Application Layer: DNS, Name space, domain resource records. Electronic Mail: SMTP, POP, IMAP,
MIME, HTTP, HTTPS, SNMP. Network Security: Security Goals, Attacks, Attack prevention techniques,
Firewall, IDS, DMZ, IPsec.
References:
1. B. A. Forouzan, TCP/IP Protocol Suite, (4e), TMH, 2010.
2. A. S. Tanenbaum, Computer Networks, (5e), Pearson, 2010.](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-9-320.jpg)
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CS3130: DESIGN & ANALYSIS OF ALGORITHMS LAB [0 0 2 1]
Sorting & Searching Algorithm: insertion sort, selection sort, binary search. Basic data structures: stacks
and queues, graphs and trees, binary trees. Algorithmic paradigms: Recursion, divide-and-conquer,
Merge sort, Quick sort. Greedy: Knapsack, Huffman encoding, dynamic programming, lower bounds and
optimal algorithms. Heaps: Heaps, priority queues, min-max heaps, heap sort. Dynamic search
structures: Binary search trees, height balancing, B-trees. Algorithms on arrays: Linear-time median
finding, sorting in linear time (counting sort, radix sort, bucket sort), String matching (Rabin-Karp and
Knuth-Morris-Pratt algorithms). Graph algorithms Traversal: (BFS, DFS, topological sort), Minimum
spanning trees (Prim and Kruskal algorithms), shortest paths (Dijkstra’s and Floyd-Warshal algorithms).
Mini-Projects & Case Studies.
References:
1. E. Horowitz, S. Sahni, S. Rajasekaran, Fundamental of Computer Algorithms, (2e), Universities
Press, 2007.
2. T. H. Cormen, C. E. Leiserson, R.L. Rivest , C. Stein, Introduction to Algorithms, (3e), MIT press,
2009.
CS3131: ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING LAB [0 0 2 1]
Introduction to Python: basic variable declaration, loops, inbuilt functions. Basic Programming using
Python for AI techniques: global and local heuristics, Crypt arithmetic, Python syntax, Constraint
satisfaction Problem, Preposition and inference. Travelling Salesman Problem using Branch & Bound
/Nearest Neighbor. Character recognition using Neural Networks. Optimization using Genetic
Algorithms. Mini-Projects & Case Studies.
References:
1. A. K. Mackworth, D. L. Poole, Artificial Intelligence: Foundations of Computational Agents, (2e),
Cambridge University Press, 2017.
2. P. Joshi, Artificial Intelligence with Python, Packt Publishers, 2018.
3. I. Bratko, PROLOG: Programming for Artificial Intelligence, (3e), Pearson Publication, 2011.
CS3132: COMPUTER NETWORKS LAB [0 0 2 1]
Cisco Packet Tracer: Introduction to packet tracer and networking device components, Router mode,
Switch/Router basic commands; designing of star topology using HUB and Switch, IP configuration of
end devices, Configuring DHCP server, Static routing, RIP, OSPF, VLAN and NAT. Network
programming: Transmission control protocol and User datagram protocol. Network Utilities: PING,
NETSTAT, IPCONFIG, IFCONFIG, ARP, TRACE-ROUTE
References:
1. B. A. Forouzan, TCP/IP Protocol Suite, (5e), Tata McGraw Hill, 2013.
2. A. S.Tanenbaum, Computer Networks, (5e), Pearson Education, 2010.
BB 0026: ORGANISATION AND MANAGEMENT [3 0 0 3]
Meaning and definition of an organization, Necessity of Organization, Principles of Organization, Formal
and Informal Organizations. Management: Functions of Management, Levels of Management,
Managerial Skills, Importance of Management, Models of Management, Scientific Management, Forms
of Ownership, Organizational Structures, Purchasing and Marketing Management, Functions of
Purchasing Department, Methods of Purchasing, Marketing, Functions of Marketing, Advertising.
Introduction, Functions of Personal Management, Development of Personal Policy, Manpower Planning,
Recruitment and Selection of manpower. Motivation – Introduction, Human needs, Maslow’s Hierarchy
of needs, Types of Motivation, Techniques of Motivation, Motivation Theories, McGregor’s Theory,
Herzberg’s Hygiene Maintenance Theory. Leadership - Introduction Qualities of a good Leader,
Leadership Styles, Leadership Approach, Leadership Theories. Entrepreneurship-Introduction,
Entrepreneurship Development, Entrepreneurial Characteristics, Need for Promotion of
Entrepreneurship, Steps for establishing small scale unit. Data and Information; Need, function and
Importance of MIS; Evolution of MIS; Organizational Structure and MIS, Computers and MIS,
Classification of Information Systems, Information Support for functional areas of management.
Reference:
1.Koontz, Harold, Cyril O’Donnell, and Heinz Weihrich, Essentials of Management,(1e)](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-10-320.jpg)
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Tata McGraw-Hill, New Delhi, 1978.
2. Robbins, Stephen P, and Mary Coulter, Management, Prentice Hall, (2e) New Delhi, 1997.
3. E. S. Buffa and R. K. Sarin, Modern Production / Operations Management, (8e), Wiley, 1987
4. H. J. Arnold and D. C. Feldman, Organizational Behavior, McGraw – Hill, 1986.
5. Aswathappa K, Human Resource and Personnel Management, Tata McGraw Hill, 2005.
6. William Wether & Keith Davis, Human Resource and Personnel Management, McGraw Hill,
1986.
CS3201: SOFTWARE ENGINEERING [3 1 0 4]
Introduction: The Evolving Role of Software, The changing nature of software, Legacy software, Software
Myths. Software Engineering: A Layered Technology, a Process Framework, the Capability Maturity
Model Integration (CMMI), Specialized Process Models, and the Unified Process. Agile development:
Agile Process Models Software Engineering Practice, Communication Practice, Planning Practices,
Modeling Practices, Construction Practice, Deployment Computer–Based Systems, The System
Engineering Hierarchy, Business Process Engineering: An Overview. Product Engineering: An
Overview, Data Modeling Concepts, Object Oriented Analysis, Flow-Oriented Modeling, Taxonomy of
Quality Attributes, Perspectives of Quality, Quality System, Software Quality Assurance, Capability
Maturity Model Observation on Estimation, The Project Planning Process, Software Scope and
Feasibility, Human Resources, Empirical Estimation Model ,Introduction To DevOps, Cloud Computing
And Virtualization, Migration to DevOps, DevOps Tools.
References:
1. R. Pressman, Software Engineering: A Practitioners Approach, (8e), McGrawHill Pubs, 2019.
2. M. Walls, Building a Dev Ops Culture, O’Reilly Publications, 2013.
3. J. Joyner, Dev Ops for Beginners, Dev Ops Software Development Method guide for software
developers and IT professionals, Mihails Konoplovs, 2015.
CS3202: INFORMATION SYSTEMS SECURITY [3 1 0 4]
Introduction: Basic objectives of cryptography, Secret-key and public-key cryptography, One-way
trapdoor one-way functions, Cryptanalysis, Attack models, Classical cryptography. Block ciphers: Modes
of operation, DES and its variants, AES, Linear and differential cryptanalysis. Message digest: Properties
of hash functions, MD2, MD5 and SHA-1, Keyed hash functions, Attacks on hash functions. Public-key
parameters: Modular arithmetic, Primality testing, Chinese remainder theorem, Modular square roots,
Finite field. Intractable problems: Integer factorization problem, RSA problem, Modular square root
problem, Discrete logarithm problem, Diffie-Hellman problem, Known algorithms for solving the
intractable problems. Public-key encryption: RSA, Rabin and EIGamal schemes, Elliptic and hyper-
elliptic curve cryptography, Side channel attacks, Diffie-Hellman and MQV key exchange. Digital
signatures: RSA, DSA and NR signature schemes, blind and undeniable signatures. Entity
authentication: Passwords, Challenge-response algorithms, Zero-knowledge protocols. Network
security: Certification, public-key infrastructure (PKI), secure socket layer (SSL), Kerberos.
References:
1. B. A. Forouzan, D. Mukhopadhyay, Cryptography and Network Security, (2e), Mc-Graw Hill, , 2008.
2. W. Stallings, Cryptography and Network Security: Principles and Practice, (5e),Prentice Hall, 2010.
3. J. Pieprzyk, T. Hardjono, J. Seberry, Fundamentals of Computer Security, Springer International
Edition, 2003.
4. A. J. Menezes, P. C. V. Oorschot ,S. A. Vanstone, Handbook of Applied Cryptography, CRC Press.
CS3203: DATA SCIENCE AND MACHINE LEARNING [3 0 0 3]
Data Science: Descriptive Statistics, Probability Distribution, regression analysis, ANOVA. Machine
Learning: Goals, Applications of ML, developing a learning system, training data, concept representation,
function approximation. Decision Tree Learning: Representing concepts as decision trees, Recursive
induction of decision trees, best splitting attribute, entropy, information gain., Occam's razor, Overfitting,
noisy data, and pruning. Artificial Neural Networks: Neurons and biological motivation. Linear threshold
units, Perceptron, representational limitation and gradient descent training, Multilayer networks and
backpropagation. Hidden layers and constructing intermediate, distributed representations, Overfitting,
learning network structure, recurrent networks. Comparing learning algorithms: cross-validation, learning
curves, and statistical hypothesis testing. Support Vector Machines: Maximum margin linear separators.](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-11-320.jpg)
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Kernels for learning non-linear functions. Bayesian Learning: Probability theory and Bayes rule. Naive
Bayes learning algorithm, Logistic regression, Bayes nets and Markov nets for representing
dependencies. Instance-Based Learning: k-Nearest-neighbor algorithm, Case-based learning,
Relevance feedback and Rocchio algorithm. Naive Bayes for text. Clustering and Unsupervised
Learning: Hierarchical Agglomerative Clustering, k-means partitioned clustering, expectation
maximization (EM) for soft clustering. Ensemble Learning: Bagging, boosting, and Decorate. Active
learning with ensembles.
References:
1. G. James, D. Witten, T Hastie, R Tibshirani, An introduction to statistical learning with applications
in R, Springer, 2013.
2. J. Han, M. Kamber, J. Pei, Data Mining concepts and techniques, (2e), Morgan Kaufmann- Elsevier,
2011.
3. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, (2e), Springer, 2009.
4. K. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
5. T. M. Mitchell, Machine Learning, (Indian Edition), MacGraw Hill, 2017.
6. C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 2019
CS3230: SOFTWARE ENGINEERING LAB [0 0 2 1]
Introduction: Agile development: Agile Process Models Software, Communication Practice, Planning
Practices, Modeling Practices, Construction Practice, Deployment of Computer–Based Systems, The
System Engineering Hierarchy. Business Process Engineering: An Overview, Product Engineering: An
Overview, Data Modeling Concepts, Object Oriented Analysis, Flow-Oriented Modeling, Taxonomy of
Quality Attributes, Perspectives of Quality, Quality System, Software Quality Assurance, Capability
Maturity Model Observation on Estimation using Projects, The Project Planning Process, Software
Scope and Feasibility, Human Resources, Empirical Estimation Model ,Introduction To DevOps, Cloud
Computing And Virtualization, Migration to DevOps, DevOps Tools, All above will be facilitated using
Software Projects assigned to the students.
References:
1. R. Pressman, Software Engineering: A Practitioners Approach, (8e), McGrawHill Pubs, 2019.
2. M. Walls, Building a Dev Ops Culture, O’Reilly Publications, 2013.
3. J. Joyner, Dev Ops for Beginners, Dev Ops Software Development Method guide for software
developers and IT professionals, Mihails Konoplovs, 2015.
CS3231: INFORMATION SYSTEMS SECURITY LAB [0 0 2 1]
Substitution and Transposition Cipher Implementation: Caesar Cipher, Playfair Cipher, Hill Cipher,
Vigenere Cipher, Rail fence. Symmetric and Asymmetric Cipher Implementation: DES, RSA, Diffie-
Hellman, MD5, SHA-1. Signature Schemes Implementation: Digital Signature Standard, GnuPG API.
Demonstration of secure data storage: Setup of honey pot and monitoring on network using KF sensors.
Installation of rootkits. Wireless audit on an access point or a router, WEP and WPA (Net Stumbler).
Intrusion detection system using snort.
References:
1. B. A. Forouzan, D. Mukhopadhyay, Cryptography and Network Security, (2e), Mc-Graw Hill, 2008.
2. W. Stallings, Cryptography and Network Security: Principles and Practice, (5e), Prentice Hall, 2010.
3. J. Pieprzyk, T. Hardjono, J. Seberry, Fundamentals of Computer Security, Springer, 2003.
CS3080: PRINCIPLES OF PROGRAMMING LANGUAGES [3 0 0 3]
Preliminary Concepts: Concepts of programming languages. Syntax and Semantics: general Problem of
describing Syntax and Semantics. Data types: Primitive, character, user defined, array, associative
record, union, pointer and reference types. Expressions and Statements: Assignment Statements,
Control Structures. Subprograms and Blocks: Fundamentals of sub-programs, Scope of life time of
variables, static and dynamic scope, design issues of sub-programs and operations. Abstract Data types:
Abstractions and encapsulation, introductions to data abstraction, design issues, language examples.
Concurrency: Subprogram level concurrency, semaphores, monitors, massage passing, Java threads,
C# threads. Exception handling: Exceptions, exception Propagation, Exception handler in Ada, C++ and
Java, Logic Programming Language: Introduction and overview of logic programming.
References:](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-12-320.jpg)
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1. R. W. Sebesta, Concepts of Programming Languages, (10e), Pearson Education, 2008.
2. D. A. Watt, Programming Language Design Concepts, Wiley, (2e), 2007.
3. B. Tucker, R. E. Noonan, Programming Languages, (2e), TMH, 2007.
4. K. C. Louden, Programming Languages, (2e), Thomson, 2003.
5. T. W. Pratt, M. V. Zelkowitz, T. V. Gopal, Programming Languages: Design and Implementation,
(4e), PHI, 2006
CS3081: ENTERPRISE RESOURCE PLANNING [3 0 0 3]
ERP Overview: ERP Components, ERP Benefits. Business Process Reengineering (BPA): BPA life
cycle, BPA components. Data warehousing, Datamining, Supply chain Management; ERP: evolution, a
Manufacturing Perspective, ERP Module, ERP Market, ERP implementation life cycle, Options of various
paradigms, Identification of suitable platforms. SDLC/SSAD: Role of SDLC/SSAD, Object oriented
architecture. ERP Implementation: introduction, pre evaluation screening, package evaluation, project
planning phase, Gap analysis, Hidden costs, Major Vendors, Consultant Employees, Human Resource.
ERP & E-Commerce: Future Directives- in ERP, ERP and Internet, Critical Factors guiding selection and
evaluation of ERP, Strategies for its successful implementation, Impediments and initiatives to achieve
success, Critical success and failure factors, Integrating of ERP into organizational culture. Using ERP
tool: Case study of a system using SAP or ORACLE or open source ERP.
References:
1. S. R. Magal, J. Word, Integrated Business Processes with ERP Systems, (2e), John Wiley & Sons,
2011.
2. M. Sumner, Enterprise Resource Planning, Pearson Education, (2e), 2006.
3. E. Monk, B. Wagner, Concepts in Enterprise Resource Planning, (3e), Thomson Course
Technology, 2006.
CS3082: PRINCIPLES OF MACHINE LEARNING [3 0 0 3]
Introduction to Artificial Intelligence: Foundations, scope, problems. Problem-solving through Searching:
forward and backward, state-space, blind, heuristic, problem-reduction, minimax. Supervised Learning:
Process for feature selection, over-parameterization and the curse of dimensionality, regularization,
cross validation. Classification: operation of classifiers, regression as a classifier, metrics used to
evaluate classifiers, SVM, Naïve Bayes, KNN. Regression: operation of regression models, prediction
and forecasting, metrics used to evaluate regression models. Neural networks: Feed forward NN, Feed
backward NN, Convolutional Neural network. Unsupervised Learning: K-mean clustering. Algorithmic
Learning Theory and Applications: Mistake bound model, PAC Model.
References:
1. G. F. Luger, W. A. Stubblefield, Artificial Intelligence - Structures and Strategies for Complex
Problem Solving. (5e), Addison Wesley, 2005.
2. P Baldi, S Brunak, Bioinformatics: A Machine Learning Approach, (2e) MIT Press, 2002.
3. T. M. Mitchell, Machine Learning, McGraw-Hill Education, 2017.
4. Y Abu-Mostafa, M. Magdon-Ismail, H.T. Lin, H-T. Learning from Data. AML Book, 2012.
CS3140: INFORMATION CODING [3 0 0 3]
Information Theory: Entropy, Characterization and related properties, Huffman codes, Shannon-Fano
coding, Robustness of coding techniques, Information measure-noiseless coding, Discrete memoryless
channel, Channel capacity, Fundamental theorem of information theory. Coding Theory: Error correcting
codes, Minimum distance principles, Hamming bound, General binary code, Group code, Linear group
code. Convolution Encoding: Algebraic structure, Gilbert bound, Threshold decoding, Threshold
decoding for block codes. Cyclic binary codes: BCH codes, Generalized BCH code and decoding,
Optimum codes, Concepts of non-cyclic codes. Combinatorial Designs: Definitions of BIBD, Hadamard
Designs, Latin Squares, Mutually Orthogonal Latin Squares, Orthogonal Arrays. Network Coding:
Fundamentals of Network Coding, Butterfly networks, Graphs and networks, Max-flow min-cut theorem,
Multi-source multicast problem, Deterministic code design for network coding, Randomized network
coding, Application of network coding.
References:
1. T. M. Cover, J.A. Thomas, Elements of Information Theory, Wiley, (2e), 2006.](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-13-320.jpg)
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2. M. Kelbert, Y. Suhov, Information Theory and Coding by Example, Cambridge University Press,
2013.
3. D. Stinson, Combinatorial Designs: Constructions and Analysis, Springer, 2003.
4. P. J. Cameron , J. H. Lint, Designs, Graphs, Codes and their Links, Cambridge University Press,
2010.
CS3240: PRINCIPLES OF SECURE PROGRAMMING [3 0 0 3]
Introduction: Security goals, Secure system design, Secure design principles, Worms and other
malware. Secure Programming Techniques: Anatomy of a buffer overflow, Safe string libraries,
Stackguard, Static analysis tools, Heap-based overflow, Other memory corruption vulnerabilities, SQL
Injection attack scenario and solutions, Password security. Cross-Domain Security in Web Applications:
Interaction between webpages from different domains, Attack patterns, preventing cross-site request
forgery, Cross-site script inclusion, Cross-site scripting. Other Web Vulnerabilities: Cookie protocol
problems, SSL/TLS vulnerabilities, Session hijacking, Guninski attack, Defenses. Trusted Execution
Environment: Case study on TrustZone, Security vulnerability tools, Exploit development with metasploit.
References:
1. N. Daswani, C. Kern, A. Kesavan, Foundations of Security, What Every Programmer Needs to Know,
Apress, 2007.
2. J. C. Foster, V. T. Liu, Writing Security Tools and Exploits, Syngress Publishing, 2006.
3. J. Ericson, Hacking: The Art of Exploitation, (2e), No Starch Press, 2008.
4. C. Anley, J. Heasman, F. Linder, G. Richarte, The Shellcoder’s Handbook: Discovering and
Exploiting Security Holes, (2e), Addison-Wiley, 2011.
CS3270: MINOR PROJECT [0 0 6 3]
In this course student has to select a project work based on a topic of interest. Periodically the supervisor
will evaluate the implementation. This work, started in eighth semester of which, the student will be
evaluated internally and externally.
CS4140: CYBER SECURITY [3 0 0 3]
Introduction to cyber security: Computer Security, threats, harm, vulnerabilities, controls, Authentication,
Access Control and Cryptography, Web User Side, Browser Attacks, Web Attacks Targeting Users,
Email Attacks. Security in operating system and networks: Security in Operating Systems, Security in
the Design of Operating Systems, Rootkit, Network security attack, Threats to Network Communications,
Wireless Network Security, Denial of Service, Distributed Denial-of Service. Security Countermeasures:
Cryptography in Network Security, Firewalls, Intrusion Detection and Prevention Systems, Network
Management, Databases, Security Requirements of Databases Reliability and Integrity, Database
Disclosure, Data Mining and Big Data. Privacy in Cyberspace: Privacy Concepts, Privacy Principles and
Policies, Authentication and Privacy, Data Mining, Privacy on the Web, Email Security. Cyber Policies:
Policies to mitigate cyber risks, Reducing Supply Chain Risks, Mitigate Risks through Human Resource
Development, Information sharing Implementing a Cyber security framework, Digital Signature.
References:
1. M.S. Merkov, J. Breithaupt, Information Security: Principles & Practices, (2e), Pearson, 2014.
2. C.P. Pfleeger, S.L. Pfleeger, J. Margulies, Security in Computing, (5e), Pearson, 2015.
3. V. Sood, Cyber Laws Simplified, (2e) McGraw Hill, 2017.
4. N. Godbole, Information Systems Security, (2e), Wiley, 2017.
CS4141: DIGITAL FORENSICS & CYBER CRIMES [3 0 0 3]
Introduction to Computer Forensics: Computer crimes, evidence, extraction, preservation, overview of
hardware and operating systems, structure of storage media/devices, uncovering attacks that evade
detection by event viewer, task manager, and other Windows GUI tools, data acquisition, disk imaging,
recovering swap files, temporary and cache files. Computer Forensic tools: Encase, Helix, FTK, Autopsy,
Sleuth kit Forensic Browser, FIRE, Found stone Forensic ToolKit, WinHex, Linux and other open source
tools. Mobile and Network Forensics: Collecting and analyzing network-based evidence, reconstructing
web browsing, email activity, and windows registry changes, intrusion detection, tracking offenders,
Mobile Network Technology, Investigations, Collecting Evidence, Interpretation of Digital Evidence on
Mobile Network. Software Reverse Engineering: Defend against software targets for viruses, worms and
other malware, improving third-party software library, identifying hostile codes-buffer overflow, provision](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-14-320.jpg)
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of unexpected inputs. Computer crime and Legal issues: Intellectual property, privacy issues, Criminal
Justice system for forensic, audit/investigative situations and digital crime scene, investigative
procedure/standards for extraction, preservation, and deposition of legal evidence in a court of law.
References:
1. C. Altheide, H. Carvey, Digital Forensics with Open Source Tools, Syngress, 2011.
2. M.T. Britz, Computer Forensics and Cyber Crime: An Introduction, (3e), Kindle Edition, 2013.
3. S. Davidoff, J. Ham, Network Forensics: Tracking Hackers through Cyberspace, Prentice Hall,
2012.
4. B. Nelson, A. Phillips, F. Enfinger, C. Steua, Guide to Computer Forensics and Investigations,
Thomson, (4e), 2009.
CS3141: CLOUD COMPUTING AND VIRTUALIZATION [3 0 0 3]
Introduction: Distributed Computing and Enabling Technologies, Cloud Fundamentals: Cloud Definition,
Evolution, Architecture, Applications, deployment models, and service models. Virtualization: Issues with
virtualization, virtualization technologies and architectures, Internals of virtual machine
monitors/hypervisors, virtualization of data centers, and Issues with Multi-tenancy. Implementation:
Study of Cloud Computing Systems like Amazon EC2 and S3, Google App Engine, and Microsoft Azure,
Build Private/Hybrid Cloud using open source tools, Deployment of Web Services from Inside and
Outside a Cloud Architecture. MapReduce and its extensions to Cloud Computing, HDFS, and GFS.
Interoperability and Service Monitoring: Issues with interoperability, Vendor lock-in, Interoperability
approaches. SLA Management, Metering Issues, and Report generation. Resource Management and
Load Balancing: Distributed Management of Virtual Infrastructures, Server consolidation, Dynamic
provisioning and resource management, Resource Optimization, Resource dynamic reconfiguration,
Scheduling Techniques for Advance Reservation, Capacity Management to meet SLA Requirements,
and Load Balancing, various load balancing techniques. Migration and Fault Tolerance: Broad Aspects
of Migration into Cloud, Migration of virtual Machines and techniques. Fault Tolerance Mechanisms.
Advances: Grid of Clouds, Green Cloud, Mobile Cloud Computing.
References:
1. R. Buyya, J. Broberg, A. Goscinski , Cloud Computing Principles and Paradigms , Wiley Publishers,
2013.
2. B. Sosinsky, Cloud Computing Bible, Wiley, 2011.
3. M. Miller, Cloud Computing: Web-based Applications that change the way you work and collaborate
online, Pearson, 2008.
4. D. S. Linthicum, Cloud Computing and SOA Convergence in Your Enterprise: A Step-by-Step Guide,
Addision Wesley Information Technology Series, 2010.
5. T. Velte, A. T. Velte, R. Elsenpeter, Cloud Computing: A Practical Approach, McGraw Hill, 2017.
CS3241: CLOUD INFRASTRUCTURE AND SERVICES [3 0 0 3]
Introduction: Clouds and Cloud Computing: Basic Concepts, Types of Services, deployment models.
Classic Data Center (CDC): DBMS concepts, CDC drawbacks, CDC Management and case studies.
Virtualized Data Center (VDC): Compute virtualization overview, Compute virtualization techniques,
Virtual Machines, VM Resource management techniques, Virtual Infrastructure Requirements. Storage:
Storage virtualization overview, Virtual Machine Storage, Virtual provisioning and automated storage
tiering. Networking: VDC networking overview, VDC networking components, VLAN and VSAN
technologies. Business Continuity in VDC, Fault tolerance mechanism in VDC. Cloud Security: Access
control and identity management in Cloud, Governance, risk, and compliance, Security best practices
for Cloud, Cloud Migration. Issues in Cloud Development: Migration etc.
References:
1. B. Jackson, K. Saurabh, Cloud Computing, (2e), Wiley India, 2012.
2. V. Joysula, M. Orr, G. Page, Cloud Computing: Automating the Virtualized Data Center, Cisco
Press, 2012.
3. R. K. Buyya, Cloud Computing: Principles and Paradigms, Wiley Press, 2011.
4. M. Miller, Cloud Computing, (8e), Que Publishers, 2008.
5. Course materials from EMC² Education Services](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-15-320.jpg)
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CS4142: CLOUD COMPUTING APPLICATIONS [3 0 0 3]
Cloud Based Applications: Introduction, Contrast traditional software development and development
for the cloud. Public v private cloud apps. Understanding Cloud ecosystems – what is SaaS/PaaS,
popular APIs, mobile; Desktop and Application: Cloud Application Architectures, Desktop virtualization,
Application virtualization, Web Application design, Cloud app, Benefits of cloud apps, cloud API, Cloud
apps vs. web apps, Cloud apps vs. desktop apps, Testing of cloud apps; Designing Code for the cloud:
Class and Method design to make best use of the Cloud infrastructure; Web Browsers and the
Presentation Layer- Understanding Web browsers attributes and differences. Building blocks of the
presentation layer: HTML, HTML5, CSS, Silverlight, and Flash. Web Development Techniques and
Frameworks: Building Ajax controls, introduction to Javascript using JQuery, working with JSON, XML,
REST. Application development Frameworks e.g. Ruby on Rails, .Net, Java API's or JSF; Deployment
Environments – Platform As A Service (PAAS),Amazon, vmForce, Google App Engine, Azure, Heroku,
AppForce; Cloud Application Performance Mangaement: Managing applications in the cloud, cloud
application migration, Resource vs. application performance , Private and public instances, Topology
discovery, First generation CAPM tools and problems, Second generation CAPM tools and
advantages, Cloud application performance components, Agents and applications, Internet as part of
the infrastructure, Hosted SaaS CAPM advantages, Root cause analysis challenges, case studies.
References:
1. G. Reese, Cloud Application Architectures, O’Reilly Media, Inc, 2009.
2. E. Pace, D. Betts, S. Densmore, R. Dunn, M. Narumoto, Developing Applications for the Cloud on
the Microsoft Windows Azure Platform, Microsoft Press, 2010.
3. V. Joysula, M. Orr, G. Page, Cloud Computing: Automating the Virtualized Data Center, Cisco
Press, 2012.
4. Mei- Ling Liu, Distributed Computing: Principles and Application, Pearson, Education, Inc. New
Delhi. 2004
CS4143: CLOUD SECURITY AND PRIVACY [3 0 0 3]
Introduction: Cloud Computing Defined, The SaaS, PaaS and IaaS (SPI) Framework for Cloud
Computing, Key Drivers to Adopting the Cloud, The Impact of Cloud Computing on Users, Governance
in the Cloud, Barriers to Cloud Computing Adoption in the Enterprise. Security: Infrastructure Security:
Network, Host and Application. Data Security and Storage: Aspects and Mitigation, Provider Data and
Its Security. Identity and Access Management (IAM): Trust Boundaries, IAM Challenges, Definitions,
Architecture and Practices, IAM Standards and Protocols for Cloud Services, Cloud Authorization
Management. Security Management: Security Management Standards, Security Management in the
Cloud, Availability Management: SaaS Availability Management, PaaS Availability Management, IaaS
Availability Management, Access Control, Security Vulnerability, Patch, and Configuration Management.
Privacy: Privacy Standards, Data Life Cycle, Key Privacy Concerns in the Cloud, Legal and Regulatory
Implications. Cloud Morphing: Shaping the Future of Cloud Computing Security and Auditing the Cloud
for Compliance. Audit and Compliance: Internal Policy Compliance, Governance, Risk, and Compliance,
Illustrative Control Objectives for Cloud Computing, Incremental CSP-Specific Control Objectives,
Additional Key Management Control Objectives, Control Considerations for CSP Users,
Regulatory/External Compliance Cloud Security Alliance, Auditing the Cloud for Compliance.
References:
1. S. Pearson , G. Yee , Privacy and Security for Cloud Computing, Springer, 2013.
2. T. Mather, Subra Kumaraswamy, Shahed Latif, Cloud Security and Privacy, O'Reilly Media, 2009.
3. B. Halpert, Auditing Cloud Computing: A Security and Privacy Guide, Wiley, 2011.
4. K. Saurabh, Cloud Computing, (2e), Wiley, 2012.
CS4144: INFORMATION RETRIEVAL [3 0 0 3]
Introduction to IR: IR Concepts, Boolean Retrievals- An Example Information Retrieval Problem, A First
Take at Building an Inverted Index, Processing Boolean Queries. The Term Vocabulary and Postings
Lists: Document Delineation and Character Sequence Decoding, Determining the Vocabulary of Terms.
Dictionaries and Tolerant Retrieval: Search Structures for Dictionaries, Wildcard Queries, Spelling
Correction, Phonetic Correction. Index Construction: Hardware Basics Blocked Sort-Based Indexing.
Scoring, Term Weighting and the Vector Space Model: Parametric and Zone Indexes, Term Frequency
and Weighting, The Vector Space Model for Scoring. Evaluation in Information Retrieval: Information
Retrieval System Evaluation, Standard Test Collections, Evaluation of Unranked Retrieval Sets,](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-16-320.jpg)
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Evaluation of Ranked Retrieval Results. XML Retrieval: Basic XML Concepts, Challenges in XML
Retrieval, A Vector Space Model for XML Retrieval, Evaluation of XML Retrieval, Text-Centric vs. Data-
Centric XML Retrieval. Web Search Basics: Web Characteristics, Advertising as the Economic Model,
The Search User Experience, Index Size and Estimation, Near-Duplicates and Shingling. Web Crawling
and Indexes: Overview, Crawling, Distributing Indexes, Connectivity Servers. Link Analysis: The Web as
a Graph, Page Rank, Hubs and Authorities.
References:
1. C. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University
Press, 2009.
2. R. Baeza-Yate, B. Ribeiro-Neto, Modern Information Retrieval, (2e), Addison Wesley, 2012.
3. S. Chakrabarti, Mining the Web: discovering knowledge from hypertext data, (2e), Morgan
Kaufmann, 2002.
4. D. A. Grossman , O. Frieder, Information Retrieval: Algorithms, and Heuristics, (2e), Springer, 2004.
CS4145 : COMPUTER GRAPHICS & MULTIMEDIA [3 0 0 3]
Basics of Computer Graphics: Pixel, Frame buffer, Application of computer graphics. Graphic Display
Devices: Cathode Ray Tube, Light emitting diode, DVST, Random and Raster Scan displays. Scan
Conversion: Line Generation using digital differential analyzer (DDA), bresenham’s Algorithm, Circle
generation algorithm, Ellipse generation algorithm, Polygon generation and filling algorithms. Two
Dimensional Transformations: Introduction, homogeneous representation of points, basic transformation
like Translation, Rotation, Scaling, Reflection, Shear. Clipping and Windowing: Cohen Sutherland
Algorithm, liang Barsky algorithm, Sutherland Hodgman Algorithm. Three dimensional transformation:
Translation, Rotation, Scaling and Reflection. Projection: Introduction, Types of projection. Hidden
Surface elimination: Depth comparison, Back face detection algorithm, Painter’s Algorithm, Z-Buffer
Algorithm. Basic Illumination Model: Diffuse reflection, Specular reflection, Phong and Gouraud shading.
Introduction to Multimedia: Concepts and uses, hypertext and hypermedia, image, video and audio
standards, text compression algorithm. Animation: types, techniques, key frame animation, utility,
morphing.
References:
1. D. Hearn, M. P. Baker, Computer Graphics with OpenGL, (4e), Pearson Education, 2014.
2. R. Steinmetz, K. Nahrstedt, Multimedia Systems, Springer, 2004
3. J. F. Hughes, J. D. Foley, Computer graphics Principles and Practice,(3e), Pearson Education, 2014.
4. R. Steinmetz, K. Nahrstedt, Multimedia Fundamentals: Media Coding and Content Processing, (2e),
Pearson Education, 2004
CS4146: USER INTERFACE DESIGN [3 0 0 3]
UI Design Process: Design Process Introduction, Designing to Address a Problem w/o Solution Ideas,
Designing for a known solution direction, Designing to iterate on/improve an existing solution, Common
Elements: Usability, Engineering and Task-Centered Approaches, Use Cases, Personas, Tasks, and
Scenarios, Design-Centered Methods & When They Work Best, Pulling it all Together, Psychology and
Human Factors for User Interface Design: Introduction, Fitts' Law, Short- and long-term memory,
attention, Perception and visualization, hierarchy, Mistakes, Errors, and Slips, Conceptual models, The
Gulf of Execution and the Gulf of Evaluation, Design Principles: Visibility, Feedback, Mappings,
Constraints, Interacting beyond individuals (social psychology), High-Level Models: Distributed
Cognition, Activity Theory, Situated Action, User Research Methods: User Research to Design,
Introduction to User Research, Interview and Focus Groups, Observations, Contextual Inquiry, Ethics
and Consent, Design a User Research Protocol, Log Analysis, Surveys and Questionnaires, Analyzing
and Delivering User Research: Introduction: Translating User Research to Support Design, Qualitative
Analysis, Quantitative Analysis, Personas I: What They Are; How They're Used, Personas II: Walking
Through Examples, Use Cases, Tasks and Walkthrough Scenarios, Implications for Design, Ideation
and Idea Selection: rom Research to Ideas, Ideation, Idea Selection, Communicating Ideas to
Stakeholders, Good User Interfaces principles: learnability, visibility, error prevention, efficiency, and
graphic design) and the human capabilities that motivate them (including perception, motor skills, color
vision, attention, and human error), Implementation of UI: building user interfaces, including low-fidelity
prototypes, etc Empirical research involving novel user interfaces.
References:
1. Norman, A. Donald, The Design of Everyday Things. MIT Press, 2014.](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-17-320.jpg)
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2. Coursera platform and internet resources
CS4147: DIGITAL IMAGE PROCESSING [3 0 0 3]
Introduction to image processing: steps in image processing, Image file formats, Basic relationships
between pixels, Colour Models. Image Enhancement and Restoration: Image histogram, Spatial domain
enhancement, point operations, Log transformation, Power-law transformation. Frequency domain
enhancement: introduction to image transforms, Fourier transform, 2D-DFT. Restoration: Noise models,
Restoration using Inverse filtering and Wiener filtering. Image Coding and Compression: Lossless
compression, Lossy compression, JPEG, MPEG. Image Segmentation and Representation: Grey level
features, edges and lines, similarity, correlation, template matching, edge detection using templates,
Representation scheme, boundary descriptors, regional descriptors, Image Morphology. Biometric
Authentication, Object Detection.
References:
1. K. R. Castleman, Digital Image Processing, (2e), Pearson Education, 2011.
2. R. C. Gonzalez, R. E. Woods, Digital Image Processing, (4e), Pearson Education, 2018.
3. A. K. Jain, Fundamentals of Digital Image Processing, Pearson Education, Reprint 2015.
4. S. Jayaraman, S. Esakkirajan, T Veerakumar, Digital Image Processing, Tata McGraw Hill
Education, 2009.
5. R. C. Gonzalez, R. E. Woods, S. Eddins, Digital Image Processing using MATLAB, (2e), Pearson
Education.
6. A. McAndrew, Introduction to Image processing using MATLAB, Cengage Learning Publisher,
2007.
7. Prateek Joshi, OpenCV with Python By Example, (1e) PACKT Publishing, 2018.
CS4148: INTERNET OF THINGS [3 0 0 3]
Introduction: Analog and digital signals, serial communication, RF and sensors; Introduction to
JSON/XML. Programming on Development Boards: Understanding of the board, tool chain and
development environment setup; Sensors and Actuators: Understanding and using analog, digital, SPI,
UART, I2C. Nodes and communication protocols: Understanding usage of nodes and gateways for
sensor communication and external communication, RF, Zigbee, BT, WI-FI, GSM. IoT Cloud Platform,
Cloud using Web Services, Cloud Computing Services for Sensor Management, Python Script; Data
Analytics: Mongo DB, Map Reduce, Using cloud APIs for analytics, Visualization, NVD3, Mobile
interfacing.
References:
1. V. Madisetti, A. Bahga, Internet of Things: A Hands-On- Approach, VPT, 2014.
2. R. Buyya, A. V. Dastjerdi, Internet of Things Principles and Paradigms, 2016.
3. H. Geng, Internet of Things Principles and Data Analytics Handbook, Wiley, 2017.
4. P. Raj, A. C. Raman, The Internet of Things Enabling Technologies, Platforms, and Use Cases, CRC
Press, 2017.
CS4149: BIG DATA ANALYTICS [2 0 1 3]
INTRODUCTION: Introduction to big data, definition, need and evolution of BDA, Applications of Big
Data. Analysing big data: Sources of big data, Characteristics of Big Data(4 V’s), Drivers of BDA,
Structured vs. Unstructured data, Data Marts, Differences between traditional DWDM and BDA. Data
Processing: Data Wrangling, Data Munging, Data Jujitsu. Data Visualisation: Why to visualize data. Data
Analytics Life Cycle. Advanced Analytics Algorithms: Introduction using R – Theory and Methods
Overview: K-means clustering, Association Rules, Linear Regression, Logistic Regression, Naïve
Bayesian Classifiers, Decision Trees, Time Series Analysis, Text Analytics; Statistics for Model Building
and Evaluation: Statistics in the Analytic Lifecycle, Hypothesis Testing, Difference of means. Hadoop
Framework: Introduction to Hadoop, HDFS - Hadoop Distributed File system, Map Reduce
Programming, Pig. ETL & Batch Processing with Hadoop: ETL & Data Warehousing, Ingesting data into
Big Data Platforms using Apache Sqoop & Flume, Big Data Analytics using Apache Hive, NoSQL
databases for Big Data Storage Applications (HBase), Workflow management for Hadoop using Oozie
Spark: Introduction to Spark, SparkSQL, MLLib: Regression, Clustering & Classification using Spark
MLLib.
References:
1. B. Schmarzo, Big Data: Understanding How Data Powers Big Business, Wiley.2013](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-18-320.jpg)
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2. A. Jorgensen, J. Rowland-Jones, J. Welch, Microsoft Big Data Solutions, Wiley.,2014
3. J. Thompson, S. P. Rogers, Analytics: How to Win with Intelligence, Technics, LLC Publications ,
2017
CS4150: SOFTWARE DEFINED NETWORKS [3 0 0 3]
Software Defined Networking (SDN): Separation of control plane and data plane, IETF forces, Active
networking. Control and Data Plane Separation: Concepts, Advantages and disadvantages, the Open
flow protocol. Control Plane: Overview, Existing SDN controllers including floodlight and open daylight
projects. Customization of Control Plane: Switching and firewall implementation using SDN concepts.
Data Plane: Software-based and Hardware-based, Programmable network, Mininet based examples.
Programming SDNs: Northbound application programming interface, current languages and tools,
Composition of SDNs. Network Functions Virtualization (NFV): Concepts, Implementation, Applications.
Use Cases of SDNs: Data Centers, Internet exchange points, Backbone networks, Home networks,
Traffic engineering. Programming Assignments for implementing some of the theoretical concepts listed
above.
References:
1. T. D. Nadeau, K. Gray, SDN: Software Defined Networks, An Authoritative Review of Network
Programmability Technologies, (1e) O'Reilly Media, 2013.
2. P. Goransson, C. Black, Software Defined Networks: A Comprehensive Approach, (2e) Morgan
Kaufmann, 2016.
3. F. Hu, Network Innovation through Open Flow and SDN: Principles and Design, CRC Press, 2014.
4. V. Tiwari, SDN and Open Flow for Beginners, Amazon Digital Services, Inc., ASIN, 2013.
5. S Subramanian, Software Defined Networking with OpenStack, Packt Publishing, 2016.
CS4151: DEEP NEURAL NETWORK [3 0 0 0]
Introduction of Deep Learning, Basics of Machine Learning, Neural Network, Activation function,
Gradient Descent, Stochastic Gradient Descent, backpropagation, Deep Convolution Neural network:
convolution operation, ReLU Layer, Pooling Layer, Flattening, fully connected layer, softmax and cross
entropy, Recurrent Neural network: Vanishing Gradient Problem, LSTMs, LSTM variations, Self-
organizing Map (SOM), K-means clustering, Boltzmann Machine, Energy-based Models, Contrastive
Divergence, Deep Belief Networks, autoencoders, training of auto encode, over complete hidden layers,
sparse autoencoders, denoising autoencoders, contractive autoencoders, stacked autoencoders, deep
autoencoders.
References:
1. I. Goodfellow, Y. Bengio , A. Courville, Deep Learning, MIT Press 2016.
2. S. Haykin, Neural Networks and Learning Machines, (3e), PHI, 2008.
CS4152: SOCIAL NETWORK ANALYSIS [2 0 1 3]
Introduction to Social Web: Nodes, Edges and Network measures, Describing Nodes and Edges,
Describing Networks, Layouts. Visualizing Network features: The role of Tie Strength, Measuring Tie
Strength, Tie Strength and Network Structure, Tie Strength and Network Propagation, Link Prediction,
Entity Resolution. Link Prediction: Case Study Friend recommendation. Communities: Introduction,
Communities in Context, Quality Functions. Algorithms: Clustering-based, Newman and Girvan- Divisive
clustering, Newman-Modularity maximization, Clauset-Greedy optimization of modularity, Louvain
Method-Hierarchical clustering, Agglomerative clustering, Falkowski(DENGRAPH)-Density-based
clustering, Nikolaev-Entropy centrality-based clustering, Clique-based Methods for Overlapping
Community Detection, Palla- Clique percolation method, Lancichinetti-Fitness function, Du-Kernels-
based clustering, Shen-Agglomerative hierarchical clustering, Evans-Line graph, clique graph, Label
Propagation-based Community Detection. Introduction to Social Influence: Influence Related Statistics,
Social Similarity and Influence, Homophile, Existential Test for Social Influence, Influence and Actions,
Influence and Interaction, Influence Maximization in Viral Marketing.
References:
1. J. Goldbeck, Analyzing the Social Web, Morgan Kaufmann Publications, 2013.
2. C. C. Aggarwal, Social Network Data Analytics, Springer Publications, 2011.
3. J. Scott, Social Network Analysis, (3e), SAGE Publications Limited, 2013.
4. J. Goldman, Facebook Cookbook, O'Reilly, 2009.
5. S. Kumar, F. Morstatter, H. Liu, Twitter Data Analytics, Springer Publications, 2013.](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-19-320.jpg)
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CS4153: SOFTWARE TESTING [3 0 0 3]
Introduction and concept learning: Basic definitions, Testing axioms, Purpose of Software Testing,
Software Testing Principles, The Tester’s Role in a Software Development Organization, Origins of
Defects, Cost of defects, Defect Classes, Defect Prevention strategies, Defect Repository, Strategies for
Software Testing, Testing Activities, Mistakes, Faults & Failures, Verification and Formal Methods,
Planning for Verification and Validation. White-Box Testing: Test Adequacy Criteria, Static Testing,
Structural Testing, Code Complexity Testing, Mutation Testing, Data Flow Testing. Black-Box Testing:
Test Case Design Criteria, Requirement Based Testing, Positive and Negative Testing, Boundary Value
Analysis, Equivalence Partitioning State Based Testing, Domain Testing. Functional Testing: Test Plan,
Test Management, Test Execution and Reporting, Test Specialist Skills, Tester’s Workbench and Tool
Categories, Debugging, Test Bed, Traceability and Testability, Attributes of Testable Requirements, Test
Matrix, Types of Testing Documentation, Verification Testing, Validation Testing, Integration Testing,
System and Acceptance Testing, GUI Testing, Regression Testing, Selection, Minimization and
Prioritization of Test Cases for Regression Testing, Creating Test Cases from Requirements and Use
cases, Test Design. Test Automation: Software test automation – skill needed for automation – scope of
automation – design and architecture for automation – requirements for a test tool – challenges in
automation – Test metrics and measurements – project, progress and productivity metrics.
References:
1. W. E. Perry, Effective Methods for Software Testing, John Wiley and Sons, 2000.
2. R. Patton, Software Testing, Sams Publishing, 2005.
3. A. P. Mathur, Foundations of Software Testing, Pearson Education, 2013.
4. J. L. Mitchell, R. Black, Advanced Software Testing—Vol. 3, Rocky Nook, 2015.
CS4154: LINUX SYSTEM AND SHELL PROGRAMMING [3 0 0 3]
Fundamentals: Processes in Linux, I/O system calls, select and poll functions, Filters and redirection,
Linux file system navigation, Directory access, File system implementation, Hard links and symbolic
links. Asynchronous Events: Manipulating signal masks and signal sets, Catching and ignoring signals,
Waiting for signals. Inter-Process Communication: Sockets, Remote procedure calls, Network file
system. Concurrency: POSIX thread attributes, Synchronization functions, Mutex locks, Condition
variables, Signal handling and threads. Character Device Driver Development: Driver concepts, Writing
character drivers, Interrupt handling, Interfacing with hardware. Shell Scripting: Loops, Conditional
statements, Command line arguments, test command, expr command. Advanced Scripting Techniques:
Providing command line options to scripts, Exporting variables, Arrays, Remote shell execution,
Connecting to MySQL using shell, Essential system administration.
References:
1. W. R. Stevens, S. A. Rago, Advanced Programming in the UNIX Environment, (3e), Addison-
Wesley, 2013.
2. R. Love, Linux System Programming: Talking Directly to the Kernel and C Library, O'Reilly, 2007.
3. S. Das, Unix Concepts and Applications, (4e), McGraw Hill, 2006.
5. W. R. Stevens, B. Fenner, UNIX Network Programming, Volume 1: The Sockets Networking API,
(3e), Pearson, 2003.
6. K. A. Robbins, S. Robbins, Unix Systems Programming: Communication, Concurrency, and
Threads, (2e), Prentice Hall, 2004.
CS4155: WIRELESS SENSOR & ADHOC NETWORK [3 0 0 3]
Introduction to ad-hoc networks: Definition, characteristics features, applications. Characteristics of
Wireless channel, Ad-hoc Mobility Models: Indoor and outdoor models. MAC Protocols: design issues,
goals and classification. Contention based protocols with reservation, scheduling algorithms, protocols
using directional antennas. IEEE standards: 802.11a, 802.11b, 802.11g, 802.15. HIPERLAN. Routing
Protocols: Design issues, goals and classification. Proactive Vs reactive routing, Unicast routing
algorithms, Multicast routing algorithms, hybrid routing algorithm, Energy aware routing algorithm,
Hierarchical Routing, QoS aware routing. Transport layer: Issues in designing- Transport layer
classification, ad-hoc transport protocols. Security issues in ad-hoc networks: issues and challenges,
network security attacks, secure routing protocols. Cross layer Design: Need for cross layer design,
cross layer optimization, parameter optimization techniques, Cross layer cautionary perspective.
Integration of ad-hoc with Mobile IP networks. Mesh Networks, Vehicular Area Networks, and Mobile Ad](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-20-320.jpg)
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Hoc Networks (MANETs). Introduction to sensor networks and its applications: Architecture and factors
influencing the sensor network design. Routing protocols- data centric routing protocols, hierarchical
routing protocols, location based routing, energy efficient routing etc. Node Scheduling and coverage
issues, topology control. Querying, data collection and processing.
References:
1. S. K. Sarkar, T G Basavaraju, C Puttamadappa, Ad Hoc Mobile Wireless Networks: Principles,
Protocols, and Applications, (2e), CRC Press, 2016.
2. C. D. Morais Cordeiro, D. P. Agrawal, Ad Hoc and Sensor Networks: Theory and Applications, (2e),
World Scientific Publishing, 2011.
3. H. Karl, A. Willing, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons,
2007.
4. R. Jurdak, Wireless Ad Hoc and Sensor Networks: A Cross-Layer Design Perspective, Springer
Publications, 2007.
5. S R Murthy, B. S. Manoj, Ad Hoc Wireless Networks Architectures and Protocols, Pearson
Education, 2008.
CS4156: MOBILE COMPUTING [3 0 0 3]
Wireless Communication Fundamentals: Introduction wireless transmission, Frequencies for radio
transmission, Signals, Antennas, Signal propagation, Multiplexing, Modulations spread spectrum, MAC,
SDMA, FDMA, TDMA, CDMA, Cellular wireless networks. Telecommunications Systems: GSM, System
architecture protocols, Connection establishment, Frequency alocation, Routing, handover, Security,
GPRS. Wireless Networks: Wireless LAN-IEEE 802.11 Standards, Architecture, Services HIPERLAN,
AdHoc Network, Bluetooth mobile network layer: Mobile IP, Dynamic host configuration protocol.
Routing: DSDV, DSR, Alternative metrics, Wireless application protocol. Mobile Ad hoc Networks:
Overview, Properties of a MANET, Spectrum of MANET applications, Routing and various routing
algorithms, Security in MANET.
References:
1. W. Stallings, Wireless Communications and Networks, (2e) Pearson Education, 2018.
2. J. Schiller, Mobile Communications, (2e), Pearson Education, 2009.
3. K. Garg, Mobile Computing: Theory and Practice, (1e) Pearson Education India, 2010.
CS4157: NATURAL LANGUAGE PROCESSING [3 0 0 3]
Introduction: Ambiguity and uncertainty in language, processing paradigms, phases in natural language
processing. Text representation in computers: encoding schemes. Linguistics resources: Introduction to
corpus, elements in balanced corpus, WordNet, VerbNet. Part of Speech tagging: Stochastic POS
tagging, HMM, Transformation based tagging (TBL), handling of unknown words, named entities, multi
word expressions. Natural language grammars: lexeme, phonemes, phrases and idioms, word order,
agreement, tense, aspect and mood and agreement, context free grammar, spoken language syntax.
Parsing- unification, probabilistic parsing, tree-bank. Semantics: meaning representation, semantic
analysis, lexical semantics. Word Sense Disambiguation: selection restriction, machine learning
approaches, dictionary based approaches. Discourse: Reference resolution, constraints on co-
reference, algorithm for pronoun resolution, text coherence, discourse structure. Real time Applications
of NLP: text to speech, text summarization, information retrieval, sentiment analysis, machine translation.
References:
1. D. Jurafsky, J. H. Martin, Speech and Language Processing, (2e), Pearson Education, 2009.
2. T. Siddiqui, U. S. Tiwary, Natural language processing and Information retrieval, Oxford University
Press, 2008.
CS4158: COMPUTER VISION [3 0 0 3]
Introduction to computer vision and its applications, Geometric Image Features: Differential Geometry,
Contour Geometry, analytical image features: Euclidean geometry, Geometric Camera Parameters,
Calibration methods, Image formation, Liner Filtering: Linear filters and convolution, shift invariant linear
systems, spatial frequency and Fourier transforms, Image transformations and Colour models, Edge
Detection methods (Laplacian detectors and Canny edge detector), Points and patches, Harris corner
detector, Histogram of Gradients, Difference of Gaussian detector, SIFT, Colour and Texture, Feature
based alignment, least squares and RANSAC, Camera models, Camera calibration, Stereo vision,](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-21-320.jpg)
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Stereo correspondence, Epipolar geometry Optical flow, Lucas Kanade method, KLT tracking method,
Mean shift method, Dense motion estimation, Support Vector Machines, Face detection and recognition,
Bag of words, Deep convolution neural network.
References:
1. R. Szeliski, Computer Vision: Algorithms and Applications, Springer 2011.
2. D. A. Forsyth, J. Ponce, Computer Vision: A Modern Approach, (2e), PHI learning, 2012
3. J. E. Solem, Programming Computer Vision with Python, O'Reilly, 2012.
CS4170: INDUSTRIAL TRAINING [0 0 2 1]
In this course the student, undergo in reputed Private / Public Sector / Government organization /
companies as industrial training for minimum 45 days to be undergone by the student in the summer
vacation of the VI semester.
Outcome of this course:
To expose students to the 'real' working environment and be acquainted with the organization structure,
business operations and administrative functions.
To have hands-on experience in the students’ related field so that they can relate and reinforce what has
been taught at the university.
To promote cooperation and to develop synergetic collaboration between industry and the university in
promoting a knowledgeable society & to set the stage for future recruitment by potential employers.
CS4270: MAJOR PROJECT [0 0 24 12]
In this course student has to select a project work based on a topic of interest. Periodically the supervisor
will evaluate the implementation. This work, started in eighth semester of which, the student will be
evaluated internally and externally.
Outcome of the course:
Investigating professional topics, including ethical, legal and security issues, related to computing
projects.
Design and Develop the software with Software Engineering practices and standards
Apply prior knowledge to design and implement solutions for computational problems while considering
numerous realistic restraints.](https://image.slidesharecdn.com/b-230515173623-d436a019/85/B-Tech-Scheme-and-Syllabus-2019-2020-onwards-docx-22-320.jpg)
B.Tech Scheme and Syllabus 2019-2020 onwards.docx
- 1. MANIPAL UNIVERSITY JAIPUR Department of Computer Science & Engineering B.Tech Scheme – 2019 Onwards Year THIRD SEMESTER FOURTH SEMESTER Sub. Code Subject Name L T P C Sub. Code Subject Name L T P C II EO2001 Economics 3 0 0 3 BB0025 Value, Ethics and Governance 2 0 0 2 MA2101 Engineering Mathematics – III 2 1 0 3 MA2201 Engineering Mathematics – IV 2 1 0 3 CS2101 Data Communications 3 1 0 4 CS2201 Operating Systems 3 1 0 4 CS2102 Computer System Architecture 3 1 0 4 CS2202 Relational Database Management Systems 3 1 0 4 CS2103 Data Structures & Algorithms 3 1 0 4 CS2203 Computer Organization 3 1 0 4 CS2104 Object Oriented Programming 3 1 0 4 *** *** Open Elective – I 3 0 0 3 CS2130 Data Structures & Algorithms Lab 0 0 2 1 CS2230 Operating Systems Lab 0 0 2 1 CS2131 Object Oriented Programming Lab 0 0 2 1 CS2231 Relational Database Management Systems Lab 0 0 2 1 CS2232 Web Technology Lab 0 0 2 1 17 5 4 24 16 4 6 23 Total Contact Hours (L + T + P) 25 Total Contact Hours (L + T + P) + OE 23+3= 26 III FIFTH SEMESTER SIXTH SEMESTER CS3101 Artificial Intelligence & Soft Computing 3 1 0 4 BB0026 Organization and Management 3 0 0 3 CS3102 Design & Analysis of Algorithms 3 1 0 4 CS3201 Software Engineering 3 1 0 4 CS3103 Automata Theory & Compiler Design 3 1 0 4 CS3202 Information Systems Security 3 1 0 4 CS3104 Computer Networks 3 1 0 4 CS3203 Data Science and Machine Learning 3 0 0 3 CS31XX Program Elective – I 3 0 0 3 CS32XX Program Elective – II 3 0 0 3 *** **** Open Elective – II 3 0 0 3 *** **** Open Elective – III 3 0 0 3 CS3130 Design & Analysis of Algorithms Lab 0 0 2 1 CS3230 Software Engineering Lab 0 0 2 1 CS3131 Artificial Intelligence & Soft Computing Lab 0 0 2 1 CS3231 Information Systems Security Lab 0 0 2 1 CS3132 Computer Networks lab 0 0 2 1 CS3270 Minor Project 0 0 6 3 18 4 6 25 18 2 8 25 Total Contact Hours (L + T + P) + OE 25+3=28 Total Contact Hours (L + T + P) + OE 25+3=28 IV SEVENTH SEMESTER EIGHTH SEMESTER CS41XX Program Elective – III 3 0 0 3 CS4270 Major Project 12 CS41XX Program Elective – IV 3 0 0 3 CS41XX Program Elective – V 3 0 0 3 CS41XX Program Elective – VI 3 0 0 3 CS41XX Program Elective – VII 3 0 0 3 CS4170 Industrial Training 0 0 2 1 15 0 2 16 12 Total Contact Hours (L + T + P) 15+ 2 = 17
- 2. Programme Electives ( Minor Specializations) I. CYBER SECURITY 1. CS3140: Information Coding 2. CS3240: Principles of Secure Programing 3. CS4140: Cyber Security 4. CS4141: Digital Forensics & Cyber Crimes II. CLOUD COMPUTING 1. CS3141 : Cloud Computing & Virtualization 2. CS3241 : Cloud Infrastructure Services 3. CS4142 : Cloud Computing Applications 4. CS4143 : Cloud Security and Privacy Programme Electives (PE5, PE6, PE7) 1. CS4144 Information Retrieval 2. CS4145 Computer Graphics & Multimedia 3. CS4146 User Interface Design 4. CS4147 Digital Image Processing 5. CS4148 Internet of Things 6. CS4149 Big Data Analytics 7. CS4150 Software Defined Networks 8. CS4151 Deep Neural Network 9. CS4152 Social Network Analysis 10. CS4153 Software Testing 11. CS4154 Linux System and Shell Programming 12. CS4155 Wireless Sensor & Adhoc Network 13. CS4156 Mobile Computing 14. CS4157 Natural Language Processing 15. CS4158 Computer Vision Open Electives 1. CS2080 Fundamentals of Databases 2. CS3080 Principles of Programming Languages 3. CS3081 Enterprise Resource Planning 4. CS3082 Principles of Machine Learning
- 3. P a g e 3 | 22 School of Computing & IT Department of Computer Science & Engineering B.Tech Syllabus– 2019 Onwards EO2001: ECONOMICS [3 0 0 3] Introduction: Definition, nature and scope of economics, introduction to micro and macro economics; Microeconomics: Consumer behaviour, cardinal and ordinal approaches of utility, law of diminishing marginal utility, theory of demand and supply, law of demand, exceptions to the law of demand, change in demand and change in quantity demanded, elasticity of demand and supply, Indifference curve, properties, consumer equilibrium, Price and income effect; Production: Law of production, production function, SR and LR production function, law of returns, Isoquant curve, characteristics, Isocost, producer’s equilibrium; Cost and revenue analysis: Cost concepts, short run and long- run cost curves, TR,AR,MR; Various market situations: Characteristics and types, Break-even analysis; Macro Economics: National Income, Monetary and Fiscal Policies,Inflation, demand and supply of money, consumption function and business cycle. References: 1. H.L Ahuja, Macroeconomics Theory and Policy, (20e) S. Chand Publication. 2. Peterson H C et.al., Managerial Economics, (9e), Pearson, 2012 3. P L Mehta, Managerial Economics, Sultan Chand & Sons, New Delhi, 2012. 4. G J Tuesen & H G Tuesen, Engineering Economics, PHI, New Delhi, 2008. 5. J. L. Riggs, D. D. Bedworth, S. U. Randhawa, Engineering Economics, Tata McGraw Hill, 2018. MA2101: ENGINEERING MATHEMATICS III [2 1 0 3] Boolean Algebra: Partial ordering relations, Poset, Lattices, Basic Properties of Lattices. Distributive and complemented lattices, Boolean lattices and Boolean Algebra. Propositional and Predicate Calculus: Wellformed formula, connectives, quantifications, Inference theory of propositional and predicate calculus. Elementary configuration: Permutations and Combinations, Generating function, Principle of inclusion and exclusion Partitions, compositions. Ordering of permutations: Lexicographical and Fikes. Graph theory: Basic definitions, Degree, regular graphs, Eulerian and Hamiltonian graphs, Trees and Properties, Center, radius and diameter of a graph, Rooted and binary trees, Matrices associated with graphs, Algorithms for finding shortest path, Algorithm. Group theory: Semi groups, Monoids, Groups subgroups, Normal Subgroups, Cosets, Lagrange's Theorem, Cyclic groups. References: 1. C. L. Liu, Elements of Discrete Mathematics, (2e), Mc Graw Hill, New Delhi, 2007. 2. J. P. Trembaly and R. Manohar, Discrete Mathematics Structures with application to computer science, Tata Mc Graw Hill, 2012. 3. E. S. Page and L. B. Wilson, An Introduction to Computational Combinatorics, Cambridge Univ. Press, 1979. 4. N. Deo, Graph theory with Applications to computer science, PHI, 2012. CS2101: DATA COMMUNICATIONS [3 1 0 4] Introduction: Data communications, Networks, Network types, Standards. Protocol Layering: Protocol, Need for protocol architecture, OSI Model, TCP/IP protocol architecture. Data Transmission: Concepts and terminology, Analog and digital data transmission, Transmission impairments, Channel capacity, Transmission Media: Guided transmission media, Wireless transmission, Wireless propagation, Line-of-Sight transmission. Signal Encoding Techniques: Analog and digital Signals, Digital-to-digital conversion: Line coding schemes, Block coding, scrambling, Analog-To-Digital Conversion: Pulse code modulation, Delta modulation. Digital Data Communication Techniques: asynchronous and synchronous transmission, Types of errors, Error detection, Error correction, Line configurations. Data Link Control Protocols: Flow control, Error control, High-level data link control. Multiplexing: Frequency-division multiplexing, Time-division multiplexing, Code-division multiple access. Space division multiplexing. Multiple Access: Random access, Aloha, Carrier sense multiple access, Carrier sense multiple access with collision detection, Carrier sense multiple access with collision avoidance, Code-division multiple access.
- 4. P a g e 4 | 22 References: 1. B. Forouzan, Data Communication & Networking, (5e), McGraw Hill Education, 2013. 2. W. Stallings, Data and Computer Communications, (10e), Pearson Education,2018. CS2102: COMPUTER SYSTEM ARCHITECTURE [3 1 0 4] Digital Logic Circuits: Logic Gates, Boolean algebra, Map Simplification, Combinational Circuits, Flip- Flops, Sequential Circuits. Digital Components: Integrated Circuits, Decoders, Multiplexers, Registers, Shift Registers, Binary Counters, Memory Unit. Basic Structure of Computers: Computer Types, Functional Units, Basic Operational Concepts, Software, Performance. Machine Instructions and Programs: Numbers, Arithmetic Operations and Characters, Memory Locations and Addresses, Instructions and Instruction Sequencing, Addressing Modes, Assembly Language, Additional Instructions, Encoding of Machine Instructions. Arithmetic: Addition and Subtraction of Signed Numbers, Design of Fast Adders, Multiplication of Positive Numbers, Signed Operand Multiplication, Fast Multiplication, Integer Division, Floating Point Numbers and Operations. References: 1. M. Morris Mano, Computer System Architecture, (3e), Pearson, 2017. 2. C. Hamacher, Z. Vranesic, S. Zaky, Computer Organization and Embedded Systems, (6e), McGraw Hill, 2012. 3. J. P. Hayes, Computer Architecture and Organization, (3e), McGraw Hill TMH, 2012. CS2103: DATA STRUCTURES & ALGORITHMS [3 1 0 4] Introduction: Data structures classification, time and space complexity, pointers and pointer applications, Accessing variables through pointers, structures, functions. Array: introduction, Linear array, representation of an array in memory, multi-dimensional arrays, pointer arrays, matrix operations. Linked Lists: Introduction, single linked list, representation of a linked list in memory, Different Operations on a Single linked list, Reversing a single linked list, Advantages and disadvantages of single linked list, circular linked list, doubly linked list and Header linked list. Applications: polynomial operations and Josephus problem. Stacks: Basic Stack Operations, implementation of a Stack using Static Array, Dynamic Array and linked list, Multiple stack implementation using single array, Stack Applications, Reversing list, Factorial Calculation, Infix to postfix conversion, evaluation of Arithmetic Expressions and Towers of Hanoi. Queues: Basic Queue Operations, Representation of a Queue using array and linked list, Implementation of Queue Operations using Stack, Applications of Queues, Round Robin Algorithm, Circular Queues, DeQueues, Priority Queues. Trees: Definition of tree, Properties of tree, Binary Tree, Representation of Binary trees using arrays and linked lists, Operations on a Binary Tree, Threaded binary tree, Binary Tree Traversals (recursive and using stack), Binary search tree, AVL tree, m-way tree, B- tree, B+ tree Graphs: Basic concepts, Different representations of Graphs, Graph Traversals (BFS & DFS), Minimum Spanning Tree (Prims & Kruskal). Searching Techniques: Sequential and binary search. Hashing: Hash function, Address calculation techniques, and Common hashing functions, Collision resolution, Linear and Quadratic probing, Double hashing. Sorting Techniques: Basic concepts, Sorting by: bubble sort, Insertion sort, selection sort, quick sort, heap sort, merge sort, radix sort. References: 1. A. S. Tannenbaum, J. Augenstein, Data Structures using C, Pearson India, 2018. 2. E. Horowitz, S. Sahni, Fundamentals of Data Structures in C, (2e), Universities Press, 2008. 3. A. Forouzan, R. F. Gilberg, A Structured Programming Approach Using C, (3e), Cengage Learning, 2006. CS2104: OBJECT ORIENTED PROGRAMMING [3 1 0 4] The History and Evolution of object-oriented technology: benefits of object-oriented programming (OOP), application of object-oriented programming (OOP), introduction to object-oriented programming language like Java, C# and C++. Programming Fundamentals: Control flow statements, operators, datatypes, Type conversion, Wrapper Classes, Arrays. Introduction to classes: Class fundamentals, declaring objects, Assigning Object reference variables, Introduction
- 5. P a g e 5 | 22 to methods, Constructors, Method Overloading, objects as parameters, argument passing, returning objects, recursion, access control, final, nested and inner classes. I/O Basics: Reading Console Input, Writing Console Output, Files handling. Inheritance: base and derived class, multilevel hierarchy, access modifier in inheritance, method overriding, abstract classes. Exception Handling: Exception types, creating exception, Try Catch construct, Throw and throws keyword. Multithreaded programming: Creating and running threads, synchronise methods, inter thread communication, suspending, resuming and stopping thread. References: 1. M. Weisfeld, The object-oriented thought process, (4e), Pearson, 2013. 2. H. Schildt, The Complete Reference Java, (10e), Oracle Press, 2018. 3. C. Horstmann, Core Java Volume I—Fundamentals, (10e), Prentice Hall, 2006. 4. H. Schildt, The Complete Reference C++, (4e), Mcgraw Hill, 2003. CS2130: DATA STRUCTURES & ALGORITHMS LAB [0 0 2 1] Implementation of array operations: insertion, deletion, linear search and binary search, matrix operation. Implementation of singly, doubly and circular linked lists: inserting, deleting, and inverting a linked list, Polynomial addition, subtraction and sparse matrix implementation by linked list, Josephus problem. Stacks and Queues: adding, deleting elements. Circular Queue: Adding & deleting elements, conversion of infix to postfix and Evaluation of postfix expressions using stacks & queues, Implementation of stacks & queues using linked lists. Recursive and Non-recursive traversal of Trees: Threaded binary tree traversal, BST and AVL tree implementation. Implementation of sorting and searching algorithms: bubble sort, Insertion sort, selection sort, quick sort, heap sort, merge sort, radix sort, Hash table implementation. References: 1. A. S. Tannenbaum, J. Augenstein, Data Structures using C, Pearson India, 2018. 2. E. Horowitz, S. Sahni, Fundamentals of Data Structures in C, (2e), Universities Press, 2008. 3. A. Forouzan, R. F. Gilberg, A Structured Programming Approach Using C, (3e), Cengage Learning, 2006. CS2131: OBJECT ORIENTED PROGRAMMING LAB [0 0 2 1] Introduction to object-oriented programming language: Basic programming construct, flow control, loops, data type and arrays. Introduction to classes and object: creating class and object, using object to access class members, declaring method in class, recursion, argument passing and returning, declaring constructor, constructor overloading and method overloading. Input-output: Basic technique for input and output, type casting, file handling. Inheritance: creating base class and derive class, use of different access modifier, overriding base class methods, creating abstract classes/interfaces. Exception handling: try catch construct, creating own exception, raising exception. Multi thread programming: creating and running thread, stopping thread, use of wait, inter thread communication. References: 1. H. Schildt, The Complete Reference Java, (10e), Oracle Press, 2018. 2. C. Horstmann, Core Java Volume I—Fundamentals, (10e), Prentice Hall, 2006. 3. H. Schildt, The Complete Reference C++, (4e), Mcgraw Hill, 2003. BB 0025: VALUE, ETHICS & GOVERNANCE [2 0 0 2] Relevance of Value Education in day-to-day life. Mantra for success - Value, Moral and Ethics. Determinants of human nature (Three Gunas) and its impact on human life. Relevance of Personality, Attitude, Behavior, Ego, Character, introspection, Motivation, Leadership and 4 Qs with relevant Case Studies*.Governance: Understanding of Public and Private sector Governance systems; Courts & CAG. Public Sector Governance: Need, relevance, stakeholders. Private Sector Governance: Proprietary, Partnership, Company (Pvt Ltd & Ltd), Company’ Act 2013, Board of Directors; its Roles and Responsivities. Regulatory bodies; its role in ethical governance. Projects on PPP mode-relevance & prospects.CSR: Relationship with Society, Philanthropy and Business strategy, CSR Policy, Triple Bottom Line. Suggestive Case Studies:Uphar Theatre Tragedy- Engineering Ethics, Bhopal Gas
- 6. P a g e 6 | 22 Tragedy- Operational Engineering Ethics, Satyam Case- Financial Reporting Ethics, Enron Case- Business Ethics, Navin Modi Case- Financial Fraudulence. References: 1. Professional Module of ICSI. 2. Ghosh B.N., Business Ethics & Corporate Governance, (1e) McGraw Hill, 2011. 3. Mandal S.K., Ethics in Business & Corporate Governance, (2e), McGraw Hill, 2012. 4. Ray C.K., Corporate Governance, Value & Ethics, Vaya Education of India, 2012. 5. Chatterjee Abha, Professional Ethics, (2e) Oxford Publications. MA2201: ENGINEERING MATHEMATICS IV [2 1 0 3] Basic Set theory, Axioms of probability, Sample space, conditional probability, total probability theorem, Baye's theorem. One dimensional and two dimensional random variables, mean and variance, properties, Chebyschev's inequality, correlation coefficient, Distributions, Binomial, Poisson, Normal and Chisquare. Functions of random variables: One dimensional and Two dimensional, F & T distributions , Moment generating functions, Sampling theory, Central limit theorem, Point estimation, MLE, Interval estimation, Test of Hypothesis : significance level, certain best tests; Chi square test. References: 1. P. L. Meyer, Introduction to probability and Statistical Applications, (2e), Oxford and IBH publishing, 1980. 2. Miller, Freund and Johnson, Probability and Statistics for Engineers, (8e), PHI, 2011. 3. Hogg and Craig, Introduction to mathematical statistics, (6e), Pearson education, 2012. 4. S. M. Ross, Introduction to Probability and Statistics for Engineers and Scientists, Elseveir, 2010. CS2201: OPERATING SYSTEMS [3 1 0 4] Introduction: Definition of operating systems, Single and multi-processor systems, Operating system Services, System commands and system calls, Interrupt, System boot. OS Structure: Simple, Layered, Microkernel, Hybrid, Modules, Types of OS, Multi-user, Multitasking, Embedded, Real-time, Network, Distributed. Virtualization: Introduction, Hypervisor, Data center, Virtual data center, VMware virtualization products. Process and Thread: Process concept, Operations on processes, Inter-process communication, UNIX pipes, Multithreading, Multithreaded models, PThread API. Process Scheduling: Basic concepts, Scheduling criteria, Scheduling algorithms. Synchronization: Critical section problem, Peterson solution, Synchronization hardware, Semaphores, Classical problems of synchronization, Deadlock, Methods for handling deadlock. Memory Management: Swapping, Contiguous memory allocation, Paging, Structure of Page Table, Segmentation, Demand Paging, Page Replacement Policies, Allocation of Frames, Thrashing. File System Interface and Implementation: File Concept, Access Methods, Directory and Disk Structure, File System Mounting, File System Structure, File System Implementation, Allocation Methods, Free Space Management. Disk Management: Disk Scheduling Algorithms, Disk Management, Swap Space Management. References: 1. A. Silberschatz, P. B. Galvin, G. Gagne, Operating System Concepts, (9e), Wiley, 2014. 2. A.S. Tanenbaum, H. Bos, Modern Operating Systems, (4e), Pearson, 2015. 3. W. Stallings, Operating Systems: Internals and Design Principles, (9e), Pearson, 2018. CS2202: RELATIONAL DATABASE MANAGEMENT SYSTEMS [3 1 0 4] Introduction: Database systems, RDBMS Definition, data models, 3-schema architecture, challenges in building RDBMS, different components of a RDBMS. Relational Data Model: Concept of relations and its characteristics, schema-instance, integrity constraints, E/R Model, Extended E/R Model, converting the database specification in E/R and Extended E/R notation to the relational schema. Relational Query Language: Relational Algebra operators- selection, projection, cross product, various types of joins, division, example queries, tuple relation calculus, domain relational calculus, introduction to SQL, data definition in SQL, table and different types of constraints definitions, data manipulation in SQL, nested queries, notion of aggregation. Relational Database Design: functional dependencies and Normal forms, Armstrong's axioms for FD's, closure of a set of FD's, minimal covers, definitions of 1NF, 2NF, 3NF and BCNF, decompositions and desirable properties of them, algorithms for 3NF and BCNF normalization, multi-valued dependencies and 4NF. Transaction Processing: concepts of transaction processing, ACID properties, concurrency control, locking based protocols, recovery and logging methods. Data Storage
- 7. P a g e 7 | 22 and Indexing: file organizations, primary, secondary index structures, hash-based indexing, dynamic hashing techniques, multi-level indexes, B-tree and B+ trees. References: 1. A. Silberschatz, H. F. Korth, S. Sudarshan, Database System Concepts, (6e), McGraw Hill, 2013. 2. R. Elmasri, S. B. Navathe, Fundamentals of Database Systems, (6e), Addison-Wesley, 2010. 3. R. Ramakrishnan, J. Gehrke, Database Management Systems, (3e), McGraw Hill, 2014. 4. I. Bayross, SQL, PL/SQL The Programming Language of Oracle, (4e), BPB Publications, 2010. 5. C. J. Date, An Introduction to Database Systems, (8e), Prentice Hall of India, 2006. CS2203: COMPUTER ORGANIZATION [3 1 0 4] Processor Datapath and Control: Logic Design Conventions, Building a Datapath, Implementation Schemes, Exceptions, Microprogramming. Pipelining: Overview, Pipelined Datapath, Pipelined Control, Data Hazards and Forwarding, Data Hazards and Stalls, Branch Hazards. Memory Hierarchy: Basics of Caches, Measuring and Improving Cache Performance, Virtual Memory, Address Translation. Storage and Other Peripherals: Disk Storage and Dependability, Networks, Connecting I/O Devices to Processor and Memory, Interfacing I/O Devices to the Memory, Processor, and Operating System, I/O Performance Measures, Redundant Array of Inexpensive Disks. Multicores, Multiprocessors and Clusters: Shared Memory Multiprocessors, Clusters and other Message-Passing Multiprocessors, Hardware Multithreading, SISD, MIMD, SIMD, SPMD and Vector Processors. References: 1. D. A. Patterson, J. L. Hennessy, Computer Organization and Design: The Hardware and Software Interface, (5e), Elsevier, 2017. 2. J. L. Hennessy, D. A. Patterson, Computer Architecture: A Quantitative Approach, (6e), Morgan Kaufmann Publishers, 2019. 3. W. Stallings, Computer Organization and Architecture –Designing for Performance, (9e), Pearson, 2013. CS2230: OPERATING SYSTEMS LAB [0 0 2 1] Basic Linux commands: Illustration of shell functions, wild cards, redirection, pipes, sequencing, grouping, background processing, command substitution, sub shells, Shell programming. System Calls: File and process, I/O Redirection, IPC using Pipe and Signals. PThread API: Multithreaded programs, Synchronization programs using PThreads and Semaphores, CPU Scheduling, Deadlock, Memory Management. Creating a Virtual Machine: Virtual Machine Files and Snapshots, Virtual Machine Cloning and Exporting. References: 1. W. R. Stevens , S. A. Rago, Advanced Programming in the UNIX Environment, (3e), Addison- Wesley, 2013. 2. S. Das, Unix Concepts and Applications, (4e), McGraw Hill, 2006. 3. K. A. Robbins, S. Robbins, Unix Systems Programming: Communication, Concurrency, and Threads, (2e), Prentice Hall, 2004. CS2231: RELATIONAL DATABASE MANAGEMENT SYSTEMS LAB [0 0 2 1] Introduction to SQL and its different command categories i.e. DDL, DML, DQL and DCL, Data Integrity Constraints and Built-in Functions, Design and implementing the data requirements of a simple DB application, Experiments on views, indexing, triggers, stored procedures, transaction. References: 1. I. Bayross, Teach yourself SQL & PL/SQL using Oracle 8i & 9i with SQLJ, BPB Publications, 2010. 2. A. Silberschatz, H. F. Korth, S. Sudarshan, Database System Concepts, (6e), McGraw Hill, 2013. 3. R. Elmasri, S. B. Navathe, Fundamentals of Database Systems, (6e), Addison-Wesley, 2010. CS2232: WEB TECHNOLOGY LAB [0 0 2 1] Introduction to WWW: Web Design, Web site design principles, planning the site and navigation. HTML: The development process, html tags, forms, web site structure. XHTML: XML, move to XHTML, meta
- 8. P a g e 8 | 22 tags, character entities, frames and frame sets, inside browser. Style Sheets: CSS1, CSS2, CSS3. JavaScript: How to develop javascript, variables, functions, conditions, loops and repetition. Advance Javascript: Javascript and objects, javascript own objects, the DOM and web browser environments, forms and validations. DHTML: Combining HTML, CSS and Javascript, events and buttons, controlling your browser. Ajax: Introduction, advantages, purpose of it, Ajax based web application, alternatives of Ajax. XML: Introduction to XML, DTD and Schemas, Well formed, using XML with application. XSL: Introduction to XSL, XML transformed simple example, XSL elements, transforming with XSLT. PHP: Starting to script on server side, arrays, function and forms, advance PHP. Databases: Connection to server, creating database, performing data and schema related operations, PHP myadmin and database bugs. Advanced topics: E-Commerce models and architecture. m-Commerce: WAP and Mobile agents, search engines and search engine optimization, Introduction to web services and technology. Introduction, pros and cons of the above technology with advance technology: JQuery, WebRTC, Web socks, Angularjs, NodeJS, JSON, Bootstrap. All above will be facilitated using Web/Mobile application projects assigned to the students. References: 1. R. Connolly, R. Hoar, Fundamentals of Web Development, Pearson Education India, 2015. 2. R. Nixon, Learning PHP, MySQL & JavaScript with jQuery, CSS and HTML5, (5e), O’Reilly Publications, 2018. 3. L. Welling, L. Thomson, PHP and MySQL Web Development, (5e), Pearson Education, 2017. 4. N. C. Zakas, Professional JavaScript for Web Developers, (3e), Wrox/Wiley India, 2019. 5. D. S. Mcfarland, JavaScript & jQuery: The Missing Manual, (3e), O’Reilly/Shroff Publishers & Distributors Pvt Ltd, 2014. 6. Z. R. A. Boehm, Murach's HTML5 and CSS3, (4e), Murach's/Shroff Publishers & Distributors Pvt Ltd, 2018. CS2080: FUNDAMENTAL OF DATABASES [3 0 0 3] Introduction: Database-System Applications, Relational Databases, Database Design, Data Storage and Querying, Transaction Management, Database Architecture. File Management System: Indexing and Hashing. Relational Algebra: Algebra, Tuple Calculus, Domain Calculus. SQL: Data Definition Language, Data manipulation language , SQL Data Types and Schemas, Integrity Constraints, Basic Structure of SQL Queries, Set Operations, Aggregate Functions, Null Values, Nested Sub-queries, Correlated queries. Join: Inner, Outer, Left, Right and Natural. The Entity-Relationship Model: Constraints, Entity-Relationship Diagrams, Entity-Relationship Design Issues, Weak Entity Sets, Extended E-R Features. Normalization: Normal Forms, BCNF. References: 1. R. Elmasri, S. B. Navathe, Fundamentals of Database Systems, (6e), Addison-Wesley, 2010. 2. R. Ramakrishnan, J. Gehrke, Database Management Systems, (3e), McGraw Hill, 2014. CS3101: ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING [3 1 0 4] Fundamental Concepts: Agents, environments, general model, Problem solving techniques. Search Techniques: Uninformed search, heuristic search, adversarial search and game trees, Solution of constraint satisfaction problems using search. Knowledge Representation: Propositional and predicate calculus, semantics for predicate calculus, inference rules, unification, Resolution, semantic networks, conceptual graphs/Dependency, structured representation. Learning: Inductive learning, decision tree learning. Natural language processing: introduction, parsing using context free grammars, Chomsky hierarchy, case grammar. Soft computing: Fuzzy set theory, Fuzzy sets, set-theoretic operations, membership functions, Union, intersection and complement, fuzzy rules, reasoning and interference. Neural networks: Perceptron, Back Propagation. Evolutionary techniques: genetic algorithms, Swarm Algorithm, ant colony optimization. References: 1. S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach,(3e) PHI, 2011. 2. E. Rich, K. Knight, S. B. Nair, Artificial Intelligence, (3e), Tata McGraw Hill, 2009. 3. G. F. Luger, Artificial Intelligence-Structures and Strategies for Complex Problem Solving, (6e), Addison-Wesley Pearson Education, 2012.
- 9. P a g e 9 | 22 CS3102: DESIGN & ANALYSIS OF ALGORITHMS [3 1 0 4] Introduction: Fundamentals of Algorithms, Important Problem Types, Analysis of algorithm efficiency. Analysis Framework: Asymptotic Notations and Basic Efficiency Classes, Mathematical Analysis of Nonrecursive and Recursive Algorithms, Brute force Techniques, Divide and Conquer. Decrease and Conquer: Insertion Sort, Depth First Search, Breadth First Search, Topological Sorting. Transform and Conquer: Presorting, BST, Heapsort. Space and Time tradeoffs: Input Enhancement in String Matching. Dynamic Programming: Warshall's and Floyd's Algorithms, The Knapsack Problem. Greedy Techniques: Prim's, Kruskal's and Dijkstra's Algorithm, Huffman Trees, Coping with limitations of algorithmic power. Backtracking: nQueens problem, Hamiltonian Circuit Problem, SubsetSum Problem. BranchandBound: Assignment Problem, Knapsack Problem, TSP. Complexity Clasess: P, NP,and NP- complete Problems. References: 1. E. Horowitz, S. Sahni, S. Rajasekaran, Fundamental of Computer Algorithms, (2e), Universities Press, 2007. 2. T. H. Cormen, C. E. Leiserson, R.L. Rivest and C. Stein, Introduction to Algorithms, (3e), MIT press, 2009. CS3103: AUTOMATA THEORY & COMPILER DESIGN [3 1 0 4] Introduction: Automata Theory, Mathematical Preliminaries and Notation, Review of set theory, function, relation. Finite Automata: Deterministic and Non Deterministic Finite Automata (FA), Regular languages, Mealy and Moore machine; Regular Sets and Regular Grammars: Chomsky Hierarchy, Regular Expressions, Regular Grammar and FA, Pumping Lemma for Regular Languages; Context Free Languages (CFL) and Grammars: Ambiguity, Methods for Transforming Grammars; Push Down Automata: Nondeterministic Pushdown Automata (NPDA), Design of NPDA, PDA and CFLs; Introduction to Turing machine; Introduction to Compiler Design: Structure of a Compiler, Lexical Analysis, Recognition of Tokens; Introduction to LR Parsing: Simple LR, More Powerful LR Parsers, Parser Generators; Syntax Directed Translations; Type Checking: Rules for Type Checking; Storage Organization. References: 1. P. Linz, An Introduction to Formal Languages and Automata, (6e), Jones and Bartlett Student Edition, 2016. 2. A. V. Aho, J. Ullman, M. S. Lam, R. Sethi, Compilers: Principles, Techniques and Tools, (2e), Pearson Education, 2015. 3. M. Sipser, Introduction to the Theory of Computation, (3e), Cengage Learning, 2014. 4. J. C. Martin, Introduction to Languages and the Theory of Computation, (4e), McGraw Hill, 2010. CS3104: COMPUTER NETWORKS [3 1 0 4] Network Layer: Network layer design issues, routing algorithms, congestion control algorithms, Quality of service, MPLS, Classfull addressing, Sub-netting, Classless addressing. Protocols: ARP & DHCP, Introduction, Packet Format, message types, IPV4 header format, fragmentation, options, checksum. ICMP: Message format, message types. Dynamic routing protocols: RIP, OSPF & BGP. Multicasting Protocol: IGMP, Introduction to IPV6. Transport Layer: Transport services, state diagram, Elements of Transport Protocols, addressing, Connection establishment, connection release, Error control and Flow Control, Multiplexing. Congestion Control: Bandwidth allocation, regulating the sending rate, UDP, TCP. Application Layer: DNS, Name space, domain resource records. Electronic Mail: SMTP, POP, IMAP, MIME, HTTP, HTTPS, SNMP. Network Security: Security Goals, Attacks, Attack prevention techniques, Firewall, IDS, DMZ, IPsec. References: 1. B. A. Forouzan, TCP/IP Protocol Suite, (4e), TMH, 2010. 2. A. S. Tanenbaum, Computer Networks, (5e), Pearson, 2010.
- 10. P a g e 10 | 22 CS3130: DESIGN & ANALYSIS OF ALGORITHMS LAB [0 0 2 1] Sorting & Searching Algorithm: insertion sort, selection sort, binary search. Basic data structures: stacks and queues, graphs and trees, binary trees. Algorithmic paradigms: Recursion, divide-and-conquer, Merge sort, Quick sort. Greedy: Knapsack, Huffman encoding, dynamic programming, lower bounds and optimal algorithms. Heaps: Heaps, priority queues, min-max heaps, heap sort. Dynamic search structures: Binary search trees, height balancing, B-trees. Algorithms on arrays: Linear-time median finding, sorting in linear time (counting sort, radix sort, bucket sort), String matching (Rabin-Karp and Knuth-Morris-Pratt algorithms). Graph algorithms Traversal: (BFS, DFS, topological sort), Minimum spanning trees (Prim and Kruskal algorithms), shortest paths (Dijkstra’s and Floyd-Warshal algorithms). Mini-Projects & Case Studies. References: 1. E. Horowitz, S. Sahni, S. Rajasekaran, Fundamental of Computer Algorithms, (2e), Universities Press, 2007. 2. T. H. Cormen, C. E. Leiserson, R.L. Rivest , C. Stein, Introduction to Algorithms, (3e), MIT press, 2009. CS3131: ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING LAB [0 0 2 1] Introduction to Python: basic variable declaration, loops, inbuilt functions. Basic Programming using Python for AI techniques: global and local heuristics, Crypt arithmetic, Python syntax, Constraint satisfaction Problem, Preposition and inference. Travelling Salesman Problem using Branch & Bound /Nearest Neighbor. Character recognition using Neural Networks. Optimization using Genetic Algorithms. Mini-Projects & Case Studies. References: 1. A. K. Mackworth, D. L. Poole, Artificial Intelligence: Foundations of Computational Agents, (2e), Cambridge University Press, 2017. 2. P. Joshi, Artificial Intelligence with Python, Packt Publishers, 2018. 3. I. Bratko, PROLOG: Programming for Artificial Intelligence, (3e), Pearson Publication, 2011. CS3132: COMPUTER NETWORKS LAB [0 0 2 1] Cisco Packet Tracer: Introduction to packet tracer and networking device components, Router mode, Switch/Router basic commands; designing of star topology using HUB and Switch, IP configuration of end devices, Configuring DHCP server, Static routing, RIP, OSPF, VLAN and NAT. Network programming: Transmission control protocol and User datagram protocol. Network Utilities: PING, NETSTAT, IPCONFIG, IFCONFIG, ARP, TRACE-ROUTE References: 1. B. A. Forouzan, TCP/IP Protocol Suite, (5e), Tata McGraw Hill, 2013. 2. A. S.Tanenbaum, Computer Networks, (5e), Pearson Education, 2010. BB 0026: ORGANISATION AND MANAGEMENT [3 0 0 3] Meaning and definition of an organization, Necessity of Organization, Principles of Organization, Formal and Informal Organizations. Management: Functions of Management, Levels of Management, Managerial Skills, Importance of Management, Models of Management, Scientific Management, Forms of Ownership, Organizational Structures, Purchasing and Marketing Management, Functions of Purchasing Department, Methods of Purchasing, Marketing, Functions of Marketing, Advertising. Introduction, Functions of Personal Management, Development of Personal Policy, Manpower Planning, Recruitment and Selection of manpower. Motivation – Introduction, Human needs, Maslow’s Hierarchy of needs, Types of Motivation, Techniques of Motivation, Motivation Theories, McGregor’s Theory, Herzberg’s Hygiene Maintenance Theory. Leadership - Introduction Qualities of a good Leader, Leadership Styles, Leadership Approach, Leadership Theories. Entrepreneurship-Introduction, Entrepreneurship Development, Entrepreneurial Characteristics, Need for Promotion of Entrepreneurship, Steps for establishing small scale unit. Data and Information; Need, function and Importance of MIS; Evolution of MIS; Organizational Structure and MIS, Computers and MIS, Classification of Information Systems, Information Support for functional areas of management. Reference: 1.Koontz, Harold, Cyril O’Donnell, and Heinz Weihrich, Essentials of Management,(1e)
- 11. P a g e 11 | 22 Tata McGraw-Hill, New Delhi, 1978. 2. Robbins, Stephen P, and Mary Coulter, Management, Prentice Hall, (2e) New Delhi, 1997. 3. E. S. Buffa and R. K. Sarin, Modern Production / Operations Management, (8e), Wiley, 1987 4. H. J. Arnold and D. C. Feldman, Organizational Behavior, McGraw – Hill, 1986. 5. Aswathappa K, Human Resource and Personnel Management, Tata McGraw Hill, 2005. 6. William Wether & Keith Davis, Human Resource and Personnel Management, McGraw Hill, 1986. CS3201: SOFTWARE ENGINEERING [3 1 0 4] Introduction: The Evolving Role of Software, The changing nature of software, Legacy software, Software Myths. Software Engineering: A Layered Technology, a Process Framework, the Capability Maturity Model Integration (CMMI), Specialized Process Models, and the Unified Process. Agile development: Agile Process Models Software Engineering Practice, Communication Practice, Planning Practices, Modeling Practices, Construction Practice, Deployment Computer–Based Systems, The System Engineering Hierarchy, Business Process Engineering: An Overview. Product Engineering: An Overview, Data Modeling Concepts, Object Oriented Analysis, Flow-Oriented Modeling, Taxonomy of Quality Attributes, Perspectives of Quality, Quality System, Software Quality Assurance, Capability Maturity Model Observation on Estimation, The Project Planning Process, Software Scope and Feasibility, Human Resources, Empirical Estimation Model ,Introduction To DevOps, Cloud Computing And Virtualization, Migration to DevOps, DevOps Tools. References: 1. R. Pressman, Software Engineering: A Practitioners Approach, (8e), McGrawHill Pubs, 2019. 2. M. Walls, Building a Dev Ops Culture, O’Reilly Publications, 2013. 3. J. Joyner, Dev Ops for Beginners, Dev Ops Software Development Method guide for software developers and IT professionals, Mihails Konoplovs, 2015. CS3202: INFORMATION SYSTEMS SECURITY [3 1 0 4] Introduction: Basic objectives of cryptography, Secret-key and public-key cryptography, One-way trapdoor one-way functions, Cryptanalysis, Attack models, Classical cryptography. Block ciphers: Modes of operation, DES and its variants, AES, Linear and differential cryptanalysis. Message digest: Properties of hash functions, MD2, MD5 and SHA-1, Keyed hash functions, Attacks on hash functions. Public-key parameters: Modular arithmetic, Primality testing, Chinese remainder theorem, Modular square roots, Finite field. Intractable problems: Integer factorization problem, RSA problem, Modular square root problem, Discrete logarithm problem, Diffie-Hellman problem, Known algorithms for solving the intractable problems. Public-key encryption: RSA, Rabin and EIGamal schemes, Elliptic and hyper- elliptic curve cryptography, Side channel attacks, Diffie-Hellman and MQV key exchange. Digital signatures: RSA, DSA and NR signature schemes, blind and undeniable signatures. Entity authentication: Passwords, Challenge-response algorithms, Zero-knowledge protocols. Network security: Certification, public-key infrastructure (PKI), secure socket layer (SSL), Kerberos. References: 1. B. A. Forouzan, D. Mukhopadhyay, Cryptography and Network Security, (2e), Mc-Graw Hill, , 2008. 2. W. Stallings, Cryptography and Network Security: Principles and Practice, (5e),Prentice Hall, 2010. 3. J. Pieprzyk, T. Hardjono, J. Seberry, Fundamentals of Computer Security, Springer International Edition, 2003. 4. A. J. Menezes, P. C. V. Oorschot ,S. A. Vanstone, Handbook of Applied Cryptography, CRC Press. CS3203: DATA SCIENCE AND MACHINE LEARNING [3 0 0 3] Data Science: Descriptive Statistics, Probability Distribution, regression analysis, ANOVA. Machine Learning: Goals, Applications of ML, developing a learning system, training data, concept representation, function approximation. Decision Tree Learning: Representing concepts as decision trees, Recursive induction of decision trees, best splitting attribute, entropy, information gain., Occam's razor, Overfitting, noisy data, and pruning. Artificial Neural Networks: Neurons and biological motivation. Linear threshold units, Perceptron, representational limitation and gradient descent training, Multilayer networks and backpropagation. Hidden layers and constructing intermediate, distributed representations, Overfitting, learning network structure, recurrent networks. Comparing learning algorithms: cross-validation, learning curves, and statistical hypothesis testing. Support Vector Machines: Maximum margin linear separators.
- 12. P a g e 12 | 22 Kernels for learning non-linear functions. Bayesian Learning: Probability theory and Bayes rule. Naive Bayes learning algorithm, Logistic regression, Bayes nets and Markov nets for representing dependencies. Instance-Based Learning: k-Nearest-neighbor algorithm, Case-based learning, Relevance feedback and Rocchio algorithm. Naive Bayes for text. Clustering and Unsupervised Learning: Hierarchical Agglomerative Clustering, k-means partitioned clustering, expectation maximization (EM) for soft clustering. Ensemble Learning: Bagging, boosting, and Decorate. Active learning with ensembles. References: 1. G. James, D. Witten, T Hastie, R Tibshirani, An introduction to statistical learning with applications in R, Springer, 2013. 2. J. Han, M. Kamber, J. Pei, Data Mining concepts and techniques, (2e), Morgan Kaufmann- Elsevier, 2011. 3. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, (2e), Springer, 2009. 4. K. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012. 5. T. M. Mitchell, Machine Learning, (Indian Edition), MacGraw Hill, 2017. 6. C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 2019 CS3230: SOFTWARE ENGINEERING LAB [0 0 2 1] Introduction: Agile development: Agile Process Models Software, Communication Practice, Planning Practices, Modeling Practices, Construction Practice, Deployment of Computer–Based Systems, The System Engineering Hierarchy. Business Process Engineering: An Overview, Product Engineering: An Overview, Data Modeling Concepts, Object Oriented Analysis, Flow-Oriented Modeling, Taxonomy of Quality Attributes, Perspectives of Quality, Quality System, Software Quality Assurance, Capability Maturity Model Observation on Estimation using Projects, The Project Planning Process, Software Scope and Feasibility, Human Resources, Empirical Estimation Model ,Introduction To DevOps, Cloud Computing And Virtualization, Migration to DevOps, DevOps Tools, All above will be facilitated using Software Projects assigned to the students. References: 1. R. Pressman, Software Engineering: A Practitioners Approach, (8e), McGrawHill Pubs, 2019. 2. M. Walls, Building a Dev Ops Culture, O’Reilly Publications, 2013. 3. J. Joyner, Dev Ops for Beginners, Dev Ops Software Development Method guide for software developers and IT professionals, Mihails Konoplovs, 2015. CS3231: INFORMATION SYSTEMS SECURITY LAB [0 0 2 1] Substitution and Transposition Cipher Implementation: Caesar Cipher, Playfair Cipher, Hill Cipher, Vigenere Cipher, Rail fence. Symmetric and Asymmetric Cipher Implementation: DES, RSA, Diffie- Hellman, MD5, SHA-1. Signature Schemes Implementation: Digital Signature Standard, GnuPG API. Demonstration of secure data storage: Setup of honey pot and monitoring on network using KF sensors. Installation of rootkits. Wireless audit on an access point or a router, WEP and WPA (Net Stumbler). Intrusion detection system using snort. References: 1. B. A. Forouzan, D. Mukhopadhyay, Cryptography and Network Security, (2e), Mc-Graw Hill, 2008. 2. W. Stallings, Cryptography and Network Security: Principles and Practice, (5e), Prentice Hall, 2010. 3. J. Pieprzyk, T. Hardjono, J. Seberry, Fundamentals of Computer Security, Springer, 2003. CS3080: PRINCIPLES OF PROGRAMMING LANGUAGES [3 0 0 3] Preliminary Concepts: Concepts of programming languages. Syntax and Semantics: general Problem of describing Syntax and Semantics. Data types: Primitive, character, user defined, array, associative record, union, pointer and reference types. Expressions and Statements: Assignment Statements, Control Structures. Subprograms and Blocks: Fundamentals of sub-programs, Scope of life time of variables, static and dynamic scope, design issues of sub-programs and operations. Abstract Data types: Abstractions and encapsulation, introductions to data abstraction, design issues, language examples. Concurrency: Subprogram level concurrency, semaphores, monitors, massage passing, Java threads, C# threads. Exception handling: Exceptions, exception Propagation, Exception handler in Ada, C++ and Java, Logic Programming Language: Introduction and overview of logic programming. References:
- 13. P a g e 13 | 22 1. R. W. Sebesta, Concepts of Programming Languages, (10e), Pearson Education, 2008. 2. D. A. Watt, Programming Language Design Concepts, Wiley, (2e), 2007. 3. B. Tucker, R. E. Noonan, Programming Languages, (2e), TMH, 2007. 4. K. C. Louden, Programming Languages, (2e), Thomson, 2003. 5. T. W. Pratt, M. V. Zelkowitz, T. V. Gopal, Programming Languages: Design and Implementation, (4e), PHI, 2006 CS3081: ENTERPRISE RESOURCE PLANNING [3 0 0 3] ERP Overview: ERP Components, ERP Benefits. Business Process Reengineering (BPA): BPA life cycle, BPA components. Data warehousing, Datamining, Supply chain Management; ERP: evolution, a Manufacturing Perspective, ERP Module, ERP Market, ERP implementation life cycle, Options of various paradigms, Identification of suitable platforms. SDLC/SSAD: Role of SDLC/SSAD, Object oriented architecture. ERP Implementation: introduction, pre evaluation screening, package evaluation, project planning phase, Gap analysis, Hidden costs, Major Vendors, Consultant Employees, Human Resource. ERP & E-Commerce: Future Directives- in ERP, ERP and Internet, Critical Factors guiding selection and evaluation of ERP, Strategies for its successful implementation, Impediments and initiatives to achieve success, Critical success and failure factors, Integrating of ERP into organizational culture. Using ERP tool: Case study of a system using SAP or ORACLE or open source ERP. References: 1. S. R. Magal, J. Word, Integrated Business Processes with ERP Systems, (2e), John Wiley & Sons, 2011. 2. M. Sumner, Enterprise Resource Planning, Pearson Education, (2e), 2006. 3. E. Monk, B. Wagner, Concepts in Enterprise Resource Planning, (3e), Thomson Course Technology, 2006. CS3082: PRINCIPLES OF MACHINE LEARNING [3 0 0 3] Introduction to Artificial Intelligence: Foundations, scope, problems. Problem-solving through Searching: forward and backward, state-space, blind, heuristic, problem-reduction, minimax. Supervised Learning: Process for feature selection, over-parameterization and the curse of dimensionality, regularization, cross validation. Classification: operation of classifiers, regression as a classifier, metrics used to evaluate classifiers, SVM, Naïve Bayes, KNN. Regression: operation of regression models, prediction and forecasting, metrics used to evaluate regression models. Neural networks: Feed forward NN, Feed backward NN, Convolutional Neural network. Unsupervised Learning: K-mean clustering. Algorithmic Learning Theory and Applications: Mistake bound model, PAC Model. References: 1. G. F. Luger, W. A. Stubblefield, Artificial Intelligence - Structures and Strategies for Complex Problem Solving. (5e), Addison Wesley, 2005. 2. P Baldi, S Brunak, Bioinformatics: A Machine Learning Approach, (2e) MIT Press, 2002. 3. T. M. Mitchell, Machine Learning, McGraw-Hill Education, 2017. 4. Y Abu-Mostafa, M. Magdon-Ismail, H.T. Lin, H-T. Learning from Data. AML Book, 2012. CS3140: INFORMATION CODING [3 0 0 3] Information Theory: Entropy, Characterization and related properties, Huffman codes, Shannon-Fano coding, Robustness of coding techniques, Information measure-noiseless coding, Discrete memoryless channel, Channel capacity, Fundamental theorem of information theory. Coding Theory: Error correcting codes, Minimum distance principles, Hamming bound, General binary code, Group code, Linear group code. Convolution Encoding: Algebraic structure, Gilbert bound, Threshold decoding, Threshold decoding for block codes. Cyclic binary codes: BCH codes, Generalized BCH code and decoding, Optimum codes, Concepts of non-cyclic codes. Combinatorial Designs: Definitions of BIBD, Hadamard Designs, Latin Squares, Mutually Orthogonal Latin Squares, Orthogonal Arrays. Network Coding: Fundamentals of Network Coding, Butterfly networks, Graphs and networks, Max-flow min-cut theorem, Multi-source multicast problem, Deterministic code design for network coding, Randomized network coding, Application of network coding. References: 1. T. M. Cover, J.A. Thomas, Elements of Information Theory, Wiley, (2e), 2006.
- 14. P a g e 14 | 22 2. M. Kelbert, Y. Suhov, Information Theory and Coding by Example, Cambridge University Press, 2013. 3. D. Stinson, Combinatorial Designs: Constructions and Analysis, Springer, 2003. 4. P. J. Cameron , J. H. Lint, Designs, Graphs, Codes and their Links, Cambridge University Press, 2010. CS3240: PRINCIPLES OF SECURE PROGRAMMING [3 0 0 3] Introduction: Security goals, Secure system design, Secure design principles, Worms and other malware. Secure Programming Techniques: Anatomy of a buffer overflow, Safe string libraries, Stackguard, Static analysis tools, Heap-based overflow, Other memory corruption vulnerabilities, SQL Injection attack scenario and solutions, Password security. Cross-Domain Security in Web Applications: Interaction between webpages from different domains, Attack patterns, preventing cross-site request forgery, Cross-site script inclusion, Cross-site scripting. Other Web Vulnerabilities: Cookie protocol problems, SSL/TLS vulnerabilities, Session hijacking, Guninski attack, Defenses. Trusted Execution Environment: Case study on TrustZone, Security vulnerability tools, Exploit development with metasploit. References: 1. N. Daswani, C. Kern, A. Kesavan, Foundations of Security, What Every Programmer Needs to Know, Apress, 2007. 2. J. C. Foster, V. T. Liu, Writing Security Tools and Exploits, Syngress Publishing, 2006. 3. J. Ericson, Hacking: The Art of Exploitation, (2e), No Starch Press, 2008. 4. C. Anley, J. Heasman, F. Linder, G. Richarte, The Shellcoder’s Handbook: Discovering and Exploiting Security Holes, (2e), Addison-Wiley, 2011. CS3270: MINOR PROJECT [0 0 6 3] In this course student has to select a project work based on a topic of interest. Periodically the supervisor will evaluate the implementation. This work, started in eighth semester of which, the student will be evaluated internally and externally. CS4140: CYBER SECURITY [3 0 0 3] Introduction to cyber security: Computer Security, threats, harm, vulnerabilities, controls, Authentication, Access Control and Cryptography, Web User Side, Browser Attacks, Web Attacks Targeting Users, Email Attacks. Security in operating system and networks: Security in Operating Systems, Security in the Design of Operating Systems, Rootkit, Network security attack, Threats to Network Communications, Wireless Network Security, Denial of Service, Distributed Denial-of Service. Security Countermeasures: Cryptography in Network Security, Firewalls, Intrusion Detection and Prevention Systems, Network Management, Databases, Security Requirements of Databases Reliability and Integrity, Database Disclosure, Data Mining and Big Data. Privacy in Cyberspace: Privacy Concepts, Privacy Principles and Policies, Authentication and Privacy, Data Mining, Privacy on the Web, Email Security. Cyber Policies: Policies to mitigate cyber risks, Reducing Supply Chain Risks, Mitigate Risks through Human Resource Development, Information sharing Implementing a Cyber security framework, Digital Signature. References: 1. M.S. Merkov, J. Breithaupt, Information Security: Principles & Practices, (2e), Pearson, 2014. 2. C.P. Pfleeger, S.L. Pfleeger, J. Margulies, Security in Computing, (5e), Pearson, 2015. 3. V. Sood, Cyber Laws Simplified, (2e) McGraw Hill, 2017. 4. N. Godbole, Information Systems Security, (2e), Wiley, 2017. CS4141: DIGITAL FORENSICS & CYBER CRIMES [3 0 0 3] Introduction to Computer Forensics: Computer crimes, evidence, extraction, preservation, overview of hardware and operating systems, structure of storage media/devices, uncovering attacks that evade detection by event viewer, task manager, and other Windows GUI tools, data acquisition, disk imaging, recovering swap files, temporary and cache files. Computer Forensic tools: Encase, Helix, FTK, Autopsy, Sleuth kit Forensic Browser, FIRE, Found stone Forensic ToolKit, WinHex, Linux and other open source tools. Mobile and Network Forensics: Collecting and analyzing network-based evidence, reconstructing web browsing, email activity, and windows registry changes, intrusion detection, tracking offenders, Mobile Network Technology, Investigations, Collecting Evidence, Interpretation of Digital Evidence on Mobile Network. Software Reverse Engineering: Defend against software targets for viruses, worms and other malware, improving third-party software library, identifying hostile codes-buffer overflow, provision
- 15. P a g e 15 | 22 of unexpected inputs. Computer crime and Legal issues: Intellectual property, privacy issues, Criminal Justice system for forensic, audit/investigative situations and digital crime scene, investigative procedure/standards for extraction, preservation, and deposition of legal evidence in a court of law. References: 1. C. Altheide, H. Carvey, Digital Forensics with Open Source Tools, Syngress, 2011. 2. M.T. Britz, Computer Forensics and Cyber Crime: An Introduction, (3e), Kindle Edition, 2013. 3. S. Davidoff, J. Ham, Network Forensics: Tracking Hackers through Cyberspace, Prentice Hall, 2012. 4. B. Nelson, A. Phillips, F. Enfinger, C. Steua, Guide to Computer Forensics and Investigations, Thomson, (4e), 2009. CS3141: CLOUD COMPUTING AND VIRTUALIZATION [3 0 0 3] Introduction: Distributed Computing and Enabling Technologies, Cloud Fundamentals: Cloud Definition, Evolution, Architecture, Applications, deployment models, and service models. Virtualization: Issues with virtualization, virtualization technologies and architectures, Internals of virtual machine monitors/hypervisors, virtualization of data centers, and Issues with Multi-tenancy. Implementation: Study of Cloud Computing Systems like Amazon EC2 and S3, Google App Engine, and Microsoft Azure, Build Private/Hybrid Cloud using open source tools, Deployment of Web Services from Inside and Outside a Cloud Architecture. MapReduce and its extensions to Cloud Computing, HDFS, and GFS. Interoperability and Service Monitoring: Issues with interoperability, Vendor lock-in, Interoperability approaches. SLA Management, Metering Issues, and Report generation. Resource Management and Load Balancing: Distributed Management of Virtual Infrastructures, Server consolidation, Dynamic provisioning and resource management, Resource Optimization, Resource dynamic reconfiguration, Scheduling Techniques for Advance Reservation, Capacity Management to meet SLA Requirements, and Load Balancing, various load balancing techniques. Migration and Fault Tolerance: Broad Aspects of Migration into Cloud, Migration of virtual Machines and techniques. Fault Tolerance Mechanisms. Advances: Grid of Clouds, Green Cloud, Mobile Cloud Computing. References: 1. R. Buyya, J. Broberg, A. Goscinski , Cloud Computing Principles and Paradigms , Wiley Publishers, 2013. 2. B. Sosinsky, Cloud Computing Bible, Wiley, 2011. 3. M. Miller, Cloud Computing: Web-based Applications that change the way you work and collaborate online, Pearson, 2008. 4. D. S. Linthicum, Cloud Computing and SOA Convergence in Your Enterprise: A Step-by-Step Guide, Addision Wesley Information Technology Series, 2010. 5. T. Velte, A. T. Velte, R. Elsenpeter, Cloud Computing: A Practical Approach, McGraw Hill, 2017. CS3241: CLOUD INFRASTRUCTURE AND SERVICES [3 0 0 3] Introduction: Clouds and Cloud Computing: Basic Concepts, Types of Services, deployment models. Classic Data Center (CDC): DBMS concepts, CDC drawbacks, CDC Management and case studies. Virtualized Data Center (VDC): Compute virtualization overview, Compute virtualization techniques, Virtual Machines, VM Resource management techniques, Virtual Infrastructure Requirements. Storage: Storage virtualization overview, Virtual Machine Storage, Virtual provisioning and automated storage tiering. Networking: VDC networking overview, VDC networking components, VLAN and VSAN technologies. Business Continuity in VDC, Fault tolerance mechanism in VDC. Cloud Security: Access control and identity management in Cloud, Governance, risk, and compliance, Security best practices for Cloud, Cloud Migration. Issues in Cloud Development: Migration etc. References: 1. B. Jackson, K. Saurabh, Cloud Computing, (2e), Wiley India, 2012. 2. V. Joysula, M. Orr, G. Page, Cloud Computing: Automating the Virtualized Data Center, Cisco Press, 2012. 3. R. K. Buyya, Cloud Computing: Principles and Paradigms, Wiley Press, 2011. 4. M. Miller, Cloud Computing, (8e), Que Publishers, 2008. 5. Course materials from EMC² Education Services
- 16. P a g e 16 | 22 CS4142: CLOUD COMPUTING APPLICATIONS [3 0 0 3] Cloud Based Applications: Introduction, Contrast traditional software development and development for the cloud. Public v private cloud apps. Understanding Cloud ecosystems – what is SaaS/PaaS, popular APIs, mobile; Desktop and Application: Cloud Application Architectures, Desktop virtualization, Application virtualization, Web Application design, Cloud app, Benefits of cloud apps, cloud API, Cloud apps vs. web apps, Cloud apps vs. desktop apps, Testing of cloud apps; Designing Code for the cloud: Class and Method design to make best use of the Cloud infrastructure; Web Browsers and the Presentation Layer- Understanding Web browsers attributes and differences. Building blocks of the presentation layer: HTML, HTML5, CSS, Silverlight, and Flash. Web Development Techniques and Frameworks: Building Ajax controls, introduction to Javascript using JQuery, working with JSON, XML, REST. Application development Frameworks e.g. Ruby on Rails, .Net, Java API's or JSF; Deployment Environments – Platform As A Service (PAAS),Amazon, vmForce, Google App Engine, Azure, Heroku, AppForce; Cloud Application Performance Mangaement: Managing applications in the cloud, cloud application migration, Resource vs. application performance , Private and public instances, Topology discovery, First generation CAPM tools and problems, Second generation CAPM tools and advantages, Cloud application performance components, Agents and applications, Internet as part of the infrastructure, Hosted SaaS CAPM advantages, Root cause analysis challenges, case studies. References: 1. G. Reese, Cloud Application Architectures, O’Reilly Media, Inc, 2009. 2. E. Pace, D. Betts, S. Densmore, R. Dunn, M. Narumoto, Developing Applications for the Cloud on the Microsoft Windows Azure Platform, Microsoft Press, 2010. 3. V. Joysula, M. Orr, G. Page, Cloud Computing: Automating the Virtualized Data Center, Cisco Press, 2012. 4. Mei- Ling Liu, Distributed Computing: Principles and Application, Pearson, Education, Inc. New Delhi. 2004 CS4143: CLOUD SECURITY AND PRIVACY [3 0 0 3] Introduction: Cloud Computing Defined, The SaaS, PaaS and IaaS (SPI) Framework for Cloud Computing, Key Drivers to Adopting the Cloud, The Impact of Cloud Computing on Users, Governance in the Cloud, Barriers to Cloud Computing Adoption in the Enterprise. Security: Infrastructure Security: Network, Host and Application. Data Security and Storage: Aspects and Mitigation, Provider Data and Its Security. Identity and Access Management (IAM): Trust Boundaries, IAM Challenges, Definitions, Architecture and Practices, IAM Standards and Protocols for Cloud Services, Cloud Authorization Management. Security Management: Security Management Standards, Security Management in the Cloud, Availability Management: SaaS Availability Management, PaaS Availability Management, IaaS Availability Management, Access Control, Security Vulnerability, Patch, and Configuration Management. Privacy: Privacy Standards, Data Life Cycle, Key Privacy Concerns in the Cloud, Legal and Regulatory Implications. Cloud Morphing: Shaping the Future of Cloud Computing Security and Auditing the Cloud for Compliance. Audit and Compliance: Internal Policy Compliance, Governance, Risk, and Compliance, Illustrative Control Objectives for Cloud Computing, Incremental CSP-Specific Control Objectives, Additional Key Management Control Objectives, Control Considerations for CSP Users, Regulatory/External Compliance Cloud Security Alliance, Auditing the Cloud for Compliance. References: 1. S. Pearson , G. Yee , Privacy and Security for Cloud Computing, Springer, 2013. 2. T. Mather, Subra Kumaraswamy, Shahed Latif, Cloud Security and Privacy, O'Reilly Media, 2009. 3. B. Halpert, Auditing Cloud Computing: A Security and Privacy Guide, Wiley, 2011. 4. K. Saurabh, Cloud Computing, (2e), Wiley, 2012. CS4144: INFORMATION RETRIEVAL [3 0 0 3] Introduction to IR: IR Concepts, Boolean Retrievals- An Example Information Retrieval Problem, A First Take at Building an Inverted Index, Processing Boolean Queries. The Term Vocabulary and Postings Lists: Document Delineation and Character Sequence Decoding, Determining the Vocabulary of Terms. Dictionaries and Tolerant Retrieval: Search Structures for Dictionaries, Wildcard Queries, Spelling Correction, Phonetic Correction. Index Construction: Hardware Basics Blocked Sort-Based Indexing. Scoring, Term Weighting and the Vector Space Model: Parametric and Zone Indexes, Term Frequency and Weighting, The Vector Space Model for Scoring. Evaluation in Information Retrieval: Information Retrieval System Evaluation, Standard Test Collections, Evaluation of Unranked Retrieval Sets,
- 17. P a g e 17 | 22 Evaluation of Ranked Retrieval Results. XML Retrieval: Basic XML Concepts, Challenges in XML Retrieval, A Vector Space Model for XML Retrieval, Evaluation of XML Retrieval, Text-Centric vs. Data- Centric XML Retrieval. Web Search Basics: Web Characteristics, Advertising as the Economic Model, The Search User Experience, Index Size and Estimation, Near-Duplicates and Shingling. Web Crawling and Indexes: Overview, Crawling, Distributing Indexes, Connectivity Servers. Link Analysis: The Web as a Graph, Page Rank, Hubs and Authorities. References: 1. C. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2009. 2. R. Baeza-Yate, B. Ribeiro-Neto, Modern Information Retrieval, (2e), Addison Wesley, 2012. 3. S. Chakrabarti, Mining the Web: discovering knowledge from hypertext data, (2e), Morgan Kaufmann, 2002. 4. D. A. Grossman , O. Frieder, Information Retrieval: Algorithms, and Heuristics, (2e), Springer, 2004. CS4145 : COMPUTER GRAPHICS & MULTIMEDIA [3 0 0 3] Basics of Computer Graphics: Pixel, Frame buffer, Application of computer graphics. Graphic Display Devices: Cathode Ray Tube, Light emitting diode, DVST, Random and Raster Scan displays. Scan Conversion: Line Generation using digital differential analyzer (DDA), bresenham’s Algorithm, Circle generation algorithm, Ellipse generation algorithm, Polygon generation and filling algorithms. Two Dimensional Transformations: Introduction, homogeneous representation of points, basic transformation like Translation, Rotation, Scaling, Reflection, Shear. Clipping and Windowing: Cohen Sutherland Algorithm, liang Barsky algorithm, Sutherland Hodgman Algorithm. Three dimensional transformation: Translation, Rotation, Scaling and Reflection. Projection: Introduction, Types of projection. Hidden Surface elimination: Depth comparison, Back face detection algorithm, Painter’s Algorithm, Z-Buffer Algorithm. Basic Illumination Model: Diffuse reflection, Specular reflection, Phong and Gouraud shading. Introduction to Multimedia: Concepts and uses, hypertext and hypermedia, image, video and audio standards, text compression algorithm. Animation: types, techniques, key frame animation, utility, morphing. References: 1. D. Hearn, M. P. Baker, Computer Graphics with OpenGL, (4e), Pearson Education, 2014. 2. R. Steinmetz, K. Nahrstedt, Multimedia Systems, Springer, 2004 3. J. F. Hughes, J. D. Foley, Computer graphics Principles and Practice,(3e), Pearson Education, 2014. 4. R. Steinmetz, K. Nahrstedt, Multimedia Fundamentals: Media Coding and Content Processing, (2e), Pearson Education, 2004 CS4146: USER INTERFACE DESIGN [3 0 0 3] UI Design Process: Design Process Introduction, Designing to Address a Problem w/o Solution Ideas, Designing for a known solution direction, Designing to iterate on/improve an existing solution, Common Elements: Usability, Engineering and Task-Centered Approaches, Use Cases, Personas, Tasks, and Scenarios, Design-Centered Methods & When They Work Best, Pulling it all Together, Psychology and Human Factors for User Interface Design: Introduction, Fitts' Law, Short- and long-term memory, attention, Perception and visualization, hierarchy, Mistakes, Errors, and Slips, Conceptual models, The Gulf of Execution and the Gulf of Evaluation, Design Principles: Visibility, Feedback, Mappings, Constraints, Interacting beyond individuals (social psychology), High-Level Models: Distributed Cognition, Activity Theory, Situated Action, User Research Methods: User Research to Design, Introduction to User Research, Interview and Focus Groups, Observations, Contextual Inquiry, Ethics and Consent, Design a User Research Protocol, Log Analysis, Surveys and Questionnaires, Analyzing and Delivering User Research: Introduction: Translating User Research to Support Design, Qualitative Analysis, Quantitative Analysis, Personas I: What They Are; How They're Used, Personas II: Walking Through Examples, Use Cases, Tasks and Walkthrough Scenarios, Implications for Design, Ideation and Idea Selection: rom Research to Ideas, Ideation, Idea Selection, Communicating Ideas to Stakeholders, Good User Interfaces principles: learnability, visibility, error prevention, efficiency, and graphic design) and the human capabilities that motivate them (including perception, motor skills, color vision, attention, and human error), Implementation of UI: building user interfaces, including low-fidelity prototypes, etc Empirical research involving novel user interfaces. References: 1. Norman, A. Donald, The Design of Everyday Things. MIT Press, 2014.
- 18. P a g e 18 | 22 2. Coursera platform and internet resources CS4147: DIGITAL IMAGE PROCESSING [3 0 0 3] Introduction to image processing: steps in image processing, Image file formats, Basic relationships between pixels, Colour Models. Image Enhancement and Restoration: Image histogram, Spatial domain enhancement, point operations, Log transformation, Power-law transformation. Frequency domain enhancement: introduction to image transforms, Fourier transform, 2D-DFT. Restoration: Noise models, Restoration using Inverse filtering and Wiener filtering. Image Coding and Compression: Lossless compression, Lossy compression, JPEG, MPEG. Image Segmentation and Representation: Grey level features, edges and lines, similarity, correlation, template matching, edge detection using templates, Representation scheme, boundary descriptors, regional descriptors, Image Morphology. Biometric Authentication, Object Detection. References: 1. K. R. Castleman, Digital Image Processing, (2e), Pearson Education, 2011. 2. R. C. Gonzalez, R. E. Woods, Digital Image Processing, (4e), Pearson Education, 2018. 3. A. K. Jain, Fundamentals of Digital Image Processing, Pearson Education, Reprint 2015. 4. S. Jayaraman, S. Esakkirajan, T Veerakumar, Digital Image Processing, Tata McGraw Hill Education, 2009. 5. R. C. Gonzalez, R. E. Woods, S. Eddins, Digital Image Processing using MATLAB, (2e), Pearson Education. 6. A. McAndrew, Introduction to Image processing using MATLAB, Cengage Learning Publisher, 2007. 7. Prateek Joshi, OpenCV with Python By Example, (1e) PACKT Publishing, 2018. CS4148: INTERNET OF THINGS [3 0 0 3] Introduction: Analog and digital signals, serial communication, RF and sensors; Introduction to JSON/XML. Programming on Development Boards: Understanding of the board, tool chain and development environment setup; Sensors and Actuators: Understanding and using analog, digital, SPI, UART, I2C. Nodes and communication protocols: Understanding usage of nodes and gateways for sensor communication and external communication, RF, Zigbee, BT, WI-FI, GSM. IoT Cloud Platform, Cloud using Web Services, Cloud Computing Services for Sensor Management, Python Script; Data Analytics: Mongo DB, Map Reduce, Using cloud APIs for analytics, Visualization, NVD3, Mobile interfacing. References: 1. V. Madisetti, A. Bahga, Internet of Things: A Hands-On- Approach, VPT, 2014. 2. R. Buyya, A. V. Dastjerdi, Internet of Things Principles and Paradigms, 2016. 3. H. Geng, Internet of Things Principles and Data Analytics Handbook, Wiley, 2017. 4. P. Raj, A. C. Raman, The Internet of Things Enabling Technologies, Platforms, and Use Cases, CRC Press, 2017. CS4149: BIG DATA ANALYTICS [2 0 1 3] INTRODUCTION: Introduction to big data, definition, need and evolution of BDA, Applications of Big Data. Analysing big data: Sources of big data, Characteristics of Big Data(4 V’s), Drivers of BDA, Structured vs. Unstructured data, Data Marts, Differences between traditional DWDM and BDA. Data Processing: Data Wrangling, Data Munging, Data Jujitsu. Data Visualisation: Why to visualize data. Data Analytics Life Cycle. Advanced Analytics Algorithms: Introduction using R – Theory and Methods Overview: K-means clustering, Association Rules, Linear Regression, Logistic Regression, Naïve Bayesian Classifiers, Decision Trees, Time Series Analysis, Text Analytics; Statistics for Model Building and Evaluation: Statistics in the Analytic Lifecycle, Hypothesis Testing, Difference of means. Hadoop Framework: Introduction to Hadoop, HDFS - Hadoop Distributed File system, Map Reduce Programming, Pig. ETL & Batch Processing with Hadoop: ETL & Data Warehousing, Ingesting data into Big Data Platforms using Apache Sqoop & Flume, Big Data Analytics using Apache Hive, NoSQL databases for Big Data Storage Applications (HBase), Workflow management for Hadoop using Oozie Spark: Introduction to Spark, SparkSQL, MLLib: Regression, Clustering & Classification using Spark MLLib. References: 1. B. Schmarzo, Big Data: Understanding How Data Powers Big Business, Wiley.2013
- 19. P a g e 19 | 22 2. A. Jorgensen, J. Rowland-Jones, J. Welch, Microsoft Big Data Solutions, Wiley.,2014 3. J. Thompson, S. P. Rogers, Analytics: How to Win with Intelligence, Technics, LLC Publications , 2017 CS4150: SOFTWARE DEFINED NETWORKS [3 0 0 3] Software Defined Networking (SDN): Separation of control plane and data plane, IETF forces, Active networking. Control and Data Plane Separation: Concepts, Advantages and disadvantages, the Open flow protocol. Control Plane: Overview, Existing SDN controllers including floodlight and open daylight projects. Customization of Control Plane: Switching and firewall implementation using SDN concepts. Data Plane: Software-based and Hardware-based, Programmable network, Mininet based examples. Programming SDNs: Northbound application programming interface, current languages and tools, Composition of SDNs. Network Functions Virtualization (NFV): Concepts, Implementation, Applications. Use Cases of SDNs: Data Centers, Internet exchange points, Backbone networks, Home networks, Traffic engineering. Programming Assignments for implementing some of the theoretical concepts listed above. References: 1. T. D. Nadeau, K. Gray, SDN: Software Defined Networks, An Authoritative Review of Network Programmability Technologies, (1e) O'Reilly Media, 2013. 2. P. Goransson, C. Black, Software Defined Networks: A Comprehensive Approach, (2e) Morgan Kaufmann, 2016. 3. F. Hu, Network Innovation through Open Flow and SDN: Principles and Design, CRC Press, 2014. 4. V. Tiwari, SDN and Open Flow for Beginners, Amazon Digital Services, Inc., ASIN, 2013. 5. S Subramanian, Software Defined Networking with OpenStack, Packt Publishing, 2016. CS4151: DEEP NEURAL NETWORK [3 0 0 0] Introduction of Deep Learning, Basics of Machine Learning, Neural Network, Activation function, Gradient Descent, Stochastic Gradient Descent, backpropagation, Deep Convolution Neural network: convolution operation, ReLU Layer, Pooling Layer, Flattening, fully connected layer, softmax and cross entropy, Recurrent Neural network: Vanishing Gradient Problem, LSTMs, LSTM variations, Self- organizing Map (SOM), K-means clustering, Boltzmann Machine, Energy-based Models, Contrastive Divergence, Deep Belief Networks, autoencoders, training of auto encode, over complete hidden layers, sparse autoencoders, denoising autoencoders, contractive autoencoders, stacked autoencoders, deep autoencoders. References: 1. I. Goodfellow, Y. Bengio , A. Courville, Deep Learning, MIT Press 2016. 2. S. Haykin, Neural Networks and Learning Machines, (3e), PHI, 2008. CS4152: SOCIAL NETWORK ANALYSIS [2 0 1 3] Introduction to Social Web: Nodes, Edges and Network measures, Describing Nodes and Edges, Describing Networks, Layouts. Visualizing Network features: The role of Tie Strength, Measuring Tie Strength, Tie Strength and Network Structure, Tie Strength and Network Propagation, Link Prediction, Entity Resolution. Link Prediction: Case Study Friend recommendation. Communities: Introduction, Communities in Context, Quality Functions. Algorithms: Clustering-based, Newman and Girvan- Divisive clustering, Newman-Modularity maximization, Clauset-Greedy optimization of modularity, Louvain Method-Hierarchical clustering, Agglomerative clustering, Falkowski(DENGRAPH)-Density-based clustering, Nikolaev-Entropy centrality-based clustering, Clique-based Methods for Overlapping Community Detection, Palla- Clique percolation method, Lancichinetti-Fitness function, Du-Kernels- based clustering, Shen-Agglomerative hierarchical clustering, Evans-Line graph, clique graph, Label Propagation-based Community Detection. Introduction to Social Influence: Influence Related Statistics, Social Similarity and Influence, Homophile, Existential Test for Social Influence, Influence and Actions, Influence and Interaction, Influence Maximization in Viral Marketing. References: 1. J. Goldbeck, Analyzing the Social Web, Morgan Kaufmann Publications, 2013. 2. C. C. Aggarwal, Social Network Data Analytics, Springer Publications, 2011. 3. J. Scott, Social Network Analysis, (3e), SAGE Publications Limited, 2013. 4. J. Goldman, Facebook Cookbook, O'Reilly, 2009. 5. S. Kumar, F. Morstatter, H. Liu, Twitter Data Analytics, Springer Publications, 2013.
- 20. P a g e 20 | 22 CS4153: SOFTWARE TESTING [3 0 0 3] Introduction and concept learning: Basic definitions, Testing axioms, Purpose of Software Testing, Software Testing Principles, The Tester’s Role in a Software Development Organization, Origins of Defects, Cost of defects, Defect Classes, Defect Prevention strategies, Defect Repository, Strategies for Software Testing, Testing Activities, Mistakes, Faults & Failures, Verification and Formal Methods, Planning for Verification and Validation. White-Box Testing: Test Adequacy Criteria, Static Testing, Structural Testing, Code Complexity Testing, Mutation Testing, Data Flow Testing. Black-Box Testing: Test Case Design Criteria, Requirement Based Testing, Positive and Negative Testing, Boundary Value Analysis, Equivalence Partitioning State Based Testing, Domain Testing. Functional Testing: Test Plan, Test Management, Test Execution and Reporting, Test Specialist Skills, Tester’s Workbench and Tool Categories, Debugging, Test Bed, Traceability and Testability, Attributes of Testable Requirements, Test Matrix, Types of Testing Documentation, Verification Testing, Validation Testing, Integration Testing, System and Acceptance Testing, GUI Testing, Regression Testing, Selection, Minimization and Prioritization of Test Cases for Regression Testing, Creating Test Cases from Requirements and Use cases, Test Design. Test Automation: Software test automation – skill needed for automation – scope of automation – design and architecture for automation – requirements for a test tool – challenges in automation – Test metrics and measurements – project, progress and productivity metrics. References: 1. W. E. Perry, Effective Methods for Software Testing, John Wiley and Sons, 2000. 2. R. Patton, Software Testing, Sams Publishing, 2005. 3. A. P. Mathur, Foundations of Software Testing, Pearson Education, 2013. 4. J. L. Mitchell, R. Black, Advanced Software Testing—Vol. 3, Rocky Nook, 2015. CS4154: LINUX SYSTEM AND SHELL PROGRAMMING [3 0 0 3] Fundamentals: Processes in Linux, I/O system calls, select and poll functions, Filters and redirection, Linux file system navigation, Directory access, File system implementation, Hard links and symbolic links. Asynchronous Events: Manipulating signal masks and signal sets, Catching and ignoring signals, Waiting for signals. Inter-Process Communication: Sockets, Remote procedure calls, Network file system. Concurrency: POSIX thread attributes, Synchronization functions, Mutex locks, Condition variables, Signal handling and threads. Character Device Driver Development: Driver concepts, Writing character drivers, Interrupt handling, Interfacing with hardware. Shell Scripting: Loops, Conditional statements, Command line arguments, test command, expr command. Advanced Scripting Techniques: Providing command line options to scripts, Exporting variables, Arrays, Remote shell execution, Connecting to MySQL using shell, Essential system administration. References: 1. W. R. Stevens, S. A. Rago, Advanced Programming in the UNIX Environment, (3e), Addison- Wesley, 2013. 2. R. Love, Linux System Programming: Talking Directly to the Kernel and C Library, O'Reilly, 2007. 3. S. Das, Unix Concepts and Applications, (4e), McGraw Hill, 2006. 5. W. R. Stevens, B. Fenner, UNIX Network Programming, Volume 1: The Sockets Networking API, (3e), Pearson, 2003. 6. K. A. Robbins, S. Robbins, Unix Systems Programming: Communication, Concurrency, and Threads, (2e), Prentice Hall, 2004. CS4155: WIRELESS SENSOR & ADHOC NETWORK [3 0 0 3] Introduction to ad-hoc networks: Definition, characteristics features, applications. Characteristics of Wireless channel, Ad-hoc Mobility Models: Indoor and outdoor models. MAC Protocols: design issues, goals and classification. Contention based protocols with reservation, scheduling algorithms, protocols using directional antennas. IEEE standards: 802.11a, 802.11b, 802.11g, 802.15. HIPERLAN. Routing Protocols: Design issues, goals and classification. Proactive Vs reactive routing, Unicast routing algorithms, Multicast routing algorithms, hybrid routing algorithm, Energy aware routing algorithm, Hierarchical Routing, QoS aware routing. Transport layer: Issues in designing- Transport layer classification, ad-hoc transport protocols. Security issues in ad-hoc networks: issues and challenges, network security attacks, secure routing protocols. Cross layer Design: Need for cross layer design, cross layer optimization, parameter optimization techniques, Cross layer cautionary perspective. Integration of ad-hoc with Mobile IP networks. Mesh Networks, Vehicular Area Networks, and Mobile Ad
- 21. P a g e 21 | 22 Hoc Networks (MANETs). Introduction to sensor networks and its applications: Architecture and factors influencing the sensor network design. Routing protocols- data centric routing protocols, hierarchical routing protocols, location based routing, energy efficient routing etc. Node Scheduling and coverage issues, topology control. Querying, data collection and processing. References: 1. S. K. Sarkar, T G Basavaraju, C Puttamadappa, Ad Hoc Mobile Wireless Networks: Principles, Protocols, and Applications, (2e), CRC Press, 2016. 2. C. D. Morais Cordeiro, D. P. Agrawal, Ad Hoc and Sensor Networks: Theory and Applications, (2e), World Scientific Publishing, 2011. 3. H. Karl, A. Willing, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons, 2007. 4. R. Jurdak, Wireless Ad Hoc and Sensor Networks: A Cross-Layer Design Perspective, Springer Publications, 2007. 5. S R Murthy, B. S. Manoj, Ad Hoc Wireless Networks Architectures and Protocols, Pearson Education, 2008. CS4156: MOBILE COMPUTING [3 0 0 3] Wireless Communication Fundamentals: Introduction wireless transmission, Frequencies for radio transmission, Signals, Antennas, Signal propagation, Multiplexing, Modulations spread spectrum, MAC, SDMA, FDMA, TDMA, CDMA, Cellular wireless networks. Telecommunications Systems: GSM, System architecture protocols, Connection establishment, Frequency alocation, Routing, handover, Security, GPRS. Wireless Networks: Wireless LAN-IEEE 802.11 Standards, Architecture, Services HIPERLAN, AdHoc Network, Bluetooth mobile network layer: Mobile IP, Dynamic host configuration protocol. Routing: DSDV, DSR, Alternative metrics, Wireless application protocol. Mobile Ad hoc Networks: Overview, Properties of a MANET, Spectrum of MANET applications, Routing and various routing algorithms, Security in MANET. References: 1. W. Stallings, Wireless Communications and Networks, (2e) Pearson Education, 2018. 2. J. Schiller, Mobile Communications, (2e), Pearson Education, 2009. 3. K. Garg, Mobile Computing: Theory and Practice, (1e) Pearson Education India, 2010. CS4157: NATURAL LANGUAGE PROCESSING [3 0 0 3] Introduction: Ambiguity and uncertainty in language, processing paradigms, phases in natural language processing. Text representation in computers: encoding schemes. Linguistics resources: Introduction to corpus, elements in balanced corpus, WordNet, VerbNet. Part of Speech tagging: Stochastic POS tagging, HMM, Transformation based tagging (TBL), handling of unknown words, named entities, multi word expressions. Natural language grammars: lexeme, phonemes, phrases and idioms, word order, agreement, tense, aspect and mood and agreement, context free grammar, spoken language syntax. Parsing- unification, probabilistic parsing, tree-bank. Semantics: meaning representation, semantic analysis, lexical semantics. Word Sense Disambiguation: selection restriction, machine learning approaches, dictionary based approaches. Discourse: Reference resolution, constraints on co- reference, algorithm for pronoun resolution, text coherence, discourse structure. Real time Applications of NLP: text to speech, text summarization, information retrieval, sentiment analysis, machine translation. References: 1. D. Jurafsky, J. H. Martin, Speech and Language Processing, (2e), Pearson Education, 2009. 2. T. Siddiqui, U. S. Tiwary, Natural language processing and Information retrieval, Oxford University Press, 2008. CS4158: COMPUTER VISION [3 0 0 3] Introduction to computer vision and its applications, Geometric Image Features: Differential Geometry, Contour Geometry, analytical image features: Euclidean geometry, Geometric Camera Parameters, Calibration methods, Image formation, Liner Filtering: Linear filters and convolution, shift invariant linear systems, spatial frequency and Fourier transforms, Image transformations and Colour models, Edge Detection methods (Laplacian detectors and Canny edge detector), Points and patches, Harris corner detector, Histogram of Gradients, Difference of Gaussian detector, SIFT, Colour and Texture, Feature based alignment, least squares and RANSAC, Camera models, Camera calibration, Stereo vision,
- 22. P a g e 22 | 22 Stereo correspondence, Epipolar geometry Optical flow, Lucas Kanade method, KLT tracking method, Mean shift method, Dense motion estimation, Support Vector Machines, Face detection and recognition, Bag of words, Deep convolution neural network. References: 1. R. Szeliski, Computer Vision: Algorithms and Applications, Springer 2011. 2. D. A. Forsyth, J. Ponce, Computer Vision: A Modern Approach, (2e), PHI learning, 2012 3. J. E. Solem, Programming Computer Vision with Python, O'Reilly, 2012. CS4170: INDUSTRIAL TRAINING [0 0 2 1] In this course the student, undergo in reputed Private / Public Sector / Government organization / companies as industrial training for minimum 45 days to be undergone by the student in the summer vacation of the VI semester. Outcome of this course: To expose students to the 'real' working environment and be acquainted with the organization structure, business operations and administrative functions. To have hands-on experience in the students’ related field so that they can relate and reinforce what has been taught at the university. To promote cooperation and to develop synergetic collaboration between industry and the university in promoting a knowledgeable society & to set the stage for future recruitment by potential employers. CS4270: MAJOR PROJECT [0 0 24 12] In this course student has to select a project work based on a topic of interest. Periodically the supervisor will evaluate the implementation. This work, started in eighth semester of which, the student will be evaluated internally and externally. Outcome of the course: Investigating professional topics, including ethical, legal and security issues, related to computing projects. Design and Develop the software with Software Engineering practices and standards Apply prior knowledge to design and implement solutions for computational problems while considering numerous realistic restraints.