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Syllabus – M.Phil., Computer Science June 2014-15

Department of Computer Science

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.



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

http://www.dcs.gla.ac.uk/~johnson/teaching/research_skills/research.html



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.



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.



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,



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.



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.



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



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



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



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.

http://www.indiastudychannel.com/resources/35366-EC-DIGITAL-IMAGE-PROCESSING-Syllabus-Anna.aspx




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,




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.



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.



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.



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.



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).



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



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.



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.



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.

http://www.indiastudychannel.com/resources/35395-IT-CRYPTOGRAPHY-AND-NETWORK-SECURITY-Syllabus.aspx







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).



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



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



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.

http://www.mkp.com/datamining2e



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



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



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.

research methodology paper i - qmgcw.in syllabus.pdf · research methodology paper i unit i ......

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