CS6801 MULTI-CORE ARCHITECTURES AND PROGRAMMING | syllabus

CS6801 MULTI-CORE ARCHITECTURES AND PROGRAMMING L T P C 3 0 0 3

OBJECTIVES:

The student should be made to:
 Understand the challenges in parallel and multi-threaded programming.
 Learn about the various parallel programming paradigms, and solutions.

UNIT I      MULTI-CORE PROCESSORS    (9)

Single core to Multi-core architectures – SIMD and MIMD systems – Interconnection networks -
Symmetric and Distributed Shared Memory Architectures – Cache coherence - Performance Issues –
Parallel program design.

UNIT II      PARALLEL PROGRAM CHALLENGES    (9)

Performance – Scalability – Synchronization and data sharing – Data races – Synchronization
primitives (mutexes, locks, semaphores, barriers) – deadlocks and livelocks – communication
between threads (condition variables, signals, message queues and pipes).

UNIT III      SHARED MEMORY PROGRAMMING WITH OpenMP    (9)

OpenMP Execution Model – Memory Model – OpenMP Directives – Work-sharing Constructs - Library
functions – Handling Data and Functional Parallelism – Handling Loops - Performance
Considerations.

UNIT IV      DISTRIBUTED MEMORY PROGRAMMING WITH MPI    (9)

MPI program execution – MPI constructs – libraries – MPI send and receive – Point-to-point and
Collective communication – MPI derived datatypes – Performance evaluation

UNIT V      PARALLEL PROGRAM DEVELOPMENT    (9)

Case studies - n-Body solvers – Tree Search – OpenMP and MPI implementations and comparison.

                                                                                                                          TOTAL: 45 PERIODS

OUTCOMES:

At the end of the course, the student should be able to:
 Program Parallel Processors.
 Develop programs using OpenMP and MPI.
 Compare and contrast programming for serial processors and programming for parallel
processors.

TEXT BOOKS:

1. Peter S. Pacheco, “An Introduction to Parallel Programming”, Morgan-Kauffman/Elsevier, 2011.
2. Darryl Gove, “Multicore Application Programming for Windows, Linux, and Oracle Solaris”,
Pearson, 2011 (unit 2)

REFERENCES:

1. Michael J Quinn, “Parallel programming in C with MPI and OpenMP”, Tata McGraw Hill, 2003.
2. Shameem Akhter and Jason Roberts, “Multi-core Programming”, Intel Press, 2006.

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CS6811 PROJECT WORK | syllabus

CS6811 PROJECT WORK L T P C 0 0 12 6

OBJECTIVES:

 To develop the ability to solve a specific problem right from its identification and literature
review till the successful solution of the same. To train the students in preparing project reports
and to face reviews and viva voce examination.
The students in a group of 3 to 4 works on a topic approved by the head of the department under the
guidance of a faculty member and prepares a comprehensive project report after completing the work
to the satisfaction of the supervisor. The progress of the project is evaluated based on a minimum of
three reviews. The review committee may be constituted by the Head of the Department. A project
report is required at the end of the semester. The project work is evaluated based on oral
presentation and the project report jointly by external and internal examiners constituted by the Head
of the Department.

TOTAL: 180 PERIODS

OUTCOMES:

 On Completion of the project work students will be in a position to take up any challenging
practical problems and find solution by formulating proper methodology.

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CS6008 HUMAN COMPUTER INTERACTION | syllabus (ELECTIVE-IV)

CS6008    HUMAN COMPUTER INTERACTION L T P C 3 0 0 3

                                                                                        

OBJECTIVES:

The student should be made to:
 Learn the foundations of Human Computer Interaction.
 Be familiar with the design technologies for individuals and persons with disabilities.
 Be aware of mobile HCI.
 Learn the guidelines for user interface.

UNIT I      FOUNDATIONS OF HCI    (9)

The Human: I/O channels – Memory – Reasoning and problem solving; The computer: Devices –
Memory – processing and networks; Interaction: Models – frameworks – Ergonomics – styles –
elements – interactivity- Paradigms.

UNIT II       DESIGN & SOFTWARE PROCESS    (9)

Interactive Design basics – process – scenarios – navigation – screen design – Iteration and
prototyping. HCI in software process – software life cycle – usability engineering – Prototyping in
practice – design rationale. Design rules – principles, standards, guidelines, rules. Evaluation
Techniques – Universal Design.

UNIT III      MODELS AND THEORIES   (9)

Cognitive models –Socio-Organizational issues and stake holder requirements –Communication and
collaboration models-Hypertext, Multimedia and WWW.

UNIT IV      MOBILE HCI   (9)

Mobile Ecosystem: Platforms, Application frameworks- Types of Mobile Applications: Widgets,
Applications, Games- Mobile Information Architecture, Mobile 2.0, Mobile Design: Elements of Mobile
Design, Tools.

UNIT V      WEB INTERFACE DESIGN    (9)

Designing Web Interfaces – Drag & Drop, Direct Selection, Contextual Tools, Overlays, Inlays and
Virtual Pages, Process Flow. Case Studies.

                                                                                                     L: 45, T: 0,TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the student should be able to:
 Design effective dialog for HCI.
 Design effective HCI for individuals and persons with disabilities.
 Assess the importance of user feedback.
 Explain the HCI implications for designing multimedia/ ecommerce/ e-learning Web sites.
 Develop meaningful user interface.

TEXT BOOKS:

1. Alan Dix, Janet Finlay, Gregory Abowd, Russell Beale, “Human Computer Interaction”, 3rd Edition,
Pearson Education, 2004 (UNIT I , II & III).
2. Brian Fling, “Mobile Design and Development”, First Edition , O’Reilly Media Inc., 2009
(UNIT –IV).
3. Bill Scott and Theresa Neil, “Designing Web Interfaces”, First Edition, O’Reilly, 2009.(UNIT-V).

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CS6009 NANO COMPUTING | syllabus (ELECTIVE-IV)

CS6009    NANO COMPUTING L T P C 3 0 0 3

                                                       

OBJECTIVES:

The student should be made to:
 Learn nano computing challenges.
 Be familiar with the imperfections.
 Be exposed to reliability evaluation strategies.
 Learn nano scale quantum computing.
 Understand Molecular Computing and Optimal Computing.

UNIT I      NANOCOMPUTING-PROSPECTS AND CHALLENGES   (9)

Introduction - History of Computing - Nanocomputing - Quantum Computers – Nanocomputing
Technologies - Nano Information Processing - Prospects and Challenges - Physics of Nanocomputing
: Digital Signals and Gates - Silicon Nanoelectronics - Carbon Nanotube Electronics - Carbon
Nanotube Field-effect Transistors – Nanolithography.

UNIT II      NANOCOMPUTING WITH IMPERFECTIONS    (9)

Introduction - Nanocomputing in the Presence of Defects and Faults - Defect Tolerance - Towards
Quadrillion Transistor Logic Systems.

UNIT III      RELIABILITY OF NANOCOMPUTING    (9)

Markov Random Fields - Reliability Evaluation Strategies - NANOLAB - NANOPRISM - Reliable
Manufacturing and Behavior from Law of Large Numbers.

UNIT IV      NANOSCALE QUANTUM COMPUTING    (9)

Quantum Computers - Hardware Challenges to Large Quantum Computers - Fabrication, Test, and
Architectural Challenges - Quantum-dot Cellular Automata (QCA) - Computing with QCA - QCA
Clocking - QCA Design Rules.

UNIT V      QCADESIGNER SOFTWARE AND QCA IMPLEMENTATION    (9)

Basic QCA Circuits using QCA Designer - QCA Implementation - Molecular and Optical Computing:
Molecular Computing - Optimal Computing - Ultrafast Pulse Shaping and Tb/sec Data Speeds.

                                                                                                                       TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the student should be able to:
 Discuss nano computing challenges.
 Handle the imperfections.
 Apply reliability evaluation strategies.
 Use nano scale quantum computing.
 Utilize Molecular Computing and Optimal Computing.

TEXT BOOK:

1. Sahni V. and Goswami D., Nano Computing, McGraw Hill Education Asia Ltd. (2008), ISBN (13):
978007024892.

REFERNCES:

1. Sandeep K. Shukla and R. Iris Bahar., Nano, Quantum and Molecular Computing, Kluwer
Academic Publishers 2004, ISBN: 1402080670.
2. Sahni V, Quantum Computing, McGraw Hill Education Asia Ltd. 2007.
3. Jean-Baptiste Waldner,Nanocomputers and Swarm Intelligence, John Wiley & Sons, Inc. 2008,
ISBN (13): 978-1848210097.

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IT6011 KNOWLEDGE MANAGEMENT | syllabus (ELECTIVE-IV)

                                                                              

IT6011     KNOWLEDGE MANAGEMENT L T P C 3 0 0 3

OBJECTIVES:
The student should be made to:
 Learn the Evolution of Knowledge management.
 Be familiar with tools.
 Be exposed to Applications.
 Be familiar with some case studies.

UNIT I      INTRODUCTION    (9)

An Introduction to Knowledge Management - The foundations of knowledge management- including
cultural issues- technology applications organizational concepts and processes- management
aspects- and decision support systems. The Evolution of Knowledge management: From Information
Management to Knowledge Management - Key Challenges Facing the Evolution of Knowledge
Management - Ethics for Knowledge Management.

UNIT II      CREATING THE CULTURE OF LEARNING AND KNOWLEDGE SHARING    (8)

Organization and Knowledge Management - Building the Learning Organization. Knowledge Markets:
Cooperation among Distributed Technical Specialists – Tacit Knowledge and Quality Assurance.

UNIT III      KNOWLEDGE MANAGEMENT-THE TOOLS    (10)

Telecommunications and Networks in Knowledge Management - Internet Search Engines and
Knowledge Management - Information Technology in Support of Knowledge Management -
Knowledge Management and Vocabulary Control - Information Mapping in Information Retrieval -
Information Coding in the Internet Environment - Repackaging Information.

UNIT IV      KNOWLEDGEMANAGEMENT-APPLICATION    (9)

Components of a Knowledge Strategy - Case Studies (From Library to Knowledge Center, Knowledge
Management in the Health Sciences, Knowledge Management in Developing Countries).

UNIT V      FUTURE TRENDS AND CASE STUDIES     (9)

Advanced topics and case studies in knowledge management - Development of a knowledge
management map/plan that is integrated with an organization's strategic and business plan - A case
study on Corporate Memories for supporting various aspects in the process life -cycles of an
organization.

                                                                                                                          TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the student should be able to:
 Use the knowledge management tools.
 Develop knowledge management Applications.
 Design and develop enterprise applications.

TEXT BOOK:

1. Srikantaiah.T. K., Koenig, M., “Knowledge Management for the Information Professional”
Information Today, Inc., 2000.

REFERENCE:

1. Nonaka, I., Takeuchi, H., “The Knowledge-Creating Company: How Japanese Companies Create
the Dynamics of Innovation”, Oxford University Press, 1995.

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CS6010 SOCIAL NETWORK ANALYSIS | syllabus (ELECTIVE-IV)

CS6010    SOCIAL NETWORK ANALYSIS L T P C 3 0 0 3

                                                                              

OBJECTIVES:

The student should be made to:
 Understand the concept of semantic web and related applications.
 Learn knowledge representation using ontology.
 Understand human behaviour in social web and related communities.
 Learn visualization of social networks.

UNIT I      INTRODUCTION    (9)

Introduction to Semantic Web: Limitations of current Web - Development of Semantic Web -
Emergence of the Social Web - Social Network analysis: Development of Social Network Analysis -
Key concepts and measures in network analysis - Electronic sources for network analysis: Electronic
discussion networks, Blogs and online communities - Web-based networks - Applications of Social
Network Analysis.

UNIT II      MODELLING, AGGREGATING AND KNOWLEDGE
REPRESENTATION                                       (9)

Ontology and their role in the Semantic Web: Ontology-based knowledge Representation - Ontology
languages for the Semantic Web: Resource Description Framework - Web Ontology Language -
Modelling and aggregating social network data: State-of-the-art in network data representation -
Ontological representation of social individuals - Ontological representation of social relationships -
Aggregating and reasoning with social network data - Advanced representations.

UNIT III      EXTRACTION AND MINING COMMUNITIES IN WEB SOCIAL
NETWORKS                                                          (9)

Extracting evolution of Web Community from a Series of Web Archive - Detecting communities in
social networks - Definition of community - Evaluating communities - Methods for community
detection and mining - Applications of community mining algorithms - Tools for detecting communities
social network infrastructures and communities - Decentralized online social networks - Multi-
Relational characterization of dynamic social network communities.

UNIT IV      PREDICTING HUMAN BEHAVIOUR AND PRIVACY ISSUES    (9)

Understanding and predicting human behaviour for social communities - User data management -
Inference and Distribution - Enabling new human experiences - Reality mining - Context - Awareness
- Privacy in online social networks - Trust in online environment - Trust models based on subjective
logic - Trust network analysis - Trust transitivity analysis - Combining trust and reputation - Trust
derivation based on trust comparisons - Attack spectrum and countermeasures.

UNIT V      VISUALIZATION AND APPLICATIONS OF SOCIAL NETWORKS    (9)

Graph theory - Centrality - Clustering - Node-Edge Diagrams - Matrix representation - Visualizing
online social networks, Visualizing social networks with matrix-based representations - Matrix and
Node-Link Diagrams - Hybrid representations - Applications - Cover networks - Community welfare -Collaboration networks - Co-Citation networks.

                                                                                                                          TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the student should be able to:
 Develop semantic web related applications.
 Represent knowledge using ontology.
 Predict human behaviour in social web and related communities.
 Visualize social networks.

TEXT BOOKS:

1. Peter Mika, “Social Networks and the Semantic Web”, First Edition, Springer 2007.
2. Borko Furht, “Handbook of Social Network Technologies and Applications”, 1st Edition, Springer,
2010.

REFERENCES:

1. Guandong Xu ,Yanchun Zhang and Lin Li, “Web Mining and Social Networking – Techniques and
applications”, First Edition Springer, 2011.
2. Dion Goh and Schubert Foo, “Social information Retrieval Systems: Emerging Technologies and
Applications for Searching the Web Effectively”, IGI Global Snippet, 2008.
3. Max Chevalier, Christine Julien and Chantal Soulé-Dupuy, “Collaborative and Social Information
Retrieval and Access: Techniques for Improved user Modelling”, IGI Global Snippet, 2009.
4. John G. Breslin, Alexander Passant and Stefan Decker, “The Social Semantic Web”, Springer,
2009.
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MG6088 SOFTWARE PROJECT MANAGEMENT | syllabus (ELECTIVE-V)

MG6088     SOFTWARE PROJECT MANAGEMENT L T P C 3 0 0 3

                                                                                               

OBJECTIVES:

 To outline the need for Software Project Management
 To highlight different techniques for software cost estimation and activity planning.

UNIT I      PROJECT EVALUATION AND PROJECT PLANNING    (9)

Importance of Software Project Management – Activities Methodologies – Categorization of Software
Projects – Setting objectives – Management Principles – Management Control – Project portfolio
Management – Cost-benefit evaluation technology – Risk evaluation – Strategic program
Management – Stepwise Project Planning.

UNIT II      PROJECT LIFE CYCLE AND EFFORT ESTIMATION    (9)

Software process and Process Models – Choice of Process models - mental delivery – Rapid
Application development – Agile methods – Extreme Programming – SCRUM – Managing interactive
processes – Basics of Software estimation – Effort and Cost estimation techniques – COSMIC Full
function points - COCOMO II A Parametric Productivity Model - Staffing Pattern.

UNIT III      ACTIVITY PLANNING AND RISK MANAGEMENT    (9)

Objectives of Activity planning – Project schedules – Activities – Sequencing and scheduling –
Network Planning models – Forward Pass & Backward Pass techniques – Critical path (CRM) method
– Risk identification – Assessment – Monitoring – PERT technique – Monte Carlo simulation –
Resource Allocation – Creation of critical patterns – Cost schedules.

UNIT IV      PROJECT MANAGEMENT AND CONTROL    (9)

Framework for Management and control – Collection of data Project termination – Visualizing
progress – Cost monitoring – Earned Value Analysis- Project tracking – Change control- Software
Configuration Management – Managing contracts – Contract Management.

UNIT V      STAFFING IN SOFTWARE PROJECTS    (9)

Managing people – Organizational behavior – Best methods of staff selection – Motivation – The
Oldham-Hackman job characteristic model – Ethical and Programmed concerns – Working in teams –
Decision making – Team structures – Virtual teams – Communications genres – Communication
plans.

                                                                                                                         TOTAL: 45 PERIODS

OUTCOMES:

 At the end of the course the students will be able to practice Project Management principles while
developing a software.

TEXTBOOK:

1. Bob Hughes, Mike Cotterell and Rajib Mall: Software Project Management – Fifth Edition, Tata
McGraw Hill, New Delhi, 2012.

REFERENCES:

1. Robert K. Wysocki “Effective Software Project Management” – Wiley Publication,2011.
2. Walker Royce: “Software Project Management”- Addison-Wesley, 1998.
3. Gopalaswamy Ramesh, “Managing Global Software Projects” – McGraw Hill Education (India),
Fourteenth Reprint 2013.

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GE6075 PROFESSIONAL ETHICS IN ENGINEERING | syllabus (ELECTIVE-V)

GE6075    PROFESSIONAL ETHICS IN ENGINEERING L T P C  3 0 0 3

                                                                                                    

OBJECTIVES:

 To enable the students to create an awareness on Engineering Ethics and Human Values, to instill
Moral and Social Values and Loyalty and to appreciate the rights of others.

UNIT I      HUMAN VALUES    (10)

Morals, values and Ethics – Integrity – Work ethic – Service learning – Civic virtue – Respect for
others – Living peacefully – Caring – Sharing – Honesty – Courage – Valuing time – Cooperation –
Commitment – Empathy – Self confidence – Character – Spirituality – Introduction to Yoga and
meditation for professional excellence and stress management.

UNIT II      ENGINEERING ETHICS     (9)

Senses of ‘Engineering Ethics’ – Variety of moral issues – Types of inquiry – Moral dilemmas – Moral
Autonomy – Kohlberg’s theory – Gilligan’s theory – Consensus and Controversy – Models of
professional roles - Theories about right action – Self-interest – Customs and Religion – Uses of
Ethical Theories

UNIT III      ENGINEERING AS SOCIAL EXPERIMENTATION    (9)

Engineering as Experimentation – Engineers as responsible Experimenters – Codes of Ethics –
A Balanced Outlook on Law.

UNIT IV      SAFETY, RESPONSIBILITIES AND RIGHTS    (9)

Safety and Risk – Assessment of Safety and Risk – Risk Benefit Analysis and Reducing Risk -
Respect for Authority – Collective Bargaining – Confidentiality – Conflicts of Interest – Occupational
Crime – Professional Rights – Employee Rights – Intellectual Property Rights (IPR) – Discrimination

UNIT V      GLOBAL ISSUES    (8)

Multinational Corporations – Environmental Ethics – Computer Ethics – Weapons Development –
Engineers as Managers – Consulting Engineers – Engineers as Expert Witnesses and Advisors –
Moral Leadership –Code of Conduct – Corporate Social Responsibility

                                                                                                                        TOTAL: 45 PERIODS

OUTCOMES:

 Upon completion of the course, the student should be able to apply ethics in society, discuss the
ethical issues related to engineering and realize the responsibilities and rights in the society

TEXTBOOKS:

1. Mike W. Martin and Roland Schinzinger, “Ethics in Engineering”, Tata McGraw Hill, New Delhi, 2003.
2. Govindarajan M, Natarajan S, Senthil Kumar V. S, “Engineering Ethics”, Prentice Hall of India, New
Delhi, 2004.

REFERENCES:

1. Charles B. Fleddermann, “Engineering Ethics”, Pearson Prentice Hall, New Jersey, 2004.
2. Charles E. Harris, Michael S. Pritchard and Michael J. Rabins, “Engineering Ethics – Concepts and
Cases”, Cengage Learning, 2009
3. John R Boatright, “Ethics and the Conduct of Business”, Pearson Education, New Delhi, 2003
4. Edmund G Seebauer and Robert L Barry, “Fundametals of Ethics for Scientists and Engineers”,
Oxford University Press, Oxford, 2001
5. Laura P. Hartman and Joe Desjardins, “Business Ethics: Decision Making for Personal Integrity
and Social Responsibility” Mc Graw Hill education, India Pvt. Ltd.,New Delhi 2013.
6. World Community Service Centre, ‘ Value Education’, Vethathiri publications, Erode, 2011

Web sources:

1. www.onlineethics.org
2. www.nspe.org
3. www.globalethics.org
4. www.ethics.org

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CS6011 NATURAL LANGUAGE PROCESSING | syllabus (ELECTIVE-V)

CS6011    NATURAL LANGUAGE PROCESSING L T P C 3 0 0 3

                                                                                          
OBJECTIVES:

The student should be made to:
 Learn the techniques in natural language processing.
 Be familiar with the natural language generation.
 Be exposed to machine translation.
 Understand the information retrieval techniques.

UNIT I      OVERVIEW AND LANGUAGE MODELING    (8)

Overview: Origins and challenges of NLP-Language and Grammar-Processing Indian Languages-
NLP Applications-Information Retrieval. Language Modeling: Various Grammar- based Language
Models-Statistical Language Model.

UNIT II       WORD LEVEL AND SYNTACTIC ANALYSIS    (9)

Word Level Analysis: Regular Expressions-Finite-State Automata-Morphological Parsing-Spelling
Error Detection and correction-Words and Word classes-Part-of Speech Tagging.
Syntactic Analysis: Context-free Grammar-Constituency- Parsing-Probabilistic Parsing.

UNIT III      SEMANTIC ANALYSIS AND DISCOURSE PROCESSING (10)

Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense
Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and
Structure.

UNIT IV      NATURAL LANGUAGE GENERATION
AND MACHINE TRANSLATION                                   (9)
Natural Language Generation: Architecture of NLG Systems- Generation Tasks and Representations-
Application of NLG. Machine Translation: Problems in Machine Translation- Characteristics of Indian
Languages- Machine Translation Approaches-Translation involving Indian Languages.

UNIT V      INFORMATION RETRIEVAL AND LEXICAL RESOURCES    (9)

Information Retrieval: Design features of Information Retrieval Systems-Classical, Non-classical,
Alternative Models of Information Retrieval – valuation Lexical Resources: World Net-Frame Net-
Stemmers-POS Tagger- Research Corpora.

                                                                                                                      TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the student should be able to:
 Analyze the natural language text.
 Generate the natural language.
 Do machine translation.
 Apply information retrieval techniques.

TEXT BOOK:

1. Tanveer Siddiqui, U.S. Tiwary, “Natural Language Processing and Information Retrieval”, Oxford
University Press, 2008.

REFERENCES:

1. Daniel Jurafsky and James H Martin, “Speech and Language Processing: An introduction to
Natural Language Processing, Computational Linguistics and Speech Recognition”, 2nd Edition,
Prentice Hall, 2008.
2. James Allen, “Natural Language Understanding”, 2nd edition, Benjamin /Cummings publishing
company, 1995.

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CS6012 SOFT COMPUTING | syllabus (ELECTIVE-V)

CS6012    SOFT COMPUTING L T P C
                                                        3 0 0 3

OBJECTIVES:

The student should be made to:
 Learn the various soft computing frame works.
 Be familiar with design of various neural networks.
 Be exposed to fuzzy logic.
 Learn genetic programming.
 Be exposed to hybrid systems.

UNIT I      INTRODUCTION    (9)

Artificial neural network: Introduction, characteristics- learning methods – taxonomy – Evolution of
neural networks- basic models - important technologies - applications.
Fuzzy logic: Introduction - crisp sets- fuzzy sets - crisp relations and fuzzy relations: cartesian product
of relation - classical relation, fuzzy relations, tolerance and equivalence relations, non-iterative fuzzy
sets. Genetic algorithm- Introduction - biological background - traditional optimization and search
techniques - Genetic basic concepts.

UNIT II      NEURAL NETWORKS    (9)

McCulloch-Pitts neuron - linear separability - hebb network - supervised learning network: perceptron
networks - adaptive linear neuron, multiple adaptive linear neuron, BPN, RBF, TDNN- associative
memory network: auto-associative memory network, hetero-associative memory network, BAM,
hopfield networks, iterative autoassociative memory network & iterative associative memory network –unsupervised learning networks: Kohonen self organizing feature maps, LVQ – CP networks, ART
network.

UNIT III      FUZZY LOGIC    (9)

Membership functions: features, fuzzification, methods of membership value assignments-
Defuzzification: lambda cuts - methods - fuzzy arithmetic and fuzzy measures: fuzzy arithmetic -
extension principle - fuzzy measures - measures of fuzziness -fuzzy integrals - fuzzy rule base and
approximate reasoning : truth values and tables, fuzzy propositions, formation of rules-decomposition
of rules, aggregation of fuzzy rules, fuzzy reasoning-fuzzy inference systems-overview of fuzzy expert system-fuzzy decision making.

UNIT IV      GENETIC ALGORITHM    (9)

Genetic algorithm and search space - general genetic algorithm – operators - Generational cycle -
stopping condition – constraints - classification - genetic programming – multilevel optimization – real life problem- advances in GA.

UNIT V      HYBRID SOFT COMPUTING TECHNIQUES & APPLICATIONS    (9)

Neuro-fuzzy hybrid systems - genetic neuro hybrid systems - genetic fuzzy hybrid and fuzzy genetic
hybrid systems - simplified fuzzy ARTMAP - Applications: A fusion approach of multispectral images
with SAR, optimization of traveling salesman problem using genetic algorithm approach, soft
computing based hybrid fuzzy controllers.

                                                                                                                         TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the student should be able to:
 Apply various soft computing frame works.
 Design of various neural networks.
 Use fuzzy logic.
 Apply genetic programming.
 Discuss hybrid soft computing.

TEXT BOOKS:

1. J.S.R.Jang, C.T. Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI / Pearson
Education 2004.
2. S.N.Sivanandam and S.N.Deepa, "Principles of Soft Computing", Wiley India Pvt Ltd, 2011.

REFERENCES:

1. S.Rajasekaran and G.A.Vijayalakshmi Pai, "Neural Networks, Fuzzy Logic and Genetic
Algorithm: Synthesis & Applications", Prentice-Hall of India Pvt. Ltd., 2006.
2. George J. Klir, Ute St. Clair, Bo Yuan, “Fuzzy Set Theory: Foundations and Applications”
Prentice Hall, 1997.
3. David E. Goldberg, “Genetic Algorithm in Search Optimization and Machine Learning” Pearson
Education India, 2013.
4. James A. Freeman, David M. Skapura, “Neural Networks Algorithms, Applications, and
Programming Techniques, Pearson Education India, 1991.
5. Simon Haykin, “Neural Networks Comprehensive Foundation” Second Edition, Pearson
Education, 2005.

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CS6702 GRAPH THEORY AND APPLICATIONS | Syllabus

CS6702 GRAPH THEORY AND APPLICATIONS L T P C 3 0 0 3

OBJECTIVES:
 The student should be made to:

• Be familiar with the most fundamental Graph Theory topics and results.
• Be exposed to the techniques of proofs and analysis.

UNIT I                                    INTRODUCTION                                                                            ( 9)
Graphs – Introduction – Isomorphism – Sub graphs – Walks, Paths, Circuits –Connectedness – Components – Euler graphs – Hamiltonian paths and circuits – Trees – Properties of trees – Distance and centers in tree – Rooted and binary trees.


UNIT II                                 TREES, CONNECTIVITY & PLANARITY                                      (9) 
Spanning trees – Fundamental circuits – Spanning trees in a weighted graph – cut sets – Properties of cut set – All cut sets – Fundamental circuits and cut sets – Connectivity and separability – Network flows – 1-Isomorphism – 2-Isomorphism – Combinational and geometric graphs – Planer graphs – Different representation of a planer graph.

UNIT III                             MATRICES, COLOURING AND DIRECTED GRAPH                   (8)
 Chromatic number – Chromatic partitioning – Chromatic polynomial – Matching – Covering – Four color problem – Directed graphs – Types of directed graphs – Digraphs and binary relations – Directed paths and connectedness – Euler graphs.

UNIT IV                             PERMUTATIONS & COMBINATIONS                                             (9)
Fundamental principles of counting - Permutations and combinations - Binomial theorem - combinations with repetition - Combinatorial numbers - Principle of inclusion and exclusion - Derangements - Arrangements with forbidden positions.

UNIT V                             GENERATING FUNCTIONS                                                               (10)
 Generating functions - Partitions of integers - Exponential generating function – Summation operator - Recurrence relations - First order and second order – Non-homogeneous recurrence relations - Method of generating functions.
TOTAL: 45 PERIODS

TEXT BOOKS:
1. Narsingh Deo, “Graph Theory: With Application to Engineering and Computer Science”, Prentice Hall of India, 2003.
2. Grimaldi R.P. “Discrete and Combinatorial Mathematics: An Applied Introduction”, Addison Wesley, 1994.

REFERENCES:
1. Clark J. and Holton D.A, “A First Look at Graph Theory”, Allied Publishers, 1995.
2. Mott J.L., Kandel A. and Baker T.P. “Discrete Mathematics for Computer Scientists and Mathematicians” , Prentice Hall of India, 1996.
3. Liu C.L., “Elements of Discrete Mathematics”, McGraw Hill, 1985.
4. Rosen K.H., “Discrete Mathematics and Its Applications”, McGraw Hill, 2007.


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CS6010 SOCIAL NETWORK ANALYSIS|Syllabus

CS6010 SOCIAL NETWORK ANALYSIS L T P C 3 0 0 3

 OBJECTIVES: 
The student should be made to:

•  Understand the concept of semantic web and related applications.
•  Learn knowledge representation using ontology.
•  Understand human behaviour in social web and related communities
•  Learn visualization of social networks.

UNIT I INTRODUCTION (9)
Introduction to Semantic Web: Limitations of current Web - Development of Semantic Web - Emergence of the Social Web - Social Network analysis: Development of Social Network Analysis - Key concepts and measures in network analysis - Electronic sources for network analysis: Electronic discussion networks, Blogs and online communities - Web-based networks - Applications of Social Network Analysis.

 UNIT II MODELLING, AGGREGATING AND KNOWLEDGE REPRESENTATION (9) 
Ontology and their role in the Semantic Web: Ontology-based knowledge Representation - Ontology languages for the Semantic Web: Resource Description Framework - Web Ontology Language - Modelling and aggregating social network data: State-of-the-art in network data representation - Ontological representation of social individuals - Ontological representation of social relationships - Aggregating and reasoning with social network data - Advanced representations.

 UNIT III EXTRACTION AND MINING COMMUNITIES IN WEB SOCIAL NETWORKS (9) 
Extracting evolution of Web Community from a Series of Web Archive - Detecting communities in social networks - Definition of community - Evaluating communities - Methods for community detection and mining - Applications of community mining algorithms - Tools for detecting communities social network infrastructures and communities - Decentralized online social networks - Multi-Relational characterization of dynamic social network communities.

 UNIT IV PREDICTING HUMAN BEHAVIOUR AND PRIVACY ISSUES (9)
Understanding and predicting human behaviour for social communities - User data management - Inference and Distribution - Enabling new human experiences - Reality mining - Context - Awareness - Privacy in online social networks - Trust in online environment - Trust models based on subjective logic - Trust network analysis - Trust transitivity analysis - Combining trust and reputation - Trust derivation based on trust comparisons - Attack spectrum and countermeasures.

UNIT V VISUALIZATION AND APPLICATIONS OF SOCIAL NETWORKS (9) 
Graph theory - Centrality - Clustering - Node-Edge Diagrams - Matrix representation - Visualizing online social networks, Visualizing social networks with matrix-based representations - Matrix and Node-Link Diagrams - Hybrid representations - Applications - Cover networks - Community welfare - Collaboration networks - Co-Citation networks.

                                                                                                        TOTAL: 45 PERIODS

TEXT BOOKS:
 1. Peter Mika, “Social Networks and the Semantic Web”, , First Edition, Springer 2007.
 2. Borko Furht, “Handbook of Social Network Technologies and Applications”, 1st Edition, Springer, 2010.

REFERENCES: 
1. Guandong Xu ,Yanchun Zhang and Lin Li, “Web Mining and Social Networking – Techniques and applications”, First Edition Springer, 2011.
2. Dion Goh and Schubert Foo, “Social information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively”, IGI Global Snippet, 2008.
3. Max Chevalier, Christine Julien and Chantal Soulé-Dupuy, “Collaborative and Social Information Retrieval and Access: Techniques for Improved user Modelling”, IGI Global Snippet, 2009.
4. John G. Breslin, Alexandre Passant and Stefan Decker, “The Social Semantic Web”, Springer, 2009.

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IT6010 BUSINESS INTELLIGENCE|Syllabus

IT6010 BUSINESS INTELLIGENCE L T P C3 0 0 3

 OBJECTIVES:
 The student should be made to:

•  Be exposed with the basic rudiments of business intelligence system
•  understand the modeling aspects behind Business Intelligence
•  understand of the business intelligence life cycle and the techniques used in it
•  Be exposed with different data analysis tools and techniques

UNIT I                               BUSINESS INTELLIGENCE                                                        (9)
 Effective and timely decisions – Data, information and knowledge – Role of mathematical models – Business intelligence architectures: Cycle of a business intelligence analysis – Enabling factors in business intelligence projects – Development of a business intelligence system – Ethics and business intelligence.

UNIT II                           KNOWLEDGE DELIVERY                                                             (9)
 The business intelligence user types, Standard reports, Interactive Analysis and Ad Hoc Querying, Parameterized Reports and Self-Service Reporting, dimensional analysis, Alerts/Notifications, Visualization: Charts, Graphs, Widgets, Scorecards and Dashboards, Geographic Visualization, Integrated Analytics, Considerations: Optimizing the Presentation for the Right Message.

UNIT III                                            EFFICIENCY                                                                    (9)
 Efficiency measures – The CCR model: Definition of target objectives- Peer groups – Identification of good operating practices; cross efficiency analysis – virtual inputs and outputs – Other models. Pattern matching – cluster analysis, outlier analysis

UNIT IV                          BUSINESS INTELLIGENCE APPLICATIONS                             (9)
 Marketing models – Logistic and Production models – Case studies.

UNIT V                           FUTURE OF BUSINESS INTELLIGENCE                                     (9)
 Future of business intelligence – Emerging Technologies, Machine Learning, Predicting the Future, BI Search & Text Analytics – Advanced Visualization – Rich Report, Future beyond Technology.

                                                                                                                 TOTAL: 45 PERIODS
TEXT BOOK:
1. Efraim Turban, Ramesh Sharda, Dursun Delen, “Decision Support and Business Intelligence Systems”, 9th Edition, Pearson 2013.

REFERENCES:
2. Larissa T. Moss, S. Atre, “Business Intelligence Roadmap: The Complete Project Lifecycle of Decision Making”, Addison Wesley, 2003.
3. Carlo Vercellis, “Business Intelligence: Data Mining and Optimization for Decision Making”, Wiley Publications, 2009.
4. David Loshin Morgan, Kaufman, “Business Intelligence: The Savvy Manager‟s Guide”, Second Edition, 2012.
5. Cindi Howson, “Successful Business Intelligence: Secrets to Making BI a Killer App”, McGraw-Hill, 2007.
6. Ralph Kimball , Margy Ross , Warren Thornthwaite, Joy Mundy, Bob Becker, “The Data Warehouse Lifecycle Toolkit”, Wiley Publication Inc.,2007.


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IT6011 KNOWLEDGE MANAGEMENT|Syllabus

IT6011 KNOWLEDGE MANAGEMENT L T P C 3 0 0 3

OBJECTIVES: 
The student should be made to:

•  Learn the Evolution of Knowledge management.
•  Be familiar with tools.
•  Be exposed to Applications.
•  Be familiar with some case studies.

UNIT I                                     INTRODUCTION                                                      (9)
 Introduction: An Introduction to Knowledge Management - The foundations of knowledge management- including cultural issues- technology applications organizational concepts and processes- management aspects- and decision support systems. The Evolution of Knowledge management: From Information Management to Knowledge Management - Key Challenges Facing the Evolution of Knowledge Management - Ethics for Knowledge Management.

 UNIT II     CREATING THE CULTURE OF LEARNING AND KNOWLEDGE SHARING    (8) 
Organization and Knowledge Management - Building the Learning Organization. Knowledge Markets: Cooperation among Distributed Technical Specialists – Tacit Knowledge and Quality Assurance.

UNIT III             KNOWLEDGE MANAGEMENT-THE TOOLS                               (10) 
Telecommunications and Networks in Knowledge Management - Internet Search Engines and Knowledge Management - Information Technology in Support of Knowledge Management - Knowledge Management and Vocabulary Control - Information Mapping in Information Retrieval - Information Coding in the Internet Environment - Repackaging Information.

 UNIT IV                KNOWLEDGEMANAGEMENT-APPLICATION                           (9) 
Components of a Knowledge Strategy - Case Studies (From Library to Knowledge Center, Knowledge Management in the Health Sciences, Knowledge Management in Developing Countries).

UNIT V                          FUTURE TRENDS AND CASE STUDIES                                (9)
 Advanced topics and case studies in knowledge management - Development of a knowledge management map/plan that is integrated with an organization's strategic and business plan - A case study on Corporate Memories for supporting various aspects in the process life -cycles of an organization.

                                                                                                                     TOTAL: 45 PERIODS
TEXT BOOK:
1. Srikantaiah, T.K., Koenig, M., “Knowledge Management for the Information Professional” Information Today, Inc., 2000.

REFERENCE:
 1. Nonaka, I., Takeuchi, H., “The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation”, Oxford University Press, 1995.


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