bharathiar university, coimbatore 641 046 m....
TRANSCRIPT
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 1 of 53 SCAA Dt: 10.06.2016
BHARATHIAR UNIVERSITY, COIMBATORE – 641 046
M. Sc COMPUTER SCIENCE (CBCS)
UNIVERSITY DEPARTMENT
(Effective from the academic Year 2016 - 2017)
1. Eligibility for Admission to the Programme
Candidates for admission to the first year programme leading to the Degree of Master of
Science in Computer Science (M.Sc-CS) will be required to possess:
A Pass with 50% of marks in B.Sc. Computer Science / BCA /B.Sc. Computer
Technology / B.Sc. Information Technology / B.Sc. Information Science / B.Sc. Information
Systems / B.Sc. Software Science / B.Sc. Software Engineering / B.Sc. Software Systems. In
case of SC/ST candidates, a mere pass in any of the above Bachelor‟s degree will be sufficient.
2. Duration of the Programme
The programme shall be offered on a full-time basis. The programme will consist of
three semesters of course work and laboratory work and the fourth semester consists of project
work.
3. Regulations
The general Regulations of the Bharathiar University Choice Based Credit System
Programme are applicable to this programme.
4. The Medium of Instruction and Examinations
The medium of instruction and Examinations shall be in English.
5. Submission of Record Notebooks for Practical Examinations & Project Viva-Voce.
Candidates taking the Practical Examinations should submit bonafide Record Note
Books prescribed for the Examinations. Otherwise the candidates will not be permitted to take
the Practical Examinations.
Candidates taking the Project Viva Examination should submit Project Report prescribed
for the Examinations. Otherwise the candidates will not be permitted to take the Project Viva-
voce Examination.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 2 of 53 SCAA Dt: 10.06.2016
SCHEME OF EXAMINATION
Core/
Elective/
General/
General
Supportive
Suggested
Code
Sem. Title of the Paper No. of Credits
Hrs.
L P
Marks
Theor
y
Practi
cal
Core 1 16CS1C1 I Compiler Design 4 0 4 0 100
Core 2 16CS1C2 I Advanced Operating System 4 0 4 0 100
Core 3 16CS1C3 I Data Structures and Algorithms 2 2 2 4 100
Core 4 16CS1C4 I Advanced Java Programming 2 2 2 4 100
Core 5 16CS1C5 I Python Programming 2 2 2 4 100
Elective -I 16CS1EXX I Elective – I 4 0 4 0 100
General 16CS1G1 I Industry Literacy 0 1 - - 25
General
Supportive
16CSGSXX I General Supportive - I 2 0 2 0 50
Core 6 16CS2C1 II Linux Programming 2 2 2 4 100
Core 7 16CS2C2 II Data Base Administration and
Management
2 2 2 4 100
Core 8 16CS2C3 II Information Security 4 0 4 0 100
Core 9 16CS2C4 II Internet of Things 4 0 4 0 100
Core 10 16CS2C5 II Data Mining Techniques and
Tools
2 2 2 4 100
Elective -II 16CS2EXX II Elective - II 4 0 4 0 100
General 16CS2G1 II Literature Survey 0 1 - - 25
General
Supportive
16CSGSXX II General Supportive - II 2 0 2 0 50
Core 11 16CS3C1 III Wireless Networks 2 2 2 4 100
Core 12 16CS3C2 III Visual Programming 2 2 2 4 100
Core 13 16CS3C3 III Software Project Management 4 0 4 0 100
Core 14 16CS3C4 III Cloud Computing 4 0 4
0 100
Core 15 16CS3C5 III Big Data Analytics 2 2 2 4 100
Elective-III 16CS3EXX III Elective – III 4 0 4 0 100
General 16CS3G1 III Gap Analysis 0 1 - - 25
General
Supportive
16CSGSXX III General Supportive - III 2 0 2 0 50
Project 16CSPRO IV Project 9 225
Total 90 2250
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 3 of 53 SCAA Dt: 10.06.2016
Electives for M.Sc. Computer Science (CBCS)
Sem. Suggested
Code
Title of the Paper No. of
Credits
I
16CS1E01 Business Intelligence 4
16CS1E02 Mathematical Foundations of Computer Science 4
16CS1E03 System Programming 4
16CS1E04 Software Metrics 4
16CS1E05 Bio-Informatics 4
16CS1E06 Microprocessor Principles and Design 4
16CS1E07 Principles of Programming Languages 4
16CS1E08 Mainframe Computing 4
16CS1E09 Software Reliability 4
16CS1E10 Parallel Processing 4
II 16CS2E01 Operation Research 4
16CS2E02 Green Computing 4
16CS2E03 Mobile Computing 4
16CS2E04 Image Processing 4
16CS2E05 Web Services 4
16CS2E06 Artificial Intelligence and Expert Systems 4
16CS2E07 TCP/ IP 4
16CS2E08 Embedded Systems 4
16CS2E09 Software Quality Assurance 4
16CS2E10 Natural Language Processing 4
III 16CS3E01 Virtual Reality 4
16CS3E02 Machine Learning Techniques 4
16CS3E03 Human Computer Interaction 4
16CS3E04 Data Compression 4
16CS3E05 Genetic Algorithms 4
16CS3E06 Neural Networks and Fuzzy Systems 4
16CS3E07 Speech Processing 4
16CS3E08 E-Commerce 4
16CS3E09 Distributed Systems 4
16CS3E10 Open Source Technologies 4
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 4 of 53 SCAA Dt: 10.06.2016
Supportive Subjects
Suggested
Code
Sem. Title of the Paper
Hrs
Cre
dit
s
Mar
ks
14CSS01
I/II/III
Windows and MS Word 2 2 50
14CSS02 Internet and HTML Programming 2 2 50
14CSS03 Relational Database Management System 2 2 50
14CSS04 Object Oriented Programming 2 2 50
14CSS05 Software Engineering 2 2 50
14CSS06 Multimedia Systems 2 2 50
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 5 of 53 SCAA Dt: 10.06.2016
COMPILER DESIGN
Subject Code: 16CS1C1 Number of Credits: 4
Subject Description: This course presents an Introduction to compilers, different phases of
compilers and compiler construction tools.
Goals: To enable the student to learn the types of translator, phases of compiler, optimization
techniques and code generator.
Objectives: At the end of the course, the student should be able to do:
Parsing techniques and different levels of translation.
Apply the various optimization techniques.
Use the different compiler construction tools.
Contents
Unit – I Introduction to Compilers: Translators-Compilation and Interpretation-Language processors –
The Phases of Compiler-Errors Encountered in Different Phases-The Grouping of Phases-
Compiler Construction Tools – Programming Language basics.
Unit – II
Lexical Analysis: Need and Role of Lexical Analyzer-Lexical Errors-Expressing Tokens by
Regular Expressions Converting Regular Expression to DFA- Minimization of DFA-Language
for Specifying Lexical Analyzers-LEX-Design of Lexical Analyzer for a sample Language.
Unit – III
Syntax Analysis: Need and Role of the Parser-Context Free Grammars –Top Down Parsing –
General Strategies Recursive Descent Parser Predictive Parser-LL(1) Parser-Shift Reduce
Parser-LR Parser-LR (0)Item Construction of SLR Parsing Table –Introduction to LALR Parser
– Error Handling and Recovery in Syntax Analyzer-YACC-Design of a syntax Analyzer for a
Sample Language .
Unit – IV
Syntax Directed Translation & Run Time Environment: Syntax directed Definitions-
Construction of Syntax Tree-Bottom-up Evaluation of S-Attribute Definitions- Design of
predictive translator – Type Systems-Specification of a simple type checker Equivalence of Type
Expressions-Type Conversions – Run-Time Environment: Source Language Issues-Storage
Organization-Storage Allocation Parameter Passing-Symbol Tables-Dynamic Storage
Allocation.
Unit – V
Code Optimization and Code Generation: Principal Sources of Optimization-DAG-
Optimization of Basic Blocks-Global Data Flow Analysis Efficient Data Flow Algorithms-Issues
in Design of a Code Generator – A Simple Code Generator Algorithm.
REFERENCES:
1. Alfred V Aho, Monica S. Lam, Ravi Sethi and Jeffrey D Ullman, “Compilers –
Principles, Techniques and Tools”, 2nd
Edition, Pearson Education, 2007.
2. Steven S. Muchnick, “Advanced Compiler Design and Implementation”, Morgan
Kaufmann Publishers – Elsevier Science, India, Indian Reprint 2003.
3. Keith D Cooper and Linda Torczon, “Engineering a Compiler”, Morgan Kaufmann
Publishers Elsevier Science, 2004. 4. Charles N. Fischer, Richard. J. LeBlanc, “Crafting
a Compiler with C”, Pearson Education, 2008.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 6 of 53 SCAA Dt: 10.06.2016
ADVANCED OPERATING SYSTEMS
Subject Code: 16CS1C2 Number of credits: 4
Subject Description: This course presents the principles and functions of various types of
operating system and management of computer parts.
Goals: To enable the student to learn the operating system and the functioning.
Objectives: To gain knowledge on Distributed Operating Systems, to gain insight into the
components and management aspects of real time and mobile operating systems.
Contents
Unit – I
Basics of Operating Systems: What is an Operating System? – Main frame Systems – Desktop
Systems – Multiprocessor Systems – Distributed Systems – Clustered Systems – Real-Time
Systems – Handheld Systems – Feature Migration – Computing Environments - Process
Scheduling – Cooperating Processes – Inter Process Communication- Deadlocks – Prevention –
Avoidance – Detection – Recovery.
Unit – II
Distributed Operating Systems: Issues – Communication Primitives – Lamport’s Logical Clocks
– Deadlock handling strategies – Issues in deadlock detection and resolution- distributed file
systems –design issues – Case studies – The Sun Network File System-Coda.
Unit – III
Realtime Operating Systems : Introduction – Applications of Real Time Systems – Basic Model
of Real Time System – Characteristics – Safety and Reliability - Real Time Task Scheduling
Unit – IV
Operating Systems for Handheld Systems: Requirements – Technology Overview – Handheld
Operating Systems – PalmOS-Symbian Operating System- Android –Architecture of android –
Securing handheld systems
Unit – V
Case Studies :Linux System: Introduction – Memory Management – Process Scheduling –
Scheduling Policy - Managing I/O devices – Accessing Files- iOS : Architecture and SDK
Framework - Media Layer - Services Layer - Core OS Layer - File System.
REFERENCES:
1. Abraham Silberschatz; Peter Baer Galvin; Greg Gagne, “Operating System Concepts”,
Seventh Edition, John Wiley & Sons, 2004.
2. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems
– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,
2001
3. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.
4. Pramod Chandra P.Bhatt, An introduction to operating systems, concept and practice,
PHI, Third edition, 2010
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 7 of 53 SCAA Dt: 10.06.2016
5. Daniel.P.Bovet & Marco Cesati,“Understanding the Linux kernel”,3rd edition,O’Reilly,
2005
6. Neil Smyth, “iPhone iOS 4 Development Essentials – Xcode”, Fourth Edition, Payload
media, 2011
DATA STRUCTURES AND ALGORITHMS
Subject Code: 16CS1C3 Number of credits: 4
Subject Description: This course presents the principles and functions of various types of
algorithms and management of data structures.
Goals: To enable the student to learn the data structures, algorithms and it’s functioning.
Objectives: To gain knowledge in different data structures, analysis of algorithms, divide and
conquer methods and dynamic programming.
Unit-I
Introduction: Definition, Structure and Properties of algorithms – Development of an algorithm
– Data Structures and algorithms – Data Structure definition and classification. Analysis of
algorithms: Efficiency of algorithms – Apriori analysis – Asymptotic notations – Time
complexity of an algorithm using O notation – Polynomial Vs Exponential algorithms –
Average, Best and Worst case complexities – Analyzing recursive programs. X2
Unit - II
Stacks: Introduction - Stack Operations – Applications – Recursion - Evaluation of Expressions.
Queues: Introduction - Operations on Queues – Circular queues – Application of a linear queue.
Linked Lists: Introduction - Singly linked lists - Circularly linked lists - Doubly linked lists -
Applications – polynomial addition.
Unit – III
Binary Trees: Introduction – Representation of Trees – Binary Tree Traversals. Binary Search
Trees: Introduction – Operations. AVL Trees: Definition - Operations. B-Trees: Introduction –
m-way search trees - B trees definition and operations. Graphs: Introduction – Definitions –
Representation of Graphs – Graph Traversal - Depth-First and Breadth-First Algorithms -
Topological Sorting.
Unit – IV
Divide and Conquer: General Method – Binary Search – Merge Sort – Quick Sort. Greedy
Method: General Method – Knapsack Problem – Minimum Cost Spanning Tree – Single Source
Shortest Path.
Unit –V
Dynamic Programming: General Method – Multistage Graphs – All Pair Shortest Path –
Traveling Salesman Problem. Backtracking: General Method – 8- Queens Problem – Sum of
Subsets – Hamiltonian Cycles. Branch and Bound: The Method – 0/1 Knapsack Problem –
Traveling Salesperson.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 8 of 53 SCAA Dt: 10.06.2016
REFERENCES:
1. GAV Pai, Data Structures and Algorithms Concepts, Techniques and Applications, Tata
McGraw Hill.
2. Jean Paul Tremblay, Paul G. Sorenson, An Introduction to Data Structures with
Applications, Tata McGraw Hill, Second Edition.
3. Sahini, “Data Structures, Algorithms and Applications in C++”, Mc GrawHill, 1998.
4. Ellis Horowitz, Sartaj Sahni, Sanguthevar Rajasekaran, “Fundamentals of Computer
Algorithms”, Second Edition, Universities Press, 2008
5. Robert Sedgewick, Phillipe Flajolet, “An Introduction to the Analysis of Algorithms”,
Addison-Wesley Publishing Company, 1996.
6. Alfred V. Aho, John E. Hocroft, Jeffrey D. Ullman, “Data Structures and Algorithms”.
ADVANCED JAVA PROGRAMMING
Subject Code: 16CS1C4 Number of credits: 4
Subject Description: This course starts with the fundamentals of OOPs with Java and advanced
concepts such as Java swing, RMI, JSP, Hibernate and Struts architectures
Goals: To enable the student to learn the advanced programming concepts in Java.
Objectives: At the end of the course, the student should be able to do advanced programming
using Java swing, RMI, JSP, Hibernate and Struts.
Contents
Unit - I
Introduction to Java - Features – Java programming structure – Tokens - Literals - Data types-
Operators –- Control statements - Arrays. Object Oriented Concepts in JAVA: Classes – Objects
– Method Overloading – Inheritance – Method Overriding – Abstract classes – Interfaces –
Packages- Exception handling -Threads.
Unit –II
Java Swing – Features – Classes and Packages – MVC architecture – Swing basic compontents –
Buttons – Labels – List – Combobox – Menu Simple AWT application using Swing
Components.
Unit – III
RMI: RMI overview - RMI architecture - Example demonstrating RMI. Introduction to servlet -
Developing and Deploying Servlets - Handling Request and Response - Reading Servlet
Parameters - Cookies - Session Tracking- Accessing Database using JDBC.
Unit –IV
Java Server Pages: Basic JSP Architecture - Life Cycle of JSP - JSP Tags and Expressions –
Directives- JSP applications. Java Creating and using JavaBean components –Setting and
retrieving JavaBean components – Java Server Faces Application.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 9 of 53 SCAA Dt: 10.06.2016
Unit –V
Introduction to Hibernate – Advantages – Architecture –Spring Framework -Struts Framework:
Introduction to Struts- Struts Architecture.
REFERENCES:
1. Herbert Schildt - JAVA 2 ( The Complete Reference)- Ninth Edition, TMH, 2014
2. Patrick Naughton, “The Java Hand Book, Tata McGraw Hill, 1996.
3. Brian Cole, Robert Eckstein, James Elliott, Marc Loy, David Wood, Java Swing,
O’Reilly Publishers, second edition, 2002
4. Jim Keogh, “The Complete Reference J2EE, Tata McGraw-Hill, 2002.
5. Kogent Solutionss, Java Server Programming Java Ee5 Black Book,Dreamtech Press,
2008
PYTHON PROGRAMMING
Subject Code: 16CS1C5 Number of Credits: 4
Subject Description: This course presents an Introduction to Python Programming concepts and
its various features.
Goals: To enable the student to be familiar with Python Programming.
Objectives: On successful completion of the course the student should have understood the
concepts in Python and its application.
Contents:
Unit - I
Core Python: Introduction-features-Comparative study-Comments-Operators-Variables and
Assignments-Numbers-String-List and Tuple-Dictionary-Statements and Iterative statements-list
comprehensive-Errors and Exception-functions-Classes-Modules-Useful function. Basics:
Syntax and Statements-Variable Assignments-Identifier-Style-Memory Management-
Application Example. Objects: Introduction-Standard Type- Built-in-type-Internal type-Standard
type operator and Built-in functions-Categorizing standard type-Unsupported type.
Unit - II
Numbers: Introduction- Integer-Floating Point-Complex numbers-Operators-Built-in-
functions-Other numeric type-Sequence-Strings-Strings and Operator-String only operator-Built-
in-Functions-Built-in-Methods-String Features-Unicode-Related Modules.
Unit – III
List-Operators-Built-in-Functions-Built-in-Methods-Features of List-Tuple: Introduction-
Operators and Built-in-Functions-Features-Related Modules-Mapping type: Dictionaries-
Operators-Built-in and Factory Functions-Built-in- Methods. Set type: Introduction-Operators-
Built-in Function-Built-in Methods-Related Modules-Conditional and looping statement.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 10 of 53 SCAA Dt: 10.06.2016
Unit – IV
File: Objects- Built in Functions-Methods-Attributes-Standard files-Command line Argument-
File System-File Execution-Persistent Storage Modules-Related Module. Class: Introduction-
Class and Instance- Method calls. Exception and Tools: Why use it?-Exception roles-Short
story-Try/finally statement.
Unit – V
Regular Expression: Introduction-Special Symbols and characters-Regexes and Python-
Examples of Regexes. Network Programming: Architecture-Socket. Internet Client
Programming- Transferring files-Email.GUI Programming: Introduction-Tkinter and Python.
DB Programming: Introduction-Python DB-API-Non-Relational DB. Web Services:
Introduction-Microblogging with Twitter.
REFERENCES:
1. Chun, J Wesley, Core Python Programming, 2nd Edition, Pearson, 2007 Reprint 2010.
2. Wesley J Chun Core python Application Programming,3rd Edition,
3. Lutz, Mark, Learning Python, 5th Edition, O Rielly.
LINUX PROGRAMMING
Subject Code: 16CS2C1 Number of credits: 4
Subject Description: This course presents an introduction to Linux OS, shell programming
concepts and the various inter process communication mechanisms.
Goals: To enable the student to be familiar in advanced Linux OS concepts.
Objectives: On successful completion of the course the student should be able to do
Basics in Linux OS.
Process control in Linux
Inter-process communications in Linux
Socket programming
Contents
Unit - I
Introduction: History - Architecture of UNIX operating system – Features of UNIX - Basic
commands – Working with files and directories – Commands - File types - File access processes
permissions redirection - filters –What is Linux? – Distributions – The GNU Project and the
Free software Foundation.
Unit - II
Shell programming in Linux: – vi editor – Shell syntax - variables – conditions and control
structures- command execution – simple programs – System calls and library: Read – Write –
File and record locking – Adjusting the position of file I/O – Lseek - Close – File creation –
Creation of special files – Changing directory, root, owner, mode – stat and fstat.
Unit- III
Processes and Signals: Processes: Introduction of process – Process structure - Process states -
Process termination – command line arguments - Process control – Process identifiers - Process
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 11 of 53 SCAA Dt: 10.06.2016
relationships – Zombie process - Signals: Sending signals – Signal sets–Threads:
Synchronization – Thread attributes – Canceling a Threads.
Unit - IV
Inter process Communication: Process – popen() and pclose() – Pipes –Named pipes (FIFO) –
Message queues – Semaphores - Shared Memory – Client-Server application using IPC.
Unit - V
Sockets: Introduction – Socket Connections - TCP sockets -TCP echo client server – UDP
sockets - UDP echo client server - Socket options.
REFERENCES:
1. Neil Matthew, Richard Stones, Beginning Linux Programming, Third Edition, Wrox,
Wiley Publishing Inc., 2004.
2. W. Richard Stevens, Bill Fenner, Andrew Rudoff, "UNIX Network Programming", Vol.
1 , The Sockets Networking API, Third Edition, Pearson education, Nov 2003.
3. W.Richard Stevens, Stephen A. Rago, Advanced programming in the UNIX
environment, second edition, Addison Wesley, 2005.
DATABASE ADMINISTRATION AND MANAGEMENT
Subject Code: 16CS2C2 Number of credits: 4
Subject Description: This course presents the concepts of designing and management of
relational database system.
Goals: To enable the student to learn the concepts of relational database management system.
Objectives:
To understand the fundamentals of data models and conceptualize and depict a database
system using ER diagram and data storage techniques a query processing.
To make a study of SQL and relational database design
To import knowledge in transaction processing, concurrency control techniques and
recovery procedures
Learnt distributed databases
Understood database administration and security
Contents
Unit - I Introduction: Purpose of Database Systems - View of Data - Database Languages - Data Storage
and Querying -Transaction Management – Storage Management – Data Mining and Information
Retrieval - Specialty Databases - Database Users and Administrators– Relational Databases:
Introduction to the Relational Model - Structure of Relational Databases-Database Schema -
Keys-Schema Diagrams -Relational Query Languages - Relational Operations.
Unit – II
Introduction to SQL: Overview of the SQL -Data Definition – Basic Structure of SQL Queries –
Set operations - Null values-Aggregate Functions - Modification of the Database - Integrity
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 12 of 53 SCAA Dt: 10.06.2016
Constraints – Views – SQL Data Types and Schemas. Advanced SQL - Accessing SQL From a
Programming Language – Triggers - Advanced Aggregation Features-OLAP.
Unit - III
Transaction Management : Overview of Transaction Management- The ACID Properties-
Transactions and Schedules- Concurrent Execution of Transactions - Lock-Based Concurrency
Control - Performance of Locking - Introduction to Crash Recovery.
Concurrency Control: 2PL, Serializability, and Recoverability - Introduction to Lock
Management - Lock Conversions - Dealing With Deadlocks - Specialized Locking Techniques -
Concurrency Control without Locking.
Unit - IV
Distributed Database Management Systems: The Evolution of Distributed Database
Management Systems - DDBMS Advantages and Disadvantages - Distributed Processing and
Databases - Characteristics of Distributed DBMS - DDBMS Components - Levels of Data and
Process Distribution - Distribution Transparency - Transaction Transparency - Distributed
Database Design - Client/Server vs. DDBMS.
Unit - V
Business Intelligence and Data Warehouses: The Need for Data Analysis - Business Intelligence
and Architecture - Data Warehouse- OLAP - Star Schemas - Implementing a Data Warehouse -
SQL Extensions for OLAP. Database Connectivity and Web Technologies: Database
Connectivity - Internet Databases - Extensible Markup Language (XML). Database
Administration and Security: Security - Database Administration Tools - The DBA at Work:
Using Oracle for Database Administration.
REFERENCES:
1. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database SystemConcepts”, Fifth
Edition, McGraw Hill, 2006.
2. Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”, McGraw
Hill, Third Edition 2004.
3. Peter Rob, Carlos Coronel, “Database System Concepts”, Cengage Learning, 2008
INFORMATION SECURITY
Subject Code: 16CS2C3 Number of credits: 4
Subject Description: This course presents the principles and need for ensuring information
security in organizations
Goals: To enable the student to learn the concepts of information security
Objectives: On successful completion of the course the student should have to:
Understand the basics of Information Security
Know the legal, ethical and professional issues in Information Security
Know the aspects of risk management - Become aware of various standards in this area
Know the technological aspects of Information Security
Contents
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 13 of 53 SCAA Dt: 10.06.2016
Unit - I INTRODUCTION: History, What is Security, CNSS Security Model, Components of an
Information System, Balancing Information Security and Access, The Systems Development
Life Cycle, The Security Systems Development Life Cycle. Communities of interest-Need for
security: Threats, Attacks
Unit - II
LEGAL, ETHICAL AND PROFESSIONAL ISSUES: Law and Ethics in Information Security,
International Laws and Legal Bodies, Ethics and Information Security, Codes of Ethics and
Professional Organizations Risk Management: An Overview of Risk Management, Risk
Identification, Risk Assessment, Risk Control Strategies, Selecting a Risk Control Strategy
Unit - III
PLANNING FOR SECURITY: Information Security Policy, Standards and Practices, The
Information Security Blueprint, Security Education, Training and Awareness Program,
Continuity Strategies
Unit - IV
SECURITY TECHNOLOGY: Firwalls and VPNs- Intrusion Detection and Prevention Systems,
Honeypots, Honeynets and padded cell systems -Scanning and Analysis Tools- bio metric
access control.
Unit - V
Cryptography: Cipher Methods, Cryptographic Algorithms, Cryptographic Tools, Protocols for
secured communication-Attacks on Cryptosystems.
REFERENCES:
1. Michael E Whitman and Herbert J Mattord, “Principles of Information Security”, 4th
Edition, Course Technology, Cengage Learning.
2. Micki Krause, Harold F. Tipton, “Handbook of Information Security Management”, Vol
1-3 CRC Press LLC, 2008.
3. Stuart Mc Clure, Joel Scrambray, George Kurtz, “Hacking Exposed”, Tata McGraw-
Hill, 2003
4. William Stallings, Cryptography and Network Security, Pearson Education, 2000.
5. Nina Godbole, Information Systems Security, Wiley-2009.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 14 of 53 SCAA Dt: 10.06.2016
INTERNET OF THINGS
Subject Code: 16CS2C4 Number of Credits: 4
Subject Description: This course presents the communication technologies used in IoT, Web of
Things, Structural models and applications of IoT
Goals: To enable the student to learn the concepts in IoT
Objectives: On successful completion of the course the student should have to:
Understand the communication technologies in IoT
Know the IoT protocols and web of things Know the various applications of IoT
Unit - I
Introduction : Internet Layers - Protocols - Packets - Services - Performance parameters - Peer-
to-peer networks - Sensor networks - Multimedia - IOT Definitions and Functional
Requirements –Motivation – Architecture - Web 3.0 View of IoT– Ubiquitous IoT Applications
– Four Pillars of IoT – DNA of IoT - The Toolkit Approach for End-user Participation in the
Internet of Things. Middleware for IoT: Overview – Communication middleware for IoT –IoT
Information Security.
Unit - II
IoT protocols : Protocol Standardization for IoT – Efforts – M2M and WSN Protocols –
SCADA and RFID Protocols – Issues with IoT Standardization – Unified Data Standards –
Protocols – IEEE 802.15.4 – BACNet Protocol – point-to-point protocols - Ethernet protocals -
cellular Internet access protocal - Machine-to-machine protocal - Modbus – KNX – Zigbee
Architecture – Network layer – APS layer – Security.
Unit - III
Web of Things: Web of Things versus Internet of Things – Two Pillars of the Web –
Architecture Standardization for WoT– Platform Middleware for WoT – Unified Multitier WoT
Architecture – WoT Portals and Business Intelligence. Cloud of Things: Grid/SOA and Cloud
Computing – Cloud Middleware – Cloud Standards – Cloud Providers and Systems – Mobile
Cloud Computing – The Cloud of Things Architecture.
Unit - IV
Integrating IOT: Integrated Billing Solutions in the Internet of Things Business Models for the
Internet of Things - Network Dynamics: Population Models – Information Cascades - Network
Effects - Network Dynamics: Structural Models - Cascading Behavior in Networks - The Small-
World Phenomenon.
Unit - V
Applications: The Role of the Internet of Things for Increased Autonomy and Agility in
Collaborative Production Environments - Resource Management in the Internet of Things:
Clustering, Synchronisation and Software Agents. Applications - Smart Grid – Electrical
Vehicle Charging - Case studies: Sensor body-area-network and Control of a smart home.
REFERENCES:
1. The Internet of Things in the Cloud:A Middleware Perspective-Honbo Zhou–CRC Press
2012.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 15 of 53 SCAA Dt: 10.06.2016
2. Architecting the Internet of Things - Dieter Uckelmann; Mark Harrison; Florian
Michahelles- (Eds.) – Springer – 2011
3. Networks, Crowds, and Markets: Reasoning About a Highly Connected World - David
Easley and Jon Kleinberg, Cambridge University Press - 2010.
4. The Internet of Things: Applications to the Smart Grid and Building Automation by -
Olivier Hersent, Omar Elloumi and David Boswarthick - Wiley -2012 5. Olivier Hersent,
David Boswarthick, Omar Elloumi , “The Internet of Things – Key applications and
Protocols”, Wiley, 2012.
DATA MINING TECHNIQUES AND TOOLS
Subject Code: 16CS2C5 Number of Credits: 4
Subject Description: This course presents the data mining concepts, techniques in data mining,
web mining and open source tools to manipulate data mining applications.
Goals: To enable the student to learn data mining techniques and tools.
Objectives: On successful completion of the course the student should have to:
Understand the data mining techniques Know the concept of web mining
Know the usage of WEKA and R Programming to mining knowledge from domain of
interest
Contents:
Unit I
Data mining: Introduction – Definitions - KDD vs. Data mining - DM techniques – Issues and
Challenges in Data Mining – Data mining application areas. Classification Technique:
Introduction – Decision Trees: Tree Construction Principle - Decision Tree construction
Algorithm –CART – ID3 – Rainforest –CLOUDS.
Unit II
Clustering techniques: Clustering paradigms – Partitioning algorithm - K-Means – K-Medeoid
algorithms – CLARA – Hierarchical Clustering - DBSCAN – BIRCH - Categorical clustering
algorithms – STIRR - Other techniques. Introduction to neural network - learning in NN –
Unsupervised Learning - Genetic algorithm.
Unit III
Association Rules: Concepts - Methods to discover association rules - A priori algorithm –
Partition algorithm - Dynamic Item set Counting algorithm - FP-tree growth algorithm -
Incremental algorithm - Generalized association rule.
Unit IV
Web mining: Basic concepts – Web content mining – Web structure mining – Web usage mining
– Text mining: Text clustering - Sequence mining: The GSP algorithm – SPADE.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 16 of 53 SCAA Dt: 10.06.2016
Unit V
Tools: Need for data mining tools - Introduction to WEKA – The Explorer – The Experimenter –
Classification-Regression-Clustering- Nearest neighbor - Introduction to R- Data types-
Variables Operators-Decision Making-Loop Control –Function-Strings-Vectors-Lists-Matrices-
Arrays-Factors-Data Frames-Packages- Charts and graphs- Statistics.
REFERENCES
1. Arun K. Pujari, Data Mining Techniques, Third Edition, Universities Press (India)
Limited,
2. Hyderabad, 2009.
3. Margaret H. Dunham, Data Mining Introductory and Advanced Topics, Pearson
Education,
4. 2004.
5. Jaiwei Han and MichelineKamber, Data Mining Concepts and Techniques,
MorganKaufmann Publishers, 2011, 3rd Edition.
6. Pieter Adriaans, DolfZantinge, Data Mining, Addison Wesley, 2008.
7. Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning
Tools and Techniques. Elsevier, 2011.
8. Mark Gardener, Beginning R: The Statistical Programming Language
9. Robert Kabacoff, R in Action: Data Analysis and Graphics R
WIRELESS NETWORKS
Subject Code: 16CS3C1 Number of Credits: 4
Subject Description: This course will cover the fundamental aspects of wireless networks with
emphasis on current and next generation wireless networks.
Goals: To introduce the students to state-of-the-art wireless network protocols and architectures.
Objective: On successful completion of the course the student will be able to:
Explain the fundamental of cellular communication and channel allocation.
Explain the constraints and performance of wireless personal area networks, sensor
and adhoc networks
Unit -I:
Wireless Networks: Evolution of wireless networks – Challenges - Transmission fundamentals:
Analog and digital data transmission - Transmission media - Modulation techniques for wireless
systems - Multiple access for wireless systems - Performance increasing techniques for wireless
networks.
Unit -II:
Wireless LAN: Introduction to Wireless LANs – WLAN Equipment, Topologies, Technologies,
IEEE 802.11 WLAN – Architecture and Services - Physical Layer - MAC Sub Layer –MAC
Management Sub Layer, Other IEEE 802.11 Standards.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 17 of 53 SCAA Dt: 10.06.2016
Unit -III:
Wireless Personal Area Networks: Introduction – Bluetooth : Architecture - Protocol Stack -
Physical Connection – Mac mechanism – Frame format – Connection management -Low Rate
and High Rate WPAN , ZigBee Technology IEEE 802.15.4 : Components – Network topologies
– PHY – MAC.
Unit -IV:
Ad-hoc Wireless Networks: Introduction - Characteristics of Adhoc Networks - Classifications
of MAC Protocols - Table driven and Source initiated On Demand routing protocols- OLSR -
Hierarchical routing protocols – CBRP, FSR, TCP over Ad Hoc Wireless Networks.
Unit -V:
Wireless Sensor Networks : Introduction - Challenges for wireless sensor networks -
Comparison of sensor network with ad-hoc network - Single node architecture : Hardware
components - Energy consumption of sensor nodes - Network architecture: Sensor network
scenarios - Design principles – Operating systems.
REFERENCES:
1. Nicopolitidis P, Obaidat M S, Papadimitriou G S and Pomportsis A S, “Wireless Networks”,
John Wiley and Sons, New York, 2009.
2. Vijay K Garg, Wireless Communication and Networking, Morgan Kaufmann Publishers
2010.
3. Siva Ram Murthy C,. Manoj B S, “Ad Hoc Wireless Networks: Architectures and
Protocols”, Prentice Hall, 2006.
4. Holger Karl and Andreas Willig, “Protocol and Architecture for Wireless Sensor
Networks”, John Willey Publication, 2011.
VISUAL PROGRAMMING
Subject Code: 16CS3C2 Number of credits: 4
Subject Description: This course presents the introduction to .NET framework, VB.NET,
ASP.NET and Web services.
Goals: To enable the student to be familiar with visual programming concepts.
Objectives: On successful completion of the course the student should have:
Understood the concepts in VB.NET & ASP.NET.
Knowledge in developing web services
Contents:
Unit –I
Introduction to .NET – The .NET Framework – Benefits of .NET - Common Language Runtime
– Features of CLR - Compilation and MSIL – The .NET Framework libraries – The Visual
Studio Integrated Development Environment.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 18 of 53 SCAA Dt: 10.06.2016
Unit –II
Introduction to VB.NET – VB.NET fundamentals – Branching and Looping Statements -
Classes and Objects – Constructors – Overloading- Inheritance and Polymorphism – Interfaces –
Arrays – Strings – Exceptions – Delegates and Events.
Unit –III
Building Windows Applications – Creating a Windows Applications using window controls -
Windows Forms, Text Boxes, Rich Text boxes, Labels, and link labels – Buttons, Check boxes,
Radio buttons, Panels and Group Boxes, List Boxes, Checked List boxes, Combo boxes and
Picture boxes, Scroll bars – Calendar control, Timer control – Handling Menus – Dialog boxes –
Deploying an Application – Graphics.
Unit- IV
ASP.NET Basics: Features of ASP.NET – ASP.NET page directives - Building Forms with Web
server Controls – Validation Server Controls - Rich Web Controls - Custom Controls –
Collections and Lists.
Unit –V
Data Management with ADO.NET - Introducing ADO.NET - ADO.NET features - Using SQL
Server with VB.NET – Using SQL Server with ASP.NET – LINQ queries – Building ASP.NET
3.5 Enterprise Applications: Developing ASP.NET Ajax applications – ASP.NET web services.
REFERENCES:
1. Jesse Liberty, Programming Visual Basic.NET 2003, Second Edition, O Reilly, Shroff
Publishers and Distributors Pvt. Ltd.
2. Steven Holzner, Visual Basic.NET Programming Black Book, 2005 Edition, Paraglyph
press USA&Dreamtech Press, India.
3. Bill Evjen, JasonBeres, et al. Visual Basic.NET Programming Bible, 2002 Edition, IDG
books India (p) Ltd.
4. MridulaParihar et al., ASP.NET Bible,2002 Edition,Hungry Minds Inc, New York, USA.
5. Bill Evjen, Hanselman, Muhammad, Sivakumar& Rader, Professional ASP.NET 2.0,
2006 Edition, Wiley India(p) Ltd.
6. KoGENT Solutions Inc., ASP.NET 3.5 (Covers C# and VB 2008 codes) Black Book,
Platinum Edition, Dreamtech press, 2010.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 19 of 53 SCAA Dt: 10.06.2016
SOFTWARE PROJECT MANAGEMENT
Subject Code: 16CS3C3 Number of credits: 4
Subject Description: This course presents a deep insight to software project management
concepts
Goals: Enable the student to be familiar with software project management
Objectives: On successful completion of the course the student should have:
Understood the system software project management, project evaluation effort
estimation and risk management.
Contents
Unit - I Introduction: Software Project Management - Software Project Versus Other Project –
Requirement Specification – Information and Control in Organization – Introduction to step
wise Project Planning – Select – Identify Scope and Objectives - Identify Project
Infrastructure – Analyze Project Characteristics – Products and Activities – Estimate Effort for
each Activity – Identify Activity Risks – Allocate Resources - Review / Publicize Plan –
Execute Plan and Lower Levels of Planning.
Unit - II
Project Evaluation : Introduction – Strategic Assessment – Technical Assessment – Cost Benefit
Analysis – Cash Flow Forecasting – Cost Benefit Evaluation Techniques – Risk Evaluation –
Selection of an Appropriate Project App roach – Choosing Technologies – Choice of Process
Models – Structured Methods – Rap id Application Development – Waterfall Model – V-
Process Model – Spiral Model – Software Prototyping – Ways of Categorizing Prototypes –
Tools – Incremental Delivery – Selection Process Model.
Unit - III
Software Effort Estimation : Introduction – Problem s with Over and Under Estimates – Basis
for Software Estimating – Software Effort Estimation Technique – Albrecht Function Point
Analysis – Function Points – Object Points – Procedural Code Oriented Approach – COCOMO
– Activity Planning – Project Schedules - Projects and activities – Sequencing and Scheduling
Activities – Network Planning Models – Formulating a Network Planning – Adding Time
Dimension – Forward Pass – Backward Pas s – Identifying the Critical Path – Activity Float -
Shortening Project Duration – Identifying Critical Activities – Precedence Networks.
Unit - IV
Risk Management : Introduction – Nature of Risk Man aging Identification – Analysis –
Reducing – Evaluating – Z values – Resource Allocation – Nature of Resources – Requirements
– Scheduling – Critical Paths – Counting the Cost – Resource Schedule – Cost Schedule –
Scheduling Sequence – Monitoring and Control – Creating the Frame Work - Collecting the
Data – Visualizing the Progress – Cost Monitoring – Prioritizing Monitoring – Change Control.
Unit - V
Managing Contracts : Introduction – Types of Contract – Stages in Contract Placement – Terms
of Contract – Contract Management – Acceptance – Managing People and Organizing Teams –
Organizational Behavior Background – Selecting the Right Person for the Job – Instruction in
the Best Methods – Motivation – Decision Making – Leadership – Organizational Structures –
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 20 of 53 SCAA Dt: 10.06.2016
Software Quality – Importance – Practical Measures – Product Versus Process Quality
Management – External Standards – Techniques to Help Enhance Software Quality- Case Study
on Project Management.
REFERENCES:
1. Bob Hughes and Mike Cotterell, “Software Project Management , Mc Graw Hill, Second
Edition.
2. Walker Royce, “Software Project Management , Addition Wesley.
3. DerrelInce, H. Sharp and M. Woodman, “Introduction to Software Project Management
and Quality Assurance , Tata Mc Graw Hill, 1995.
CLOUD COMPUTING
Subject Code: 16CS3C4 Number of Credits : 4
Subject Description: This course presents the Introduction to cloud computing, Cloud
Computing Technology, Virtualization, Migrating into cloud and data security in cloud.
Goals: To enable the student to be familiar with the usage of cloud services, implement the
virtualization, data storage in the cloud and its security techniques.
Objectives: On successful completion of the course the student should have understood the
different concepts of cloud computing and its services, to store and retrieve the data from cloud
and can provide the security to the data in cloud.
Unit - I
Introduction: Cloud Computing Basics: Cloud Computing Overview - Applications of cloud
computing - Intranets and the cloud – First movers in the cloud - Benefits - limitations of cloud
computing – Security Concerns – Cloud Computing Services – Salesforce.com.
Unit - II
Cloud Computing Technology: Hardware and Infrastructure – Clients – Security – Network –
Services - Cloud Storage – Standards – Cloud Computing at work: Software as a Service –
Software Plus Services – Developing Applications.
Unit - III
Virtual Machines and Virtualization: Introduction - Understanding Virtualization - History of
Virtualization – Leveraging Blade Servers – Server Virtualization – Desktop Virtualization –
Virtual Networks – Data Storage Virtualization. Data Storage in Cloud: Evolution of Network
Storage – Cloud based data Storage – Advantages and disadvantages of Cloud based data
storage- Cloud based Backup systems - File Systems – Cloud based Block Storage.
Unit - IV
Migrating into a Cloud: Introduction – Broad approaches of Migrating into cloud – The Seven
Step Models of Migrating into a Cloud. Mobile Cloud Computing: Evolution of Mobile
Computing – Mobile Cloud EcoSystem – Mobile Players.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 21 of 53 SCAA Dt: 10.06.2016
Unit - V
Data security in cloud: Introduction – Current state of data security – Homo sapiens and Digital
Information – Cloud Computing and Data security Risk – Cloud Computing and Identity – The
Cloud, Digital Identity and Data Security- Content Level Security- Pros and Cons.
REFERENCES:
1. Anthony T. Velte, Toby J. Velte, Robert Elsenpeter, “ Cloud Computing: A Practical
Approach”, McGraw Hill.
2. Kris Jamsa, “ Cloud Computing” Jones and Barlett Student Edition 2014.
3. Rajkumar Byya, James Broberg, Andrzej Goscinski, “ Cloud Computing Prnciples and
Paradigms”, Wiley & sons
BIG DATA ANALYTICS
Subject Code: 16CS3C5 Number of credits: 4
Subject Description: The course provides grounding in basic and advanced methods to big data
technology and tools, including MapReduce and Hadoop and its ecosystem.
Goals: To enable the student to learn big data technologies such as Hadoop, Hbase. NoSQL and
visual analytics.
Objectives: On successful completion of the course the student should
able to apply Hadoop ecosystem components.
able to participate data science and big data analytics projects.
Contents
Unit - I What is big data – why big data – convergence of key trends – unstructured data – industry
examples of big data – web analytics – big data and marketing – fraud and big data – risk and
big data – credit risk management – big data and algorithmic trading – big data and healthcare –
big data in medicine – advertising and big data – big data technologies - open source
technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics –
inter and trans firewall analytics
Unit – II
History of Hadoop- The Hadoop Distributed File System – Components of Hadoop- Analyzing
the Data with Hadoop- Scaling Out- Hadoop Streaming- Design of HDFS-How Map Reduce
Works-Anatomy of a Map Reduce Job run-Failures-Job Scheduling-Shuffle and Sort – Task
execution - Map Reduce Types and Formats- Map Reduce Features
Unit - III
Hbase – data model and implementations – Hbase clients – Hbase examples – praxis. Cassandra
– cassandra data model – cassandra examples – cassandra clients – Hadoop integration. Pig –
Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types
and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries.
Unit – IV
Introduction to NoSQL – aggregate data models – aggregates – key-value and document data
models – relationships– schemaless databases – materialized views – distribution models -peer-
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 22 of 53 SCAA Dt: 10.06.2016
peer replication –consistency – relaxing consistency – version stamps – partitioning and
combining – composing map-reduce calculations -Document based Database – MongoDB-
Introduction- Data Model- Working with data- Replication & Sharding- Development
Unit – V
Graph databases Neo4J- Key concept and characteristics-Modelling data for neo4j-Importing
data into neo4j-Visualizations neo4j-Cypher Query Language-Data visualization- Creating
Visual analytics with Tableau-Connecting your data-Creating Calculation-Using maps-
Dashboard-Stories
REFERENCES:
1. Tom White, “Hadoop: The Definitive Guide”
2. The Defininetive Guide to Mongodb
3. Rik Van Bruggen, “Learning Neo4j”
4. Daniel G. Murray, “Tableau Your Data!: Fast and Easy Visual Analysis with Tableau
Software”
5. Dirk deRoos, Paul Zikopoulos, Bruce Brown, Roman B. Melnyk,Rafael Coss, “Hadoop
For Dummies”
6. Gaurav Vaish, “Getting Started with NoSQL”
7. Pramod J. Sadalage, Martin Fowler, “NoSQL Distilled: A Brief Guide to the Emerging
World of Polyglot Persistence”
8. Joshua N. Milligan, “Learning Tableau”
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 23 of 53 SCAA Dt: 10.06.2016
BUSINESS INTELLIGENCE
Subject Code: 16CS1E01 Number of credits: 4
Subject Description: This course presents the business intelligence architecture and design, business process and information flow and business trends and knowledge delivery. Goals: To enable the student to learn business intelligence concepts Objectives: On successful completion of the course the student should have to
Understood the industry and overall business environment. Know the business processes and information flow.
now the business intelligence techniques.
UNIT – 1
Introduction to Business Intelligence: Introduction – Data Information and Knowledge – What is
Business intelligence? – Business Intelligence and related technologies – Obstacles to Business
Intelligence – Factors driving Business Intelligence – Improving the Decision making Process –
Why a Business intelligence Program?.
UNIT – II
Business Intelligence Capabilities : Introduction – Four Synergistic capabilities – Organizational
Memory – Technologies Enabling Organizational Memory Capability - Information Integration
Capability – Insight Creation – Technologies Enabling Insight Creation Capability.
UNIT – III
The Business Intelligence Program : Business Intelligence Architecture and Design – Data
Preparation – Data Integration – Business Intelligence Platforms – Analysis – Delivery and
Presentation - The Organizational Business Framework – Metadata management – Data
Modelling – Data Profiling – Data Quality – Data Integration – Text Analysis – Predictive
Analysis – Data Security - Data Governance.
UNIT – IV
Business Processes and Information Flow : Information Processing and Information flow –
Transaction Processing – operational Processing – Batch Processing – Analytical Processing -
The Information Flow Process : Information flow model : Processing Stages - Directed
Channels - Business Process Model and Notation (BPMN) – Data Recruitment Analysis :
Business Use of Information – Metrics : Facts , Qualifiers and Models – What is Data
Requirements Analysis ?
UNIT – V
Emerging Business Intelligence Trends and Knowledge Delivery : Introduction in Searching a
Business Intelligence Technique – Text Analysis – Entity Extraction and Entity Recognition –
Sentiment Analysis – Mobile Business Intelligence – Event Stream Processing – Big Data
Analytics - Knowledge Delivery : Standard Reports –Dimensional Analysis – Visualization :
Charts , Graphs , Widgets – Score cards and Dashboards – Geographic visualization – Integrated
Analytics.
REFERENCES: Business Intelligence, Practice, Technologies and Management, Rajiv Sabherwal, Irma Becerra-
Fernandez.
Business Intelligence: The Savvy Manager's Guide, David Loshin , 2013 Edition
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 24 of 53 SCAA Dt: 10.06.2016
MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE
4 Subject Code: 16CS1E02 Number of credits: 4
Subject Description: This course presents the properties of matrices and concepts of probability. Goals: To enable the student to learn the mathematical foundations of computer science. Objectives: On successful completion of the course the student should have:
Understood the mathematical logic grammars and languages. Learned probability concepts.
Contents: Unit - I Matrices: Types of Matrices - Matrix Operations - Inverse of a Matrix - Properties of Determinants - Eigen Values - Cayley-Hamilton Theorem. Set Theory: Basic Set Operations - Relations and Functions – Relation Matrices - Principle of Mathematical Induction. Unit - II Introduction to Probability: Sample Space and Events - Axioms of Probability - Conditional Probability – Independence of Events - Bayes Theorem. Regression and Correlation : Introduction – Linear Regression – Method of Least Squares – Normal Regression Analysis – Normal Correlation Analysis. Unit - III Grammars and Languages: Context Free Grammars – Introduction – Context Free Grammars – Derivation Trees. Finite Automata: Finite State Systems – Basic Definitions – Non Deterministic Finite Automata. Unit - IV Mathematical Logic: Statements and Notations – Connectives – Consistency of Premises and Indirect Method of Proof – Automatic Theorem Proving. Unit - V Numerical Methods Finding Roots : Bisection Method - Regula–Falsi Method - Newton–RaphsonMethod. Solution of Simultaneous Linear Equations: Gaussian Elimination - Gauss-Seidal Method. Numerical Integration: Trapezoidal Rule - Simpson s Rule. REFERENCES:
1. M. K. Venkataraman, “Engineering Mathematics, Volume II, National Publishing Company.
2. John E. Freunds, Irwin Miller, Marylees Miller, “Mathematical Statistics, Pearson Education, Sixth Edition.
3. Hopcroft and Ullman, “Introduction to Automata Theory, Languages and Computation , Pearson Education, Second Edition.
4. Tremblay and Manohar, “Discrete Mathematical Structures with Applications to Computer Science , Tata McGraw-Hill.
5. Rama B. Bhat, Snehashish Chakraverty, “Numerical Analysis in Engineering , Narosa Publishing House, 2004.
6. Radha Muthu, T. Santha, “Discrete Mathematics for Computer Science and Applications, Kalaikathir Achchagam, Coimbatore, 2003.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 25 of 53 SCAA Dt: 10.06.2016
SYSTEM PROGRAMMING
Subject Code: 16CS1E03 Number of credits: 4
Subject Description: This course presents the different system programming software such as language processors, assemblers, macro processors, loaders, linkers, compilers and interpreters Goals: To enable the student to learn system programming. Objectives: On successful completion of the course the student should have:
Understood the functions of various system programming soft wares.
Able to simulate and design a simple system programming soft wares.
Contents:
UNIT I
Language Processors: Introduction - language processing activities - Fundamentals of language
processing - Fundamentals of language – Specification - language Processor development tools.
UNIT II
Assemblers and Macros and Macro Processors : Elements of assembly language programming -
A simple assembly scheme - Pass structure of assemblers - design of a two pass assembler - A
single pass assembler for IBM PC. Macro definition and call - Macro Expansion - Nested macro
calls.
UNIT III
Compilers and Interpreters, Debuggers: Aspects of compilation - compilation of expressions -
code optimization. Linkers: Relocation and linking concepts - design of a linker - Self-relocating
programs - Debuggers: Types of Errors - Debugging Procedures - Classification of
Debuggers - Dynamic/Interactive Debugger
UNIT IV
Loaders : Function of loader - general loader scheme - Absolute loader - Relocating loader -
Direct linking loader - Dynamic loading - Design of direct linking loader.
UNIT V
Scanning and Parsing : Programming Language Grammars - Classification of Grammar -
Ambiguity in Grammatic Specification – Scanning – Parsing - Top Down Parsing - Bottom up
Parsing - Language Processor Development Tools – LEX - YACC.
REFERENCES: 1. D.M. Dhamdhere, System Programming and operating systems, 2nd Edition (TMGH)
2. J. J. Donovan, System Programming, Mc-Graw Hill
3. System Software- An Introduction to Systems Programming- 3rd Edition- Leland L. Beck (Pearson Education)
4. Srimanta Pal, System Programming,OXFORD Publication
5. R.K. Maurya & A. Godbole, System Programming and Compiler Construction
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 26 of 53 SCAA Dt: 10.06.2016
SOFTWARE METRICS
Subject Code: 16CS1E04 Number of credits: 4
Subject Description: This course presents the fundamentals of software metrics, role of metrics and measurements of software engineering. Goals: To enable the student to learn software metrics. Objectives: On successful completion of the course the student should have:
Understood the goals of software metrics.
Know the role of software metrics in software development. Know the quality control and how to ensure good quality software.
Contents:
UNIT - I
Software metrics - software metrics used in industry, and how? - Limitations on applying
software metrics - Types of software metrics – Goal of Software Metrices- Software Entities-
Errors, Faults and Failures - Reliability as a Quality Attribute - Software Metrics for Reliability-
The Dark Side of Software Metrics.
UNIT - II
The role of Metrics in software and software development- Scope of Software Metrics- Cost and
Effort Estimation- Data Collection- Quality Models and Measures- Reliability Models –Security
metrics –Structural and Complexity metrics – Capability maturity Assessment- Management by
metrices – Evaluation of Methods and Tools.
UNIT - III
Static Analysis of Code - McCabe’s Cyclomatic Complexity - Static Analysis of Code - Bug
Counting using Dynamic Measurement - Estimating Failure Rates.
UNIT - IV
Measurements in Software Engineering – Scope of Software metrics – Measurements theory –
Goal based Framework – Software Measurement Validation - Reliability Growth Models -
Software Uncertainty - Software Requirements Metrics - Software Design Metrics -
Management Metrics - Algorithmic Cost Modeling - Evaluation of Management Metrics
UNIT - V
Measurement of Internet Product Attributes – Size and Structure – External Product Attributes –
Measurement of Quality –Reliability Growth Model – Model Evaluation
REFERENCES:
1. Software Metrics: A Rigorous and Practical Approach, Third Edition (Chapman & Hall/CRC
Innovations in Software Engineering and Software Development Series) 3rd Edition
2. Software Metrics and Software Metrology ,1st Edition, Alain Abran
3. Software Metrics: A Rigorous and Practical Approach, Third Edition, Norman Fenton, James
Bieman.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 27 of 53 SCAA Dt: 10.06.2016
BIOINFORMATICS
Subject Code: 16CS1E05 Number of credits: 4
Subject Description: This course presents the importance of biological concepts and biological databases. Goals: To enable the student to know about sequence analysis, all biological databases, perl programming. Objectives:
On successful completion of the course the student should have:
Understood different structure and functions. Learnt the different modeling techniques & sequence analysis.
Contents: Unit-I Introduction – importance of bioinformatics – biological concepts – DNA & protein (Structure and functions)
Unit - II Model organisms and genome projects, Biological Databases, Sequence databases, Primary, secondary, composite databases, Nucleotide sequence databases (NCBI, EBI, DDBJ), Protein sequence databases (SwissPROT, TrEMBL, PIR, Expasy), Structural databases, DNA structure databases, Protein structure database (PDB, SCOP, CATH), Genome databases, NCBI genome, Pathway database, KEGG.
Unit - III Sequence analysis – gene identification methods (Prokaryotic and eukaryotic), Needleman and Wunsch algorithm, Smith and Waterman algorithm, pair wise sequence alignment (local and global alignment), scoring a matrix (Pam and Blosum), Multiple sequence alignment, sequence motif analysis
Unit - IV Elements of PERL Programming – Data types, syntax, loops, input and outputs.
Unit - V Structural biology and molecular modeling - Molecular visualization, RasMol, ViewerPro, Swiss PDB Viewer, Protein conformational analysis, Ramachandran plot, Secondary structure prediction, 3DPSSM, Protein Domains, Blocks and Motifs, CD Search, PDB Search, PDB Format, Comparative Modeling.
REFERENCES: 1. T.K. Attwood, D.J. Parry-Smith, “Introduction to Bioinformatics”,
Pearson Education, Asia, 2003 2. Dan E.Krane, Michael L.Raymer, “Fundamental concept s of Bioinformatics”,
Pearson Education, Asia, 2003. 3. Dr. K, Mani and N. Vijayaraj, “Bioinformatics for beginners”, Kalaikathir Achchagam.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 28 of 53 SCAA Dt: 10.06.2016
MICROPROCESSOR PRINCIPLES AND DESIGN
Subject Code: 16CS1E06 Number of credits: 4
Subject Description: This course presents the architecture memory, I/Devices, interrupts, signals DMA controller and chips. Goals: To enable the student learn the programming concepts in microprocessor. Objectives:
On successful completion of the course the student should have:
Understood assembly languages. Contents: Unit - I Introduction - Microprocessor instruction set and Computer Languages – Microcomputer and Large computers –8085 Pin configuration – 8085 Architecture– Memory – Input and Output devices – Example of a microcomputer system - Review: Logic devices for interfacing – Memory Interfacing – Interfacing I/O devices.
Unit - II Instructions – Instruction format – Addressing modes –Types of Instructions – Intel 8085 instruction set –Development of Assembly Language Programs – Programming Techniques: Looping, counting and indexing – Additional data transfer and 16 bit Arithmetic instructions – Arithmetic operations related to memory - Logical Operations: Rotate and Compare – Counters and Time delays – Stack and Subroutines – BCD to Binary conversion – Binary to BCD Conversion – BCD to seven segment LED code conversion – Binary to ASCII and ASCII to Binary code conversion – BCD Arithmetic.
Unit - III 8085 interrupts – Hardware and Software interrupts – Multiple interrupts – 8259A programmable Interrupt controller- DMA controller - 8255A Programmable peripheral interface – 8254 programmable interval timer.
Unit - IV Basic concepts in serial I/O – Software controlled asynchronous serial I/O –8085 serial I/O lines: SOD and SID- Hardware controlled serial I/O using programmable chips. Microprocessor Applications: Designing a Scanned display – Interfacing a Matrix Keyboard – Memory design.
Unit - V ADC/DAC interface – Keyboard interfacing – Printer Interfacing - Contemporary 8 bit microprocessors – Single chip micro controllers – 16 bit microprocessors – 32 bit microprocessors.
REFERENCES: 1. Ramesh S.Gaonkar, “Microprocessor Architecture, Programming and Applications
with the 8085”, Penram International Publications, Fourth Edition. 2. Mohammad Rafiguzzaman, “Microprocessor and microcomputer based system Design”,
Universal Bookstall, 1990.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 29 of 53 SCAA Dt: 10.06.2016
PRINCIPLES OF PROGRAMMING LANGUAGES
Subject Code: 16CS1E07 Number of credits: 4
Subject Description: This course presents the various principles of programming languages. Goals: To enable the student to learn the Language Design Issues, data types, inheritance, control structure, and storage management. Objectives:
On successful completion of the course the student should have:
Understood the structure and operation of a computer with various programming Languages.
Contents: Unit - I Language Design Issue: The structure and operation of a computer – Virtual Computers and Binding Times – Language Paradigms. Language Translation Issues: Programming Language Syntax – Stages in Translation – Formal Translation Models.
Unit - II Data Types: Properties of Types and Objects – Elementary Data Types – Structured Data Types. Abstraction: Abstract Data Types – Encapsulation by Subprograms – Type Definitions – Storage Management.
Unit - III Sequence Control: Implicit and Explicit Sequence Control – Sequencing with Arithmetic and Non arithmetic Expressions – Sequence Control between Statements. Subprogram Control: Subprogram Sequence Control – Attributes of Data Control – Shared Data in Subprograms
Unit - IV Inheritance: Inheritance – Polymorphism, Advances i n Language Design: Variation on Subprogram Control – Parallel Programming - Language Semantics – Software Architecture.
Unit - V Logic Programming Language: PROLOG – Overview – Data Objects – Sequence Control - Subprograms and Storage Management –Abstraction and Encapsulation – Sample Program. Functional Language: LISP –Overview-Data Objects – Sequence Control- Subprograms and Storage Management –Abstraction and Encapsulation – Sample Program.
REFERENCES: 1. Terrance W. Pratt, Marvin V. Zelkowitz, “ Programming Languages, Design and
Implementation”, PHI, 3 rd
Edition.
2. A.B.Tucker, “Programming Languages”, McGraw Hill. 3. D. Appleby, J. J. Vandekopple, “Programming Languages – Paradigm and Practices,
McGrawHill, Second Edition.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 30 of 53 SCAA Dt: 10.06.2016
MAINFRAME COMPUTING
Subject Code: 16CS1E0 8 Number of credits: 4
Subject Description: This course presents the introduction to COBOL, DB2, CICS Goals: To enable the student to familiar with to be familiar with the concepts in databases Objectives: On successful completion of the course the student should have:
Understood concepts of COBOL, JCL, and DB2. Contents:
Unit-I History of MVS- Basic concepts of JCL- Introduction to ISPF-JOB statement-EXEC statement-DD statement- Procedures-GDG-Utility program-VSAM-JES2 and JES3-ALTER, DELETE, EXPORT, IMPORT command in VSAM-SMS. Unit-II Introduction to COBOL- INDENTIFICATION DIVISIION-ENVIRONMENT DIVISION-DATA DIVISION-PROCEDURE DIVISION-SYNCHRONIZED clause-JUSTIFIED clause-REDEFINES clause- RENAMES clause- SIGN clause- VERBS-CONDITIONAL and SEQUENCE CONTROL VERBS. Unit-III Table Handling- Sequential files-sorting and merging of files-EXAMINE verbs-INSPECT verb-STRING and UNSTRING verb- Direct access files- Report. Unit-IV Introduction to DB2- Data types-Literals-Scalar operators and functions- assignment and comparison - DDL statement-DML statement: Simple queries, sub queries correlated queries; join queries, quantified comparison-Catalog: Introduction, Quantifying catalog, Updating catalog, Aliases and Synonyms, labels- Views- Security and Authorization –Integrity – Embedded SQL-Transaction processing –Lock and Dead Lock – Dynamic SQL. Unit–V Introduction to CICS-House Keeping: HANDLE CONDITION, IGNORE CONDITION, PUSH & POP, Alternates to HANDLE CONDITION, SERVICE RECORD, ADDRESS, ASSIGN, EXEC Interface Block-Program control- File control-Terminal control-BMS-Transient Data Control- Systems security –Recovery and Restart - Test and Debugging – Inter communication.
REFERENCES:
1. Kip R. Irvine, “COBOL for the IBM Personal Computers, Prentice Hall, 1988. 2. Craig S. Mullins, “Developers Guide DB2 , Tech Media Publications, Third Edition, 1997. 3. Yukihisa Kageyama, “CICI Hand Book , Tata McGraw H ill, 1997. 4. C. J. Date, Colin J. White, “A Guide to DB2 , Addis ion Wesley Publication, Fourth Edition,
1993. 5. Alexis Leon, Gibu Thomas, “IBM Mainframe and Solutions, Comdex
Publishing Company, 1997. 6. Stren, Stren, “Structured COBOL Programming . 7. Mainframe Handbook 8. M. K. Roy, “COBOL Programming .
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 31 of 53 SCAA Dt: 10.06.2016
SOFTWARE RELIABILITY
Subject Code: 16CS1E09 Number of credits: 4
Subject Description: This course provides the insight in to the reliability factors of the Software. Goals: To enable the students to learn about the principle and concepts of Software reliability. Objectives: On successful completion of the course the students must have
understood the concepts of Software reliability to analyze the quality standards
Contents:
Unit - I Software Reliability Definitions - software disasters - Errors - faults - failures - different views of software reliability – software requirements specification - Causes of unreliability in software - Dependable systems: reliable, safe, secure, maintainable, and available - Software maintenance.
Unit - II The phases of a Software Project - Monitoring the development process – The software life cycle models - software engineering - Structured Analysis and structured Design - Fault tolerance - Inspection - Software cost and schedule. Unit - III Software quality modeling - Diverse approaches and sources of information - Fault avoidance, removal and tolerance - Process maturity levels (CMM) - Software quality assurance (SQA) - Monitoring the quality of software - Total quality management (TQA) - Measuring Software Reliability - The statistical approach - Software reliability metrics.
Unit - IV Data Trends - Complete prediction Systems - overview of some software reliability models - The recalibration of the models - Analysis of model accuracy - Reliability growth models and trend analysis - Software Costs Models - Super models. Unit -V Testing and maintaining more reliable software –logical testing – functional testing – algorithm testing – regression testing - fault tree analysis – failure mode effects and critical analysis – reusability - case studies.
REFERENCES: 1. J.D. Musa, A. Iannino and K.Okumoto, Software Reliability, Measurement, Prediction,
Application, McGraw Hill, 1990. 2. J.D. Musa, Software Reliability Engineering, McGraw Hill, 1998. 3. Michael R. Lyer, Handbook of Software Reliability Engineering, McGraw Hill, 1995.
4. Xie, M., Software Reliability Modelling, World Scientific, London, 1991.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 32 of 53 SCAA Dt: 10.06.2016
PARALLEL PROCESSING Subject Code: 16CS1E10 Number of credits: 4 Subject Description: This course presents an Introduction to parallel processing, Memory and input/output system, Pipeline computers, Array Processors, Multiprocessor architecture. Goals: To enable the student to familiar definition and functions of parallel processing, Interrupt Mechanism and special hardware, principles of linear pipelining. Objectives: On successful completion of the course the student should have:
Understood concepts and principles of parallel processing, Multiprocessor architecture.
Contents: Unit - I Introduction to parallel processing – definition an d functions of parallel processing – uni-processor and parallel processing systems – parallel computers – pipeline computers – array processor – multiprocessor systems – performance of parallel computers – application of parallel processor. Unit - II Memory and input/output system – memory system for parallel processor computers – hierarchical memory structures – virtual memory system – paged system – segmented system with paged segments – memory management policies – fixed partitioning and variable partitioning – cache memories and management – characteristics of cache memories – cache memory organization – input/output subsystem – characteristics of I/O subsystem – Interrupt Mechanism and special hardware – I/O processor and channel architecture. Unit - III Pipeline computers – principles of linear pipelining – pipelined structures of a typical central processing unit – classification of pipeline processors – interleaved memory organization – S access memory organization – C access memory organization – C & S access memory organization – Static & dynamic pipelining – principles of designing static pipeline processors – Instruction prefetch and branch handling – data buffering and busing structures – Internal forwarding and register tagging – vector processing – requirements and characteristics of pipelined vector processing methods. Unit - IV Array Processors – Single Instruction stream – Multiple data stream – SIMD processors – Types of SIMD computer organization – Array process or organization and associative processors – Array processor computer organization – SIMD interconnection networks – Static and Dynamic networks – Linear array, mesh, ring, star, tree, systolic, completely connected, chordalring and cube networks – Parallel algorithms for array processors – SIMD matrix multiplication – Parallel sorting on array processors. Unit - V Multiprocessor architecture – Functional structures of a multiprocessor system loosely and tightly coupled multiprocessor – Processor characteristics of multiprocessing – Inter processor communication mechanism – Instruction set – Interconnection networks – Time shared or common bus – cross bar switch and multi p ort memories and multistage networks for multiprocessor – Parallel memory organization – Interleaved memory configurations – classification of multiprocessor operating system. REFERENCES:
1. Kai Hwang, Faye A.Briggs, “Computer Architecture and Parallel Processing , Prentice Hall of India, 1985.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 33 of 53 SCAA Dt: 10.06.2016
OPERATION RESEARCH
Subject Code: 16CS2E01 Number of credits: 4
Subject Description: This course provides a formal quantitative approach to problem solving Goals: To enable the students to learn about the techniques applied to business decision-making Objectives: On successful completion of the course the students must have understood
understood the concepts of quantitative tools and techniques applied to business decision-making
a formal quantitative approach to problem solving Contents:
Unit - I Introduction to Operations Research: Basics definition, scope, objectives, phases, models and limitations of Operations Research. Linear Programming Problem – Formulation of LPP, Graphical solution of LPP. Simplex Method, artificial variables, simplex Gauss-Jordan reduction process in simplex methods, Big-M method, two-phase method, degeneracy and unbound solutions. Unit - II Transportation Problem. Formulation, solution, unbalanced Transportation problem. Finding basic feasible solutions – Northwest corner rule, least cost method and Vogel‟s approximation method. Optimality test: the stepping stone method and MODI method, Minimization and Maximization problem Unit - III Dual Problem : Relation between primal and dual problems, Dual simplex method, Sensitivity analysis Transportation algorithms –Assignment problem –Hungarian Method (Minimization and Maximization), Branch & Bound technique. Unit - IV Shortest route – minimal spanning tree - maximum flow models – project network- CPM and PERT network-critical path scheduling. Unit - V Games Theory. Competitive games, rectangular game, saddle point, minimum (maximum) method of optimal strategies, value of the game. Solution of games with saddle points, dominance principle. Rectangular games without saddle point – mixed strategy for 2 X 2 games. REFERENCES:
1. Handy A Taha, Operations Research – An Introduction, Pearson Education 2. P. Sankara Iyer, ”Operations Research”, Tata McGraw-Hill, 2008. 3. A.M. Natarajan, P. Balasubramani, A. Tamilarasi, “Operations Research”, Pearson
Education 4. J K Sharma., “Operations Research Theory & Applications , 3e”, Macmillan India Ltd, 2007.
5. P. K. Gupta and D. S. Hira, “Operations Research”, S. Chand & co., 2007. 6. J K Sharma., “Operations Research, Problems and Solutions, 3e”, Macmillan India Ltd. 7. N.V.S. Raju, “Operations Research”, HI-TECH, 2002.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 34 of 53 SCAA Dt: 10.06.2016
GREEN COMPUTING
Subject Code: 16CS2E02 Number of credits: 4
Objective To provide graduate students with an understanding of the role of ICTs and impact on the global carbon footprint.
To estimate the carbon footprint of the ICT operations of an organization and access ways to reduce the carbon footprint by
changes to policies for procurement of ICT, changes.
To make ICT operations and revising business processes.
UNIT I
Fundamentals of Green IT : Business, IT, and the Environment – Green computing: carbon
foot print, scoop on power – Green IT Strategies: Drivers, Dimensions, and Goals –
Environmentally Responsible Business: Policies, Practices, and Metrics - Approaches to green
computing - Middleware Support - Compiler Optimization - Product longevity - Software
induced energy consumption - its measurement and rating.
UNIT II
Green Assets and Modeling : Green Assets: Buildings, Data Centers, Networks, and Devices –
Green Business Process Management: Modeling, Optimization, and Collaboration – Green
Enterprise Architecture – Environmental Intelligence – Green Supply Chains – Green
Information Systems: Design and Development Models.
UNIT III
Grid Framework : Virtualizing of IT systems – Role of electric utilities, Telecommuting,
teleconferencing and teleporting – Materials recycling – Best ways for Green PC – Green Data
center – Green Grid framework.
UNIT IV
Green Compliance and Green Mobile : Socio-cultural aspects of Green IT – Green Enterprise
Transformation Roadmap – Green Compliance: Protocols, Standards, and Audits – Emergent
Carbon Issues: Technologies and Future - Green mobile - optimizing for minimizing battery
consumption - Web, Temporal and Spatial Data Mining Materials recycling.
UNIT V
Case Studies : The Environmentally Responsible Business Strategies (ERBS) – Case Study
Scenarios for Trial Runs – Case Studies – Applying Green IT Strategies and Applications to a
Home, Hospital, Packaging Industry and Telecom Sector.
REFERENCES:
1. Bhuvan Unhelkar, “Green IT Strategies and Applications-Using Environmental
Intelligence”, CRC Press, June 2011.
2. Woody Leonhard, Katherrine Murray, “Green Home computing for dummies”, August
2009. REFERENCES: 1. Alin Gales, Michael Schaefer, Mike Ebbers, “Green Data Center:
steps for the Journey”, Shoff/IBM rebook, 2011. 2. John Lamb, “The Greening of IT”, Pearson
Education, 2009.
3. Green Computing and Green IT Best Practices on Regulations and Industry Initiatives,
Virtualization, Power Management, Materials Recycling and Telecommuting by Jason Harris,
Emereo Publishing
4. Jason Harris, “Green Computing and Green IT- Best Practices on regulations & industry”,
Lulu.com, 2008.
5. Wu Chun Feng (editor), “Green computing: Large Scale energy efficiency”, CRC Press,
2012.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 35 of 53 SCAA Dt: 10.06.2016
MOBILE COMPUTING
Subject Code: 16CS2E03 Number of Credits: 4
Subject Description: This course presents an introduction to mobile communications, Digital cellular system, Mobile switching systems, and Network management systems Goals: To enable the student learn Digital cellular system. Objectives:
On successful completion of the course the student should have:
Understood the generation of mobile communication.
Contents: Unit - I Introduction: Introduction to mobile communications – generation of mobile communication FM, TDMA, CDMA – basic cellular architecture.
Unit - II Digital cellular system infrastructure: global system for mobile communication (GSM) – GSM architecture – principles of synchronous digital hierarchy – principles of Pleisosynchronous digital hierarchy – principles of fiber optics communications.
Unit - III Mobile switching systems: Mobile service switching centre (MSC) – inter working functions (IWF) – home location register (HLR) and Vister Location register (VLR) – Gateway MSC – Signaling transfer point (STP)
Unit - IV Base station sub systems: Base station controller (BSC) – base transceiver station (BTS) – transcoder rate adaptation unit (TRAU) – open system interconnection – frequency management.
Unit - V Network management systems: Operating sub systems – network operation, maintenance and administration – subscription management and charging – mobile equipment management.
REFERENCES: 1. J. Schiller, Mobile Communications, Addison Wesley, 2000.
2. William C.Y.Lee, Mobile Cellular telecommunication, Mc Graw Hill, Int. Edition.
3. William C.Y.Lee, Mobile Communication Engineering, Mc Graw Hill, Inter. Edition.
4. Rajan Kurupillai and others, Wireless PCS, Mc Graw Hill, Inter. Edition
5. Johan Powers, Fiber optics systems, Mc Graw Hill Inter. Edition
6. William Stallings, Wireless Communications and networks, Pearson education
7. Joachim Tisal, GSM radio telephony, John Wiley.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 36 of 53 SCAA Dt: 10.06.2016
IMAGE PROCESSING
Subject Code: 16CS2E04 Number of credits: 4
Subject Description: This course presents the introduction image processing fundamentals,
segmentation, image enhancements and image perception. Goals: To enable the student to be familiar with image data compression, image recognition, image
filtering and segmentation Objectives: On successful completion of the course the student should have understood image
processing fundamentals, segmentation, image enhancements and image perception, filtering and
compression.
Contents:
UNIT- I
Introduction: Digital image processing - Fundamental steps in digital image processing -
components of image processing system. Digital Image Fundamentals: A simple image
formation model - image sampling and quantization - basic relationships between pixels.
UNIT- II
Image enhancement in the spatial domain : Basic gray-level transformation - histogram
processing, enhancement using arithmetic and logic operators - basic spatial filtering -
smoothing and sharpening spatial filters - combining the spatial enhancement.
UNIT- III
Image restoration: A model of the image degradation/restoration process - noise models -
restoration in the presence of noise–only spatial filtering - Weiner filtering - constrained least
squares filtering - geometric transforms; Introduction to the Fourier transform and the
frequency domain - estimating the degradation function.
UNIT- IV
Color Image Processing: Color fundamentals - color models - pseudo color image processing -
basics of full – color image processing - color transforms - smoothing and sharpening - color
segmentation. Image Compression: Fundamentals - image compression models - error-free
compression - lossy predictive coding - image compression standards.
UNIT- V
Morphological Image Processing: Preliminaries - dilation, erosion, open and closing, hit or
miss transformation, basic morphologic algorithms. Image Segmentation: Detection of
discontinuous - edge linking and boundary detection – thresholding - region–based
segmentation. Object Recognition : Patterns and patterns classes - recognition based on
decision – theoretic methods – matching - optimum statistical classifiers - neural networks -
structural methods – matching shape numbers - string matching.
REFERENCES: 1.
Rafeal C.Gonzalez, Richard E.Woods, Digital Image Processing, Second Edition, Pearson
Education/PHI.
2.
Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis, and Machine Vision,
Second Edition, Thomson Learning.
3.
Alasdair McAndrew, Introduction to Digital Image Processing with Matlab, Thomson Course
Technology
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 37 of 53 SCAA Dt: 10.06.2016
4.
Adrian Low, Computer Vision and Image Processing, Second Edition, B.S.Publications
5.
Rafeal C.Gonzalez, Richard E.Woods, Steven L. Eddins, Digital Image Processing using Matlab,
Pearson Education.
WEB SERVICES
Subject Code: 16CS2E05 Number of credits: 4
Subject Description: This course presents an Overview of Distributed Computing, XML, web
services Goals: To enable the student to be familiar with distributed services, XML and web services Objectives: On successful completion of the course the student should have:
Understood the concepts of web services Contents:
Unit - I Overview of Distributed Computing. Introduction to web services – Industry standards, Technologies and concepts underlying web services – their support to web services. Applications that consume web services.
Unit - II XML – its choice for web services – network protocols to back end databases- technologies – SOAP, WSDL – exchange of information between applications in distributed environment – locating remote web services – its access and usage. UDDI specification – an introduction.
Unit - III A brief outline of web services – conversation – static and interactive aspects of system interface and its implementation, work flow – orchestration and refinement, transactions, security issues – the common attacks – security attacks facilitated within web services quality of services – Architecting of systems to meet users requirement with respect to latency, performance, reliability, QOS metrics, Mobile and wireless services – energy consumption, network bandwidth utilization, portals and services management.
Unit - IV Building real world enterprise applications using web services – sample source codes to develop web services – steps necessary to build and deploy web services and client applications to meet customer s requirement – Easier development, customization, maintenance, transactional requirements, seamless porting to multiple devices and platforms. Unit - V Deployment of Web services and applications onto Tomcat application server and axis SOAP server (both are free wares) – Web services platform as a set of enabling technologies for XML based distributed computing. REFERENCES:
1. Sandeep Chatterjee, James Webber, “Developing Enterprise Web Services : An Architects Guide , Prentice Hall, Nov 2003.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 38 of 53 SCAA Dt: 10.06.2016
ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS
Subject Code: 16CS2E06 Number of credits: 4
Subject Description: This course presents the problem solving and AI, search methods and expert
systems. Goals: Enable the student to be familiar with theorems and algorithms. Objectives: On successful completion of the course the student should have:
Understood the problem states and AI, state space methods, problem reduction search methods, predicate calculus, and knowledge engineering in expert systems.
Contents: Unit- I Problem solving and AI – Puzzles and Games – Problem States and operators – Heuristic programming – state space representations – state descriptions – graph notations – non-deterministic programs
Unit - II State space search methods – breadth first and dept h first search – heuristic – admissibility – optimality of algorithms – performance measures – problem reduction representations – AND/OR graphs and higher level state space
Unit - III Problem reduction search methods – cost of solution trees – ordered search – alpha beta and minimum procedure – theorem proving in predicate calculus – syntax, semantics, Herbrand universe: variables, qualifiers, unification, resolvents
Unit - IV Predicate calculus in problem solving – answer extraction process – resolution – Automatic program writing – predicate calculus – proof finding methods
Unit - V Expert systems: Expert systems and conventional programs – expert system organization – Knowledge engineering: knowledge representation techniques – knowledge acquisition – acquiring knowledge from experts – automating knowledge acquisition –Building an expert system – difficulties in developing an expert system
REFERENCES: 1. E Charnail, CK Reiesbeck and D V Medermett, “Artificial Intelligence
Programming”, Lawrence Erlbaum Associates, N J, 198 0 2. N J Nilson, “Principles of Artificial Intelligence” , Tiega Press, Polo Alto, 1980 3. Elain Rich and Kevin Knight, “Artificial Intelligence”, McGraw Hill, 1991 4. Donald A Waterman, “A Guide to Expert Systems”, Tech knowledge series in
knowledge engineering, 1986
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 39 of 53 SCAA Dt: 10.06.2016
TCP/IP
Subject Code: 16CS2E07 Number of credits: 4
Subject Description: This course presents the introduction of TCP/IP architecture. Goals: To enable the student to learn routing mechanisms and applications. Objectives:
On successful completion of the course the student should have:
Understood OSI layers, features and its applications.
Contents: Unit - I Introduction: TCP /IP Internet – Internet Services – Internet Protocols and Standardization – Application level interconnection-Network Level Interconnection –Internet Architecture – Interconnection through IP Routers – TCP /IP Intern et Address Concepts.
Unit - II Technical Features: Mapping Internet addresses to Physical addresses – RARP-Connectionless Datagram Delivery – Routing IP Datagrams – Error and Control Messages (ICMP)
Unit - III Subnet and Supernet address Extensions-User Datagram Protocol (UDP) – Internet Multicasting – The Domain Name System (DNS)
Unit - IV Routing: Introduction to Routing and the Origin of Routing Tables – Core Routers – Peer Backbones – Gateway-to-Gateway Protocol – GGP Messages Formats – Link State Routing and Protocols-Exterior gateway protocol.
Unit - V Applications: Remote Login (Telnet) – File Transfer and Access (FTP, NFS) –Electronic Mail (SMTP) – Internet Management (SNMP).
REFERENCES:
1. Douglas E. Comer, “Internetworking with TCP /IP”, P rentice-Hall. 2. W. Richard Stevens, “TCP/IP Illustrated”, Addison W esley. 3. Pete Loshin, “TCP/IP Clearly Explained”, Morgan Kau fmann.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 40 of 53 SCAA Dt: 10.06.2016
EMBEDDED SYSTEMS
Subject Code: 16CS2E08 Number of credits: 4
Subject Description: This course presents the Hardware Fundamentals, Software Architecture Interrupts, RTOS Operating System Services, Embedded software life cycle and tools. Goals: To enable the student to learn fundamentals, and concepts of operating system. Objectives:
On successful completion of the course the student should have:
Understood: Hardware fundamentals, Software Architecture, Interrupts, Embedded software lifecycle and tools.
Contents: Unit - I Hardware Fundamentals Hardware Fundamentals: Terminology-Gates-Timing Diagrams-Memory Advanced Hardware Fundamentals: Microprocessors-Microprocessor architecture-Direct Memory Access-Conventions and Schematics-Introduction to embedded systems: An embedded system-Processor in the system-Exemplary embedded systems.
Unit - II Interrupts and Software Architecture Interrupts: Interrupt basics-Interrupt service routines Survey of Software Architectures: Round Robin with interrupts-Function-Queue-Scheduling Architecture-Real Time Operating Systems Architecture Introduction to Real Time Operating Systems: Selecting in RTOS-Tasks and Task States-Tasks and Data-Semaphores and shared data
Unit - III Concepts of RTOS More Operating System Services: Interrupt process communication-Message queues-Mailboxes and pipes-Timer functions-Events-Memory management-interrupt routines in an RTOS environment Basic design using a Real Time Operating System: Principles-encapsulating semaphores and queues-hard real time scheduling considerations-saving memory space and power-introduction to RTL & QNX
Unit - IV Embedded software life cycle and tools Embedded software Lifecycle : Software Algorithm complexity-Software development process life cycle and its models Software development tools: development tools-hosts and target machine-linker/locators for embedded software-getting embedded software into the target machine Debugging techniques: testing on your host machine-instruction set simulators-the asset macro-using laboratory tools
Unit – V Case Study
REFERENCES: 1.
David.E.Simon, “An embedded system primer”, Addison Wesley-2001
2. Raj Kamal, “Embedded Systems architecture, programming and design”, Tata McGraw Hill
Publishing Company Ltd., New Delhi, 2003.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 41 of 53 SCAA Dt: 10.06.2016
SOFTWARE QUALITY ASSURANCE
Subject Code: 16CS2E09 Number of credits: 4
Subject Description: This Course presents the essentials of Software Quality, Plan for SQA, Standards, Tools for SQA. Goals: To enable the students to learn the Concepts and Principles of SQA. Objectives: On successful completion of the course the students should have:
Understood the principles of SQA
Must be able to judge the quality of Softwares.
Unit-I Introduction to Software quality – Software modeling – Scope of the software quality program – Establishing quality goals –
purpose, quality, goals – SQA Planning software of productivity and documentation.
Unit -II
Software quality assurance plan – Purpose and Scope, Software quality assurance management
- Organization – Quality tasks – Responsibilities – Documentation.
Unit –III
Standards, Practices, Conventions and Metrics, Reviews and Audits – Management, Technical
review – Software inspection process – Wa lk through process – Audit process – Test
processes – ISO, cmm compatibility – Problem r eporting and corrective action.
Unit -IV
Tools, Techniques and methodologies, Code control, Media control, Supplier control, Records
collection, Maintenance and retention, Training and risk management.
Unit -V
ISO 9000 model, cmm model, Comparisons, ISO 9000 weaknesses, cmm weaknesses, SPICE
– Software process improvement and capability determination.
REFERENCES: 1.
Mordechai Ben – Meachem and Garry S.Marliss, “Software Quality – Producing Practical,
Consistent Software”, International Thom pson Computer Press, 1997
2.
Watt. S. Humphrey, “Managing Software Process”, Addison – Wesley, 1998.
3.
Philip.B.Crosby, “Quality is Free : The Art of making quality certain”, Mass Market,1992.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 42 of 53 SCAA Dt: 10.06.2016
NATURAL LANGUAGE PROCESSING
Subject Code: 16CS2E10 Number of credits: 4
Subject Description: This course presents the linguistic background, grammars, syntax and semantic analysis of natural language.
Goals: To enable the student to learn concepts in Natural language processing.
Objectives: On successful completion of the course the student should have:
Understood the natural language processing.
Contents: Unit - I Introduction to Natural Language Understanding – Linguistic Background – Grammars and Parsing – Features and Augmented Grammars.
Unit - II Grammars for Natural Languages – Towards Efficient Parsing – Ambiguity Resolution Statistical Methods – Semantics and Logical Forms.
Unit - III Linking Syntax and Semantics – Resolution – Strategies for Semantic Interpretation – Scoping and Interpretation of Noun Phrases.
Unit - IV Knowledge Representation and Reasoning – Local Discourse Context and Reference – World Knowledge – Discourse Structure.
Unit – V Conversational Agent – Logic and Natural Language – Model – Theoretic Semantics – Semantics of Set Theoretic Models.
REFERENCES:
1.
James Allen, “Natural Language Understanding , Pear son Education, Second
Edition.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 43 of 53 SCAA Dt: 10.06.2016
VIRTUAL REALITY
Subject Code: 16CS3E01 Number of credits: 4
Subject Description: This course presents an idea on Computer graphics, Generic VR
Systems, Physical Simulation, VR Hardware.
Goals: To enable the student to familiar with to computer graphics, 3D Computer graphics,
and simulation.
Objectives: On successful completion of the course the student should have: Understood
virtual reality in detail.
Contents:
Unit – I
Virtual Reality and Virtual Environments: Introduction – Computer Graphics – Real-time
computer Graphics – Flight Simulation – Virtual Environment – Benefits of Virtual Reality –
Historical Development of VR: Scientific Landmarks.
Unit - II
3D Computer Graphics: Virtual world Space – Positioning the Virtual Observer – The
Perspective Projection – Human Vision – Stereo Perspective Projection – 3D Clipping – Color
Theory – Simple 3D Modeling – illumination, reflection Models- Shading Algorithms –
Radiosity – Hidden surface removal – realism- stereographic Images Geometric Modeling: 3D
Space Curves – 3D boundary representation – other modeling strategies – Geometrical
Transformations: Frames of reference – Modeling – Instances – Picking, Flying, scaling –
Collision detection.
Unit - III
Generic VR System: Virtual Environment – computer Environment – VR technology – Models
of Interaction – VR Systems – Animating the Virtual Environment: The Dynamics of numbers
– animation of objects – Shape and object i n between – Free-form deformation – Particle
Systems
Unit - IV
Physical Simulation: Objects Falling in a gravitational field – Rotating wheels – Elastic
Collisions – Projectiles – Simple Pendulums – Springs – Flight dynamics of an aircraft. Human
Factors: The eye – the ear – The Somatic senses – Equilibrium.
Unit - V
VR Hardware: Sensor Hardware – Head-Coupled display s – Acoustic Hardware – Integrated
VR Systems – VR Software: Modeling Virtual World – Physical Simulation – VR Tool Kids –
VR Applications: Engineering – Entertainment – Science – training – The Future: Virtual
Environments – Modes of Interaction.
REFERENCES: 1.
John Vince, “Virtual Reality Systems”, Pearson Education Asia, 2001
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 44 of 53 SCAA Dt: 10.06.2016
MACHINE LEARNING TECHNIQUES
Subject Code: 16CS3E02 Number of credits: 4
Subject Description: This course presents the foundations of learning, linear models, distance based models, tree and rule based model and reinforcement learning.
Goals: To enable the student to learn techniques in machine learning.
Objectives: On successful completion of the course the student should have:
Understood the techniques in machine learning and apply machine learning techniques to any domain of interest.
Contents:
Unit - I
FOUNDATIONS OF LEARNING : Components of learning – learning models – geometric
models – probabilistic models – logic models – grouping and grading – learning versus design
– types of learning – supervised – unsupervised – reinforcement – theory of learning –
feasibility of learning – error and noise – training versus testing – theory of generalization –
generalization bound –bias and variance – learning curve
Unit - II
LINEAR MODELS : Linear classification – univariate linear regression – multivariate linear
regression – regularized regression – Logistic regression – perceptrons – multilayer neural
networks – learning neural networks structures – support vector machines – soft margin SVM
– generalization and over fitting – regularization – validation
Unit - III
DISTANCE-BASED MODELS : Nearest neighbor models – K-means – clustering around
medoids – silhouttes – hierarchical clustering – k- d trees – locality sensitive hashing – non -
parametric regression – ensemble learning – bagging and random forests – boosting – meta
learning
Unit -IV
TREE AND RULE MODELS : Decision trees – learning decision trees – ranking and
probability estimation trees – Regression trees – clustering trees – learning ordered rule lists –
learning unordered rule lists – descriptive rule learning – association rule mining – first -order
rule learning
Unit - V
REINFORCEMENT LEARNING : Passive reinforcement learning – direct utility estimation –
adaptive dynamic programming – temporal - difference learning – active reinforcement
learning – exploration – learning an action utility function – Generalization in reinforcement
learning – policy search – applications in game playing – applications in robot control
REFERENCES:
1. Y. S. Abu - Mostafa, M. Magdon-Ismail, and H.-T. Lin, “Learning from Data”, AMLBook
Publishers, 2012.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 45 of 53 SCAA Dt: 10.06.2016
2. P. Flach, “Machine Learning: The art and science of algorithms that make sense of data”,
Cambridge University Press, 2012.
3. K. P. Murphy, “Machine Learning: A probabilistic perspective”, MIT Press, 2012.
4. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007.
5. D. Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University Press,2012.
HUMAN COMPUTER INTERACTION
Subject Code: 16CS3E03 Number of credits: 4
Subject Description: This course presents the design process of HCI, Cognitive models,
interaction styles and design issues in HCI. Goals: To enable the student to learn concepts in Human computer interaction.
Objectives: On successful completion of the course the student should have:
Understood the design processes and issues in HCI
Able to discover models which can be used for designing systems.
Contents:
Unit - I
Introduction, The human, The computer, The interaction, Paradigms, Usability of Interactive
Systems, Guidelines, Principles, and Theories.
Unit - II
Design Process - Interaction design basics, HCI in the software process, Design rules,
Implementation support, Evaluation techniques, Universal design, User support.
Unit - III
Cognitive models, Socio-organizational issues and stakeholder requirements, Communication
and collaboration models, Task analysis, Dialogue notations and design, Models of the system,
Modelling rich interaction
Unit - IV
Interaction Styles- Direct Manipulation and Virtual Environments, Menu Selection, Form
Filling and Dialog Boxes, Command and Natural Languages, Interaction Devices,
Collaboration and Social Media Participation
Unit - V
Design Issues- Quality of Service, Balancing Function and Fashion, User Documentation and
Online Help, Information Search, Information Visualization , Information Search and
visualization - Introduction, Search in Textual Documents and Database Querying, Multimedia
Document Searches. Hypertext, Multimedia and the world wide web, Introduction,
Understanding hypertext, Web technology and issues, Static web content, dynamic web
content.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 46 of 53 SCAA Dt: 10.06.2016
REFERENCES: 1.
Human Computer Interaction, Alan Dix, Janet Finlay, Gregory Abowd and Russel Beale, Prentice
Hall Publication
2.
Designing the User Interface, Ben Shneiderman, 4th Edition, Pearson Education, 2008, ISBN 81-
7808-262-4
3.
Human Computer Interaction, Dan R. Olsen, Cengage Learning, India Edition, ISBN No.978-81-
315-1137-4
4.
The Essential Guide to User Interface Design, Second Edition, An Introduction to GUI Design
Principles and Techniques, Wilbert O. Galitz, Wiley India (P) Ltd., ISBN : 81- 265-0280-0
5.
The Essential of Interaction Design, Alan Copper, Robert Reimann, David Cronin, Wiley India (P)
Ltd., ISBN : 978-81-265-1305-
DATA COMPRESSION
Subject Code: 16CS3E04 Number of credits: 4
Subject Description: This course presents a brief introduction to compression schemes and
modulation Goals: To enable the student to familiar with Information and Coding and Compression of Still Images.
Objectives: On successful completion of the course the student should have:
Understood data compression concepts and principles
Contents:
Unit - I Information and Coding: Information and Entropy – Noiseless and Memoryless Coding Shannon – Fano Coding: Shannon Coding – Shannon-Fano Coding.
Unit - II Huffman Coding – Arithmetic Coding - Dictionary Techniques - Sampling and Quantization
Unit - III Predictive Coding: Delta Modulation – Differential Pulse Code Modulation. Transform Coding: Defining a Transform – Interpretation of Transforms – Karhenun-Loeve Transform – Hadamard Transform – Discrete Wavelet Transform. Subband Coding: Down sampling and Up sampling – Bit Allocation
Unit - IV JPEG – The Baseline System – Progressive DCT-base d Mode of Operation – Hierarchical Mode
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 47 of 53 SCAA Dt: 10.06.2016
of Operation – Sequential Losses Mode of Operation. Video Image Compression: MPEG – MPEG-1, MPEG-2, MPEG-4 and MPEG-7
Unit - V Fourier analysis: Fourier series – The Fourier Transform – The Discrete Fourier Transform - The Sampling Theorem. Wavelets: Wavelet Transforms – Multiresolution Analysis
REFERENCES:
1. Adam Drozdek, “Elements of Data Compression , Vikas Publishing House, 2002. 2. Mark Nelson, Jean-Loup Gailly, “The Data Compression Book , BPB Publication,
Second Edition, 1996.
GENETIC ALGORITHMS
Subject Code: 16CS3E05 Number of credits: 4
Subject Description: This course presents an introduction to genetic algorithms and its applications
Goals: To enable the student to familiar with the concepts of genetic algorithms Objectives:
On successful completion of the course the student should have: Understood the concepts and applications of genetic algorithms
Contents: Unit - I Introduction: Genetic algorithms (GA) – Traditional optimization and search methods – GA Vs Traditional methods – Simple GA- schemata – learning the Lingo- GA mathematical foundation: Schema processing – Two armed and K – armed bandit problem – building block hypothesis – minimal deceptive problem. Data structure – GA operations – mapping objective functions to fitness values. Fitness scaling – coding – multi parameter representation Discretization – constrains.
Unit - II Applications of GA: The Rise of GA – Bagley and Adaptive Game playing program, Tosenberg and Biological cell simulation – pattern recognition – metalevel GAs – Hollstien and Function optimization – Real genes – Box and Evolutionary operations – Evolutionary optimization techniques, programming. Function optimization – improvements in basic techniques – Current applications – Pipeline system s – Structural optimization – medical registration
Unit - III Dominance – Diploidy and Abeyance and reordering operators- other micro operators: Segregation, Translocation and multiple chromosome structure – Duplication and Deletion. Sexual determination and Differentiation – Niche an d speciation. Multi objective optimization – Knowledge based techniques – GA and Parallel Processors.
Unit - IV Genetic based Machine: Classifier system – Rule and Message system – The Bucket Brigade GA – Implementation issues.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 48 of 53 SCAA Dt: 10.06.2016
Unit - V Genetic Based Machine Learning (GBML) – Development of CS-1-Smith s Poker Player – LS – 1 Performance – GBML efforts – ANIMAT classifier system pipeline operation classifier system.
REFERENCES:
1. D.E. Goldberg, “Genetic Algorithms, Optimization, a nd Machine Learning”, Addison Wesley 2000.
NEURAL NETWORKS AND FUZZY SYSTEMS
Subject Code: 16CS3E06 Number of credits: 4
Subject Description: This course presents the fundamentals of neural networks and fuzzy systems Goals: To enable the student to familiar with to Fuzzy Set Theory, Fuzzy Systems Adaptive Resonance Theory and Back Propagation Networks Objectives:
On successful completion of the course the student should have:
Understood concepts and principles of fuzzy and neural networks.
Contents: Unit - I Fundamentals of Neural Networks : basic Concepts of Neural Networks – Human Brain – Model of an Artificial Neuron – Neural Network Architectures – Characteristics of Neural Networks – Learning Methods – Taxonomy of Neural Network Architectures – History of Neural Network Research – Easy Neural Network Architectures – Some Application Domains.
Unit - II Back Propagation Networks : Architecture of a Back Propagation Network – Back Propagation Learning – Illustration – Applications – Effects of Tuning Parameters of the Back Propagation Neural Network – Selection of Various Parameters in BPN – Variations of Standard Back Propagation Algorithm.
Unit - III Adaptive Resonance Theory: Introduction – ART1 – AR T2 – Applications.
Unit - IV Fuzzy Set Theory: Fuzzy versus Crisp – Crisp Sets – Fuzzy Sets – Crisp Relations – Fuzzy Relations.
Unit - V Fuzzy Systems: Crisp Logic – Predicate Logic – Fuzz y Logic – Fuzzy Rule Based System – Defuzzification Methods.
M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B
Page 49 of 53 SCAA Dt: 10.06.2016
REFERENCES:
1. S. Rajasekaran, G. A. Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic and Genetic
Algorithms Synthesis and Applications, Prentice Hall of India, 2003. 2. James A. Freeman, David M. Skapura, “Neural Networks – Algorithms, Applications and
Programming Techniques, Pearson Education. 3. Fredric M. Ham, Ivica Kostunica, “Principles of Neuro Computing for Science of
Engineering , Tata McGraw Hill. 4. Simon Haykin, “Neural Networks – A Comprehensive Foundation , Prentice Hall of
India.
M.Sc. Computer Science (UD)- 2016-17 onwards Annexure No.76B
Page 50 of 53 SCAA Dt: 10.06.2016
SPEECH PROCESSING
Subject Code: 16CS3E07 Number of credits: 4
Subject Description: This course presents an idea on fundamentals of speech recognition, pattern comparison techniques and processing Goals: To enable the student to familiar with to overview of signals and systems, Fundamentals of speech recognition, Pattern-comparison techniques. Objectives:
On successful completion of the course the student should have:
Understood concepts and principles in speech recognition.
Contents: Unit - I Overview of signals and systems – Review of One dimensional and two dimensional signal processing and discrete Fourier transforms and digital filters- domain models for speech processing.
Unit - II Fundamentals of speech recognition. The speech signal – production, perception and Acoustic-Phonetic characterization. Signal processing and analysis methods of speech recognition. Bank-of-filters-front-end processor-linear predictive coding model for speech recognition-vector quantization-auditory based spectral analysis models.
Unit - III Pattern-comparison techniques. Speech recognition system analysis and implementation issues: Application of source-coding techniques-template training methods-performance analysis and recognition enhancements.
Unit - IV Homomorphic speech processing-Speech Recognition algorithm: Pattern Recognition based and knowledge based – Discrete utterance and continuous speech recognition systems-Principles of speaker recognition-projects.
Unit - V Speech recognition based on connected word models-Large vocabulary continuous speech recognition – Task oriented applications of automatic speech recognition.
REFERENCES:
1. Rabiner & Schaffer, “Digital processing of speech signals”, Prentice Hall. 1980. 2. Lawrence Rabiner, ”Fundamentals of speech recognition”, Prentice Hall. 3. Samuel D.Stearns and Ruth A.David “Signal Processing algorithms”, Prentice Hall, 1988. 4. D.Shanghessuy, “Speech Communication”, Prentice Hal l,1987
M.Sc. Computer Science (UD)- 2016-17 onwards Annexure No.76B
Page 51 of 53 SCAA Dt: 10.06.2016
E-COMMERCE
Subject Code: 16CS3E08 Number of credits: 4
Subject Description: This course presents an idea on fundamentals of E-Commerce. Goals: To enable the student to familiar with network infrastructure, Information Publishing Technology, Search Engines and Directory Services. Objectives:
On successful completion of the course the student should have: Understood concepts and principles in E-Commerce
Contents: Unit - I Introduction to E-Commerce: Benefits-Impacts-Classification and Application of E-Commerce-Business Model-Architectural Frame Work
Unit - II Network Infrastructure: Local Area Network-Ethernet-Wide Area Network-Internet-TCP/IP Reference Model-Domain Name System-Internet Industry structure-Information Distribution and Messaging: FTP Application-Electronic Mail-World Wide Web Server-HTTP-Web Server Implementations
Unit - III Information Publishing Technology: Information publishing-Web Browsers-HTML-CGI-Multimedia Content - Other Multimedia Objects-VRML- Securing the Business on Internet-Why Information on Internet is vulnerable?-Security Policy-Procedures and Practices-Site Security-Protecting the Network-Firewalls-Securing the Web Service
Unit - IV Securing Network Transaction-Electronic Payment Systems: Introduction –Online Payment Systems-Pre-paid Electronic Payment System- Post-paid Electronic Payment System-Requirement Metrics of a Payment System
Unit - V Search Engines and Directory Services: Information Directories –Search Engines –Internet Adverting- Agents in Electronic Commerce: Needs and Types of Agents-Agent Technologies-Agents Standards and Protocols-Agents Applications-Case Study.
REFERENCES: 1. Bharat Bhasker, “Electronic Commerce Framework, Technologies and Applications”,
Tata McGraw Hill Publication, 2003.
M.Sc. Computer Science (UD)- 2016-17 onwards Annexure No.76B
Page 52 of 53 SCAA Dt: 10.06.2016
DISTRIBUTED SYSTEMS
Subject Code: 16CS3E09 Number of credits: 4
Subject Description: This course presents an Introduction to Distributed Systems, Client/Server Network Model and Distributed Databases Goals: To enable the student to familiar with distributed systems and client server computing Objectives:
On successful completion of the course the student should have:
Understood Distributed Systems in detail
Contents: Unit - I Distributed Systems: Fully distributed processing systems – Networks and Interconnection structures – Designing a Distributed Processing System.
Unit - II Distributed Systems: Pros and Cons of Distributed processing – Distributed databases – the challenge of distributed data – loading factors – managing the distributed resources – division of responsibilities.
Unit - III Design Considerations: Communications line loading – Line loading Calculations – Partitioning and allocation – Data flow systems – dimension analysis – network database design considerations – ration analysis – database decision trees – synchronization of network databases.
Unit - IV Client/Server Network Model: Concept – file server – printer server – an e-mail server. Unit - V Distributed Databases: An overview – Distributed Databases – Principles of Distributed Databases – levels of transparency – Distributed Database Design – The R* Project Technique Problems of Heterogeneous Distributed Databases.
REFERENCES: 1. John A. Sharp, “An Introduction to Distributed and Parallel Processing , Blackwell
Scientific Publications, 1987. 2. Uyless D.Black, “Data Communications & Distributed Networks . 3. Joel M.Crichlow, “Introduction to Distributed & Parallel Computing . 4. Stefans Ceri, Ginseppe Pelagatti, “Distributed Databases Principles and Systems,
McGraw Hill Book Co., New York, 1985.
M.Sc. Computer Science (UD)- 2016-17 onwards Annexure No.76B
Page 53 of 53 SCAA Dt: 10.06.2016
Open Source Technologies
Subject Code: 16CS3E10 Number of credits: 4 Subject Description: This course presents the principles of Open Source, platform and various
technologies and their integration. Goals: To enable the student to learn and use the available Open Source Software Technologies. Objectives: On successful completion of the course, the student should have
Understood the principles of open source technologies and the usage of Linux, Apache, PHP and MySQL
Unit I Introduction: Open Source, Free Software, Free Software vs. Open Source software, Public Domain Software, History : BSD, The Free Software Foundation and the GNU Project, Philosophy: Software Freedom, Open Source Development Model, Licenses and Patents, Economics of FOSS - Zero Marginal Cost, Income-generation opportunities, Problems with traditional commercial software, Internationalization. Unit II Open Source Platform and Technologies: The Open Source Platform – Operating Systems, Windowing Systems and Desktops, GIMP, Technologies Underlying Open source Development. Unit III Linux Application: Accessing and Running Applications-Multimedia in Linux : Listening to Audio, Playing video, Using Digital Camera, Recording music / video CDs. Publishing: Open office, Working with Graphics, Printing Documents, Displaying documents with Ghost script and Acrobat, Using Scanners driven by SANE. Unit IV PHP: Installing and Configuring PHP, Building Blocks of PHP, Flow control functions in PHP, Working with functions, arrays, objects and forms. Unit V PHP and MySQL Integration: Understanding the Database Design Process, Learning Basic SQL commands, Using Transactions and Stored Procedures in MySQL, Interacting with MySQL using PHP. REFERENCES:
1. Christopher Negus, Red Hat Linux Bible, Wiley Publishing, ISBN: 0-7645-4333-4. 2. Fadi P. Deek, James A. M. McHugh, Open Source Technology and Policy, Cambridge
University Press, 2008. 3. Julie C Melonie, PHP, MySQL and Apache, Pearson Education, ISBN: 81-297-0443-9.
4. http://en.wikibooks.org/wiki/Open_Source.