Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 1
RESEARCH METHODOLOGY
PAPER I
Unit I
Research Methodology: An introduction – Meaning of Research – Objectives of Research –
Motivation in Research – Types of Research – Research Approaches – Significance of Research –
Research methods versus methodology.
Unit II
Research and Scientific Method – Importance of knowing how Research is done – Research
process – Criteria for good Research – Problems encountered by Researchers in India. Journal
Reading Techniques - Defining the Research problem – What is the Research Problem –
Selecting the Problem – Necessity of Defining the problem – Technique involved in Defining the
Problem – An illustration – Conclusion.
Research Design – Need for Research Design – Features of good design – Important concepts
relating to Research Design – Different Research Design – Basic principles of Experimental
Designs – Conclusion – Developing a Research Plan.
Unit III
Methods of Data Collection – Collection of primary data – Collection of data through
questionnaires – Schedules – Differentiation between questionnaires and schedules – Other
methods of data collection – Collection of secondary data – Selection of appropriate method
for data collection–Guidelines for constructing questionnaire/Schedule–Guidelines for
successful Interviewing – Difference between survey and experiment – Data Collection using
Journals.
Unit IV
Significance of Report Writing – Different steps in writing Report – Layout of the Research
Report – Types of Reports – Oral presentation – Mechanics of writing a Research Report –
Precautions for writing a Research Reports – Conclusions.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 2
Unit V
Pedagogical Methods in Higher Learning - Historical Perspectives – Objectives and role of
Higher Education – Learning and Learning Hierarchy – Information processing – Learning Events
and Outcomes – Motivation.
Education Evaluation: A Conceptual Framework – Methods of Evaluation – Self Evaluation and
Student Evaluation in Higher Education – Question Banking – Diagnostic Testing and Remedial
Teaching
References
1. C.R. Kothari. “Research Methodology – Methods and Techniques”, 2nd Edition, New
Delhi : New Age International (P) Limited, 2003.
2. Eileen M. Trauth. “Qualitative Research in IS: Issues & Trends”, USA/London: IDEA
Group Publishing, 2001. (ISBN: 1-930708-06-08)
3. www.dcs.gla.ac.uk/~johnson/teaching/research_skills/research.html
4. http://www.csc.liv.ac.uk/~ullrich/COMP516
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 3
DATA WAREHOUSING AND MINING
PAPER II
UNIT I
Data 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
Data Mining: - Data Mining Functionalities – Data Preprocessing – Data Cleaning – Data
Integration and 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 Analysis – Constraint-
Based Association Mining.
UNIT III
Classification and Prediction: - Issues Regarding Classification and Prediction – Classification by
Decision Tree Introduction – Bayesian Classification – Rule Based Classification – Classification
by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners –
Other Classification Methods – Prediction – Accuracy and Error Measures – Evaluating the
Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section.
UNIT IV
Cluster Analysis: - Types of Data in Cluster Analysis – A Categorization of Major Clustering
Methods – Partitioning Methods – Hierarchical methods – Density-Based Methods – Grid-Based
Methods – Model-Based Clustering Methods – Clustering High-Dimensional Data – Constraint-
Based Cluster Analysis – Outlier Analysis.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 4
UNIT V
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.
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 ractice”, Easter
Economy Edition, Prentice Hall of India, 2006.
5. G. K. Gupta “Introduction to Data Mining with Case Studies”, Easter Economy Edition,
Prentice Hall of India, 2006.
6. Pang-Ning Tan, Michael Steinbach and Vipin Kumar “Introduction to Data Mining”, Pearson
Education, 2007.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 5
EDUCATIONAL TECHNOLOGY
Unit I
Computer Application Skills : Computer system: Characteristics, Parts and their functions –
Different generations of Computer – Operation of Computer: switching on / off / restart, Mouse
control, Use of key board and some functions of key – Information and Communication
Technology (ICT): Definition, Meaning, Features, Trends – Integration of ICT in teaching and
learning – ICT applications: Using word processors, spread sheets, Power point slides in the
classroom – ICT for Research: On-line journals, e-books, Courseware, Tutorials, Technical
reports, Theses and Dissertations
Unit II
Communication Skills : Communication: Definitions – Elements of Communication: Sender,
Message, Channel, Receiver, Feedback and Noise – Types of Communication: Spoken and
written; Non-verbal communication – Intrapersonal, Interpersonal, Group and Mass
communication – Barriers to 6 communication: Mechanical, Physical, Linguistic & Cultural –
Skills of communication: Listening, Speaking, Reading and writing – Methods of developing
fluency in oral and written communication – style, Diction and Vocabulary – Classroom
communication and dynamics
Unit III
Communication Technology : Communication Technology: Bases, Trends and Developments –
Skills of using Communication Technology – Computer Mediated Teaching: Multimedia, E-
content – Satellite-based communication: EDUSAT and ETV channels, Communication through
web: Audio and Video applications on the Internet, interpersonal communication through the
web.
Unit IV
Pedagogy : Instructional Technology: Definition, Objectives and Types – Difference between
Teaching and Instruction – Lecture Technique: Steps, Planning of a Lecture, Delivery of a lecture
– Narration in tune with the nature of different disciplines – Lecture with power point
presentation – Versatility of lecture technique – Demonstration, Characteristics, Principles,
Planning Implementation and Evaluation – Teaching – Learning Techniques: Team Teaching,
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 6
Group discussion, Seminar, Workshop, Symposium and Panel Discussion – Models of teaching:
CAI, CMI and WBI
Unit V
Teaching Skills : Teaching skill: Definition, Meaning and Nature – Types of Teaching skills: Skill of
Set Induction, Skill of Stimulus Variation, Skill of Explaining, Skill of Probing Questions, Skill of
Black Board writing and Skill of Closure – Integration of Teaching Skills – Evaluation of Teaching
Skills
References:
1. Bela Rani Sharma (2007), Curriculum Reforms and Teaching Methods, Sarup and sons, New
Delhi
2. Don Skinner (2005), Teacher Training, Edinburgh University Press Ltd., Edinburgh
3. Information and Communication Technology in Education: A Curriculum for Schools and
programme of Teacher development, Jonathan Anderson and Tom Van Weart, UNESCO, 2002 7
4. Kumar K.I (2008) Educational Technology, New Age International Publishers, New Delhi
5. Mangal, S.K. (2002) Essential of Teaching – Learning and Information Technology, Tandon
Publications, Ludhiana
6. Michael D. and William (2000), Integrating Technology into Teaching and Learning: Concepts
and Applications, Prentice Hall, New York
7. Pandey S.K. (2005) Teaching Communication, Commonwealth Publishers, New Delhi
8. Ram Babu A. and Dandapani S (2006) Microteaching (Vol.1&2) Neelakamal Publications,
Hyderabad
9. Singh V.K. and Sudarshan K.N. (1996) Computer Education, Discovery Publishing Company,
New York
10.Sharma R. A. (2006) Fundamentals of Educational Technology, Surya Publications, Meerut
11.Vanaja. M. and Rajasekar S. (2006) Computer Education, Neelkamal Publications,
Hyderabad.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 7
DESIGN OF COMPONENT BASED TECHNOLOGIES
UNIT I: INTRODUCTION : Software Component - Objects –Fundamental Properties of
Component Technology – Modules – Interfaces – Callbacks- Dictionary Services – Component
Architecture – Components and Middleware.
UNIT II: JAVA BASED COMPONENT TECHNOLOGIES : Threads – Java beans – Events and
Connections –Properties – Introspection – JAR Files – Reflection – Object Serialization –
Enterprise Java Beans – Distributed Object Models – RMI and RMI – IIOP.
UNIT III: CORBA COMPONENT TECHNOLOGIES : Java and CORBA –Interface Definition Language
– Object Request Broker – System Object Model- Portable Object Model –Portable Object
Adapter – CORBA Services – CORBA Component Model – Containers – Application Servers –
Model Driven Architecture.
UNIT IV : .NET BASED COMPONENT TECHNOLOGIES : COM-Distributed COM – Object Reuse –
Interfaces and Versioning – Dispatch Interfaces – Connectable Objects – OLE – Containers and
Servers – Active X Controls - .Net Components –Assemblies – app domains – Contexts –
Reflection – Remoting.
UNIT V: COMPONENT FRAMEWORKS AND DEVELOPMENT : Connectors – Contexts – EJB
Containers – CLR Context and Channels – Black box Components Framework – Directory
Objects – Cross Development Environment – Component – Oriented Programming –
Component Design and Implementation Tools – Testing Tools – Assembly Tools.
TEXT BOOK:
Clemens Szyperski “ Component Software : Beyond Object Oriented Programming “, Pearson
Education Publisher , 2003.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 8
ADVANCED COMPUTER TECHNOLOGY
Unit I
Security problems in Computing – Cryptography – program security – Database security –
Security in Networks
Unit II
Grid Computing organization and their role – Grid computing anatomy – Merging the Grid
service architecture with web services architecture
Unit III
Fundamental – Remote procedure cells – Distributed shared memory – Synchronization
Unit IV
Distributed Databases – Homogeneous and Heterogeneous databases – Distributed data
storage – distributed transactions – commit protocols – concurrent control – availability –
Distributed theory processing Heterogeneous distributed databases – Directory systems
Unit V
Fundamentals of Parallel processing – MIMD computers or Multiprocessor 4.1 – 4.2, 4.3
Text Books:
1. Chapter 1,2,3,6 & 7 – (Security in Computing, Charles P. Pfleeger, &
Shani Lawrence Pfeeger)
2. Joshy Joseph, Graig Felenstern „Grid Computing‟ – Pearsons 2004
3. Distributed file systems, Chapter 1,4,5,6 & 9
4. Distributed Operating Systems, Pradeep K. Sinha, PHI, 2004
5. Abraham fiberschatz & Hendry F. Korths “Data base systems concepts” Mc Graw Hill
International fifth edition, 2006
5. Distributed memory multiprocessors 5.1, 5.2, 5.3, 5.4, 5.5
Data dependence and parallelism – 7.1 – 7.2, 7.3, 7.4, 7.5
Implementing synchronization and data sharing 8.1, 8.2, 8.3, 8.4
Harry F. Jordan Gita Alaghband
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 9
SOFTWARE QUALITY ASSURANCE
UNIT I
Introduction to software quality - challenges – objectives – quality factors – components of SQA
– contract review – development and quality plans – SQA components in project life cycle –
SQA defect removal policies – Reviews
UNIT II
Basics of software testing – test generation from requirements – finite state models –
combinatorial designs - test selection, minimization and prioritization for regression testing –
test adequacy, assessment and enhancement
UNIT III
Testing strategies – white box and black box approach – integration testing – system and
acceptance testing – performance testing – regression testing – internationalization testing –
ad-hoc testing – website testing – usability testing – accessibility testing Test plan –
management – execution and reporting – software test automation – automated testing tools
UNIT IV
Hierarchical models of software quality – software quality metrics –function points -Software
product quality – software maintenance quality – effect of case tools – software quality
infrastructure – procedures – certifications – configuration management –documentation
control.
UNIT V
Project progress control – costs – quality management standards – project process
standards – management and its role in SQA – SQA unit
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 10
REFERENCES
1.Daniel Galin, Software quality assurance – from theory to implementation , Pearson
education, 2009.
2. Aditya Mathur, Foundations of software testing, Pearson Education, 2008
3. Srinivasan Desikan and Gopalaswamy Ramesh, Software testing – principles and practices ,
Pearson education, 2006
4. Ron Patton, Software testing , second edition, Pearson education, 2007
5. Alan C Gillies, “Software Quality Theory and Management”, Cengage Learning, Second
edition, 2003
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 11
DIGITAL IMAGE PROCESSING
UNIT I
DIGITAL IMAGE FUNDAMENTALS AND TRANSFORMS: Elements of visual perception – Image
sampling and quantization Basic relationship between pixels – Basic geometric transformations-
Introduction to Fourier Transform and DFT – Properties of 2D Fourier Transform – FFT –
Separable Image Transforms -Walsh – Hadamard – Discrete Cosine Transform, Haar, Slant –
Karhunen – Loeve transforms.
UNIT II
IMAGE ENHANCEMENT TECHNIQUES:Spatial Domain methods: Basic grey level transformation
– Histogram equalization – Image subtraction – Image averaging –Spatial filtering: Smoothing,
sharpening filters – Laplacian filters – Frequency domain filters : Smoothing – Sharpening filters
– Homomorphic filtering.
UNIT III
IMAGE RESTORATION: Model of Image Degradation/restoration process – Noise models –
Inverse filtering -Least mean square filtering – Constrained least mean square filtering – Blind
image restoration – Pseudo inverse – Singular value decomposition.
UNIT IV
IMAGE COMPRESSION: Lossless compression: Variable length coding – LZW coding–Bitplane
coding- predictive coding- DPCM. Lossy Compression: Transform coding – Wavelet coding –
Basics of Image compression standards: JPEG, MPEG,Basics of Vector quantization.
UNIT V
IMAGE SEGMENTATION AND REPRESENTATION: Edge detection – Thresholding - Region Based
segmentation – Boundary representation: chair codes- Polygonal approximation – Boundary
segments – boundary descriptors: Simple descriptors-Fourier descriptors - Regional descriptors
–Simple descriptors- Texture.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 12
TEXT BOOKS
1. Rafael C Gonzalez, Richard E Woods 2nd Edition, Digital Image Processing - Pearson
Education 2003.
REFERENCES
1. William K Pratt, Digital Image Processing John Willey (2001)
2. Image Processing Analysis and Machine Vision – Millman Sonka, Vaclav hlavac, Roger Boyle,
Broos/colic, Thompson Learniy (1999).
3. A.K. Jain, PHI, New Delhi (1995)-Fundamentals of Digital Image Processing.
4. Chanda Dutta Magundar – Digital Image Processing and Applications, Prentice Hall of India,
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 13
BIO INFORMATICS
UNIT I
INTRODUCTORY CONCEPTS: The Central Dogma – The Killer Application – Parallel Universes –
Watson’s Definition – Top Down Versus Bottom up – Information Flow – Convergence –
Databases – Data Management – Data Life Cycle – Database Technology – Interfaces –
Implementation – Networks – Geographical Scope – Communication Models – Transmissions
Technology – Protocols – Bandwidth – Topology – Hardware – Contents – Security – Ownership
– Implementation – Management.
UNIT II
SEARCH ENGINES AND DATA VISUALIZATION :The search process – Search Engine Technology –
Searching and Information Theory –Computational methods – Search Engines and Knowledge
Management – Data Visualization – sequence visualization – structure visualization – user
Interface – Animation Versus simulation – General Purpose Technologies.
UNIT III
STATISTICS AND DATA MINING Statistical concepts – Microarrays – Imperfect Data –
Randomness – Variability – Approximation – Interface Noise – Assumptions – Sampling and
Distributions – Hypothesis Testing – Quantifying Randomness – Data Analysis – Tool selection
statistics of Alignment – Clustering and Classification – Data Mining – Methods – Selection and
Sampling – Preprocessing and Cleaning – Transformation and Reduction – Data Mining
Methods – Evaluation – Visualization – Designing new queries – Pattern Recognition and
Discovery – Machine Learning – Text Mining – Tools.
UNIT IV
PATTERN MATCHING : Pairwise sequence alignment – Local versus global alignment – Multiple
sequence alignment – Computational methods – Dot Matrix analysis – Substitution matrices –
Dynamic Programming – Word methods – Bayesian methods – Multiple sequence alignment –
Dynamic Programming – Progressive strategies – Iterative strategies – Tools – Nucleotide
Pattern Matching – Polypeptide pattern matching – Utilities – Sequence Databases.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 14
UNIT V
MODELING AND SIMULATION : Drug Discovery – components – process – Perspectives –
Numeric considerations – Algorithms – Hardware – Issues – Protein structure – AbInitio
Methods – Heuristic methods – Systems Biology – Tools – Collaboration and Communications –
standards - Issues – Security – Intellectual property.
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.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 15
INFORMATION SECURITY
UNIT - I
Conventional Encryption : Classical Technique – Modern technique – Algorithms; Public Key
Cryptography : Public Key Cryptography – Introduction to Number Theory – Message
Authentication and Hash Function – HASH and MAC Algorithm – Digital Signature and
Authentication protocol.
UNIT - II
Network Security Practice: Authentication Application – Electronic Mail Security – IP Security
Program Security and System Security: Secure programs – Nonmalicious program errors –
viruses and Worms – Memory and address protection – control access to general objects – File
protection mechanism – user authentication – Trusted operating system design and assurance
– Intrusion Detection system.
UNIT - III
System Security and Web Security: Intruders,– Firewall - Managing Access – Password
management - Web Security requirements – SSL and TLS – SET; Client Side Security : Using SSL –
Active Content – Web Privacy. Database Security: The Database as a Networked Server –
Securing database-to-database communication – Reliability and Integrity of database –
sensitive data – inference – multilevel databases
UNIT - IV
Wireless Network Security: Mobile Security – Encryption Schemes in WLANs – Basic approach
to WLAN security and Policy Development – WLAN intrusion process – WLAN security solutions.
Digital Watermarking and Steganography: Models of Watermarking – Basic Message Coding –
Watermark Security – Content Authentication – Steganography.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 16
UNIT - V
Cyber Crimes: Introduction – computer crime and cyber crimes; Classification of cyber crimes,
Cyber crime and Related Concepts: Distinction between cyber crime and conventional crimes,
Reasons for commission of cyber crime, Cyber forensic : Cyber criminals and their objectives,
Kinds of cyber crimes – cyber stalking; cyber pornography; forgery and fraud; crime related to
IPRs; Cyber terrorism; computer vandalism, Regulation of cyber crimes: Issues relating to
investigation, Issues relating to Jurisdiction, Issues relating to Evidence , Relevant provisions
under Information Technology Act, 2000, Indian Penal Code, Pornography Act and Evidence Act
etc
TEXT BOOKS:
1. Charrles P. Pfleeger, Shari Lawrence Pfleegner, “Security in Computing”, Prentice Hall of
India, 2007.
2. William Stallings, “Cryptography and Network Security”, 5th Edition, Pearson.
3. John W.Rittinghouse, James F.Ransome, “Wireless Operaional Security”, Elsevier, 2004.
4. Ron Ben Natan,”Implementing Database Security and Auditing”, Elsevier, 2005.
5. Lincoln D. Stein, “Web Security”, Addison Wesley, 1999.
6. Ingemar J.Cox, Matthew L. Miller Jeffrey A.Bloom, Jessica Fridrich,Ton Kalker,“Digital
Watermarking and Steganography”, 2nd Edition, Elsevier.
7. Dr.R.K.Tiwari, P.K.Sastri, K.V.Ravikumar, “ Computer Crime and Computer Forensics”,
1st Edition, Selective Publishers, 2002.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 17
PATTERN RECOGNITION
UNIT - I
Introduction : Machine perception, pattern recognition example, pattern recognition systems,
the design cycle, learning and adaptation (Text book-1, p.nos: 1-17).
Bayesian Decision Theory : Introduction, continuous features – two categories classifications,
minimum error-rate classification- zero–one loss function, classifiers, discriminant functions,
and decision surfaces (Text Book, p.nos: 20-27, 29-31).
UNIT-II
Normal density : Univariate and multivariate density, discriminant functions for the normal
density different cases, Bayes decision theory – discrete features, compound Bayesian decision
theory and context (Text book-1, p.nos: 31-45,51-54,62-63).
Maximum likelihood and Bayesian parameter estimation : Introduction, maximum likelihood
estimation, Bayesian estimation, Bayesian parameter estimation–Gaussian case (Text book-1,
p.nos: 84-97).
UNIT-III
Un-supervised learning and clustering : Introduction, mixture densities and identifiability,
maximum likelihood estimates, application to normal mixtures, K-means clustering. Date
description and clustering – similarity measures, criteria function for clustering (Text book-1,
p.nos: 517 – 526, 537 – 546).
UNIT-IV
Component analyses : Principal component analysis, non-linear component analysis; Low
dimensional representations and multi dimensional scaling (Text book-1, p.nos: 568-570,573 –
576,580-581).
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 18
UNIT-V
Discrete Hidden Morkov Models : Introduction, Discrete–time markov process, extensions to
hidden Markov models, three basic problems for HMMs. (Text book -2, p.nos: 321 – 344)
Continuous hidden Markov models : Observation densities, training and testing with continuous
HMMs, types of HMMs. (Text book-2, p.nos: 348 – 352)
TEXT BOOKS :
1. Pattern classifications, Richard O. Duda, Peter E. Hart, David G. Stroke. Wiley
student edition, Second Edition, 2001.
2. Fundamentals of speech Recognition, Lawerence Rabiner, Biing – Hwang
Juang Pearson education, 1993.
REFERENCE :
1. Pattern Recognition and Image Analysis – Earl Gose, Richard John baugh, Steve Jost PHI 2004
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 19
LANGUAGE TECHNOLOGY
UNIT I
INTRODUCTION :Natural language processing - Linguistic background - Spoken language input
and output technologies - Written language input - Mathematical methods - Statistical
modeling and classification finite state methods grammar for natural language processing -
Parsing - Semantic and logic Form - Ambiguity resolution - Semantic interpretation.
UNIT II
INFORMATION RETRIEVAL : Information retrieval architecture - Indexing - Storage -
Compression techniques - Retrieval approaches - Evaluation - Search engines - Commercial
search engine - Features - Comparison - Performance measures - Document processing - NLP
based information retrieval - Information extraction.
UNIT III
TEXT MINING: Categorization - Extraction based categorization - Clustering - Hierarchical
clustering - Document classification and Routing - Finding and organizing answers from text
search - Use of categories and clusters for organizing retrieval results - Text categorization and
efficient summarization using lexical chains - Pattern extraction.
UNIT IV
GENERIC ISSUES : Multilinguality - Multilingual information retrieval and speech processing
Multimodality - Text and images - Modality integration - Transmission and storage – Speech
coding - Evaluation of systems - Human factors and user acceptability.
UNIT V
APPLICATIONS : Machine translation - Transfer metaphor - Interlingual and statistical
approaches - Discourse processing - Dialog conversational agents - Natural language generation
- Surface realization and discourse planning.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 20
TEXT BOOKS
1. Daniel Jurafsky And James H.Martin , "Speech and Language Processing" , Prentice Hall ,
2008.
2. Ron Cole and J.Mariani, "Survey of the state of the art in Human SSS language
technology", Cambridge University Press, 1997.
3. Michal W. Berry, "Survey Of Text Mining : Clustering, Classification And Retrieval"
Springer Verlag, 2003.
4. Christopher D, Manning and Hinrich Schutze, "Foundations of Statistical Natural
Language Processing", MIT Press, 1999.
REFERENCES
1. James Allen, "Natural Language Understanding ", Benjamin / Cummings Publishing Co,
1995.
2. Gerald J. Kowalski and Mark .T.Marubury, "Information Storage and Retrieval Systems",
Kluwer Academic Publishers, 2000.
3. Tomek Strzalkowski, "Natural Language Information Retrieval", Kluwer Academic
Publishers, 1999.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 21
CRYPTOGRPAHY & NETWORK SECURITY
UNIT I
INTRODUCTION : OSI Security Architecture - Classical Encryption techniques – Cipher Principles
– Data Encryption Standard – Block Cipher Design Principles and Modes of Operation -
Evaluation criteria for AES – AES Cipher – Triple DES – Placement of Encryption Function –
Traffic Confidentiality
UNIT II
PUBLIC KEY CRYPTOGRAPHY : Key Management - Diffie-Hellman key Exchange – Elliptic Curve
Architecture and Cryptography - Introduction to Number Theory – Confidentiality using
Symmetric Encryption – Public Key Cryptography and RSA.
UNIT III
AUTHENTICATION AND HASH FUNCTION : Authentication requirements – Authentication
functions – Message Authentication Codes – Hash Functions – Security of Hash Functions and
MACs – MD5 message Digest algorithm - Secure Hash Algorithm – RIPEMD – HMAC Digital
Signatures – Authentication Protocols – Digital Signature Standard
UNIT IV
NETWORK SECURITY : Authentication Applications: Kerberos – X.509 Authentication Service
– Electronic Mail Security – PGP – S/MIME - IP Security – Web Security.
UNIT V
SYSTEM LEVEL SECURITY : Intrusion detection – password management – Viruses and related
Threats – Virus Counter measures – Firewall Design Principles – Trusted Systems.
TEXT BOOK
1. William Stallings, “Cryptography And Network Security – Principles and Practices”,
Prentice Hall of India, Third Edition, 2003.
REFERENCES
1. Atul Kahate, “Cryptography and Network Security”, Tata McGraw-Hill, 2003.
2. Bruce Schneier, “Applied Cryptography”, John Wiley & Sons Inc, 2001.
3. Charles B. Pfleeger, Shari Lawrence Pfleeger, “Security in Computing”, Third Edition,
Pearson Education, 2003.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 22
WEB MINING & BUSINESS INTELLIGENCE
Unit I :
Document Indexing: Indexing of Web sites and text operations used by search engines -
document linearization – tokenization - stop word filtration - stemming and parsing - Search
Engine Optimization: Mining search engine relevance algorithms for ranking high Web pages.
Unit II :
Intelligence Searching: Covers undocumented (smart) searches in Google and other search
engines - Hacking and Penetration through customized searches - Keyword Research and
Clustering: Discovery of word patterns and keywords for branding and marketing through
Association - Scalar and Metric Clusters.
Unit III :
Term Matching Algorithms: Vector Space Models used by search engines - Scoring of local -
global and entropy term weights. Concept Matching Algorithms: Singular Value Decomposition
(SVD) - Latent Semantic Indexing (LSI) models for clustering and ranking.
Unit IV:
Link Analysis Models: Google’s PageRank, Hubs & Authorities, and other link-based models.
Spam Intelligence: Tools and techniques for spamming search engines and web sites -
techniques based on scripts – cloacking - keyword spam techniques - link-bombs - email
marketing - viral marketing, Web 2.0, and Web 3.0.
Unit V:
Introduction to Business Dashboards (BDs): Overview of dashboard technology - open source
and customized add-on components. Special Topics: On-Topic Analysis - Co-Occurrence Theory
- Latent Graphs.
Reference Books:
1. Modern Information Retrieval (Baeza-Yates and Ribeiro-Neto; Addison Wesley).
2. Information Retrieval – Algorithms and Heuristics (Grossman and Frieder; Springer).
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 23
WEB DATA MINING
UNIT I
Introduction: world wide web-web and the Internet-Web Data Mining.Association Rules and
Sequential Patterns: Basics Concepts of Association Rules-Apriori Algorithm-Data Formats for
Association Rule Mining-Mining with Multiple Minimum Supports-Mining Class Association
Rules-Basic Concepts of Sequential Patterns-Mining Sequential Patterns Based on GSP-Mining
Sequential Patterns Based on PrefixSpan-Generating Rules from Sequential Patterns.
Supervised Learning: Basic Concepts-Decision Tree Induction-Classifier Evaluation-Rule
Induction-Classification Based on Associations-Naïve Bayesian Classification-Naïve Bayesian
Text Classification-Support Vector Machines-K-Nearest Neighbor Learning-Ensemble of
Classifiers.
UNIT II
Unsupervised Learning: Basic Concepts-K-means Clustering-Representation of Clusters-
Hierarchical Clustering-Distance Functions-Data Standardization-Handling of Mixed Attributes-
Clustering Algorithm -Cluster Evaluation-Discovering Holes and Data Regions.Partially
Supervised Learning: Learning from Labeled and Unlabeled Examples-Learning from Positive
and Unlabeled Examples.
UNIT III
Information Retrieval and Web Search: Basic Concepts of Information Retrieval-Information
Retrieval Models-Relevance Feedback-Evaluation Measures-Text and Web Page Pre-Processing-
Inverted Index and its Compression-Latent Semantic Indexing-Web Search-Meta-Search:
Combining Multiple Rankings-Web Spamming.Social Network Analysis: Social Network Analysis-
Co-Citation and Bibliographic Coupling-PageRank-HITS-Community discovery. Web Crawling: A
Basic Crawler Algorithm-Implementation Issues-Universal Crawlers-Focused Crawlers-Topical
Crawlers-Evaluation-Crawler Ethics and Conflicts-Some New Developments.
UNIT IV
Structured Data Extraction: Wrapper Generation--- Preliminaries-Wrapper Induction- Instance-
Based Wrapper Learning- Automatic Wrapper Generation: Problems- String Matching and Tree
Matching-Multiple Alignment-Building DOM Trees- Extraction Based on a Single List Page: Flat
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 24
Data Records- Extraction Based on a Single List Page: Nested Data Records- Extraction Based on
Multiple Pages-Some Other Issues. Information Integration: Introduction to Schema Matching-
Pre-Processing for Schema Matching- Schema-Level Matching- Domain and Instance-Level
Matching- Combing Similarities- Some Other Issues-Integration of Web Query Interfaces-
Constructing a Unified Global Query Interface.
UNIT V
Opinion Mining and Sentiment Analysis: The Problem of Opinion Mining-Document Sentiment
Classification-Sentence Subjectivity and Sentiment Classification-Sentence Subjectivity
Classification-Sentence Subjectivity and Sentiment Classification-Opinion Lexicon Expansion-
Aspect-Based Opinion Mining- Mining Comparative Opinions-Some Other Problems-Opinion
Search and Retrieval-Opinion Spam Detection-Utility of Reviews. Web Usage Mining: Data
Collection and Pre-Processing- Data Modeling for Web Usage Mining- Discovery and Analysis of
Web Usage Patterns- Recommender Systems and Collaborative Filtering-Query Log Mining-
Computational Advertising.
TEXT BOOK
1. Bing Liu,”web data mining” , Springer edition,2011
REFERENCE BOOK
1. Bo Pang,Lillian Lee, ” Opinion Mining and Sentiment Analysis” , Now Publishers Inc, 2008
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 25
ADVANCED APPROACHES IN ANALYZING UNSTRUCTURED DATA
Unit –I
Introduction to Text Mining-General Architecture of Text Mining Systems-Core Text Mining
Operations-Text Mining Preprocessing Techniques: Task-Oriented Approaches.
Unit – II
Categorization: Applications – Definition – Document Representation-Knowledge Engineering
Approach- Machine Learning Approach-Evaluation of Text Classifiers. Clustering: Clustering
Tasks in Text Analysis-Clustering Algorithms-Clustering of Textual Data.
Unit – III
Information Extraction: Introduction-IE Examples-Architecture of IE Systems-Anaphora
Resolution-Inductive Algorithms- Structural Information Extraction.
Unit – IV
Probabilistic Models of Information Extraction: Hidden Markov Models - Stochastic Context-
Free Grammers-Maximal Entropy Modeling-Maximal Entropy Markov Models-Conditional
Random Fields.
Unit – V
Preprocessing Applications, Presentation Layer: Browsing – Query Refinement, Visualization
Approaches, Text Mining Applications – Case Studies.
Books for References:
1.”The Text Mining Handbook – Advanced Approached in Analyzing Unstructured Data”, Ronen
Feldman and James Sanger, Cambridge University Press, 2006
2.”Data Mining: Concepts and Techniques”, 2nd edition, Jiawei Han and Micheline Kamber,
Morgan Kaufmann Publishers, Second Edition, 2010
3. “Text Mining-Predictive Methods for Analyzing Unstructured Information”, Sholom M. Weiss,
Nitin Indurkhya, Tong Zhang, I Fred J. Damerau – Springer International Edition, 2010
4. “Survey of Text Mining – Clustering, Classification and Retrieval”, Michael W. Berry – Springer
International Edition
5. “Information Storage and Retriecal System – Theory and Implementation”, Second Edition,
Gerald J. Kowalski, Mark T. Maybury – Springer International Edition, 2012.
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 26
MACHINE LEARNING
UNIT -I
Introduction to machine learning.Classification: nearest neighbor – decision trees – perception
– support vector machines – VC-dimension. Regression: Linear least square – regression –
support vector regression.
UNIT - II
Additional learning problems: multiclass classification – ordinal regression – ranking. Ensemble
methods: boosting. Probabilistic models: classification – regression – mixture models
(Conditional, unconditional), parameter estimation – EM algorithm.
UNIT - III
Beyond IID: directed graphical models: Hidden markov models, Bayesian networks, Beyond IID,
Undirected graphical models: Markov random fields, conditional random fields.
UNIT - IV
Learning and inference in Bayesian networks and MRFs: parameter estimation, exact inference
(variable elimination, belief propagation), approximate inference.
UNIT - V
Additional topics: Semi-supervised learning, active learning, structured prediction, Principles of
Pattern recognition: feature vectors, decision regions, learning algorithm and optimization
methods.
Reference Book:
1. Pattern Recognition and Machine Learning by Christopher M.Bishop, Springer,2007
2. Data Mining: Practical Machine Learning Tools and Techniques by Ian H.Witten & Eibe
Frank Third Edition, 2011
3. Machine Learning by Mitchell, Mc Graw Hill,, 1997
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 27
ADVANCE DATABASE SYSTEMS
UNIT I
INTRODUCTION TO DATABASE: Introduction-Traditional File Based Systems-Database
Approach-Roles in the Database Environment-Advantages and Disadvantages of DBMSs
DATABASE ENVIRONMENT: The Three-Level ANSI_SPARC Architecture Database Languages-
Data Models and Conceptual Modeling-Functions of a DBMS-Components of a DBMS-Multi-
User DBMS Architectures. THE RELATIONAL MODEL AND LANGUAGES: Brief History of the
Relational Model-Terminology-Integrity Constraints-Views-Relational Languages:
Introduction to Relational Algebra-Introduction to Relational Calculas-SQL: Introduction to
SQL.
UNIT II
ENTITY_RELATIONSHIP MODELING: Entity Types-Relationship Types-Attributes-Strong and
Weak Entity Types-Attributes on Relationships-Structural Constraints-Problems With ER
Models.ENHANCED ENTITY_RELATIONSHIP MODELING: Specialization/Generalization-
Aggregation-Composition.
UNIT III
NORMALIZATION: The purpose of Normalization-How Normalization Supports Database
Design-Data Redundancy and Update Anomalies-Functional Dependencies-The Process of
Normalization-First Normal Form(1NF)-Second Normal Form(2NF)-Third Normal Form(3NF)-
General definitions of 2NF and 3NF.ADVANCED NORMALIZATION: More on Funtional
Dependencies-Boyce_Codd Normal Form(BCNF)-Review of Normalization up to BCNF-Fourth
Normal Form(4NF)-Fifth Normal Form(5NF).
UNIT IV
METHODOLOGY_CONCEPTUAL DATABASE DESIGN: Introduction to the Database Design
Methodology-Overview of the Databse Design Methodology-Conceptual Database Design
Methodology.METHODOLOGY_LOGICAL DATABASE DESIGN FOR THE RELATIONAL MODEL:
Logical Database Design Methodology for the Relational Model.METHODOLOGY_PHYSICAL
DATABASE DESIGN FOR RELATIONAL DATABASE: Comparison of Logical and Physical
Syllabus – M.Phil., Computer Science June 2014-15
Department of Computer Science Page 28
Database Design-Overview of Physical Database Design Methodology-The Physical Database
Design Methodology for Relational Database.
UNIT V
Database Security-Countermeasures-Computer-Based Controls.TRANSACTION
MANAGEMENT: Transaction Support-Concurrency Control-Database Recovery-Advanced
Transaction Models-Concurrency Control and Recovery in Oracle.OUERY PROCESSING:
Overview of Query Processing-Query Decomposition-Heuristical Approach to Query
Optimization-Cost Estimation for the Relational Algebra Operations-Enumeration of
Alternative Execution Strategies-Query Optimization in Oracle.
TEXT BOOKS:
1. DATABASE SYSTEMS A Practical Approach to Design, Implementation and
Management, Fourth Edition-Thomas Connolly, Carolynbegg.