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Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
COURSE CURRICULUM
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DEPARTMENT OF COMPUTER ENGINEERING
& APPLICATIONS
GLA UNIVERSITY, MATHURA (U.P.) INDIA
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
COURSE STRUCTURE
M.TECH.(CSE)
FULL TIME
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
First Semester
S.
No.
CODE
1
MCS1001
2
TEACHING
SCHEME
SUBJECT
CREDITS
Contact
Hrs/wk
L
T
P
Theory of Computation
4
0
0
4
4
MCS1002
Software Engineering Methodologies
4
0
0
4
4
3
MCS1003
Advanced Concepts in Data Mining
4
0
0
4
4
4
MCS1004
Advanced Concepts in Networking
4
0
0
4
4
5
MCS1005
Probability and Stochastic Process
4
0
0
4
4
Practicals
6
MCS1081
Advanced Concepts in Networking Lab
0
0
2
1
2
7
MCS1082
Seminar-I
0
0
2
1
2
20
0
4
22
24
TOTAL
Second Semester
TEACHING
SCHEME
L
T
P
S.
No.
CODE
CREDITS
Contact
Hrs/wk
1
MCS2001
Mobile Ad-hoc Networks
4
0
0
4
4
2
MCS2002
Intelligent Systems
4
0
0
4
4
3
MCS2003
Information Retrieval
4
0
0
4
4
4
MCS2004
Image Processing and Analysis
4
0
0
4
4
5
MCS2005
Design of Distributed Systems
4
0
0
4
4
SUBJECT
Practicals
6
MCS2081
Image Processing and Analysis Lab
0
0
2
1
2
7
MCS2082
Seminar-II
0
0
2
1
2
20
0
4
22
24
CREDITS
Contact
Hrs/wk
TOTAL
Third Semester
S.
No.
CODE
TEACHING
SCHEME
SUBJECT
L
T
P
1
Elective-I
4
0
0
4
4
2
Elective - II
4
0
0
4
4
3
MCS3081
Dissertation – I
0
0
-
4
4
MCS3082
Colloquium
0
0
4
2
4
20
0
4
14
12
TOTAL
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
1
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
ELECTIVE-I - Third Semester
TEACHING
SCHEME
L
T
P
S.
No.
CODE
1
MCS3021
Computer Vision
4
0
2
MCS3022
4
3
MCS3023
Wireless Sensor Networks
Software and Service Oriented
Architecture
4
SUBJECT
CREDITS
Contact
Hrs/wk
0
4
4
0
0
4
4
0
0
4
4
CREDITS
Contact
Hrs/wk
ELECTIVE-II - Third Semester
TEACHING
SCHEME
L
T
P
S.
No.
CODE
1
MCS3041
Pattern Recognition
4
0
0
4
4
2
MCS3042
High Performance Computing
4
0
0
4
4
3
MCS3043
Web Mining
4
0
0
4
4
CREDITS
Contact
Hrs/wk
SUBJECT
Fourth Semester
S. No.
CODE
1
MCS4081
TEACHING
SCHEME
L
T
P
0
0
-
SUBJECT
Dissertation – II
TOTAL
0
0
0
14
14
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
2
-
Course Curriculum (w.e.f. Session 2015-16)
M.tech. (CSE)
COURSE STRUCTURE
M.TECH.(CSE)
PART TIME
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
First Semester
S.
NO.
CODE
1.
MCS1001
2.
MCS1002
3.
MCS1005
4.
MCS1082
SUBJECT
LECTURE
Theory of Computation
Software Engineering
Methodologies
Probability and Stochastic
Process
Seminar-I
TOTAL
TEACHING SCHEME
TUTORIALS PRACTICALS
CREDITS
4
0
0
4
4
0
0
4
4
0
0
4
0
0
2
1
12
0
2
13
Second Semester
S.
NO.
1.
MCS2002
2.
MCS2004
3.
MCS2005
4.
MCS2004
CODE
SUBJECT
LECTURE
4
Intelligent Systems
Image Processing and
Analysis
Design of Distributed
Systems
Image Processing and
Analysis Lab
TOTAL
TEACHING SCHEME
TUTORIALS PRACTICALS
0
0
CREDITS
4
4
0
0
4
4
0
0
4
0
0
2
1
12
0
2
13
Third Semester
S.
NO.
CODE
1.
MCS1003
2.
MCS1004
3.
4.
SUBJECT
LECTURE
Advanced Concepts in Data
Mining
Advanced Concepts in
Networking
Elective-I
MCS1081
Advanced Concepts in
Networking Lab
TOTAL
TEACHING SCHEME
TUTORIALS PRACTICALS
CREDITS
4
0
0
4
4
0
0
4
4
0
0
4
0
0
2
1
12
0
2
13
Fourth Semester
S.
NO.
1.
2.
3.
4.
CODE
MCS2001
MCS2003
MCS2082
SUBJECT
Mobile Ad-hoc Networks
Information Retrieval
Elective - II
Seminar-II
TOTAL
LECTURE
4
4
4
0
12
TEACHING SCHEME
TUTORIALS PRACTICALS
0
0
0
0
0
0
0
2
0
2
CREDITS
4
4
4
1
13
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
3
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
ELECTIVE-I - Third Semester
S.
NO.
1.
CODE
SUBJECT
MCS3021
Computer Vision
2.
MCS3022
Wireless Sensor Networks
4
0
0
4
3.
MCS3023
Software & Service
Oriented Architecture
4
0
0
4
LECTURE
4
TEACHING SCHEME
TUTORIALS PRACTICALS
0
0
CREDITS
4
ELECTIVE-II - Fourth Semester
S.
NO.
1.
CODE
MCS3041
2.
MCS3042
3.
MCS3043
SUBJECT
Pattern Recognition
High Performance
Computing
Web Mining
LECTURE
4
TEACHING SCHEME
TUTORIALS PRACTICALS
0
0
CREDITS
4
4
0
0
4
4
0
0
4
Fifth Semester
S.
NO.
CODE
SUBJECT
1.
MCS3081
Dissertation - I
0
0
-
8
2.
MCS3082
Colloquium
0
0
4
2
Total
0
0
-
10
LECTURE
TEACHING SCHEME
TUTORIALS PRACTICALS
CREDIT
S
Sixth Semester
S.
NO.
CODE
1.
MCS4081
SUBJECT
LECTURE
TEACHING SCHEME
TUTORIALS PRACTICALS
CREDITS
Dissertation - II
0
0
-
10
Total
0
0
-
10
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
4
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
SYLLABUS
M.TECH. (CSE)
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
5
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS1001: THEORY OF COMPUTATION
Prerequisite: Basic Mathematics, Discrete Structure and Data structure.
Credits: 04
Module
No.
Semester I
L–T–P: 4–0–0
Contents
Automata & Languages Chomsky Hierarchy of Grammars and the
corresponding acceptors, Turing Machines, Recursive and Recursively
Enumerable Languages; Operations on Languages, closures with respect
to the operations.
I
Computability Theory Decidability – Decidable languages, The Halting
Problem, Undecidable Problems about Turing Machines, Post’s
Correspondence Problem, Reducibility.
II
Complexity Theory Time Complexity - Measuring Complexity, The
class P, The class NP, NP-Completeness, Reduction, co-NP, Polynomial
Hierarchy Space Complexity -- Savich's Theorem, The class PSPACE,
PSPACE Completeness, Intractability
III
Teaching
Hours
13
14
13
References:




Michael Sipser, (2012), “Introduction to The Theory of Computation”, CENGAGE Learning
John E. Hopcroft, Rajeev Motwani, Jeffery D. Ullman (2009), “Automata Theory, Languages, and
Computation”, 3/e Pearson Education
John C. Martin, (2003), “ Introduction to Languages and the Theory of Computation”, McGraw Hill
Harry R. Lewis, Christos H. Papadimitriou, (1997), “Elements of the Theory of Computation”, 2/e,
Prentice-Hall
Outcome:
On successful completion of this module, students should be able to:





Design, manipulate, and reason about formal computational models.
Describe the limitations of different types of computing devices.
Identify relations between classes of computational problems, formal languages, and computational
models
Account for the inherent complexity of many computational problems of practical importance
Conduct formal reasoning about machines, problems and algorithms, including reduction-based
proof
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
6
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS1002: SOFTWARE ENGINEERING METHODOLOGIES
Prerequisite Ubderstanding of basics of software development, algorithms and programming
Credits: 04
Semester I
L–T–P : 4–0–0
Module
No.
Contents
Teaching
Hours
I
Introduction: Motivation – Software Attributes – Complexity - Software
Metrics- Software Process, Requirement Engineering, Formal
requirement specification, requirement modeling and specification
Design Metrics and Configuration Management. Formal Specification
and program verification, Software Process, Requirement Engineering,
Formal requirement specification, requirement modeling and
specification.
13
II
Software Design Patterns
Issues in software design: modularity based cohesion & coupling
Function oriented analysis & design. Software Architecture description
languages - Product-line architectures; Component based development
Software Quality Engineering
Testing Techniques – Test Case Generation, Software Maintenance
schemes
Software testing: strategies and assessment, COTS, Software reliability
metrics & modeling, Software quality: models and assurance
framework, Software Maintenance.
14
III
Introduction to formal methods Formal specifications Techniques –
Verification and Validation – Theorem Provers - Model checking –
Temporal logics – CTL & LTL and model checking
Software Metrics - COTS Integration - Distributed, Internet-scale and
Web-based Software Engineering
Empirical Studies of Software Tools And Methods
Software Reengineering - Software Reuse - Software Safety - Enterprise
Architectures, Zachman's Framework; Architectural Styles.
13
References:





Ghezzi, Jazayeri, Mandrioli, (2002) “Fundamentals of Software Engineering”, 2/E,Pearson
ducation.
Ian Sommerville (2006), “Software Engineering”, 6/E,Pearson Education.
Roger S Pressman (2005), “Software Engineering – A Practitioner’s Approach” , 6/E,MGH,
Schmidt, Stal, Rohnert, and Buschmann (2000), “Pattern-Oriented Software Architecture” Volume
2: Patterns for Concurrent and Network ed Objects”, Wiley
Len Bass, Paul Clements, Rick Katzman, Ken Bass (2003), “Software Architecture in Practice”, 2/E,
Addiwon –Wesley Professional.
Outcomes:
Upon successful completion of this course it is expected that graduates will be able to demonstrate:
 ability to develop, maintain and evaluate large-scale software systems
 ability to produce efficient, reliable, robust and cost-effective software solutions
 ability to critically evaluate assumptions and arguments
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
7
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)






ability to perform independent research and analysis
ability to work as an effective member or leader of software engineering teams
ability to apply the principles, tools and practices of IT project management
ability to manage time, processes and resources effectively by prioritising competing demands to
achieve personal and team goals
ability to understand and meet ethical standards and legal responsibilities
ability to rapidly learn and apply emerging technologies
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
8
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS1003: ADVANCED CONCEPTS IN DATA MINING
Prerequisite: Algorithm and Data structure.
Credits: 04
Semester I
Module
No.
Content
I
II
III
L–T–P: 4–0–0
Introduction of Data Mining: Fundamentals, Data Mining Functionalities,
Classification of Data Mining systems, Major issues in Data Mining, Data Mining
Primitives.
Association Rules: Basic Concepts, Apriori Algorithm, Data Formats for
Association Rule Mining, Mining with Multiple Minimum Supports- Extended
Model and Mining Algorithm, Mining Class Association Rules- Problem
Definition and Mining Algorithm.
Sequential Patterns: Basic Concepts, Mining Sequential Patterns Based on GSP,
GSP Algorithm, Mining Sequential Patterns Based on PrefixSpan- PrefixSpan
Algorithm, Generating Rules from Sequential Patterns-Sequential Rules and
Label Sequential Rules.
Supervised Learning: Basic concepts.
Decision Tree Induction: Learning Algorithm, Impurity Function, Classifier
Evaluation- Evaluation Methods, Precision, Recall, F-score and Breakeven Point.
Classification Based on Associations: Classification using Class Association
rules, Class Association Rules as Features,
Naïve Bayesian Classification: Basic Concepts, Naïve Bayesian Text
Classification, Probabilistic Framework, Naïve Bayesian Model.
Support Vector Machines: Linear SVM, Nonlinear SVM, K-Nearest Neighbor
Learning.
Unsupervised Learning: Basic Concepts, K-means Clustering- K-means
Algorithm, Disk Version of the K-means Algorithm. Representation of ClustersCommon Ways of Representing Clusters and Clusters of Arbitrary Shapes.
Hierarchical Clustering: Single-Link Method, Complete-Link Method.
Distance Functions: Numeric Attributes, Binary and Nominal Attributes,
Text Documents, Data Standardization. Handling of Mixed Attributes, Which
Clustering Algorithm to Use, Cluster Evaluation.
Partially Supervised Learning: Learning from Labeled and Unlabeled
Examples, EM Algorithm with Naïve Bayesian Classification, Co-Training, SelfTraining.
Teaching
Hours
13
14
13
Reference Books:



Bing Liu, (2007) “Web Data Mining”, First Edition, Springer.
Jiawei Han and. Micheline Kamber (2003) “Data Mining – Concepts and Techniques”, 3rd Edition
Morgan Kaufmann.
Arun K Pujari (2010) “Data Mining Techniques”, 2nd Edition University Press.
Outcome:
On successful completion of this module, students should be able to:
 Implement the role of data warehousing and enterprise intelligence in industry and government.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
9
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS1004: ADVANCED CONCEPTS IN NETWORKING
Prerequisite: Computer Networks
Credits: 04
Module
No.
Semester I
L–T–P: 4–0–0
Content
Introduction: Networking overview, MAC layer issues, Ethernet 802.3, ARP, IP
addressing and Subnetting, NAT and PAT, Variable Length Subnet Masking, CIDR
Advanced routing in the Internet and traffic engineering: Intra domain
routing: OSPF and IS-IS, Inter domain routing: BGP, Traffic Engineering
MPLS network: MPLS basics, MPLS signaling, MPLS VPN
Internet multicasting: IP multicasting, Application layer (Overlay) multicasting
TCP connection establishment and termination: Sliding window concepts,
other issues: wrap around, silly window syndrome, Nagle’s algorithm, adaptive
retransmission, TCP extensions.
End-to-End Congestion Control: Tahoe, Reno, Vegas,
Network based congestion control: RED and ECN, Multicast congestion control.
Multimedia networking: Introduction to multimedia networking, Video
streaming over the Internet.
Internet QoS: QoS fundamentals, Internet Differentiated services, Internet
Integrated Services.
Peer-to-Peer networks and applications: Peer-to-Peer file sharing networks,
Peer-to-Peer streaming networks, Concept of overlays, Unstructured Overlays:
Gnutella, Concepts of Distributed Hash Table, Structured Overlays: Chord, CAN,
Pastry.
Wireless mobile networks: Introduction to wireless networks, Wireless LAN,
Cellular Networks, Mobile IP
I
II
III
Teaching
Hours
13
14
13
Reference Books:


Peterson and Davie (2011) “Computer Networks: A Systems Approach”, 5th Ed. Morgan Kauffman.
Kurose and Ross (2011) “Computer Networking: Top Down Approach”, 6th Ed. Pearson Education,
Reading List





V. Paxson. "End – to - end Internet packet dynamics," in IEEE/ACM Transactions on Networking, Vol. No. 3,
June, 1999.
W. Stevens, “TCP Slow Start, Congestion Avoidance, Fast Retransmit, and Fast Recovery Algorithms,”
RFC2001.
K. Fall and S. Floyd, “Simulation - based comparison of Tahoe, Reno, and SACK TCP," Computer
Communication Review, vol. 26, pp. 5 -- 21, July 1996.
L. Brakmo and L. Peterson, " TCP Vegas: End – to - End Congestion Avoidance on a Global Internet," IEEE
Journal on Selected Areas in Communications, 13(8), October 1995, 1465 -- 1480.
A. Rowstron, P. Druschel,”Pastry: Scalable, decentralized object location and routing for large - scale peer –
to - peer systems”. Middleware, 2001, 329—350.
Outcome:
After the completion of the course, the student will be able to:

Understand the protocols, algorithms and tools needed to support the development and delivery of advanced
network services over networks.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
10
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS1005: Probability & Stochastic Process
Prerequisite Discrete Mathematics and Basic Mathematics
Credits: 04
Module
No.
Semester I
L–T–P: 4–0–0
Content
Basic Probability: Introduction, definitions of probability, set theory, axioms
of probability, Conditional probability, Total probability and Bayes' theorem.
Moments of Random variables: Mean and varience of random variable,
Coefficients of variation, Skewness and kurtosis, Moments, Covariance and
correlation coefficient.
I
Random Variables: Definition, Cumulative Distribution Function (CDF),
continuous, discrete and mixed Random Variables, Probability Density
Function (PDF), Probability Mass Function (PMF), Properties of Distribution
Functions, Specific Random Variables: Gaussian, Exponential, Rayleigh,
Uniform, Binomial and Poisson Distributions.
II
Stochastic Processes: Definition and Classification of Stochastic Processes,
Poisson
process, Birth and Death Process, Applications to Queues, Discrete Time
Markov Chains, Limiting Distributions – Theory of M/M/1 and M/M/m queues
– Little’s Theorem
III
Teaching
Hours
13
14
13
Text Book:
 Kishore S. Trivedi, “ Probability and Statistics with Reliability, Queuing and Computer Science
Applications” Wiley
References:







Papoulis, S. U. Pillai, “Probability, Random Variables and Stochastic Processes”, Tata McGraw Hill
A L Garcia, “Probability and Random Process for Electrical Engineers”, Pearson Education
R. M. Gray, L. D. Davisson (2004), “An Introduction to Statistical Signal Processing”, Cambridge
University Press.
H. Stark and J. W. Woods, “Probability and Random Processes with Applications to Signal
Processing “, Pearson Education.
P.Z.Peebles, “Probability, Random variables and Random signal principles”, Tata McGraw Hill
S L Miller, D G.Childers, “Probability and Random Processes”, Academic press.
Y. Viniotis, “Probability and Random Processes for Electrical Engineers”, McGraw Hill.
Outcome:
At the end of this course the students will be able to:
 Apply concept of probability and random variables in the area of computer networks, Image
processing etc.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
11
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS1081: ADVANCED CONCEPTS IN NETWORKING LAB
Prerequisite: Computer Networks, Concepts of Computer Programming(C, C++)
Credits: 01
Semester I
Content








Sockets programming; client/server; peer-to-peer; Internet
addressing; TCP sockets; UDP sockets; raw sockets. Finger, DNS, HTTP,
and ping clients and servers
Internetwork setup: network topology, wireless internetworking,
Packet Sniffers: Network protocol analyzers, traffic generation.
BGP Simulation using C-BGP Simulator
Introduction to Network Simulation: NS-2, OMNET++
Installation and understanding of these simulators
Implementation of existing protocols using these simulators
Implementation of modifications to existing protocols
L–T–P: 0–0–2
Teaching
Hours
24
Reference Books:



W. R. Stevens (2004), “UNIX Network Programming”, Addison-Wesley Professional
Issariyakul, Teerawat, Hossain, Ekram (2012), “Introduction to Network Simulator NS2”, Springer
Klaus Wehrle, Mesut Günes, James Gross (2010), “Modeling and Tools for Network Simulation“,
Springer
Outcome:
On successful completion of this Lab, the student should be able to:


Experimentally evaluate different network protocols using simulation.
Suggest modifications on exiting protocols and implement and validate the new protocol using
simulation.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
12
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS2001: MOBILE AD-HOC NETWORKS
Prerequisite: Basic understanding of Computer Networks.
Credits: 04
Module
No.
Semester II
L–T–P: 4–0–0
Contents
Ad Hoc Wireless Networks: Issues in Ad Hoc Wireless Networks, Ad
Hoc Wireless Internet;
MAC Protocols for Ad Hoc Wireless Networks: Issues in Designing a
MAC Protocol for Ad Hoc Wireless Networks, Classifications of MAC
Protocols;
Routing Protocols for Ad Hoc Wireless Networks: Issues in
Designing a Routing Protocol for Ad Hoc Wireless Networks,
Classifications of Routing Protocols, Power Aware Routing Protocols.
I
Multi cast routing in Ad Hoc Wireless Networks: Issues in Designing
a Multicast Routing Protocol, Classifications of Multicast Routing
Protocols, Energy Efficient Multicasting, Multicasting with Quality of
Service Guarantees, Application Dependent Multicast Routing;
Transport Layer: Issues and Design Goals, Split TCP, Ad-Hoc TCP,
TCP-Bus
II
Key Management. Secure Routing in Ad Hoc Wireless Networks
Energy Management in Ad Hoc Wireless Networks: Classification of
Energy Management Schemes, Transmission Power Management
Schemes, System Power Management Schemes.
QoS in Ad-hoc Networks: Issues, PHY, MAC, Network Layer Solutions
Cross Layer Design
III
Teaching
Hours
13
14
13
References:




C S. Ram Murthy, B. S. Manoj (2005), “Ad Hoc Wireless Networks: Architectures and Protocols”,
Second Edition, Prentice Hall of India.
R. Hekmat (2006), “Ad hoc Networks: Fundamental Properties and Network Topologies”, First
Edition, Springer.
B. Tavli and W. Heinzelman (2006), “Mobile Ad Hoc Networks: Energy Efficient Real Time Data
Communications”, First Edition, Springer.
G. Anastasi, E. Ancillotti, R. Bernasconi, and E. S. Biagioni (2008) “Multi Hop Ad Hoc Networks from
Theory to Reality”, Nova Science Publishers.
Outcome:
At the end of this course the students will be able to:
 Understand applications and research issues of Mobile Ad-Hoc Networks.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
13
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS2002: INTELLIGENT SYSTEMS
Prerequisite Discrete Mathematics, General Mathematics, Programming logics
Credits: 04
Module
No.
I
II
III
Semester II
L–T–P : 4–0–0
Contents
Introduction to Artificial Intelligence - Introduction to AI, Intelligent
agents, Problem Solving by Searching, Informed Search & Exploration –
Heuristic Search Strategies, Heuristic Functions, Hill climbing, Simulated
Annealing search.
Knowledge & Reasoning – Propositional Logic, reasoning patterns in
Propositional Logic, First order Logic, Inference in First Order Logic –
Unification & Lifting, Forward & backward Chaining, resolution.
Knowledge Representation – Ontological Engineering, Categories &
Objects, Action, Situation & Events, Semantic Networks.
Uncertainity –, Basic Probability notion, The Axiom’s of probability,
Bayes’ Rule & its use.
Probabilistic reasoning – Bayesian Networks, Exact & Approximate
Inference in Bayesian Networks,
Artificial Neural Networks: Introduction, Neuron Physiology, Artificial
Neurons, Learning, Feed forward & feedback networks, Training
algorithms: Delta rule, Perceptron learning rules, Back propagation,
RBFN.
Expert Systems - Introduction, Expert system: features, characteristics,
development, activities, difference with conventional methods.
Genetic Algorithm Introduction to GA, representation, initialization and
selection, operators of GA,
Fuzzy Logic - Introduction to Fuzzy logic, Fuzzy Sets, Classical Relations
and Fuzzy Relations, Properties of Membership Functions, Fuzzification,
and
Defuzzification, Logic and Fuzzy Systems.
Teaching
Hours
13
14
13
References:




Stuart Russell, Peter Norvig (2009), “Artificial Intelligence – A Modern Approach”, Pearson
Elaine Rich & Kevin Knight (1999), “Artificial Intelligence”, TMH, 2nd Edition
NP Padhy(2010), “Artificial Intelligence & Intelligent System”, Oxford
ZM Zurada, “Introduction to Artificial Neural Systems”, West Publishing Company
 Timothy J Ross (2004), “Fuzzy Logic with Engineering Applications”, John Wiley & Sons Ltd.
Outcome:
At the end of this course the students will be able to:



Describe the attributes of various search techniques and the situations to which they are well-suited
Understand a range of techniques of intelligent systems across artificial intelligence (AI) and intelligent agents (IA);
both from a theoretical and a practical perspective.
describe and apply various techniques for logic programming and machine learning
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
14
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS2003: INFORMATION RETRIEVAL
Prerequisite: Basic understanding of data structures and algorithms, linear algebra, probability theory,
concepts of Internet & Web Technology.
Credits: 04
Semester II
L–T–P: 4–0–0
Module
No.
Content
Teaching
Hours
I
Introduction: Basic Concepts, Retrieval Process
Modeling – A Formal Characterization of IR Models, Classic Information
Retrieval (Boolean model, Vector Model, Probabilistic Model), Alterative Set
Theoretic Models, Alternative Algebraic Models (Generalized Vector Space
Model, Latent Semantic Indexing Model).
14
Query Languages and Operations: Keyword based Querying, Pattern
Matching, Structural Queries, User Relevance Feedback.
Text Operations: Document Preprocessing, Document Clustering, Text
Compression.
Evaluation in Information Retrieval: Retrieval Performance Evaluation
Recall, Precision, Mean average Precision, F-Measure, User Oriented Measures,
Discounted Cumulated Gain. TREC Web Collections.
Searching the Web: Characterizing the web, Crawling the Web, Mercator: A
Scalable, Extensible Web Crawler, Parallel Crawlers, Different Types of Web
Crawler, Anatomy of a Large-Scale Hypertextual Web Search Engine, Page Rank
Algorithm.
IR Applications: Summarization and Question Answering.
II
III
13
13
References:



Ricardo Baeza-Yate, Berthier Ribeiro-Neto, (2011) “Modern Information Retrieval”, Second
Edition, Addison Wesly.
G. G. Chowdhury (2003) “Introduction to Modern Information Retrieval”, Second Edition, NealSchuman Publishers.
David A. Grossman, Ophir Frieder, (2004) “Information Retrieval: Algorithms, and Heuristics”,
Springer.
Outcome:
At the end of this course the students will be able to:

To apply Information retrieval concepts, algorithms and approaches for efficient and meaningful
information retrieval from digital library, search engine etc.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
15
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS2004: IMAGE PROCESSING AND ANALYSIS
Prerequisite: This course requires basic knowledge of Linear algebra and Probability theory.
Credits: 04
Semester II
L–T–P: 4–0–0
Module
No.
Content
Teaching
Hours
I
Digital Image Fundamentals: Image sampling & quantization; Basic
relationships between pixels, Some mathematical tools used in digital image
processing.
Image perception: Light, luminance, brightness and contrast, Human Visual
System, Colour representation, Chromaticity diagram, Colour Coordinate
Systems.
Image Enhancement: Overview, Contrast Intensification, Smoothing,
Sharpening, Basic intensity Transformation functions, Histogram processing,
Spatial filters, Image Restoration
13
II
Image Transforms: Discrete Fourier Transform, DCT Transform, KL
Transform, Wavelet Transform. Image Enhancement in Frequency Domain
Image Compression: Fundamentals, Lossless Compression: Huffman Coding,
Arithmetic Coding, Run-length Coding. Lossy Compression: JPRG Coding.
Image Registration: Geometric Transformation, Registration by Mutual
Information Maximization.
13
III
Image Analysis: Fundamental concepts, Segmentation: Region extraction,
Pixel based approach, Thresholding, Region based approach. Canny Edge
Detection,
Feature Extraction: Representation, Topological Attributes, Geometrical
Attributes, Spatial Moments, Boundary based Description, Region based
Description, and Intensity based Description.
Object Recognition: Patterns and pattern classes, Recognition based on
decision-theoretic methods, structural methods.
14
References:



R. C. Gonzalez and R.E.Woods, (2011) “Digital Image Processing”, Third Edition, Prentice Hall.
Bhabatosh Chanda, D. Dutta Majumder, (2013) “Digital image processing and analysis, Second
Edition, PHI.
Anil K. Jain, (2011) “Fundamentals of Digital Image Processing”, Prentice-Hall.
Outcome:
Upon successful completion of this course, students will be able to:
 Understand an image analysis problem so as to identify the requirements for its solution.
 To apply knowledge of mathematics and computing to design a solution.
 Develop and systematically test different basic methods of Image Analysis.
 Have a broad knowledge base so to easily read the related literature.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
16
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS2005: DESIGN OF DISTRIBUTED SYSTEMS
Prerequisite: Basic concepts and understanding of the courses like Operating systems, Data communication
and Computer Network.
Credits: 04
Semester II
L–T–P: 4–0–0
Module
No.
Content
Teaching
Hours
I
Introduction: Introduction, Types, Design Issues, Models, Theoretical
foundations of DS, Case Study of Amobea.
Distributed Mutual Exclusion: Classification, Requirements, Performance
Measurement, Non-Token Based Algorithm & Token Based Algorithm, Shared
Memory based Mutual Exclusion
Communication in Distributed System: Communication Between Distributed
Objects, Events, Inter Process Communication- RPC, Distributed Objects and
Middleware – Overview of trends. Challenges and Opportunities.
Distributed File Systems – Introduction, Issues, Mechanism for building
distributed file systems, Reliability & Performance in traditional DFS, Case Study –
AFS, NFS, CODA
14
II
Failure Recovery: Introduction, types, Recovery in concurrent and replicated
distributed database system, Checkpoint Based Recovery
Fault Tolerance: Issues, Commit Protocols, Voting Protocols
Distributed Scheduling – Issues in Load Distributing, Components, Stability,
Load Distributing algorithm, Performance Comparison, Task Migration and issues
Distributed shared memory- Architecture & Motivation, Memory coherence,
Coherence Protocol, Design Issues Case Study- IVY
13
III
Distributed Web-Based Systems – Architecture, Processes, Naming,
Synchronization, Consistency and Replication
Distributed Coordination-Based Systems- Introduction To Coordination
Models, Architectures, Processes, Communication
Grid Computing – Definition, Benefits, Issues, Types of Resources, Scheduling,
reservation, and scavenging, Grid architecture models, Grid topologies, Case Study
– Globus Toolkit
Cloud Computing – Definition, Properties, Characteristics & Disadvantages,
Cloud Computing Architecture, Service Models, Deployment Models, Resource
Virtualization, Case Study – Amazon EC2
14
Text Book:
 Singhal & Shivaratri (2001) “Advanced Concept in Operating Systems", McGraw Hill.
Reference Books:
 Coulouris, Dollimore, Kindberg (2011) "Distributed System: Concepts and Design”, Pearson
Ed.Gerald Tel "Distributed Algorithms", Cambridge University Press.
 Tannenbaum (2004) “Distributed Systems: Principles and Paradigms”, Pearson Education.
Outcome:
 Understanding of the technical demands and potential solutions for distributed systems that impose
high requirements on data storage and transport. A sound understanding of the principles and
concepts involved in designing distributed systems and Internet applications Ability to implement a
distributed application.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
17
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS2081: IMAGE PROCESSING & ANALYSIS LAB.
Prerequisite: Basics of Computer Programming
Credits: 01
Semester II
L–T–P : 0–0–2
Experiment 1: This objective of this lab is to understand
 Basic commands of MATLAB
 Basic Relationship of Pixel i.e. Connectivity based on following two method:
a) 4-Adjacency
b) 8-Adjacency
Experiment 2: Perform Following Operation in Image
 How to read an image in Matlab.
 How to show an image in Matlab.
 How to access Image Pixels in Matlab.
 How to write Image in Matlab.
 Mirror Image generation.
 Flipped Image generation.
Experiment 3:
The objective of this lab is to understand & implement Image enhancement in spatial domain through
Gray level Transformation function
Experiment 4:
The objective of this lab is to understand & implement
1. Image enhancement in frequency domain.
2. Low Pass Filters
3. High Pass Filters
Experiment 5:
The objective of this lab is to understand & implement
 Histogram Equalization
 Histogram Specification
Experiment 6:
The objective of this lab is to understand & implement
 Use of Second Derivate for Image Enhancement: The Laplacian
 Use of First Derivate for Image Enhancement: The Gradient
Experiment 7:
The objective of this lab is to understand & implement morphological operation Dilation, Erosion, Closing,
Opening etc.
Experiment 8:
The objective of this lab is to understand & implement Object Representation methods
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
18
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS3021: COMPUTER VISION
Prerequisite: This course requires basic knowledge of Image Processing, Linear algebra and Probability
theory.
Credits: 04
Semester III
L–T–P : 4–0–0
Module
No.
Content
Teaching
Hours
I
Introduction: Overview to Computer Vision, Image formation – Geometric
primitives and transformations, Photometric Image formation.
Digital Camera: Sampling & aliasing, Colour, Compression, Camera model &
caliberation, Epipolar Geometry, Stereopsis.
2D Shape – Hough transform, Shape numbers, Pyramids, Quad Trees, Medial
Axix Transform.
13
II
Recognition: Detectors and Descriptors, Clustering (K-Mean and Mean Shift),
Interest Point Detection, Harris Corner Detector, SIFT, Template Matching,
Detection with sliding windows: Viola Jones, Object recognition (Eigenfaces,
Active appearance models).
Classification: K-nearest Neighbours Algorithm, Statistical Classification, Bagof-Words Models, Overview of methods for building Classifies, a part-based
generative model (Constellation model) and a part-based discriminative model
(Latent SVM),
14
III
Motion Analysis: Motion estimation using Optic Flow, Video Change Detection,
Moving object detection - Background Subtraction approach, Moving object
detection using Gaussians Mixture Model (GMM) approach. Object Tracking,
Kernel (Mean Shift) based Object Tracking, Motion Models to aid tracking (
Kalman Filtering, particle filtering), Data Association, Applications of Object
Tracking.
13
References:





Richard Szeliski (2010) “Computer Vision: Algorithms and Applications”, Springer.
D.A. Forsyth and J. Ponce (2002), “Computer Vision: A Modern Approach”, Prentice Hall.
Milan Sonka, Vaclav Hlavae, Roger Boyle (2008), “Image Processing, Analysis and Machine
Vision”, Second Edition, Thomson.
R. Hartley, and A. Zisserman (2004), “Multiple View Geometry in Computer Vision”, 2nd Edition,
Cambridge University Press.
R.O. Duda, P.E. Hart, and D.G. Stork (2000), “Pattern Classification” (2nd Edition), WileyInterscience.
Outcome:

The outcome of this course is to develop the theoretical and algorithmic basis by which useful
information about the world can be automatically extracted and analyzed from a single image, a set
of images or video.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
19
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS3022: WIRELESS SENSOR NETWORKS
Prerequisite: Computer Networks
Credits: 04
Module
No.
Semester III
L–T–P: 4–0–0
Content
Applications and Design Model: Examples of available sensor nodes, Sample
sensor networks applications, Design challenges, Contemporary network
architectures, Operational and computational models, Performance metrics,
Software and hardware setups.
Network Bootstrapping: Sensor deployment mechanisms, Issues of coverage,
Node discovery protocols
Physical and Link layers: Radio energy consumption model, Power
Management, Medium access arbitration: Low duty cycle protocols and
wakeup concepts, Contention-based protocols, Schedule-based protocols,
Optimization mechanisms
Localization and Positioning: Properties of positioning, Possible approaches,
Mathematical basics for the lateration problem, Single-hop localization,
Positioning in multi-hop environments, Impact of anchor placement.
Topology control: Motivation and basic ideas, Flat network topologies,
Hierarchical networks by dominating sets, Hierarchical networks by clustering,
Combining hierarchical topologies and power control, Adaptive node activity.
Naming and Addressing: Address and name management in wireless sensor
networks, Assignment of MAC addresses, Distributed assignment of locally
unique addresses, Content-based and geographic addressing.
Routing protocols: The many faces of forwarding and routing, Gossiping and
agent-based unicast forwarding, Energy-efficient unicast, Broadcast and
multicast, Geographic routing, Coping with energy constraints, Mobile nodes.
Data-centric and content-based networking: Introduction, Data-centric
routing, Data aggregation, Data-centric storage.
Dependability Issues: Security challenges, Threat and attack models, Quality
of service provisioning, Time Synchronization: Introduction to the time
synchronization problem, Protocols based on sender/receiver synchronization,
Protocols based on receiver/receiver synchronization, Supporting fault
tolerant operation.
I
II
III
Teaching
Hours
13
13
14
Reference Books:





Dorothea Wagner and Roger Wattenhofer (2007), “Algorithms for Sensor and Ad Hoc Networks,
Advanced Lectures”, Lecture Notes in Computer Science 4621
Waltenegus Dargie, Christian Poellabauer (2010), “Fundamentals of Wireless Sensor Networks: Theory
and Practice”, John Wiley & Sons
Carlos De Morais Cordeiro, Dharma Prakash Agrawal (2011), “Ad Hoc and Sensor Networks: Theory and
Applications”, World Scientific
Holger Karl, Andreas Willig (2005) “Protocols and Architectures for Wireless Sensor Networks”, Wiley
Publications
Cauligi S. Raghavendra, Krishna Sivalingam, Taieb M. Znati (2005) “Wireless Sensor Networks”,
Springer,
Outcome:
After the completion of the course, the student will be able to:

Assess coverage and conduct node deployment planning, devise appropriate data dissemination protocols and
model links cost, determine suitable medium access protocols and radio hardware.

Provision quality of service, fault-tolerance, security and other dependability requirements and conduct tradeoff analysis between performance and resources..

Evaluate the performance of sensor networks and identify bottlenecks.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
20
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS3023: SOFTWARE AND SERVICE ORIENTED ARCHITECTURE
Prerequisite: Software Engineering, Web Technologies
Credits: 04
Module
No.
Semester III
L–T–P: 4–0–0
Content
Software Architecture – Types of IT Architecture, SOA Evolution, Key
components, perspective of SOA, Enterprise-wide SOA Architecture,
Enterprise Applications , Solution Architecture for enterprise application
Software platforms for enterprise Applications, Patterns for SOA, SOA
programming models.
Service-oriented Analysis and Design – Design of Activity, Data, Client and
business process services. Technologies of SOA – SOAP, WSDL. Service
integration with ESB
Web Services and Contemporary SOA- Message exchange patterns, Service
activity, coordination. Atomic transactions, Business activities, Orchestration
and Choreography- Issues
Introduction to XML – Overview and Security,
Introduction to Web Services and Security, SOA implementation and
Governance strategy, trends in SOA, event-driven architecture, software as a
service. SOA Delivery Strategies- SOA delivery lifecycle phases.
Transaction processing – paradigm, protocols and coordination, transaction
specifications, SOA in mobile, research issues in SOA
I
II
III
Teaching
Hours
13
14
13
Reference Books:






Shankar Kambhampaly (2008), “Service –Oriented Architecture for Enterprise Applications”, Wiley
India Pvt. Ltd.
Eric Newcomer, Greg Lomow, “Understanding SOA with Web Services”, Pearson Education.
Mark O’ Neill, et al. , “Web Services Security”, Tata McGraw-Hill Edition, 2003.
Thomas Erl (2008),”Service-Oriented Architecture: Concepts, Technology & Design”, Pearson
Education Pvt. Ltd.
Thomas Erl (2007), “SOA Principles of Service Design”, Pearson Exclusives.
Thomas Erl and Grady Booch, (2008) “SOA Design Patterns”, Prentice Hall.
Outcome:
After the completion of the course, the student will be able to:
 Understand primary concepts of SOA
 Know the integration of SOA technological points with Web Services.
 Implement SOA in development cycle of Web Services
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
21
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS3041: PATTERN RECOGNITION
Prerequisite Image Processing, General Mathematics, Artificial Intelligence
Credits: 04
Module
No.
Semester III
Contents
Introduction: Basics of pattern recognition, Design principles of
pattern recognition system, Learning and adaptation, Pattern
recognition approaches, Mathematical foundations – Linear
algebra, Probability Theory, Expectation, mean and covariance,
Normal distribution, multivariate normal densities, Chi squared
test.
Statistical Patten Recognition: Bayesian Decision Theory,
Classifiers, Normal density and discriminant functions,
Parameter
estimation
methods:
Maximum-Likelihood
estimation, Bayesian Parameter estimation, Dimension reduction
methods - Principal Component Analysis (PCA), Fisher Linear
discriminant analysis, Expectation-maximization (EM), Hidden
Markov Models (HMM), Gaussian mixture models.
Nonparametric Techniques: Density Estimation, Parzen
Windows, K-Nearest Neighbor Estimation, Nearest Neighbor
Rule, Fuzzy classification.
Unsupervised Learning & Clustering: Criterion functions for
clustering, Clustering Techniques: Iterative square - error
partitional clustering – K means, agglomerative hierarchical
clustering, Cluster validation.
I
II
III
L–T–P: 4–0–0
Teaching Hours
14
13
13
References:



Richard O. Duda, Peter E. Hart and David G. Stork (2006), “Pattern Classification”, 2nd Edition,
John Wiley.
C. M. Bishop (2009), “Pattern Recognition and Machine Learning”, Springer.
S. Theodoridis and K. Koutroumbas (2009), “Pattern Recognition”, 4th Edition, Academic Press.
Outcome:
After the completion of the course, the student will be able to:

Understand a variety of pattern recognition algorithms, along with pointers on which algorithms
work best under what conditions, so that students can make sound decisions on what approaches to
take when faced with a real world problem.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
22
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS3042: HIGH PERFORMANCE COMPUTING
Prerequisite: Distributed Systems
Credits: 04
Module
No.
Semester III
Contents
Overview of Parallel Techniques: Classification of Instruction Set
Architectures, Instruction level, Thread level and Process level.
Pipelining: Instruction and functional pipelines, Hazards in a pipeline,
Branch prediction techniques; Superscalar Techniques.
Memory Hierarchies: Basic hierarchical memory concepts, Cache
design, Virtual memory design & uses, Memory hierarchy performance.
Parallel Programming Concepts: Abstract Machine Models – RAM&
PRAM, various parallel algorithms on them.
Introduction: Cloud Computing, Computing Platforms and
Developments, Virtualization.
Cloud Computing Architecture: Reference Model, Types of Cloud,
Concurrent Computing, High Throughput Computing.
Cloud Applications: Application in Industry, General Cloud
Applications, Advanced Topics in Cloud Computing.
Introduction: Definition of Grid Computing, Grid Architecture Standard
for Grid, Data Management in Grid, Grid Scheduling
Grid Security & Middleware: Trust and Security in Grid, Grid Middle
ware, Architectural Overview of Grid Projects.
Grid Computing Methods: Monte Carlo Method, Partial Differential
Equations, Some Grid Tool- Globus, glite.
I
II
III
L–T–P: 4–0–0
Teaching Hours
14
12
14
Reference Books:




John L Hennessy & David A, (2011) “Patterson-Computer Architecture: A Quantitative Approach”,
Morgan Kaufmann.
Kai Hwang (2013) “Advanced Computer Architecture”, Tata McGraw Hill Edition.
Rajkumar Buyya, Christian Vecchiola & S, Thamarai Selvi (2013)”Mastering Cloud Computing”,
Tata McGraw Hill Edition.
Fredric Magoules, Jie Pan, Kiat-An Tan & Abhinit Kumar (2007) “Introduction to Grid Computing”,
CRC Press, Taylor & Francis Group.
Outcome:
At the end of this course the students will be able to:
 Understand the concept and terminology of High Performance Computing.
 Write and analyze the behavior of High Performance Parallel programs for distributed memory
architecture, share memory architecture using MPI, Open MP and Pthreads.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
23
Course Curriculum (w.e.f. Session 2015-16)
M.Tech. (CSE)
MCS3043: WEB MINING
Prerequisite Concepts of Internet, Web Technology and Information Retrieval.
Credits: 04
Module
No.
Semester III
L–T–P : 4–0–0
Content
Introduction: Basic Concepts of Web Mining, Classification of Web Mining:
Web Content Mining, Web Structure Mining, Web Usage Mining, Issues in Web
Mining, Crawling the Web, Hyperlink Analysis, Basics of HTML, HTTP, HTTPS
and scripting.
Web Content Mining: document indexing and retrieval in the web
environment, web documents categorization and clustering, Text and Web
Page Pre- Processing.
Web Structure Mining: Anchor Text, Hyperlink Analysis, Static and Dynamic
Hyperlinks, Web Graph, Web Search, Query Expansion, Primary web browsing
(crawling), Link topology analysis.
Social Network Analysis:
Social Sciences and Bibliometry, Prestige,
Centrality, Co- citation, PageRank and HITS, Stochastic HITS and Other
Variants, Enhanced Models and Techniques, Avoiding Two- Party Nepotism,
Outlier Elimination, Exploiting Anchor Text.
Evaluation of Topic Distillation: HITS Algorithm.
Web Usage Mining Process and Techniques: Data collection and Pre
Processing, Data modeling for web usage mining, Discovery and analysis of web
usage patterns, Session and visitor analysis, Cluster analysis and visitor
segmentation.
Resource Discovery: Collecting important pages preferentially, Crawling as
guided search in a graph, Keyword-Based graph search, Similarity search using
Link Topology.
The Future Of Web Mining: Natural Language Processing, Lexical Networks
and Ontologies, Part- of- Speech and Sense Tagging , Parsing and Knowledge
Representation, Profiles, Personalization, Collaboration, Opinion mining.
I
II
III
Teaching
Hours
13
13
14
References:





Soumen Chakrabarti (2010) “Mining the Web: discovering knowledge from hypertext data,
Part 2”, Morgan Kaufmann Publisher.
Bing Liu (2007) “Web Data Mining: exploring hyperlinks, contents, and usage data”, Springer.
Gordon Linoff and Michael Berry (2002) “Mining the Web: Transforming Customer Data into
Customer Value”, John Wiley & Sons.
C. Manning, P. Raghavan, and H. Schütze (2008) “Introduction to Information Retrieval”,
Cambridge University Press.
Ricardo Baeza-Yate, Berthier Ribeiro-Neto, (2011) “Modern Information Retrieval”, Second
Edition, Addison Wesly.
Outcome:
At the end of this course the students will be able to:
To apply Web Mining concepts, algorithms and approaches for efficient ranking of web pages from digital
library, search engine etc.
DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONS, Institute of Engineering & Technology
24