UNIVERSITY DEPARTMENTS ANNA UNIVERSITY CHENNAI : : CHENNAI 600 025 REGULATIONS - 2009 CURRICULUM I TO IV SEMESTERS (FULL TIME) M.E. COMPUTER SCIENCE & ENGINEERING SPECIALIZATION IN KNOWLEDGE ENGINEERING & COMPUTATIONAL LINGUISTICS SEMESTER I (5+1) SL. NO COURSE CODE COURSE TITLE L T P C THEORY 1 MA9110 Operations Research 3 1 0 4 2 CP9112 Advanced Data Structures and Algorithms 3 0 0 3 3 CP9113 Advanced Computer Architecture 3 0 0 3 4 CP9114 Object Oriented Systems Engineering 3 0 0 3 5 CP9115 Network Engineering and Management 3 0 0 3 PRACTICAL 6 CK9113 Computer Laboratory - I 0 0 3 2 TOTAL 15 1 3 18 SEMESTER II (6+1) SL. NO COURSE CODE COURSE TITLE L T P C THEORY 1 CP9124 Parallel Algorithms 3 0 0 3 2 CP9122 Compiler Optimization 3 0 0 3 3 CP9123 Advanced Database Technology 3 0 0 3 1
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UNIVERSITY DEPARTMENTS
ANNA UNIVERSITY CHENNAI : : CHENNAI 600 025
REGULATIONS - 2009
CURRICULUM I TO IV SEMESTERS (FULL TIME)
M.E. COMPUTER SCIENCE & ENGINEERING
SPECIALIZATION IN KNOWLEDGE ENGINEERING &
COMPUTATIONAL LINGUISTICS
SEMESTER I (5+1)
SL. NOCOURSE
CODECOURSE TITLE L T P C
THEORY
1 MA9110 Operations Research 3 1 0 4
2 CP9112 Advanced Data Structures and Algorithms 3 0 0 3
3 CP9113 Advanced Computer Architecture 3 0 0 3
4 CP9114 Object Oriented Systems Engineering 3 0 0 3
5 CP9115 Network Engineering and Management 3 0 0 3
PRACTICAL
6 CK9113 Computer Laboratory - I 0 0 3 2
TOTAL 15 1 3 18
SEMESTER II (6+1)
SL. NOCOURSE
CODECOURSE TITLE L T P C
THEORY
1 CP9124 Parallel Algorithms 3 0 0 3
2 CP9122 Compiler Optimization 3 0 0 3
3 CP9123 Advanced Database Technology 3 0 0 3
4 CP9153 Knowledge Engineering 3 0 0 3
5 CK9123 Computational Linguistics 3 0 0 3
6 E1 Elective – I 3 0 0 3
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PRACTICAL
7 CK9125 Language Technology Laboratory 1 0 3 3
TOTAL 19 0 3 21
SEMESTER III (3+1)
SL. NOCOURSE
CODECOURSE TITLE L T P C
THEORY
1 CP9131 Security Principles and Practice 3 0 0 3
2 E2 Elective – II 3 0 0 3
3 E3 Elective – III 3 0 0 3
PRACTICAL
4 CK9134 Project Phase - I 0 0 12 6
TOTAL 9 0 12 15
SEMESTER IV (0+1)
SL. NOCOURSE
CODECOURSE TITLE L T P C
PRACTICAL
1 CK9141 Project Phase - II 0 0 24 12
TOTAL 0 0 24 12
Total No of Credits : 67
No of Theory courses : 14
No of Lab Courses : 02
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UNIVERSITY DEPARTMENTS
ANNA UNIVERSITY CHENNAI : : CHENNAI 600 025
REGULATIONS - 2009
CURRICULUM I TO VI SEMESTERS (PART TIME)
M.E. COMPUTER SCIENCE & ENGINEERING
SPECIALIZATION IN KNOWLEDGE ENGINEERING &
COMPUTATIONAL LINGUISTICS
SEMESTER I
SL. NOCOURSE
CODECOURSE TITLE L T P C
THEORY
1 MA9110 Operations Research 3 1 0 4
2 CP9112 Advanced Data Structures and Algorithms 3 0 0 3
3 CP9114 Object Oriented Systems Engineering 3 0 0 3
TOTAL 9 1 0 10
SEMESTER II
SL. NOCOURSE
CODECOURSE TITLE L T P C
THEORY
1 CP9123 Advanced Database Technology 3 0 0 3
2 CK9123 Computational Linguistics 3 0 0 3
3 E1 Elective I 3 0 0 3
PRACTICAL
4 CK9125 Language Technology Laboratory 1 0 0 3
TOTAL 10 0 0 12
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SEMESTER III
SL. NOCOURSE
CODECOURSE TITLE L T P C
THEORY
1 CP9115 Network Engineering and Management 3 0 0 3
2 CP9113 Advanced Computer Architecture 3 0 0 3
PRACTICAL
3 CK9113 Computer Laboratory I 0 0 3 2
TOTAL 6 0 3 8
SEMESTER IV
SL. NOCOURSE
CODECOURSE TITLE L T P C
THEORY
1 CP9124 Parallel Algorithms 3 0 0 3
2 CP9122 Compiler Optimization 3 0 0 3
3 CP9153 Knowledge Engineering 3 0 0 3
TOTAL 9 0 0 9
SEMESTER V
SL. NO
COURSE CODE
COURSE TITLE L T P C
THEORY
1 CP9131 Security Principles and Practice 3 0 0 3
2 E2 Elective II 3 0 0 3
3 E3 Elective III 3 0 0 3
PRACTICAL
4 CK9134 Project Work (phase I) 0 0 12 6
TOTAL 9 0 12 15
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SEMESTER VI
SL. NOCOURSE
CODECOURSE TITLE L T P C
PRACTICAL
1 CK9141 Project Work (Phase II) 0 0 24 12
TOTAL 0 0 24 12
List of Electives
Knowledge Engineering and Computational Linguistics Stream
SL. NOCOURSE
CODECOURSE TITLE L T P C
1 IT9155 Ontology and Semantic Web 3 0 0 3
2 CK9152 Human Language Technology 3 0 0 3
3 CK9153 Information Retrieval Techniques 3 0 0 3
4 CK9154 Statistical Natural Language Processing 3 0 0 3
5 CP9173 Machine Learning 3 0 0 3
6 CK9156 Natural Language Generation 3 0 0 3
7 CK9157 Text Mining 3 0 0 3
Computer Science and Engineering Stream
SL. NOCOURSE
CODECOURSE TITLE L T P C
8 CP9157 Speech Processing 3 0 0 3
9 CP9161 Knowledge Management 3 0 0 3
10 CP9164 Data Warehousing And Data Mining 3 0 0 3
11 CP9154 Visualization Techniques 3 0 0 3
12 CP9156 User Interface Design 3 0 0 3
13 CP9158 Bio Informatics 3 0 0 3
14 CP9159 Soft Computing 3 0 0 3
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SL. NOCOURSE
CODECOURSE TITLE L T P C
15 CK9161 Service Oriented Computing 3 0 0 3
16 SW9160 XML and Web Services 3 0 0 3
17 CK9163 Pervasive Computing 3 0 0 3
18 SW9161 Software Agents 3 0 0 3
19 IT9161 Artificial Intelligence 3 0 0 3
20 CP9176 Human Resources Management 3 0 0 3
The Students are required to take at least two Electives from Knowledge Engineering and Computational Linguistics Stream
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MA9110 OPERATIONS RESEARCH L T P C 3 1 0 4
UNIT I QUEUEING MODELS 9Poisson Process – Markovian Queues – Single and Multi-server Models – Little’s formula – Machine Interference Model – Steady State analysis – Self Service Queue.
UNIT II ADVANCED QUEUEING MODELS 9Non-Markovian Queues – Pollaczek Khintchine Formula – Queues in Series – Open Queuing Networks –Closed Queuing networks.
UNIT III SIMULATION 9Discrete Even Simulation – Monte – Carlo Simulation – Stochastic Simulation – Applications to Queuing systems.
UNIT IV LINEAR PROGRAMMING 9Formulation – Graphical solution – Simplex method – Two phase method -Transportation and Assignment Problems.
UNIT V NON-LINEAR PROGRAMMING 9Lagrange multipliers – Equality constraints – Inequality constraints – Kuhn – Tucker conditions – Quadratic Programming.
Binary Search Trees – AVL Trees – Red-Black trees – Multi-way Search Trees –B-Trees – Splay Trees – Tries.
UNIT IV `MULTIMEDIA STRUCTURES 9Segment Trees – k-d Trees – Point Quad Trees – MX-Quad Trees – R-Trees – TV-Trees.
UNIT V ALGORITHMS 9Huffman Coding – Convex Hull – Topological Sort – Tree Vertex Splitting – Activity Networks – Flow Shop Scheduling – Counting Binary Trees – Introduction to Randomized Algorithms.
TOTAL = 45
REFERENCES1. E. Horowitz, S.Sahni and Dinesh Mehta, Fundamentals of Data structures in C+
+, Uiversity Press, 2007.2. E. Horowitz, S. Sahni and S. Rajasekaran, Computer Algorithms/C++, Second
Edition, University Press, 2007.3. G. Brassard and P. Bratley, Algorithmics: Theory and Practice, Printice –Hall,
1988.4. V.S. Subramanian, Principles of Multimedia Database systems, Morgan
Kaufman, 1998.
CP9113 ADVANCED COMPUTER ARCHITECTURE L T P C
3 0 0 3
UNIT I PIPELINING AND ILP 9Fundamentals of Computer Design - Measuring and Reporting Performance - Instruction Level Parallelism and Its Exploitation - Concepts and Challenges - Overcoming Data Hazards with Dynamic Scheduling – Dynamic Branch Prediction - Speculation - Multiple Issue Processors – Case Studies.
UNIT II ADVANCED TECHNIQUES FOR EXPLOITING ILP 9Compiler Techniques for Exposing ILP - Limitations on ILP for Realizable Processors - Hardware versus Software Speculation - Multithreading: Using ILP Support to Exploit Thread-level Parallelism - Performance and Efficiency in Advanced Multiple Issue Processors - Case Studies.
UNIT III MULTIPROCESSORS 9Symmetric and distributed shared memory architectures – Cache coherence issues - Performance Issues – Synchronization issues – Models of Memory Consistency - Interconnection networks – Buses, crossbar and multi-stage switches.
UNIT IV MULTI-CORE ARCHITECTURES 9Software and hardware multithreading – SMT and CMP architectures – Design issues – Case studies – Intel Multi-core architecture – SUN CMP architecture – IBM cell architecture.- hp architecture.
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UNIT V MEMORY HIERARCHY DESIGN 9Introduction - Optimizations of Cache Performance - Memory Technology and Optimizations - Protection: Virtual Memory and Virtual Machines - Design of Memory Hierarchies - Case Studies.
TOTAL - 45
REFERENCES1. John L. Hennessey and David A. Patterson, “ Computer Architecture – A
quantitative approach”, Morgan Kaufmann / Elsevier, 4th. edition, 2007.2. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture : A
hardware/ software approach” , Morgan Kaufmann / Elsevier, 1997.3. William Stallings, “ Computer Organization and Architecture – Designing for
CP9114 OBJECT ORIENTED SYSTEMS ENGINEERING L T P C 3 0 0 3
UNIT I CLASSICAL PARADIGMSystem Concepts – Project Organization – Communication – Project Management
UNIT II PROCESS MODELSLife cycle models – Unified Process – Iterative and Incremental – Workflow – Agile Processes
UNIT III ANALYSISRequirements Elicitation – Use Cases – Unified Modeling Language, Tools – Analysis Object Model (Domain Model) – Analysis Dynamic Models – Non-functional requirements – Analysis Patterns
UNIT IV DESIGNSystem Design, Architecture – Design Principles - Design Patterns – Dynamic Object Modeling – Static Object Modeling – Interface Specification – Object Constraint Language
UNIT V IMPLEMENTATION, DEPLOYMENT AND MAINTENANCEMapping Design (Models) to Code – Testing - Usability – Deployment – Configuration Management – Maintenance
REFERENCES1. Bernd Bruegge, Alan H Dutoit, Object-Oriented Software Engineering, 2nd ed,
Pearson Education, 2004. 2. Craig Larman, Applying UML and Patterns 3rd ed, Pearson Education, 2005. 3. Stephen Schach, Software Engineering 7th ed, McGraw-Hill, 2007. 4. Ivar Jacobson, Grady Booch, James Rumbaugh, The Unified Software
Development Process, Pearson Education, 1999.5. Alistair Cockburn, Agile Software Development 2nd ed, Pearson Education, 2007.
CP9115 NETWORK ENGINEERING AND MANAGEMENT L T P C 3 0 0 3
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UNIT I FOUNDATIONS OF NETWORKING 9Communication Networks – Network Elements – Switched Networks and Shared media Networks – Probabilistic Model and Deterministic Model – Datagrams and Virtual Circuits – Multiplexing – Switching - Error and Flow Control – Congestion Control – Layered Architecture – Network Externalities – Service Integration – Modern Applications
UNIT II QUALITY OF SERVICE 9Traffic Characteristics and Descriptors – Quality of Service and Metrics – Best Effort model and Guaranteed Service Model – Limitations of IP networks – Scheduling and Dropping policies for BE and GS models – Traffic Shaping algorithms – End to End solutions – Laissez Faire Approach – Possible improvements in TCP – Significance of UDP in inelastic traffic
UNIT III HIGH PERFORMANCE NETWORKS 9Integrated Services Architecture – Components and Services – Differentiated Services Networks – Per Hop Behaviour – Admission Control – MPLS Networks – Principles and Mechanisms – Label Stacking – RSVP – RTP/RTCP
UNIT IV HIGH SPEED NETWORKS 9Optical links – WDM systems – Optical Cross Connects – Optical paths and Networks – Principles of ATM Networks – B-ISDN/ATM Reference Model – ATM Header Structure – ATM Adaptation Layer – Management and Control – Service Categories and Traffic descriptors in ATM networks
UNIT V NETWORK MANAGEMENT 9ICMP the Forerunner – Monitoring and Control – Network Management Systems – Abstract Syntax Notation – CMIP – SNMP Communication Model – SNMP MIB Group – Functional Model – Major changes in SNMPv2 and SNMPv3 – Remote monitoring – RMON SMI and MIB
Education, 20006. Kasera and Seth, ‘ATM Networks: Concepts and Protocols’, Tata McGraw Hill,
2002.
CK9113 COMPUTER LABORATORY- I L T P C0 0 3 2
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1. Implementation of multi-dimensional structures such as matrices, triangular matrices, diagonal matrices, etc into a one dimensional array (atleast any two)
2. Implementation of any two of the following Heap structures Deaps (Insertion, Delete Min, Delete Max) Leftist Heap (All Meldable Priority Queue operations) Skew Heap (All Meldable Priority Queue operations) Fibonacci Heap (All Meldable Priority Queue operations)
3. Implementation of any two of the following Search Structures AVL Trees (Insertion, Deletion and Search) Splay Trees (Insertion, Deletion and Search) Tries for any specified alphabet (Insertion, Deletion and Search) B-Trees (Insertion, Deletion and Search)
4. Implementation of any two of the following multimedia structures 2-d Trees (Insertion, Deletion and Range Queries) Point Quad-Trees (Insertion, Deletion and Range Queries) Segment Trees (Insertion, Deletion – Show list of nodes where in
insertion and deletion took place)
5. Finding Convex-hull.
CP9124 PARALLEL ALGORITHMS L T P C 3 0 0 3
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UNIT I 9PRAM Model – PRAM Algorithms – Parallel Reduction – Prefix Sums – List Ranking – Preorder Tree Traversal – Merging Two Sorted Lists – Graph Coloring – Reducing Number of Processors – NC Class.
UNIT II 9Classifying MIMD Algorithms – Hypercube SIMD Model – Shuffle Exchange SIMD Model – 2D Mesh SIMD Model – UMA Multiprocessor Model – Broad case – Prefix Sums.
UNIT III 9Enumeration Sort – Lower Bound on Parallel Sorting – Odd-Even Transposition Sort – Bitonic Merge – Parallel Quick Sort – Complexity of Parallel Search – Searching on Multiprocessors.
UNIT IV 9P-Depth Search – Breadth Death Search – Breadth First Search – Connected Components – All pair Shortest Path – Single Source Shortest Path – Minimum Cost Spanning Tree.
UNIT V 9Matrix Multiplication on 2-D Mesh, Hypercube and Shuffle Exchange SIMD Models – Algorithms for Multiprocessors – Algorithms for Multi computers – Mapping Data to Processors.
TOTAL : 45
REFERENCES 1. Michael J. Quinn, Parallel Computing : Theory & Practice, Tata McGraw Hill
Edition, 2003.2. Ananth Grame, George Karpis, Vipin Kumar and Anshul Gupta, Introduction to
UNIT I 9Principles Of Compiler – Compiler Structure – Properties of a Compiler – Optimization – Importance of Code optimization – Structure of Optimizing compilers – placement of optimizations in optimizing compilers – ICAN – Introduction and Overview – Symbol table structure – Local and Global Symbol table management
UNIT II 9Intermediate representation – Issues – High level, medium level, low level intermediate languages – MIR, HIR, LIR – ICAN for Intermediate code – Optimization – Early optimization – Constant folding – scalar replacement of aggregates – Simplification – value numbering – constant propagation – redundancy elimination – loop optimization
UNIT III 9Procedure optimization – in-line expansion – leaf routine optimization and shrink wrapping – register allocation and assignment – graph coloring – code scheduling –
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control flow and low level optimizations – inter-procedural analysis and optimization – call graph – data flow analysis – constant propagation – alias analysis – register allocation – global references – Optimization for memory hierarchy
UNIT IV 9Code Scheduling – Instruction scheduling – Speculative scheduling – Software pipelining – trace scheduling – percolation scheduling – Run-time support – Register usage – local stack frame – run-time stack – Code sharing – position–independent code – Symbolic and polymorphic language support
UNIT V 9Case Studies – Sun Compilers for SPARC – IBM XL Compilers – Alpha compilers – PA –RISC assembly language – COOL – ( Classroom Object oriented language) - Compiler testing tools – SPIM
TOTAL : 45
TEXT BOOKS1. Steven S. Muchnick, “Advanced Compiler Design Implementation”, Morgan
Koffman – Elsevier Science, India, Indian Reprint 20032. Keith D Cooper and Linda Torczon, “ Engineering a Compiler, Elsevier Science,
India.
REFERENCES1. Allen Holub “Compiler Design in C”, Prentice Hall of India, 1990.2. Alfred Aho, V. Ravi Sethi, D. Jeffery Ullman, “Compilers Principles, Techniques
and Tools”, Addison Wesley, 1988.3. Charles N. Fischer, Richard J. Leblanc, “Crafting a compiler with C”, Benjamin
Cummings, 1991.
CP9123 ADVANCED DATABASE TECHNOLOGY L T P C
3 0 0 3
UNIT I PARALLEL AND DISTRIBUTED DATABASES 9Database System Architectures: Centralized and Client-Server Architectures – Server
System Architectures – Parallel Systems- Distributed Systems – Parallel Databases: I/O
Parallelism – Inter and Intra Query Parallelism – Inter and Intra operation Parallelism –
and Design: ODMG Model – ODL – OQL – Object Relational and Extended – Relational
Systems : Object Relational featuresinSQL/Oracle – Case Studies.
UNIT III XML DATABASES 9
XML Databases: XML Data Model – DTD - XML Schema - XML Querying – Web
Databases – JDBC – Information Retrieval – Data Warehousing – Data Mining
UNIT IV MOBILE DATABASES 9
Mobile Databases: Location and Handoff Management - Effect of Mobility on Data
Management - Location Dependent Data Distribution - Mobile Transaction Models -
Concurrency Control - Transaction Commit Protocols- Mobile Database Recovery
Schemes
UNIT V MULTIMEDIA DATABASES 9
Multidimensional Data Structures – Image Databases – Text/Document Databases-
Video Databases – Audio Databases – Multimedia Database Design.
TOTAL = 45
REFERENCES
1. R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”, Fifth Edition, Pearson Education/Addison Wesley, 2007.
2. Thomas Cannolly and Carolyn Begg, “ Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education, 2007.
3. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database System Concepts”, Fifth Edition, McGraw Hill, 2006.
4. C.J.Date, A.Kannan and S.Swamynathan,”An Introduction to Database Systems”, Eighth Edition, Pearson Education, 2006.
5. V.S.Subramanian, “Principles of Multimedia Database Systems”, Harcourt India Pvt Ltd., 2001.
6. Vijay Kumar, “ Mobile Database Systems”, John Wiley & Sons, 2006.
CP9153 KNOWLEDGE ENGINEERING L T P C 3 0 0 3
UNIT I INTRODUCTION 9Key concepts – Why knowledge Representation and Reasoning – Language of first order Logic – Syntax, Semantics Pragmatics – Expressing Knowledge – Levels of Representation – Knowledge Acquisition and Sharing – Sharing Ontologies – Language Ontologies –Language Patterns – Tools for Knowledge Acquisition UNIT II RESOLUTION AND REASONING 9
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Proportional Case – Handling Variables and Qualifies – Dealing with Intractability – Reasoning with Horn Clauses - Procedural Control of Reasoning – Rules in Production – Description Logic - Vivid Knowledge – Beyond Vivid.
UNIT III REPRESENTATION 9Object Oriented Representations – Frame Formalism – Structured Descriptions – Meaning and Entailment - Taxonomies and Classification – Inheritance – Networks –Strategies for Defensible Inheritance – Formal Account of Inheritance Networks.
UNIT IV DEFAULTS, UNCERTAINTY AND EXPRESSIVENESS 9Defaults – Introduction – Closed World Reasoning – Circumscription – Default Logic Limitations of Logic – Fuzzy Logic – Nonmontonic Logic – Theories and World – Semiotics – Auto epistemic Logic - Vagueness – Uncertainty and Degrees of Belief – Noncategorical Reasoning – Objective and Subjective Probability.
UNIT V ACTIONS AND PLANNING 9Explanation and Diagnosis – Purpose – Syntax, Semantics of Context – First Order Reasoning – Modal Reasoning in Context – Encapsulating Objects in Context – Agents – Actions – Situational Calculus – Frame Problem – Complex Actions – Planning – Strips – Planning as Reasoning – Hierarchical and Conditional Planning.
TOTAL=45
REFERENCES1. Ronald Brachman, Hector Levesque “Knowledge Representation and
Reasoning “, The Morgan Kaufmann Series in Artificial Intelligence 20042. John F. Sowa, “ Knowledge Representation: Logical, Philosophical, and
Computational Foundations”, 2000 3. Arthur B. Markman, “Knowledge Representation”, Lawrence Erlbaum
Associates,1998
CK9123 COMPUTATIONAL LINGUISTICS L T P C 3 0 0 3
UNIT I INTRODUCTION 9Issues – Motivation – Theory of Language -Features of Indian Languages – Issues in Font – Coding Techniques – sorting & searching issues.
UNIT II MORPHOLOGY AND PARTS-OF-SPEECH 9Phonology – Computational Phonology - Words and Morphemes – Segmentation – Categorization and Lemmatisation – Word Form Recognition – Valency - Agreement - Regular Expressions and Automata – Morphology- Morphological issues of Indian Languages – Transliteration.
UNIT III PROBABILISTIC MODELS 9Probabilistic Models of Pronunciation and Spelling – Weighted Automata – N- Grams – Corpus Analysis – Smoothing – Entropy - Parts-of-Speech – Taggers – Rule based – Hidden Markov Models – Speech Recognition
UNIT IV SYNTAX 9
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Basic Concepts of Syntax – Parsing Techniques – General Grammar rules for Indian Languages – Context Free Grammar – Parsing with Context Free Grammars – Top Down Parser – Earley Algorithm – Features and Unification - Lexicalised and Probabilistic Parsing.
UNIT V SEMANTICS AND PRAGMATICS 9Representing Meaning – Computational Representation – Meaning Structure of Language – Semantic Analysis – Lexical Semantics – WordNet – Pragmatics – Discourse – Reference Resolution – Text Coherence – Dialogue Conversational Agents.
TOTAL =45
REFERENCES1. Daniel Jurafskey and James H. Martin “Speech and Language Processing”,
Prentice Hall, 2000. 2. Ronald Hausser “Foundations of Computational Linguistics”, Springer-Verleg,
1999.3. James Allen “Natural Language Understanding”, Benjamin/Cummings Publishing
Co. 1995.4. Steve Young and Gerrit Bloothooft “Corpus – Based Methods in Language and
CK9125 LANGUAGE TECHNOLOGY LABORATORY L T P C1 0 3 3
1. Design and implement a FSA that recognizes the following date and time expressions. Each edge of the graph should have a word or set of words on it. Use classes of words wherever applicable(e.g furniture desk, chair, table)
Simple date expressions like March15, the 22nd of November, Christmas. Extend to handle deictic expressions like yesterday, tomorrow, a week
from tomorrow, the day before yesterday, Sunday, next Monday, three weeks from Saturday
Handle time of the day expressions like eleven o’clock, twelve thirty, midnight, quarter to ten etc.
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Modify your FSA to tackle the same for any Indian Language
2. The Viterbi algorithm can be used to extend a simplified version of spelling error correction algorithm. Implement the Viterbi algorithm to extend the Kernigham algorithm to handle multiple spelling errors assuming only three confusion matrices (del, ins and sub)
3. Write a program (use of Perl preferred) to compute unsmoothed unigrams and bigrams.
Extend the program for a N-gram case. Run the N-gram program for two different corpora and compare statistics Add options to:
o Generate random sentenceso Do Witten-Bell discountingo Compute entropy of test set
4. Collect a reasonable amount of text written by different authors and break up individual texts (e.g., term papers) into smaller pieces to get a large enough set. Build a decision tree that automatically determines whether you are the author of a piece of text. Note that it is often the `little' words that give an author away (for example, the relative frequencies of words like because and though)
5. Implement the k-means algorithm in MATLAB. Write a matlab function in a file named kmeans.m, with the following syntax: function [a,M] = kmeans(S,k,T). This function should behave as follows: • Input:S - An m × n matrix, where each row represents an instance, k - A positive integer, the number of clusters, T - A positive integer, the number of iterations.• Output: a - A column vector of length , where a(i) is an integer between 1 and k, which is the index of the cluster to which instance i is assigned. – M - A k × n matrix, where each row is one of the “means”, namely, the center of a cluster.
6. Write a program to form clusters of Usenet newsgroup messages. Cluster the messages by first employing a dimensionality reduction technique, then running a clustering algorithm such as k-means. The provided code should also evaluate the success of your clustering by computing the purity of your clusters.
7. The goal of this exercise was to illustrate the difficulties of making consistent decisions in the process of annotating a treebank, by letting the students annotate 50 English sentences according to the Penn Treebank Guidelines [Penn-guide].
Sample Projects
1. Develop an approach to automate the highlighting process of papers. Imagine a person highlighting interesting passages in a paper. If a person highlights two or three things, can you highlight the rest. (See Wang et. al. 2005 in BioLink and Marc Light’s work at U of Iowa.)
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2. Develop a conditional random field approach for an information extraction task (e.g., McDonald and Pereira, BioCreative 2004, Sha and Pereira 2003, Lafferty et al 2001)
3. Spam filtering4. Bookmark page generator, give the system a few examples of pages that are
interesting, then crawl the web to find more and create a page of links to pages that contain related information
5. Meeting scheduler, parse an email message to determine if a meeting is being scheduled and generate a calendar event for the meeting
6. Process the Enron email dataset (http://www.cs.cmu.edu/_enron/) and identify social networks (networks of individuals in the company), categorize messages, etc.
7. Build a more intelligent search engine for a specific corpus8. Explore applications of WordNet (http://wordnet.princeton.edu/w3wn.html)9. Natural language interfaces to programming (NaturalJava by Price et al, Metafor
http://web.media.mit.edu/ _hugo/research/index.html#metafor)10. Intelligent Writing Aid
Total = 60
CP9131 SECURITY PRINCIPLES AND PRACTICE L T P C
3 0 0 3
UNIT I INTRODUCTION & MATHEMATICAL FOUNDATION 9Beginning with a simple communication game – wresting between safeguard and attack – Probability and Information Theory - Algebraic foundations – Number theory.
UNIT II ENCRYPTION – SYMMETRIC TECHNIQUES 9Substitution Ciphers - Transposition Ciphers - Classical Ciphers – DES – AES – Confidentiality Modes of Operation – Key Channel Establishment for symmetric cryptosystems.
UNIT III ENCRYPTION –ASYMMETRIC TECHNIQUES & DATA INTEGRITY TECHNIQUES 9
Diffie-Hellman Key Exchange protocol – Discrete logarithm problem – RSA cryptosystems & cryptanalysis – ElGamal cryptosystem – Need for stronger Security Notions for Public key Cryptosystems – Combination of Asymmetric and Symmetric Cryptography – Key Channel Establishment for Public key Cryptosystems - Data Integrity techniques – Symmetric techniques - Asymmetric techniques UNIT IV AUTHENTICATION 9Authentication Protocols Principles – Authentication protocols for Internet Security – SSH Remote logic protocol – Kerberos Protocol – SSL & TLS – Authentication frame for
public key Cryptography – Directory Based Authentication framework – Non - Directory Based Public-Key Authentication framework .
UNIT V SECURITY PRACTICES 9Protecting Programs and Data – Information and the Law – Rights of Employees and Employers – Software Failures – Computer Crime – Privacy – Ethical Issues in Computer Security.
TOTAL : 45REFERENCES1. Wenbo Mao, “Modern Cryptography – Theory and Practice”, Pearson Education,
First Edition, 2006.2. Douglas R. Stinson ,“Cryptography Theory and Practice ”, Third Edition, Chapman &
Hall/CRC, 2006.3. Charles B. Pfleeger, Shari Lawrence Pfleeger, “Security in Computing”, Fourth
Edition, Pearson Education, 2007.
4. Wade Trappe and Lawrence C. Washington, “Intrduction to Cryptography with Coding Theory” Second Edition, Pearson Education, 2007.
IT9155 ONTOLOGY AND SEMANTIC WEBL T P C3 0 0 3
UNIT I INTRODUCTION 8
Components – Types – Ontological Commitments – Ontological Categories – Philosophical Background -Sample - Knowledge Representation Ontologies – Top Level Ontologies – Linguistic Ontologies – Domain Ontologies – Semantic Web – Need – Foundation – Layers – Architecture.
UNIT II LANGUAGES FOR SEMANTIC WEB AND ONTOLOGIES 12
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Web Documents in XML – RDF - Schema – Web Resource Description using RDF- RDF Properties – Topic Maps and RDF – Overview – Syntax Structure – Semantics – Pragmatics - Traditional Ontology Languages – LOOM- OKBC – OCML - Flogic Ontology Markup Languages – SHOE – OIL - DAML + OIL- OWL
UNIT III ONTOLOGY LEARNING FOR SEMANTIC WEB 12
Taxonomy for Ontology Learning – Layered Approach – Phases of Ontology Learning – Importing and Processing Ontologies and Documents – Ontology Learning Algorithms - Evaluation
UNIT IV ONTOLOGY MANAGEMENT AND TOOLS 8
Overview – need for management – development process – target ontology – ontology mapping – skills management system – ontological class – constraints – issues. Evolution – Development of Tools and Tool Suites – Ontology Merge Tools – Ontology based Annotation Tools.
UNIT V APPLICATIONS 5 Web Services – Semantic Web Services - Case Study for specific domain – Security issues – current trends.
TOTAL = 45
REFERENCES:
1. Asuncion Gomez-Perez , Oscar Corcho, Mariano Fernandez-Lopez, “Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web” Springer, 2004
2. Grigoris Antoniou, Frank van Harmelen, “A Semantic Web Primer (Cooperative Information Systems)”, The MIT Press, 2004
3. Alexander Maedche, “Ontology Learning for the Semantic Web”, Springer; 1 edition, 2002
4. John Davies, Dieter Fensel, Frank Van Harmelen, “Towards the Semantic Web: Ontology – Driven Knowledge Management”, John Wiley & Sons Ltd., 2003.
5. John Davies (Editor), Rudi Studer (Co-Editor), Paul Warren (Co-Editor) “Semantic Web Technologies: Trends and Research in Ontology-based Systems”Wiley Publications, Jul 2006
6. Dieter Fensel (Editor), Wolfgang Wahlster, Henry Lieberman, James Hendler, “Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential”, The MIT Press, 2002
7. Michael C. Daconta , Leo J. Obrst, Kevin T. Smith, “The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management”, Wiley, 2003
8. Steffen Staab (Editor), Rudi Studer, “Handbook on Ontologies (International Handbooks on Information Systems)”, Springer 1st edition, 2004
9. Dean Allemang (Author), James Hendler (Author) “Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL” (Paperback), Morgan Kaufmann, 2008
UNIT I INTRODUCTION 9Definition - Need for language technologies – spectrum – commercial applications - Mathematical foundations – Probability and statistics in computational linguistics – Set theory foundations – Statistical modeling, classification, and clustering – issues in language modeling – Stochastic analysis – latent semantic analysis – Finite state technology
UNIT II SPOKEN AND WRITTEN LANGUAGE INPUT 9Overview – Speech recognition – robustness – HMM methods – Language representation – Speaker recognition – Speech coding – Speech enhancement – Document image analysis – OCR – Handwriting analysis
UNIT III LANGUAGE ANALYSIS, UNDERSTANDING AND GENERATION 9Sub sentential processing – Grammar formalism – lexicons parsing – Semantics – Document retrieval – Information Extraction – Summarization – Syntactic generation – Deep generation – Discourse, dialogue and Spoken output – discourse modeling – dialogue modeling – spoken language dialogue, synthetic speech generation – text interpretation for text to speech – spoken language generation
UNIT IV MULTI-LINGUALITY AND MULTIMODALITY 9Multilinguality – Multilingual Information Retrieval and Speech Processing –automatic language identification - Multimodality – Text and Images – Modality Integration – Transmission and Storage.
UNIT V LANGUAGE RESOURCES AND EVALUATION 9Written and Spoken language corpora – lexicons –Evaluation overview – task oriented text analysis evaluation – Evaluation of machine translation and translation tools – evaluation of broad coverage natural language parsers – Human factors – user acceptability – speech analysis and synthesis evaluation – Current Trends.
TOTAL = 45
REFERENCES:
1. Ron Cole, J.Mariani, et al., “Survey of the State of the Art in Human Language Technology”, Cambridge University Press, 1997.
2. Mark Johnson, Sanjeev P. Khudaupur, Mari Ostendorf, “Mathematical foundations of Speech and language processing”, Springer Verlag, 2004
3. Daniel Jurafsky and James H. martin, “Speech and Language Processing”, 2000.4. Christopher D.Manning and Hinrich Schutze, “ Foundations of Statistical Natural
Language Processing “, MIT Press, 1999.
CK9153 INFORMATION RETRIEVAL TECHNIQUES
21
L T P C 3 0 0 3
UNIT I INTRODUCTION 9Basic Concepts – Retrieval Process – Modeling – Classic Information Retrieval – Set Theoretic, Algebraic and Probabilistic Models – Structured Text Retrieval Models – Retrieval Evaluation –Word Sense Disambiguation
UNIT II QUERYING 9
Languages – Key Word based Querying – Pattern Matching – Structural Queries – Query Operations – User Relevance Feedback – Local and Global Analysis – Text and Multimedia languages
UNIT III TEXT OPERATIONS AND USER INTERFACE 9
Document Preprocessing – Clustering – Text Compression - Indexing and Searching – Inverted files – Boolean Queries – Sequential searching – Pattern matching – User Interface and Visualization – Human Computer Interaction – Access Process – Starting Points –Query Specification - Context – User relevance Judgment – Interface for Search
UNIT IV MULTIMEDIA INFORMATION RETRIEVAL 9
Data Models – Query Languages – Spatial Access Models – Generic Approach – One Dimensional Time Series – Two Dimensional Color Images – Feature Extraction
UNIT V APPLICATIONS 9
Searching the Web – Challenges – Characterizing the Web – Search Engines – Browsing – Meta-searchers – Online IR systems – Online Public Access Catalogs – Digital Libraries – Architectural Issues – Document Models, Representations and Access – Prototypes and Standards
2. G.G. Chowdhury, “Introduction to Modern Information Retrieval”, Neal-
Schuman Publishers; 2nd edition, 2003.
3. Daniel Jurafsky and James H. Martin, “Speech and Language Processing”, Pearson Education, 2000
4. David A. Grossman, Ophir Frieder, “ Information Retrieval: Algorithms, and Heuristics”, Academic Press, 2000
5. Charles T. Meadow, Bert R. Boyce, Donald H. Kraft, “Text Information Retrieval Systems”, Academic Press, 2000
.
22
CK9154 STATISTICAL NATURAL LANGUAGE PROCESSING L T P C
3 0 0 3
UNIT I INTRODUCTION 9Statistical Natural Language Processing – Mathematical Foundations – Linguistic Essentials – Corpus based issues
UNIT II WORD COLLOCATIONS 9Statistical Measures – N-gram models – Statistical estimators – Word sense Disambiguation – Lexical Acquisition
UNIT III STATISTICAL PARSING 9Markov Models – Hidden Markov Models – Part of speech Tagging – Probabilistic Context Free Grammars – Probabilistic Parsing
UNIT IV STATISTICAL TECHNIQUES 9
23
Statistical Alignment and machine translation – Clustering – Hierarchical and Non – hierarchical Clustering
UNIT V APPLICATIONS 9Information Retrieval – Vector Space Models – Latent Semantic Indexing – Text categorization – Decision trees – maximum Entropy modeling – Statistical Speech Processing – Hidden Markov Models in Speech Processing
Total = 45
REFERENCES:
1. Christopher D. Manning and Hinrich Schutze , “Foundations of Statistical Natural Language Processing”, MIT Press, 1999.
2. Steve Young and Gerrit Bloothooft, “Corpus Based Methods in Language and Speech Processing”, Kluwer Academic Publishers, 1997.
CP9173 MACHINE LEARNING
L T P C
3 0 0 3
UNIT I INTRODUCTION 9
Learning Problems – Perspectives and Issues – Concept Learning – Version Spaces and Candidate Eliminations – Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space Search. UNIT II NEURAL NETWORKS AND GENETIC ALGORITHMS 9Neural Network Representation – Problems – Perceptrons – Multilayer Networks and Back Propagation Algorithms – Advanced Topics – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evalution and Learning.
UNIT III BAYESIAN AND COMPUTATIONAL LEARNING 9 Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum Description Length Principle – Bayes Optimal Classifier – Gibbs Algorithm – Naïve Bayes Classifier – Bayesian Belief Network – EM Algorithm – Probability Learning – Sample Complexity – Finite and Infinite Hypothesis Spaces – Mistake Bound Model.
UNIT IV INSTANT BASED LEARNING 9K- Nearest Neighbour Learning – Locally weighted Regression – Radial Bases Functions – Case Based Learning.
24
UNIT V ADVANCED LEARNING 9Learning Sets of Rules – Sequential Covering Algorithm – Learning Rule Set – First Order Rules – Sets of First Order Rules – Induction on Inverted Deduction – Inverting Resolution – Analytical Learning – Perfect Domain Theories – Explanation Base Learning – FOCL Algorithm – Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning
Total =45
REFERENCES:
1. Tom M. Mitchell , “Machine Learning”, McGraw-Hill Science /Engineering /Math; 1 edition, 1997
2. Ethem Alpaydin , “Introduction to Machine Learning (Adaptive Computation and Machine Learning)”, The MIT Press 2004
3. T. Hastie , R. Tibshirani, J. H. Friedman, “The Elements of Statistical Learning”, Springer; 1 edition, 2001
CK9156 NATURAL LANGUAGE GENERATION
L T P C
3 0 0 3
UNIT I INTRODUCTION 9 The Research Perspective – The Application Perspective – Some Example NLG Systems History of NLG - National Language Generation in Practice – Appropriateness of NLG Techniques – Using a Corpus to Determine User Requirements - Evaluating NLG Systems – Fielding NLG Systems
UNIT II ARCHITECTURE AND PLANNING 9Inputs and Outputs of Natural Language Generation An Informal Characterization of the Architecture – An Overview of the Architecture - The Architecture and its Representation – Broad Structure and Terminology – Messages – Other Architectures - The Document Planner – Representing Information in the Domain – Domain Models – Implementation – Defining Messages – Domain Modeling and Message Definition – Content Determination- Document Structuring
UNIT III MICRO PLANNING 9Micro Planning – Architecture of Microplanner - Lexicalisation – Contextual and Pragmatic Influences on Lexical Choice – Expressing Discourse Relations – Fine Grained Lexicalisation – Aggregation – Mechanisms for Sentence Formation – Choosing Between Possible Aggregation – Order of Presentation – Paragraph Formation – Generating Referring Expression - Forms of Referring Expressions and their Uses – Requirements for Referring Expression Generation – Generating Pronouns Generating Subsequent References Limitation and other Approaches.
UNIT IV SURFACE REALISATION 9Realising Text Specifications – Varieties of Phrase Specifications – Skeletal Propositions – Meaning Specifications – Lexicalised Case Frames – Abstract Syntactic Structures – Canned Text – Orthographic Strings – KPML- An Overview – The Input to KPML – Using Systemic Grammar for Linguistic Realisation – SURGE – The Input to SURGE – Functional Unification Grammar – Linguistic Realisation via Unification- REAL PRO – Input to REALPROMeaning – Text Theory – Real Pro Works - Choosing a Realiser – Bidirectional Grammars.
UNIT V BEYOND TEXT GENERATION 9Typography - Integrating Text and Graphics –Hypertext – Hypertext and its uses – Implementing Hypertext based NLG Systems – Speech Output – Benefits of Speech Output – Text –to-speech systems – Implementing Concepts-to-Speech – Question Answering
Total =45REFERENCES:
1. Ehud Reiter , Robert Dale, “Building Natural Language Generation Systems (Studies in Natural Language Processing)” Cambridge University Press, 2000
2. Ralph Grishman , et.al (edited) “Computational Linguistics” (Studies in Natural Language Processing), Cambridge University Press, 1986
CK9157 TEXT MINING
L T P C3 0 0 3
UNIT I INTRODUCTION 9Overview of text mining-document classification- information retrieval- clustering and organizing documents- information extraction- prediction and evaluation-Textual information to numerical vectors -Collecting documents- document standardization- tokenization- lemmatization- vector generation for prediction- sentence boundary determination -evaluation performance
UNIT II INFORMATION RETRIEVAL AND TEXT MINING 9Information retrieval and text mining- keyword search- nearest-neighbor methods- measuring similarity- web-based document search- document -matching- inverted lists- evaluation of performance -Structure in a document collection - clustering documents by similarity- evaluation of performance - information extraction- patterns and entities from text- coreference and relationship extraction- template filling and database construction
UNIT III CLUSTERING AND CLASSIFICATION 9
Cluster-preserving dimension reduction methods for efficient classification of text data - Dimension reduction in the vector space model- Orthogonal basis of centroids- discriminant analysis - Trace optimization using an orthogonal basis of centroids - Automatic Discovery of similar words - Simultaneous clustering and dynamic weighting - simultaneous soft clustering and term weighting - robustness in the presence of noise -Feature selection and document clustering
Vector space models (VSM) for search and cluster mining - Major and minor cluster discovery - Discovering hot topics from dirty text - Thesaurus assistant- sentence identifier- sentence extractor- mining case excerpts for hot topics -Combining families of information retrieval algorithms using metalearning
UNIT V TRENDS IN TEXT MINING 9
Trend and behavior detection from web queries - query data and analysis- Zipf’s law- vocabulary growth - ETD systems-technology opportunities analysis(TOA)- constructive collaborative inquiry-based multimedia E-learning (CIMEL)- TimeMines- New event detection- ThemeRiver- PatentMiner- HDDI- Commercial software overview -Summarization- active learning- learning with unlabeled data- different ways of collecting samples- question answering - Case studies : market intelligence from the web- lightweight document matching for digital libraries- generating model cases for help desk applications- assigning topics to new articles- E-mail filtering- search engines- extracting named entities from documents- customized newspapers
REFERENCES :
1. Michael Berry, “Survey of Text Mining: Clustering- Classification- and Retrieval”- Springer, 2003
UNIT I INTRODUCTION 9Spoken Language System Architecture and Structure – Sound and Human Speech System – Phonetics and Phonology – Syllables and Words – Syntax and Semantics –Probability Theory – Estimation Theory – Significance Testing.
UNIT II SPEECH SIGNAL REPRESENTATION AND CODING 9
Short Time Fourier Analysis – Acoustic Model of Speech Production - Linear Predictive Coding – Cepstral Processing – Perceptual Motivated Representations – Formant Frequencies – Role of Pitch – Scalar Waveform Coders – Scalar Frequency Domain Coders – Code excited linear Prediction – Low – Bit rate Speech coders.
27
UNIT III SPEECH RECOGNITION 9
Hidden Markov Models (HMM) – Practical Issues in Using HMMs – HMM Limitations Acoustic Modeling – Phonetic Modeling – Language Modeling - Speaker Recognition Algorithms – Signal Enhancement for Mismatched Conditions.
UNIT IV SPEECH SYNTHESIS 9
Formant Speech Synthesis – Concatenative Speech Synthesis – Prosodic Modification Of Speech – Source Filter Models For Prosody Modification – Evaluation Of Text To Speech System.
1. Thomas F.Quatieri, “Discrete-Time Speech Signal Processing”, Pearson Education, 2002.
2. Xuedong Huang, Alex Acero, Hsiad, Wuen Hon, “ Spoken Language Processing”, Prentice Hall ,2001.
REFERENCES:
1. B.Gold and N.Morgan, “Speech and Audio Signal Processing”, Wiley and Sons, 2000.
2. M.R.Schroeder, “Computer Speech – Recognition, Compression, Synthesis”, Springer Series in Information Sciences, 1999.
3. A Brief Introduction to Speech Analysis and Recognition, An Internet Tutorial - http://www.mor.itesm.mx/~omayora/Tutorial/tutorial.html
4. Daniel Jurafsky & James H.Martin, “Speech and Language Processing”, Pearson Education ,2000.
CP9161 KNOWLEDGE MANAGEMENT
L T P C 3 0 0 3
UNIT I INTRODUCTION 9The value of Knowledge – Knowledge Engineering Basics – Knowledge Economy – The Task and Organizational Content – Knowledge Management – Knowledge Management Ontology.
Knowledge Model Components – Template Knowledge Models –Reflective Knowledge Models– Knowledge Model Construction – Types of Knowledge Models.
UNIT III TECHNIQUES OF KNOWLEDGE MANAGEMENT 8
Knowledge Elicitation Techniques – Modeling Communication Aspects – Knowledge Management and Organizational Learning.
UNIT IV KNOWLEDGE SYSTEM IMPLEMENTATION 11
Case Studies – Designing Knowledge Systems – Knowledge Codification – Testing and Deployment – Knowledge Transfer and Knowledge Sharing – Knowledge System Implementation.
UNIT V ADVANCED KM 8
Advanced Knowledge Modeling – Value Networks – Business Models for Knowledge Economy – UML Notations – Project Management.
TOTAL = 45
TEXT BOOKS:
1. Guus Schreiber, Hans Akkermans, Anjo Anjewierden, Robert de Hoog, Nigel Shadbolt, Walter Van de Velde and Bob Wielinga, “Knowledge Engineering and Management”, Universities Press, 2001.
2. Elias M.Awad & Hassan M. Ghaziri, “Knowledge Management”, Pearson Education, 2003.
REFERENCES:
1. C.W. Holsapple, “Handbooks on Knowledge Management”, International Handbooks on Information Systems, Vol 1 and 2, 2003.
CP9164 DATA WAREHOUSING AND DATA MININGL T P C3 0 0 3
UNIT I 9Data Warehousing and Business Analysis: - Data warehousing Components –Building a Data warehouse – Mapping the Data Warehouse to a Multiprocessor Architecture – DBMS Schemas for Decision Support – Data Extraction, Cleanup, and Transformation Tools –Metadata – reporting – Query tools and Applications – Online Analytical Processing (OLAP) – OLAP and Multidimensional Data Analysis.
UNIT II 9
Data Mining: - Data Mining Functionalities – Data Preprocessing – Data Cleaning – Data Integration and Transformation – Data Reduction – Data Discretization and Concept
29
Hierarchy Generation.
Association Rule Mining: - Efficient and Scalable Frequent Item set Mining Methods – Mining Various Kinds of Association Rules – Association Mining to Correlation Analysis – Constraint-Based Association Mining.
UNIT III 9
Classification and Prediction: - Issues Regarding Classification and Prediction – Classification by Decision Tree Introduction – Bayesian Classification – Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section.
UNIT IV 9
Cluster Analysis: - Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical methods – Density-Based Methods – Grid-Based Methods – Model-Based Clustering Methods – Clustering High-Dimensional Data – Constraint-Based Cluster Analysis – Outlier Analysis.
UNIT V 9
Mining Object, Spatial, Multimedia, Text and Web Data:
Multidimensional Analysis and Descriptive Mining of Complex Data Objects – Spatial Data Mining – Multimedia Data Mining – Text Mining – Mining the World Wide Web.
Total = 45
REFERENCES
1. Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques” Second Edition,
2. Elsevier, Reprinted 2008.
3. Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAP”, Tata McGraw – Hill Edition, Tenth Reprint 2007.
4. K.P. Soman, Shyam Diwakar and V. Ajay “Insight into Data mining Theory and Practice”, Easter Economy Edition, Prentice Hall of India, 2006.
5. G. K. Gupta “Introduction to Data Mining with Case Studies”, Easter Economy Edition, Prentice Hall of India, 2006.
6. Pang-Ning Tan, Michael Steinbach and Vipin Kumar “Introduction to Data Mining”, Pearson Education, 2007.
CP9154 VISUALIZATION TECHNIQUESL T P C 3 0 0 3
UNIT I VISUALIZATION 9 Introduction – Issues – Data Representation – Data Presentation - Interaction
30
UNIT II FOUNDATIONS FOR DATA VISUALIZATION 9 Visualization stages – Experimental Semiotics based on Perception Gibson‘s Affordance theory – A Model of Perceptual Processing – Types of Data.
UNIT III COMPUTER VISUALIZATION 9
Non-Computer Visualization – Computer Visualization: Exploring Complex Information Spaces – Fisheye Views – Applications – Comprehensible Fisheye views – Fisheye views for 3D data – Non Linear Magnificaiton – Comparing Visualization of Information Spaces – Abstraction in computer Graphics – Abstraction in user interfaces.
UNIT IV MULTIDIMENSIONAL VISUALIZATION 9
One Dimension – Two Dimensions – Three Dimensions – Multiple Dimensions – Trees – Web Works – Data Mapping: Document Visualization – Workspaces.
UNIT V CASE STUDIES 9Small interactive calendars – Selecting one from many – Web browsing through a key hole – Communication analysis – Archival analysis
TOTAL = 45
TEXT BOOKS:
1. Colin Ware, “Information Visualization Perception for Design” Margon Kaufmann Publishers, 2004, 2nd edition.
2. Robert Spence “Information visualization – Design for interaction”, Pearson Education, 2 nd Edition, 2007
REFERENCES: 1. Stuart.K.Card, Jock.D.Mackinlay and Ben Shneiderman, “Readings in
Information Visualization Using Vision to think”, Morgan Kaufmann Publishers.
CP9156 USER INTERFACE DESIGNL T P C
3 0 0 3UNIT I INTRODUCTION 8Human–Computer Interface – Characteristics Of Graphics Interface –Direct Manipulation Graphical System – Web User Interface –Popularity –Characteristic & Principles.
UNIT II HUMAN COMPUTER INTERACTION 7 User Interface Design Process – Obstacles –Usability –Human
Characteristics In Design – Human Interaction Speed –Business Functions –Requirement Analysis – Direct – Indirect Methods – Basic Business Functions – Design Standards – General Design Principles – Conceptual Model Design – Conceptual Model Mock-Ups
UNIT III WINDOWS 12Characteristics– Components– Presentation Styles– Types– Managements– Organizations– Operations– Web Systems– System Timings - Device– Based Controls
31
Characteristics– Screen – Based Controls –– Human Consideration In Screen Design – Structures Of Menus – Functions Of Menus– Contents Of Menu– Formatting – Phrasing The Menu – Selecting Menu Choice– Navigating Menus– Graphical Menus. Operate Control – Text Boxes– Selection Control– Combination Control– Custom Control– Presentation Control.
UNIT IV MULTIMEDIA 9Text For Web Pages – Effective Feedback– Guidance & Assistance– Internationalization– Accessibility– Icons– Image– Multimedia – Coloring.
UNIT V EVALUATION 9Conceptual Model Evaluation – Design Standards Evaluation – Detailed User Interface Design Evaluation
Total = 45
TEXT BOOKS:
1. Wilbent. O. Galitz ,“The Essential Guide To User Interface Design”, John Wiley& Sons, 2001.
2. Deborah Mayhew, The Usability Engineering Lifecycle, Morgan Kaufmann, 1999Ben Shneiderman, “Design The User Interface”, Pearson Education, 1998.
REFERENCES:
1. Alan Cooper, “The Essential Of User Interface Design”, Wiley – Dream Tech Ltd., 2002. Sharp, Rogers, Preece, ‘Interaction Design’, Wiley India Edition, 2007
CP9158 BIO INFORMATICS L T P C3 0 0 3
UNIT I INTRODUCTORY CONCEPTS 9
The Central Dogma – The Killer Application – Parallel Universes – Watson’s Definition – Top Down Versus Bottom up – Information Flow – Convergence – Databases – Data Management – Data Life Cycle – Database Technology – Interfaces – Implementation – Networks – Geographical Scope – Communication Models – Transmissions Technology – Protocols – Bandwidth – Topology – Hardware – Contents – Security – Ownership – Implementation – Management.
UNIT II SEARCH ENGINES AND DATA VISUALIZATION 9
The search process – Search Engine Technology – Searching and Information Theory – Computational methods – Search Engines and Knowledge Management – Data Visualization – sequence visualization – structure visualization – user Interface – Animation Versus simulation – General Purpose Technologies.
32
UNIT III STATISTICS AND DATA MINING 9
Statistical concepts – Microarrays – Imperfect Data – Randomness – Variability – Approximation – Interface Noise – Assumptions – Sampling and Distributions – Hypothesis Testing – Quantifying Randomness – Data Analysis – Tool selection statistics of Alignment – Clustering and Classification – Data Mining – Methods – Selection and Sampling – Preprocessing and Cleaning – Transformation and Reduction – Data Mining Methods – Evaluation – Visualization – Designing new queries – Pattern Recognition and Discovery – Machine Learning – Text Mining – Tools.
Fuzzy Sets – Operations on Fuzzy Sets – Fuzzy Relations – Membership Functions- Fuzzy Rules and Fuzzy Reasoning – Fuzzy Inference Systems – Fuzzy Expert Systems – Fuzzy Decision Making.
UNIT V NEURO-FUZZY MODELING 9
Adaptive Neuro-Fuzzy Inference Systems – Coactive Neuro-Fuzzy Modeling – Classification and Regression Trees – Data Clustering Algorithms – Rulebase Structure Identification – Neuro-Fuzzy Control – Case studies.
TOTAL = 45
TEXT BOOKS:
1. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, “Neuro-Fuzzy and Soft Computing”, Prentice-Hall of India, 2003.
2. George J. Klir and Bo Yuan, “Fuzzy Sets and Fuzzy Logic-Theory and Applications”,
Prentice Hall, 1995.
3. James A. Freeman and David M. Skapura, “Neural Networks Algorithms, Applications, and Programming Techniques”, Pearson Edn., 2003.
REFERENCES:
1. Mitchell Melanie, “An Introduction to Genetic Algorithm”, Prentice Hall, 1998.2. David E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine
Learning”, Addison Wesley, 1997.3. S. N. Sivanandam, S. Sumathi and S. N. Deepa, “Introduction to Fuzzy
Logic using MATLAB”, Springer, 2007.4. S.N.Sivanandam · S.N.Deepa, “ Introduction to Genetic Algorithms”,
Springer, 2007.5. Jacek M. Zurada, “Introduction to Artificial Neural Systems”, PWS
Publishers, 1992.
CK9161 SERVICE ORIENTED COMPUTINGL T P C3 0 0 3
34
UNIT I INTRODUCTION TO COMPUTING 9Basics - Computing with Services - Basic Standards for Web Services - Programming Web Services - Principles of Service-Oriented Computing – Description - Modeling and Representation - Resource Description Framework - Web Ontology Language - Ontology Management.
UNIT II ENGAGEMENT 9Execution Models - Transaction Concepts - Coordination Frameworks for Web Services - Process SpecificationsBPEL4WS, BPML and ebxml- Formal Specification and Enactment.
UNIT III STANDARDS AND WEB SERVICES PROGRAMMING 9SOAP-WSDL-UDDI- Programming WSDL – JAVA based web services –. NET environment for web services – web service interoperability.
UNIT IV COLLABORATION AND SOLUTIONS 9 Agents - Multiagent Systems – Organizations - Communication - Solutions - Semantic Service Solutions - Social Service Selection - Economic Service Selection.
UNIT V APPLICATIONS AND DIRECTIONS 9 Building SOC Applications- Service Management - Security – Directions -
Challenge and ExtensionsTotal=45
REFERENCES:1. Munindar P.Singh, Michael N.Huhns ,”Service –Oriented Computing:
Semantics,Processes,Agents, John Wiley and Sons, 20052. Sowa, John F. Knowledge Representation: Logical, Philosophical, and
3. Douglas K Barry “Web Services and Service-Oriented Architecture: Your Road Map to Emerging IT” , 2003
4. Ron Schmelzer etal , “XML and Web Services”, Pearson Education, 2002.
5. Zakaria Maamar, David Martin, Boualem Benatallah, Lawrence Cavedon “Extending Web Services Technologies: The Use of Multi-Agent Approaches”, Springer, 2004
6. Mark O'Neill “Web Services Security” McGraw-Hill Professional, 20037. Zoran Stojanovic, Ajantha Dahanayake “Service Oriented Software System
Engineering: Challenges and Practices” - Idea Group Inc (IGI), 20058. James McGovern, Sameer Tyagi, Sunil Mathew, Michael Stevens “Java Web
Services Architecture” - Morgan Kaufmann, 2003
SW9160 XML AND WEB SERVICES L T P C 3 0 0 3UNIT I XML TECHNOLOGY FAMILY 9
XML – benefits – Advantages of XML over HTML – EDL –Databases – XML based standards – DTD –XML Schemas – X- Files – XML processing – DOM –SAX- presentation technologies – XSL – XFORMS – XHTML – voice XML – Transformation – XSLT – XLINK – XPATH –XQ
UNIT II ARCHITECTING WEB SERVICES 9Business motivations for web services – B2B – B2C- Technical motivations – limitations of CORBA and DCOM – Service – oriented Architecture (SOA) – Architecting web services – Implementation view – web services technology stack – logical view – composition of web services – deployment view – from application server to peer to peer – process view – life in the runtime
UNIT III WEB SERVICES BUILDING BLOCK 9Transport protocols for web services – messaging with web services – protocols – SOAP – describing web services – WSDL – Anatomy of WSDL – manipulating WSDL – web service policy – Discovering web services – UDDI – Anatomy of UDDI- Web service inspection – Ad-Hoc Discovery – Securing web services.
UNIT IV IMPLEMENTING XML IN E-BUSINESS 9B2B - B2C Applications – Different types of B2B interaction – Components of e-business XML systems – ebXML – Rosetta Net Applied XML in vertical industry – Web services for mobile devices.
UNIT V XML AND CONTENT MANAGEMENT 9Semantic Web – Role of Meta data in web content – Resource Description Framework – RDF schema – Architecture of semantic web – content management workflow – XLANG –WSFL.
TOTAL: 45 PERIODS
TEXT BOOKS:1. Ron schmelzer et al, “XML and Web Services”, Pearson Education, 2002.2. Sandeep Chatterjee and James Webber, “Developing Enterprise Web Services: An
Architect’s Guide”, Prentice Hall, 2004.
REFERENCES:1. Frank P. Coyle, “XML, Web Services and the Data Revolution”, Pearson Education,
2002.2. Keith Ballinger, “.NET Web Services Architecture and Implementation”, Pearson
Education, 2003.3. Henry Bequet and Meeraj Kunnumpurath, “Beginning Java Web Services”, Apress,
2004.4. Russ Basiura and Mike Batongbacal, “Professional ASP.NET Web Services”,
Apress,2. ASP .NET Web Services”, Apress, 2003.
36
CK9163 PERVASIVE COMPUTING L T P C
3 0 0 3
UNIT I 9
Pervasive Computing Application - Pervasive Computing devices and Interfaces - Device technology trends, Connecting issues and protocols
UNIT II 9Pervasive Computing and web based Applications - XML and its role in Pervasive Computing - Wireless Application Protocol (WAP) Architecture and Security - Wireless Mark-Up language (WML) – Introduction
UNIT III 9Voice Enabling Pervasive Computing - Voice Standards - Speech Applications in Pervasive Computing and security
User Interface Issues in Pervasive Computing, Architecture - Smart Card- based Authentication Mechanisms - Wearable computing Architecture
TOTAL = 45TEXT BOOKS
2. Jochen Burkhardt, Horst Henn, Stefan Hepper, Thomas Schaec & Klaus Rindtorff. Pervasive Computing Technology and Architecture of Mobile Internet Applications, Addision Wesley, Reading, 2002.
2. Uwe Ha nsman, Lothat Merk, Martin S Nicklous & Thomas Stober: Principles of Mobile Computing, Second Edition, Springer- Verlag, New Delhi, 2003. Reference Books
REFERENCES
1. Rahul Banerjee: Internetworking Technologies: An Engineering Perspective, Prentice –Hall of India, New Delhi, 2003. (ISBN 81-203-2185-5)
Interface Agents – Agent Communication Languages – Agent Knowledge Representation – Agent Adaptability – Belief Desire Intension – Mobile Agent Applications.UNIT V AGENTS AND SECURITY 9
Agent Security Issues – Mobile Agents Security – Protecting Agents against Malicious Hosts – Untrusted Agent – Black Box Security – Authentication for agents – Security issues for Aglets.
TOTAL = 45
REFERENCES:
1. Bigus & Bigus, " Constructing Intelligent agents with Java ", Wiley, 1997.2. Bradshaw, " Software Agents ", MIT Press, 2000.3. Russel, Norvig, "Artificial Intelligence: A Modern Approach", Second Edition,
Pearson Education, 2003.4. Richard Murch, Tony Johnson, "Intelligent Software Agents", Prentice Hall, 2000.5. Gerhard Weiss, “Multi Agent Systems – A Modern Approach to Distributed
Artificial Intelligence”, MIT Press, 2000.
IT9161 ARTIFICIAL INTELLIGENCE
L T P C3 0 0 3
UNIT I INTRODUCTION 8
38
Intelligent Agents – Agents and environments – Good behavior – The nature of environments – structure of agents – Problem Solving – problem solving agents – example problems – searching for solutions – uniformed search strategies – avoiding repeated states – searching with partial information.
UNIT II SEARCHING TECHNIQUES 10
Informed search strategies – heuristic function – local search algorithms and optimistic problems – local search in continuous spaces – online search agents and unknown environments – Constraint satisfaction problems (CSP) – Backtracking search and Local search – Structure of problems – Adversarial Search – Games – Optimal decisions in games – Alpha – Beta Pruning – imperfect real-time decision – games that include an element of chance.
UNIT III KNOWLEDGE REPRESENTATION 10
First order logic - syntax and semantics – Using first order logic – Knowledge engineering – Inference – prepositional versus first order logic – unification and lifting – forward chaining – backward chaining – Resolution – Knowledge representation – Ontological Engineering – Categories and objects – Actions – Simulation and events – Mental events and mental objects.
UNIT IV LEARNING 9
Learning from observations – forms of learning – Inductive learning - Learning decision trees – Ensemble learning – Knowledge in learning – Logical formulation of learning – Explanation based learning – Learning using relevant information – Inductive logic programming - Statistical learning methods – Learning with complete data – Learning with hidden variable – EM algorithm – Instance based learning – Neural networks – Reinforcement learning – Passive reinforcement learning – Active reinforcement learning – Generalization in reinforcement learning.
UNIT V APPLICATIONS 8
Communication – Communication as action – Formal grammar for a fragment of English – Syntactic analysis – Augmented grammars – Semantic interpretation – Ambiguity and disambiguation – Discourse understanding – Grammar induction – Probabilistic language processing – Probabilistic language models – Information retrieval – Information Extraction – Machine translation.
Total = 45REFERENCES
1. Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, Second Edition, Pearson Education / Prentice Hall of India, 2004.
2. Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd., 2000.
3. Elaine Rich and Kevin Knight, “Artificial Intelligence”, Second Edition, Tata McGraw Hill, 2003.
4. George F. Luger, “Artificial Intelligence-Structures And Strategies For Complex Problem Solving”, Pearson Education / PHI, 2002.
39
CP9176 HUMAN RESOURCE MANAGEMENT L T P C
3 0 0 3UNIT I PERSPECTIVES IN HUMAN RESOURCE MANAGEMENT 9 Evolution of human resource management – the importance of the human factor – objectives of human resource management – role of human resource manager – human resource policies – computer applications in human resource management.
UNIT II THE CONCEPT OF BEST FIT EMPLOYEE 9Importance of human resource planning – forecasting human resource requirement – internal and external sources. Selection process-screening – tests - validation – interview - medical examination – recruitment introduction – importance – practices – socialization benefits.
UNIT III TRAINING AND EXECUTIVE DEVELOPMENT 9Types of training, methods, purpose, benefits and resistance. Executive development programmes – common practices - benefits – self development – knowledge management.
UNIT IV SUSTAINING EMPLOYEE INTEREST 9Compensation plan – reward – motivation – theories of motivation – career management – development, mentor – protégé relationships.
UNIT V PERFORMANCE EVALUATION AND CONTROL PROCESS 9Method of performance evaluation – feedback – industry practices. Promotion, demotion, transfer and separation – implication of job change. The control process – importance – methods – requirement of effective control systems grievances – causes – implications – redressal methods.
TOTAL = 45
TEXT BOOKS:
1. Decenzo and Robbins, Human Resource Management, Wilsey, 6th edition, 2001. 2. Biswajeet Pattanayak, Human Resource Management, Prentice Hall of India,2001.
REFERENCES:
1. Human Resource Management, Eugence Mckenna and Nic Beach, Pearson Education Limited, 2002.
2. Dessler Human Resource Management, Pearson Education Limited, 2002.3. Mamoria C.B. and Mamoria S.Personnel Management, Himalaya Publishing
Company, 1997.4. Wayne Cascio, Managing Human Resource, McGraw Hill, 1998.5. Ivancevich, Human Resource Management, McGraw Hill 2002.
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