DEGREE OF MASTER OF ENGINEERING

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UNIVERSITY DEPARTMENTS

ANNA UNIVERSITY CHENNAI : : CHENNAI 600 025


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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

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2

CP9122

Compiler Optimization

3

0

0

3

3

CK
912
1

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

PRACTICAL

7

CK9125

Language Technology Laboratory


1

0

3

3

TOTAL

19

0

3

21




SEMESTER III (3+1)

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 Phase
-

I

0

0

12

6

TOTAL

9

0

12

15


SEMESTER IV (0+1)

SL. NO

COURSE
CODE

COURSE 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


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No of Lab Courses


:

02






UNIVERSITY DEPARTMENTS

ANNA UNIVERSITY CHENNAI : : CHENNAI 600 025

REGULATIONS
-

2009

CURRICULUM I TO
V
I

SEMESTERS (
PART

TIME)

M.E
.

COMPUTER SCIENCE & ENGINEERING

SPEC
IALIZATION IN KNOWLEDGE ENGINEERING &
COMPUTATIONAL

LINGUISTICS



SEMESTER I


SL. NO

COURSE
CODE

COURSE TITLE

L

T

P

C

THEORY

1

MA9128

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

1
0



SEMESTER II






SL. NO

COURSE
CODE

COURSE TITLE

L

T

P

C

THEORY

1

CK9121

Advanced Database Technology

3

0

0

3

2

CK9123

Computational Linguistics

3

0

0

3

3

E1

Elective I

3

0

0

3

PRACTICAL

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4

CK9125

Language Techn
ology Laboratory

1

0

0

3

TOTAL

10

0

0

1
2



SEMESTER III






SL. NO

COURSE
CODE

COURSE 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. 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

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

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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

1
5




SEMESTER VI

SL. NO

COURSE
CODE

COURSE 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. NO

COURSE
CODE

COURSE TITLE

L

T

P

C

1

IT
9155

Ontology and Sem
antic 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. NO

COURSE
CODE

COURSE TITLE

L

T

P

C

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SL. NO

COURSE
CODE

COURSE 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

15

CK916
1

Service Oriented Computing

3

0

0

3

16

CK9162

XML and Web Services

3

0

0

3

17

CK9163

Pervasive Computing


3

0

0

3

18

CK9164

Software Agents

3

0

0

3

19

CK9165

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
Computationa
l Linguistics Stream





MA9110

OPERATIONS RESEARCH



L T P C





3 1 0 4


UNIT I


QUEUEING MODELS






9

Poisson Process


Markovian Queues


Single and Multi
-
server Models


Little’s
formula


Machine Interference Model


Steady State analysis


Self Ser
vice Queue.


UNIT II

ADVANCED QUEUEING MODELS






9

Non
-
Markovian Queues


Pollaczek Khintchine Formula


Queues in Series


Open
Queuing Networks

Closed Queuing networks.


UNIT III

SIMULATION









9

Discrete Even Simulation


Monte



Carlo Simulation


Stochastic Simulation


Applications to Queuing systems.


UNIT IV

LINEAR PROGRAMMING







9

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Formulation


Graphical solution


Simplex method


Two phase method
-
Transportation and Assignment Problems.


UNIT V

NON
-
LINEAR P
ROGRAMMING






9

Lagrange multipliers


Equality constraints


Inequality constraints


Kuhn


Tucker
conditions


Quadratic Programming.












L + T: 45+15 =60

TEXT BOOKS

1.

Winston.W.L. “Operations Research”, Fourth Edition, Thomson


Broo
ks/Cole,
2003.

2.

Taha, H.A. “Operations Research: An Introduction”, Ninth Edition, Pearson
Education Edition, Asia, New Delhi, 2002.


REFERENCES

1.

Robertazzi. T.G. “Computer Networks and Systems


Queuing Theory and
Performance Evaluation”, Third Edition, Spri
nger, 2002 Reprint.

2.

Ross. S.M., “Probability Models for Computer Science”, Academic Press, 2002.




CP9112


ADVANCED DATA STRUCTURES AND ALGORITHMS L T P C



3 0 0 3


UNIT I


FUNDAMENTALS









9

Mathematical Induction
-

Asymptotic Notations


Properties of Big
-
oh Notation


Conditional Asymptotic Notation


Algorithm Analysis


Amortized Analysis


N
P
-
Completeness


NP
-
Hard


Recurrence Equations


Solving Recurrence Equations


Memory Representation of Multi
-
dimensional Arrays


Time
-
Space Tradeoff.


UNIT II

HEAP STRUCTURES








9

Min/Max heaps


Deaps


Leftist Heaps


Binomial Heap
s


Fibonacci Heaps


Skew
Heaps


Lazy
-
Binomial Heaps.


UNIT III

SEARCH STRUCTURES








9

Binary Search Trees


AVL Trees


Red
-
Black trees


Multi
-
way Search Trees

B
-
Trees


Splay Trees


Tries.


UNIT IV

`MULTIMEDIA STRUCTURES








9

Segment Trees


k
-
d Trees


Point Quad Trees


MX
-
Quad Trees


R
-
Trees


TV
-
Trees.


UNIT V

ALGORITHMS









9

Huffman Coding


Convex Hull


Topological Sort


Tree Vertex Splitting


Activity
Networks


Flow Shop Scheduling



Counting Binary Trees


Introduction to
Randomized Algorithms.























TOTAL = 45


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REFERENCES

1.

E. Horowitz, S.Sahni and Dinesh Mehta, Fundamentals of Data structures in
C++, Uiversity Press, 2007.

2.

E. Horowitz, S. Sahni and S. Raj
asekaran, 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








9

Fundamentals of Computer Design
-

Measuring and Reporting Performance
-

Instruction
Level Parallelism and Its Exploitation
-

Concepts and Challenges
-

Overc
oming Data
Hazards with Dynamic Scheduling


Dynamic Branch Prediction
-

Speculation
-

Multiple
Issue Processors


Case Studies.


UNIT II

ADVANCED TECHNIQUES FOR EXPLOITING ILP



9

Compiler Techniques for Exposing ILP
-

Limitations on ILP for Real
izable 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







9

Symmetric 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 ARCHITEC
TURES








9

Software and hardware multithreading


SMT and CMP architectures


Design issues


Case studies


Intel Multi
-
core architecture


SUN CMP architecture


IBM cell
architecture.
-

hp architecture.


UNIT V

MEMORY HIERARCHY DES
IGN







9

Introduction
-

Optimizations of Cache Performance
-

Memory Technology and
Optimizations
-

Protection: Virtual Memory and Virtual Machines
-

Design of Memory
Hierarchies
-

Case Studies.











TOTAL
-

45

REFERENCES

1.

John L. Hennesse
y and David A. Patterson, “ Computer Architecture


A
quantitative approach”,
Morgan Kaufmann /
Elsevier, 4
th
. edition, 2007.

2.

David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture : A
hardware/ software approach” ,
Morgan Kaufmann /
Elsevi
er, 1997.

3.

William Stallings, “ Computer Organization and Architecture


Designing for
Performance”, Pearson Education, Seventh Edition, 2006.


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CP9114


OBJECT ORIENTED SYSTEMS ENGINEERING



L T P C














3 0 0 3


UN
IT I


CLASSICAL PARADIGM

System Concepts


Project Organization


Communication


Project Management


UNIT II

PROCESS MODELS

Life cycle models


Unified Process


Iterative and Incremental


Workflow


Agile
Processes


UNIT III

ANALYSIS

Requirements El
icitation


Use Cases


Unified Modeling Language, Tools


Analysis
Object Model (Domain Model)


Analysis Dynamic Models


Non
-
functional
requirements


Analysis Patterns


UNIT IV

DESIGN

System Design, Architecture


Design Principles
-

Design Patterns


Dynamic Object
Modeling


Static Object Modeling


Interface Specification


Object Constraint
Language


UNIT V

IMPLEMENTATION, DEPLOYMENT AND MAINTENANCE

Mapping Design (Models) to Code


Testing
-

Usability


Deployment


Configuration
Management


Mai
ntenance



REFERENCES

1.

Bernd Bruegge, Alan H Dutoit, Object
-
Oriented Software Engineering, 2
nd

ed,
Pearson Education, 2004.

2.

Craig Larman, Applying UML and Patterns 3
rd

ed, Pearson Education, 2005.

3.

Stephen Schach, Software Engineering 7
th

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 2
nd

ed, Pearson Education, 2007.


CP9115


NETWORK ENGINEERING AND MANAGEMENT

L T P C












3 0 0 3


UNIT I


FOUNDATIONS OF NETWORKING






9

Communication 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







9

Traffic Characteristics an
d 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
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solutions


Laissez Faire Approa
ch


Possible improvements in TCP


Significance of
UDP in inelastic traffic










UNIT III

HIGH PERFORMANCE NETWORKS





9

Integrated Services Architecture


Components and Services


Differentiated Services
Networks


Per Hop Behaviour


Admi
ssion Control


MPLS Networks


Principles and
Mechanisms


Label Stacking


RSVP


RTP/RTCP






UNIT IV

HIGH SPEED NETWORKS







9

Optical 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







9

ICMP 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
















TOTAL ; 45


REFERENCES

1.

Mahbub Hassan and Raj Jain, ‘High Performance TCP/IP Networking’, Pearson
Education, 2004.

2.

Larry L Peterson and Bruce S Davie, ‘Computer Networks: A Systems
Approach’, Fourth Edition, Morgan Kaufman Publishers,
2007.

3.

Jean Warland and Pravin Vareya, ‘High Performance Networks’, Morgan
Kauffman Publishers, 2002

4.

William Stallings, ‘High Speed Networks: Performance and Quality of Service’,
2
nd

Edition, Pearson Education, 2002.

5.

Mani Subramaniam, ‘Network Management:
Principles and Practices’, Pearson
Education, 2000

6.

Kasera and Seth, ‘ATM Networks: Concepts and Protocols’, Tata McGraw Hill,
2002.



CK9113

COMPUTER LABORATORY
-

I

L T P C

0 0 3 2


1.

Implementation of multi
-
dimensional structures such as matrices, triangu
lar
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 M
eldable Priority Queue operations)



Fibonacci Heap (All Meldable Priority Queue operations)


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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 s
pecified 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


UNIT I













9

PRAM 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











9

Classifying MIMD Algo
rithms


Hypercube SIMD Model


Shuffle Exchange SIMD Model


2D Mesh SIMD Model


UMA Multiprocessor Model


Broad case


Prefix Sums.


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UNIT III











9

Enumeration Sort


Lower Bound on Parallel Sorting


Odd
-
Even Transposition Sort


Biton
ic Merge


Parallel Quick Sort


Complexity of Parallel Search


Searching on
Multiprocessors.








UNIT IV












9

P
-
Depth Search


Breadth Death Search


Breadth First Search


Connected
Components


All pair Shortest Path


Single Source
Shortest Path


Minimum Cost
Spanning Tree.










UNIT V












9

Matrix 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
Parallel Computing, 2
nd

Edition
, Addison Wesley, 2003



CP9122



COMPILER OPTIMIZATION







L T P C



3 0 0 3


UNIT I












9

Principles Of Compiler


Compiler Structure


Properties of a Compiler


Optimization


Importance of Code optimization


Stru
cture of Optimizing compilers


placement of
optimizations in optimizing compilers


ICAN


Introduction and Overview


Symbol
table structure


Local and Global Symbol table management


UNIT II












9

Intermediate representation


Issues


H
igh 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


red
undancy elimination


loop optimization


UNIT III











9

Procedure optimization


in
-
line expansion


leaf routine optimization and shrink
wrapping


register allocation and assignment


graph coloring


code scheduling


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











9

Code Sched
uling


Instruction scheduling


Speculative scheduling


Software
pipelining


trace scheduling


percolation scheduling


Run
-
time support


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usage


local stack frame


run
-
time stack


Code sharing


position

independent code


Symbolic and pol
ymorphic language support


UNIT V











9

Case Studies


Sun Compilers for SPARC


IBM XL Compilers


Alpha compilers


PA

RISC assembly language


COOL


( Classroom Object oriented language)
-

Compiler
testing tools


SPIM












TOTA
L : 45


TEXT BOOKS

1.

Steven S. Muchnick, “Advanced Compiler Design Implementation”, Morgan
Koffman


Elsevier Science, India, Indian Reprint 2003

2.

Keith D Cooper and Linda Torczon, “ Engineering a Compiler, Elsevier Science,
India.


REFERENCES

1.

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.






C
K9121



ADVANCED DATABASE TECHNOLOGY


L T P C



3 0 0 3


UNIT I


QUERY AND TRANSACTIO
N PROCESSING

9

Data Storage and Querying : Storage and File Structure
-

Indexing and Hashing


Physical Database Design and Tuning
-

Query Processing Algorithms


Query
Optimization Techniques


Transaction Management: Transaction Processing Concepts


C
oncurrency Control


Recovery Techniques


Database Security.


UNIT II


PARALLEL AND DISTRIBUTED DATABASES

9

Database System Architectures: Centralized and Client
-
Server Architectures


Server
System Architectures


Parallel Systems
-

Distributed S
ystems


Parallel Databases: I/O
Parallelism


Inter and Intra Query Parallelism


Inter and Intra operation Parallelism


Distributed Database Concepts
-

Distributed Data Storage


Distributed Transactions


Commit Protocols


Concurrency Control


Distri
buted Query Processing


Three Tier
Client Server Architecture
-

Case Studies
.


UNIT III


OBJECT AND OBJECT RELATIONAL DATABASES

9

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Concepts for Object Databases: Object Identity


Object structure


Type Constructors


Encapsulation of Operations


M
ethods


Persistence


Type and Class Hierarchies


Inheritance


Complex Objects


Object Database Standards, Languages and Design:
ODMG Model


ODL


OQL


Object Relational and Extended


Relational Systems:
Object Relational features in SQL / Oracle


Case Studies.


UNIT IV
ENHANCED DATA MODELS

9

Active Database Concepts and Triggers


Temporal Databases


Spatial Databases


Multimedia Databases


Deductive Databases


XML Databases: XML Data Model


DTD
-

XML Schema
-

XML Querying
-

Geogra
phic Information Systems
-

Genome Data
Management
.


UNIT V


EMERGING TECHNOLOGIES

9

Mobile Databases: Location and Handoff Management
-

Effect of Mobility on Data
Management
-

Location Dependent Data Distribution
-

Mobile Transac
tion Models
-

Concurrency Control
-

Transaction Commit Protocols


Web Databases
-

Information
Retrieval
-

Data Warehousing
-

Data Mining.


TOTAL = 45


REFERENCES

1.

R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”, Fifth Edition,
Pearson Educati
on/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
Conc
epts”, 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.

Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”,
McGraw Hill, Third Edition 200
4.




CP9153



KNOWLEDGE ENGINEERING L T P C



3 0 0 3


UNIT I


INTRODUCTION









9

Key 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


R
ules in Production


Description Logic
-

Vivid Knowledge


Beyond Vivid.







UNIT III

REPRESENTATION








9

Object Oriented Representations


Frame Formalism


Structured Descriptions


Meaning and Entailment
-

Taxonomies and Classificatio
n


Inheritance


Networks

Strategies for Defensible Inheritance


Formal Account of Inheritance Networks.


UNIT IV

DEFAULTS, UNCERTAINTY AND EXPRESSIVENESS


9

Defaults


Introduction


Closed World Reasoning


Circumscription


Defau
lt 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







9

Explanation 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


REFERENCES

1.

Ronald Brachman, Hector Levesque “Knowledge Representation and
Reasoning “, The Morgan Kaufmann Series in Artificial Intelligence 2004

2.

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








9

Issues


Motivation


Theory of Language
-
Features of Indian Languages


Issues in
Font


Coding Techniques


sorting & searching issues.






UNIT II

MORPHOLOGY AND PARTS
-
OF
-
SPEECH





9

Phonology


Computational Phonology
-

Words and Morphemes


Segmentation


Categorization and Lemmatisation


Word Form Recognitio
n


Valency
-

Agreement
-

Regular Expressions and Automata


Morphology
-

Morphological issues of Indian
Languages


Transliteration.











UNIT III

PROBABILISTIC MODELS







9

Probabilistic Models of Pronunciation and Spelling


Weighted Auto
mata


N
-

Grams


Corpus Analysis


Smoothing


Entropy
-

Parts
-
of
-
Speech


Taggers


Rule based


Hidden Markov Models


Speech Recognition


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UNIT IV

SYNTAX









9

Basic Concepts of Syntax


Parsing Techniques


General Grammar rules for India
n
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






9

Representing Meaning


Compu
tational Representation


Meaning Structure of
Language


Semantic Analysis


Lexical Semantics


WordNet


Pragmatics


Discourse


Reference Resolution


Text Coherence


Dialogue Conversational Agents.














TOTAL =45


REFERENCES

1.

Daniel Jurafs
key 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 Yo
ung and Gerrit Bloothooft “Corpus


Based Methods in Language and
Speech Processing”, Kluwer Academic Publishers, 1997.


















CK9125

LANGUAGE TECHNOLOGY LABORATORY



L T P C

1 0 3 3



1.

Design and implement a FSA that recognizes the followin
g 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


摥獫, c桡i爬 t慢l攩



Simple date expressions like March15, the 22
nd

of November, Christmas.



Extend to ha
ndle deictic expressions like yesterday, tomorrow, a week
from tomorrow, the day before yesterday, Sunday, next Monday, three
weeks from Saturday

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Handle time of the day expressions like eleven o’clock, twelve thirty,
midnight, quarter to ten etc.



Modify yo
ur 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 sentences

o

Do Witten
-
Bell discounting

o

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 s
et.
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 pos
itive 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 mat
rix, 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 s
uch 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 tr
eebank, 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
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three things, can you highlight the rest. (See Wang et. al. 2005 in BioLink and
Marc Light’s work at U of Iowa.)

2.

Develop a conditional random field approach for an information extraction task
(e.g., McDonald and Pe
reira, BioCreative 2004, Sha and Pereira 2003, Lafferty
et al 2001)

3.

Spam filtering

4.

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 relat
ed 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 in
dividuals in the company), categorize messages,
etc.

7.

Build a more intelligent search engine for a specific corpus

8.

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



9

Beginning with a simple communication game


wresting between safeguard and attack


Probabil
ity and Information Theory
-

Algebraic foundations


Number theory.




UNIT II

ENCRYPTION


SYMMETRIC TECHNIQUES




9

Substitution Ciphers
-

Transposition Ciphers
-

Classical Ciphers


DES


AES


Confidentiality Modes of Operation


Key Channel E
stablishment for symmetric
cryptosystems.












UNIT III

ENCRYPTION

ASYMMETRIC TECHNIQUES & DATA INTEGRITY


TECHNIQUES








9

Diffie
-
Hellman Key Exchange protocol


Discrete logarithm problem


RSA
cryptosystems & cryptanalysis


E
lGamal 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
-

Asymm
etric techniques





UNIT IV

AUTHENTICATION








9

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Authentication Protocols Principles


Authentication protocols for Internet Security


SSH Remote logic protocol


Kerberos Protocol


SSL & TLS


Authentication frame for
public key Cryptograph
y


Directory Based Authentication framework


Non
-

Directory
Based Public
-
Key Authentication framework .








UNIT V

SECURITY PRACTICES







9

Protecting Programs and Data


Information and the Law


Rights of Employees and
Employers


Softw
are Failures


Computer Crime


Privacy


Ethical Issues in
Computer Security.























TOTAL : 45

REFERENCES

1.

Wenbo Mao, “Modern Cryptography


Theory and Practice”, Pearson Education,
First Edition, 2006.

2.

Douglas R. Stinson ,“Cryptography The
ory 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 T
heory” Second Edition, Pearson Education, 2007.






















IT9155


ONTOLOGY AND SEMANTIC WEB




L T P C

3 0 0 3


UNIT I


INTRODUCTION








8


Components


Types


Ontological Commitments


Ontological Categories


Philosophical Backgr
ound
-
Sample
-

Knowledge Representation Ontologies


Top Level
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Ontologies


Linguistic Ontologies


Domain Ontologies


Semantic Web


Need


Foundation


Layers


Architecture.


UNIT II

LANGUAGES FOR SEMANTIC WEB AND ONTOLOGIES


12


Web Documents i
n 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
-

DAM
L + 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 Suite
s


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 Pres
s, 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 t
o 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 St
aab (Editor), Rudi Studer, “Handbook on Ontologies (International
Handbooks on Information Systems)”, Springer 1st edition, 2004

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9.

Dean Allemang

(Author),
James Hendler

(Author) “
Semantic W
eb for the
Working Ontologist: Effective Modeling in RDFS and OWL” (Paperback),
Morgan
Kaufmann, 2008





CK9152

HUMAN LANGUAGE TECHNOLOGY







L T P C

3 0 0 3

UNIT I


INTRODUCTION








9


Definition
-

Need for language technologies


s
pectrum


commercial applications
-

Mathematical foundations


Probability and statistics in computational linguistics


Set
theory foundations


Statistical modeling, classification, and clustering


issues in
language modeling


Stochastic analysis


lat
ent semantic analysis


Finite state
technology


UNIT II

SPOKEN AND WRITTEN LANGUAGE INPUT



9


Overview


Speech recognition


robustness


HMM methods


Language
representation


Speaker recognition


Speech coding


Speech enhanceme
nt


Document image analysis


OCR


Handwriting analysis


UNIT III

LANGUAGE ANALYSIS, UNDERSTANDING AND GENERATION 9


Sub sentential processing


Grammar formalism


lexicons parsing


Semantics


Document retrieval


Information Extraction


Summ
arization


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 generatio
n






UNIT IV

MULTI
-
LINGUALITY AND MULTIMODALITY



9

Multilinguality


Multilingual Information Retrieval and Speech Processing

automatic
language identification
-

Multimodality


Text and Images


Modality Integration


Transmission and Storage.




UNIT V

LANGUAGE RESOURCES AND EVALUATION




9


Written and Spoken language corpora


lexicons

Evaluation overview


task oriented
text analysis evaluation


Evaluation of machine translation an
d 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 th
e 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.

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4.

Christopher D.Manning and Hinrich Schutze, “ Foundations of Statistical Natural
Language Processing “, MIT Press, 1999.




CK9153

INFORMATION RETRIEVAL TECHNIQUES






L T P C


3 0 0 3


UNIT I


INTRODUCTION








9

Basic 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 Sp
ecification
-

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 I
mages


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













TOTAL = 45


REFERENCES:


1.

Ricardo Baeza
-
Yate, Berthier Ribeiro
-
Neto, “Modern Information Retrieval”,
Pearson Education Asia, 2005.

2. G.G. Chowdhu
ry, “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 Ret
rieval: Algorithms, and
Heuristics”, Academic Press, 2000

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5.

Charles T. Meadow, Bert R. Boyce, Donald H. Kraft, “Text Information


Retrieval Systems”, Academic Press, 2000


.







































CK9154

STATISTICAL NATURAL LANGUAGE PRO
CESSING



L T P C














3 0 0 3


UNIT I


INTRODUCTION








9

Statistical Natural Language Processing


Mathematical Foundations


Linguistic
Essentials


Corpus based issues


UNIT II

WORD COLLOCAT
IONS







9

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Statistical Measures


N
-
gram models


Statistical estimators


Word sense
Disambiguation


Lexical Acquisition


UNIT III

STATISTICAL PARSING







9

Markov Models


Hidden Markov Models


Part of speech Tagging


Probabilis
tic
Context Free Grammars


Probabilistic Parsing


UNIT IV

STATISTICAL TECHNIQUES







9

Statistical Alignment and machine translation


Clustering


Hierarchical and Non


hierarchical Clustering


UNIT V

APPLICATIONS








9

Informat
ion 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.

Chr
istopher 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.












C
P9173

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


Dec
ision Tree learning


Representation


Algorithm


Heuristic Space Search.






UNIT II

NEURAL NETWORKS AND GENETIC ALGORITHMS



9

Neural Network Representation


Problems


Perceptrons


Multilayer Networks and
Back Propagation Algorithms


Adva
nced Topics


Genetic Algorithms


Hypothesis
Space Search


Genetic Programming


Models of Evalution and Learning.










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UNIT III

BAYESIAN AND COMPUTATIONAL LEARNING




9
Bayes Theorem


Concept Learning


Maximum Likelihood


Minimum D
escription
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





9

K
-

Nearest Neighbour Learning


Locally weighted Regression


Radial Bases
Functions


Case Based Learning.



UNIT V

ADVANCED LEARNING








9

Learning 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
-
L
earning


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”,
Spr
inger; 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 Generati
on in Practice


Appropriateness
of NLG Techniques


Using a Corpus to Determine User Requirements
-

Evaluating
NLG Systems


Fielding NLG Systems


UNIT II

ARCHITECTURE AND PLANNING






9

Inputs and Outputs of Natural Language Generation An Info
rmal 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


Domai
n Models


Implementation


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Defining Messages


Domain Modeling and Message Definition


Content
Determination
-

Document Structuring


UNIT III

MICRO PLANNING








9

Micro Planning


Architecture of Microplanner
-

Lexicalisation


Contextual an
d
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 Ref
erring 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







9

Reali
sing 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
Sy
stemic 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 Gram
mars.


UNIT V

BEYOND TEXT GENERATION






9

Typography
-

Integrating Text and Graphics

Hypertext


Hypertext and its uses


Implementing Hypertext based NLG Systems


Speech Output


Benefits of Speech
Output


Text

to
-
speech systems


Implemen
ting Concepts
-
to
-
Speech


Question
Answering











Total =45

REFERENCES:


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 Pr
ocessing), Cambridge University Press, 1986




CK9157

TEXT MINING







L T P C

3 0 0 3

UNIT I


INTRODUCTION








9

Overview of text mining
-
document classification
-

information retrieval
-

clustering and
organizing documents
-

information extrac
tion
-

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

INFO
RMATION RETRIEVAL AND TEXT MINING




9

Information 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
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similarity
-

evaluation of performance
-

information extraction
-

patterns and entities from
text
-

coreference and relationship extraction
-

template filling and database construction


UNIT III

CLUSTERING AND CLA
SSIFICATION





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 orthogona
l 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


UNIT IV

LEARNI
NG AND TEXT MINING






9

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
-
tec
hnology 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 unlabele
d 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

2.

Sholom Weiss, “Text Mining: Predictive Meth
ods for Analyzing Unstructured
Information”,
Springer, 2005




CP9157

SPEECH PROCESSING








L T P C


3 0 0 3


UNIT I


INTRODUCTION









9

http://www.wordwendang.com/en/

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, where you can download million word
documents .

Spoken Language System Architecture and Struct
ure


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 Anal
ysis


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 Predict
ion


Low


Bit rate Speech coders.


UNIT III

SPEECH RECOGNITION







9

Hidden Markov Models (HMM)


Practical Issues in Using HMMs


HMM Limitations
Acoustic Modeling


Phonetic Modeling


Language Modeling
-

Speaker Recognition
A
lgorithms


Signal Enhancement for Mismatched Conditions.



UNIT IV

SPEECH SYNTHESIS





9



Formant Speech Synthesis


Concatenative Speech Synthesis


Prosodic Modification
Of Speech


Source Fil
ter Models For Prosody Modification


Evaluation Of Text To
Speech System.




UNIT V

SPOKEN LANGUAGE UNDERSTANDING





9

Dialog Structure


Semantic Representation


Sentence Interpretation


Discourse
Analysis


Dialog Management


Response Gener
ation And Rendition


Case Study.




TOTAL = 45


TEXT BOOKS:


1.

Thomas F.Quatieri, “Discrete
-
Time Speech Signal Processing”, Pearson
Education, 2002.

2.

Xuedong H
uang, 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”,
S
pringer 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.

D
aniel Jurafsky & James H.Martin, “Speech and Language Processing”, Pearson
Education ,2000.




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documents .

CP9161

KNOWLEDGE MANAGEMENT








L T P C


3 0 0 3

UNIT I


INTRODUCTION









9

The value of Knowledge


Knowledge Eng
ineering Basics


Knowledge Economy


The
Task and Organizational Content


Knowledge Management


Knowledge Management
Ontology.


UNIT II

KNOWLEDGE MODELS







9

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 I
V

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”, I
nternational
Handbooks on Information Systems, Vol 1 and 2, 2003.

2.

http://www.epistemics.co.uk

3.

http://depts.washington.edu/pettt/papers/WIN_poster_text.pdf




CP9164

DATA WAREHOUSING AND DATA MINING





L T P C

3 0 0 3

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documents .

UNIT I












9

D
ata 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 an
d Transformation


Data Reduction


Data Discretization and Concept
Hierarchy Generation.

Association Rule Mining:
-

Efficient and Scalable Frequent Item set Mining Methods


Mining Various Kinds of Association Rules


Association Mining to Correlation Ana
lysis


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 Pre
dictor


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


De
nsity
-
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
Editio
n, Prentice Hall of India, 2006.

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documents .

6.

Pang
-
Ning Tan, Michael Steinbach and Vipin Kumar “Introduction to Data
Mining”, Pearson Education, 2007.



CP9154

VISUALIZATION TECHNIQUES




L T P C

3 0 0 3



UNIT I


VISUALIZATION








9

I
ntroduction


Issues


Data Representation


Data Presentation
-

Interaction




UNIT II

FOUNDATIONS FOR DATA VISUALIZATION



9

Visualization stages


Experimental Semiotics based on Perception Gibson‘s Affordance
theory


A Model of Per
ceptual Processing


Types of Data.


UNIT III

COMPUTER VISUALIZATION




9

Non
-
Computer Visualization


Computer Visualization: Exploring Complex Information
Spaces


Fisheye Views


Applications


Comprehensible Fisheye vie
ws


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 Dimen
sion


Two Dimensions


Three Dimensions


Multiple Dimensions


Trees


Web Works


Data Mapping: Document Visualization


Workspaces.




UNIT V

CASE STUDIES








9

Small 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, 2
nd

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 DESIGN







L T P C



3 0 0 3


UNIT I


INTRODUCTION








8

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documents .

Human

Computer Interface


Characteristics Of Graphics Interface

Direct
Manipulati
on Graphical System


Web User Interface

Popularity

Characteristic &
Principles.


UNIT II

HUMAN COMPUTER INTERACTION


7


User Interface Design Process


Obstacles

Usability

Human Characteris
tics 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

WIND
OWS








12

Characteristics


Components


Presentation Styles


Types


Managements


Organizations


Operations


Web Systems


System Timings
-

Device


Based Controls
Characteristics


Screen


Based Controls
––

Human Consi
deration 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







9

Text For Web Pages


Effective Feedback


Guidance & Assistance


Internationalization


Accessibility


Icons


Image


Multimedia


Coloring.




UNIT V

EVALUATION








9

Conceptual 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,
1999
Ben 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, ‘Inte
raction Design’, Wiley India Edition, 2007



CP9158

BIO INFORMATICS









L T P C

3 0 0 3

UNIT I


INTRODUCTORY CONCEPTS






9

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documents .

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


Hardw
are


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 K
nowledge Management


Data
Visualization


sequence visualization


structure visualization


user Interface


Animation Versus simulation


General Purpose Technologies.

UNIT III

STATISTICS AND DATA MINING






9

Statistical concepts


Microarray
s


Imperfect Data


Randomness


Variability


Approximation


Interface Noise


Assumptions


Sampling and Distributions


Hypothesis Testing


Quantifying Randomness


Data Analysis


Tool selection
statistics of Alignment


Clustering and Classificatio
n


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.

UNIT IV

PATTERN MATCHING






9

Pairwise sequence alignment


Local versus global alignment


Multiple sequence
alignment


Computational methods


Dot Matrix analysis


Substitution matrices


Dynamic Programming


Word method
s


Bayesian methods


Multiple sequence
alignment


Dynamic Programming


Progressive strategies


Iterative strategies


Tools


Nucleotide Pattern Matching


Polypeptide pattern matching


Utilities


Sequence Databases.

UNIT V

Modeling and Simulation







9

Drug Discovery


components


process


Perspectives


Numeric considerations


Algorithms


Hardware


Issues


Protein structure


AbInitio Methods


Heuristic
methods


Systems Biology


Tools


Collaboration and Communications


standar
ds
-

Issues


Security


Intellectual property.

TOTAL = 45



REFERENCES

1.

Bryan Bergeron, “Bio Informatics Computing”, Second Edition, Pearson Education,
2003.

2.

T.K.Attwood and D.J. Perry Smith, “Introduction to Bio Informatics, Longman Essen,
1999.

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documents .



CP915
9

SOFT COMPUTING








L T P C


3 0 0 3

UNIT I


INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS 9

Evolution of Computing
-

Soft Computing Constituents


From Conventional AI to
Computational Intelligence
-

Machine Learning Basics


UNIT II

GENETIC A
LGORITHMS







9

Introduction to Genetic Algorithms (GA)


Applications of GA in Machine Learning
-

Machine Learning Approach to Knowledge Acquisition.


UNIT III

NEURAL NETWORKS






9

Machine Learning Usi
ng Neural Network, Adaptive Networks


Feed forward Networks


Supervised Learning Neural Networks


Radial Basis Function Networks
-

Reinforcement Learning


Unsupervised Learning Neural Networks


Adaptive
Resonance architectures


Advances in Neural net
works.


UNIT IV

FUZZY LOGIC









9

Fuzzy Sets


Operations on Fuzzy Sets


Fuzzy Relations


Membership Functions
-

Fuzzy Rules and Fuzzy Reasoning


Fuzzy Inference Systems


Fuzzy Expert Systems


Fuzzy Decision Maki
ng.



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


Ca
se 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”,


Pren
tice Hall, 1995.

3
.
James A. Freeman and David M. Skapura, “Neural Networks Algorithms,


Applications, and Programming Techniques”, Pearson Edn., 2003.



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, where you can download million word
documents .

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, “ Introducti
on to Genetic Algorithms”,
Springer,
2007.

5.

Jacek M. Zurada, “Introduction to Artificial Neural Systems”, PWS
Publishers, 1992.




CK9161


SERVICE
ORIENTED COMPUTING





L T P C

3 0 0 3


UNIT I


INTRODUCTION TO COMPUTING






9

Basics
-

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

EN
GAGEMENT









9

Execution Models
-

Transaction Concepts
-

Coordination Frameworks for Web Services
-

Process SpecificationsBPEL4WS, BPML and ebxml
-

Formal Specification and
Enactment.


UNIT III

STANDARDS AND WEB SERVICES PROGRAMMING



9

SOAP
-
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
Extensions

















Total=45

REFERENCES:

1.

Munindar P.Singh, Michael N.Huhns ,”Service

Oriented Computing:
Semantics,Processes,Agents, John Wiley and Sons, 2005

2.

Sowa, John F. Knowledge Repr
esentation: Logical, Philosophical, and
Computational Foundations, Brooks/Cole, Pacific Grove,CA,2000 …..

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documents .

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”, Pe
arson Education, 2002.

5.

Zakaria Maamar, David Martin, Boualem Benatallah, Lawrence Cavedon

“Extending Web Services Technologies: The Use of Multi
-
Agent Approaches”,
Sp
ringer, 2004

6.

Mark O'Neill



“Web Services Security”

McGraw
-
Hill Professional
, 2003

7.

Zoran Stojanovic, Ajantha Dahanayake



“Service Oriented Software System
Engineering: Challenges and Practices”
-



Idea Group I
nc (IGI)
, 2005

8.

James McGovern, Sameer Tyagi, Sunil Mathew, Michael Stevens

“Java Web
Services Architecture”


-



Morgan Kaufmann
, 2003


CK9162

XML AND WEB SERVICES





L T P C

3 0 0

3

UNIT I


Distributed Databases Vs Conventional Databases


Architecture


Fragmentation


Query Processing


Transaction Processing


Concurrency Control


Recovery.


UNIT II

OBJECT ORIENTED DATABASES






10

Introduction to Object Oriented Data B
ases
-

Approaches
-

Modeling and Design
-

Persistence


Query Languages
-

Transaction
-

Concurrency


Multi Version Locks
-

Recovery









UNIT III

EMERGING SYSTEMS







10

Enhanced Data Models
-

Client/Server Model
-

Data Warehousing

and Data Mining
-

Web Databases


Mobile Databases.













UNIT IV

DATABASE DESIGN ISSUES






9

ER Model
-

Normalization
-

Security
-

Integrity
-

Consistency
-

Database Tuning
-

Optimization and Research Issues


Design of Temporal Database
s


Spatial
Databases.












UNIT V

CURRENT ISSUES






9

Semantic Web


Role of Meta data in web content
-

Resource Description Framework


RDF schema


Architecture of semantic web


content management workflow


XLA
NG


WSFL


BPEL4WS













TOTAL = 45

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.


REFERENCE
S:


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Please go to
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, where you can download million word
documents .

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”,Firs
t
Edition, Apress, 2004.

4.

Russ Basiura and Mike Batongbacal, “Professional ASP .NET Web Services”,
Apress, 2003.



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










9

Pervasive 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










9

Voice Enabling Pervasive Computing
-

Voice Standards
-

Speech Applications in
Pervasive Computing and security


UNIT IV










9

PDA in Pervasive Computing


Introduction
-

PDA software Components, Standards,
emerging trends
-

PDA Device characteristics
-

PDA Based Access Architecture


UNIT V











9

User Interface Issues in Pervasive Computin
g, Architecture
-

Smart Card
-

based
Authentication Mechanisms
-

Wearable computing Architecture



TOTAL = 45





TEXT 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.


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documents .

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 Ba
nerjee: Internetworking Technologies: An Engineering Perspective,
Prentice

Hall of India, New Delhi, 2003. (ISBN 81
-
203
-
2185
-
5)


2
.


Rahul Banerjee: Lecture Notes in Pervasive Computing, Outline Notes,



BITS
-
Pilani, 2003.



CK9164

SOFTWARE AGENT
S




L T P C

3 0 0 3

UNIT I


AGENTS


OVERVIEW







9

Agent Definition


Agent Programming Paradigms


Agent Vs Object


Aglet


Mobile
Agents


Agent Frameworks


Agent Reasoning.


UNIT II

JAVA AGENTS







9

Processes


T
hreads


Daemons


Components


Java Beans


ActiveX


Sockets


RPCs


Distributed Computing


Aglets Programming


Jini Architecture


Actors and
Agents


Typed and proactive messages.


UNIT III

MULTIAGENT SYSTEMS





9

Interaction between agents


Reactive Agents


Cognitive Agents


Interaction protocols


Agent coordination


Agent negotiation


Agent Cooperation


Agent Organization


Self
-
Interested agents in Electronic Commerce Applicatio
ns.



UNIT IV

INTELLIGENT SOFTWARE AGENTS




9

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, " Constr
ucting 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.

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documents .

4.

Richard Murch, Tony Johnson, "Intelligent Sof
tware Agents", Prentice Hall, 2000.

5.

Gerhard Weiss, “Multi Agent Systems


A Modern Approach to Distributed
Artificial Intelligence”, MIT Press, 2000.



CK9165

ARTIFICIAL INTELLIGENCE







L T P C

3 0 0 3

UNIT I


INTRODUCTION








8

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


searchi
ng 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 ch
ance.

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 learnin
g



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 meth
ods



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 reinforcem
ent 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 understand
ing


Grammar induction



Probabilistic
language processing



Probabilistic language models


Information retrieval


Information Extraction


Machine translation.

Total = 45

REFERENCES

3.

Stuart Russell, Peter Norvig, “Artificial Intelligence


A Modern Ap
proach”, Second
Edition, Pearson Education / Prentice Hall of India, 2004.

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documents .

4.

Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd.,
2000.

5.

Elaine Rich and Kevin Knight, “Artificial Intelligence”, Second Edition, Tata McGraw
Hil
l, 2003.

6.

George F. Luger, “Artificial Intelligence
-
Structures And Strategies For Complex
Problem Solving”, Pearson Education / PHI, 2002.





CP9176


HUMAN RESOURCE MANAGEMENT







L T P C



3 0 0 3



UNIT 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




9

Importance of human resource planning


forecasting human resource requirement


internal and external sources. Selection pr
ocess
-
screening


tests
-

validation


interview
-

medical examination


recruitment introduction


importance


practices


socialization benefits.






UNIT III

TRAINING AND EXECUTIVE DEVELOPMENT

9

Types of traini
ng, methods, purpose, benefits and resistance. Executive development
programmes


common practices
-

benefits


self development


knowledge
management.





UNIT IV

SUSTAINING EMPLOYEE INTEREST





9

Compensation plan


reward


motivation


theo
ries of motivation


career management


development, mentor


protégé relationships.





UNIT V

PERFORMANCE EVALUATION AND CONTROL PROCESS


9

Method 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 Manageme
nt, Wilsey, 6
th

edition, 2001.


2. Biswajeet Pattanayak, Human Resource Management, Prentice Hall of
India,2001.



REFERENCES:

http://www.wordwendang.com/en/

Please go to
http://www.wordwendang.com/en/
, where you can download million word
documents .


1.

Human Resource Management, Eugence Mckenna and Nic Beach, Pearson
Education Limited, 2002.

2.

Dessler Human Resource Managemen
t, 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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