Syllabus for
Master of Philosophy (Computer Science)
Academic Year  (2018)

This course is intended to assist students in planning and carrying out research projects.  The students are exposed to the principles, procedures and techniques of implementing a research project.

• foster a clear understanding about research design that enables students in analyzing and evaluating the published research.
• acquire sound knowledge in theoretical and quantitative methods.
• analyze and interpret data for evaluating alternative perspectives.
• understand the components and techniques of effective report writing.
• obtain necessary skills in preparing scientific documents using LaTeX..
• employ computers in managing and planning research activities effectively.

An introduction–meaning of research-objectives of research- motivation in research –types of research- research approaches-significance of research-research methods versus methodology-research and scientific method-importance of knowing how research done-research processes-criteria of good research-defining research problem-selecting the problem-necessity of defining the problem-technique involved in defining a problem-Research design- meaning of research design-need for research design-features of good design-different research design-basic principles of experimental design

Measurement and Scaling Techniques- Methods of Data Collection, - processing and Analysis of Data,- Sampling Fundamentals, Testing of Hypotheses - I (Parametric or Standard Tests of Hypotheses), Chi-square Test, Analysis of Variance and Covariance, Testing of Hypotheses - II (Nonparametric or Distribution - Free Test),Multivariate Analysis Techniques.

Interpretation and report writing, technique of report writing-precaution in interpretation-significance- different steps of report writing- layout of research report-oral presentation- mechanics of writing- Exposure to writing tools like Latex/PDF, Camera Ready Preparation

Originality in research, resources for research, Research skills, Time management, Role of supervisor and Scholar, Interaction with subject expert,  The Computer: Its Role in Research, Case study interpretation: minimum 5 case studies.

1. C.R.Kothari, Research Methodology- Methods and Techniques, 2nd ed., Vishwa Prakashan Publications, New Delhi, 2006.

2. R. Pannerselvam, Research methodology, 3rd Printing, New Delhi, PHI 2006.

3. Santosh Gupta,  Methodology And Statistical Techniques, 1st ed., Deep and Deep Publications, 2004.

4. E. B. Wilson Jr., An Introduction to scientific research, 1st ed., (Reprint), New York: Dover publications Inc, 2000.

5. Ram Ahuja, Research Methods, 1st ed., New Delh: Rawat Publications, 2002.

6. Gopal Lal Jain, Research Methodology, 2nd ed., Jaipur: Mangal Deep Publications, 2003.

7. B. C. Nakra and K. K. Chaudhry: Instrumentation, measurement and analysis,2nd ed., New Delhi: Tata McGraw-Hill Education, 2004.

8. S. L. Mayers,  Data analysis for Scientists, Reprint,John Wiley & Sons, 2000.

9. L. Blaxter, C. Hughes, M. Tight, How to research, 4th ed., McGraw-Hill, 2010.

10. J. Bell, Doing your research project, 5th ed., McGraw-Hill, 2010.

11. A. Thomas, J. Chataway, M. Wuyts, Finding our fast-Investigative Skills for Policy and Development, Reprint, SAGE Publications Inc., 2000.

12. P.J.M. Costello,  Effective Action Research: Developing Reflective Thinking and Practice, 2nd ed., Continuum, 2005 (NIAS)

13. B. Gilham, Case study research methods,1st ed.,Continuum,  2011.

14. S. Kleinman, M.A.Copp, Emotions and fieldwork, Reprint, SAGE Publications Inc., 2000.

15. I. Gregory, Ethics in research, Continuum, 2005 (NIAS)

16. J. Bennet, Evaluation methods in research, Continuum, 2005 (NIAS)

17. D. L. Morgan, Focus groups as qualitative research, Reprint, Sage Pub., 2000 (NIAS)

18. Illingham,Jo., Giving presentations, OUP, 2003 (NIAS)

19. M. Denscombe, The good research guide, Reprint, Viva, 2000 (NIAS)

20. D. Ezzy, Qualitative analysis, Routledge, 2002 (NIAS)

21. M. Q. Patton, Qualitative evaluation and research methods, Reprint, Sage Pub, 2000 (NIAS)

22. J. Kirk, Reliability and validity in qualitative research, Rerpint, Sage Pub, 2000 (NIAS)

This paper gives insights into the underlying concepts of operating system, data structures, data base management and emerging technologies.

Description: Virtual memory management, Synchronization and communication, File systems, Distributed Operating System.

Description: Overview of emerging database applications and challenges, Mobile Database, Management, Spatial Indexing Techniques, Data Clustering Algorithms, Stream databases

Description: AVL trees, B tree, Red-Black tree, Hashing techniques/Indexing techniques, Graph algorithms.

Description: Grid and cloud computing, Knowledge management and business intelligence, Mobile computing, Green computing, Storage technologies

*Subjected to change based on recent trends.

[2] A. S. Tanenbaum, Distributed Operating system, 3rd ed. Prentice hall 2008.

[3] Mark A. WeissAddison-Wesley, Data Structures and Algorithm Analysis in Java, 2/E,2007.

[2] E. Bertino, L. Martino,Object- Oriented Database Systems: Concepts and Architectures, Addison-Wesley Publication, 2012.

[3] R. L. Kruse,Data Structures and Program Design, PHI-2007.

[4] DoeppnerOperating Systems in Depth: Design and Programming, 1st ed. Wiley: 2010.

ESE (Weight) 50

To understand the basics of machine learning

 To understand different techniques involved in pre-processing

 To implement feature selection and machine learning classification processes

 To build and deploy machine learning classifier for predictor system.

 Use machine learning techniques to make better decisions.

 Know when to use different data preprocessing techniques.

 Know the strengths and weaknesses of diverse machine learning methods.

Introduction to machine learning – Supervised learning – Unsupervised learning – Machine learning and data mining. Neural Networks: Introduction – Use of NN – Working of NN, Genetic Algorithm: Introduction –Working of GA.

Implementation of NN and GA using any open source tool.

Preprocessing: Data Cleaning - Missing Values – Noisy Data - Data Cleaning as a Process - Data Integration and Transformation - Data Reduction-Data Cube Aggregation-Attribute Subset Selection.

Preprocessing can be done using any open source tool.

Supervised learning : Logistic regression. Perceptron. Exponential family. Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes. Support vector machines.

Unsupervised learning: K-means Clustering, EM,Principle components analysis(PCA),Independent components analysis(ICA).

Model selection and feature selection. Ensemble methods: Bagging, boosting. Evaluating and debugging learning algorithms.

2. Jaiwei Han, Michelinne Kamber , “Data Mining : Concepts and Techniques “, Second Edition, Morgan Kaufmann Publication, 2006.

3. T.Sushmita Mitra, Tir ku Acharaya , “Data Mining Multimedia , Softcomputing & ` Bioinformatics”, Wiley Interscience Publications, 2004. 4. Bittles, Alan H., Carol Bower, RafatHussain, and Emma J. Glasson. "The four ages of Down syndrome." The European Journal of Public Health17, no. 2 (2007): 221-225.

5. Zhao, Qian, Kenneth Rosenbaum, Kazunori Okada, Dina J. Zand, Raymond Sze, Marshall Summar, and Marius George Linguraru. "Automated down syndrome detection using facial photographs." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3670-3673. IEEE, 2013

ESE (Weight) 50

• To develop in-depth understanding of Medical Informatics, its goals, standards, applications, and uses in demanding clinical environment.
• To identify and solve MI problems in the best possible ways; build, run and optimize complex healthcare processes; do MI research.
• To study algorithmic examples in distributed concurrent and parallel environments.

Imaging modalities – image file formats – image sensing andacquisition – image sampling and quantization – noise models.Spatial Domain Processing - Frequency Domain Processing – filtering and smoothing techniques.

Intensity Histogram - Classification- Connected Components Labelling - Feature Extraction-Region representation, texture based features.

Image Segmentation -Thresholding techniques – region growing methods – region splitting and merging.Image Compression– image compression models – basic compression methods.

Fundamentals – Evolution –– Human body and Medical Imaging – Areas of challenges – Virtual reality - Medical Image Enhancement, Filtering Basic image processing algorithms.

Cloud infrastructures - Service provider interfaces – Cloud in Healthcare domain – Implementation of Medical Image Processing in Cloud using Map Reduce Processing and Cloud Storage.

2. Digital Image Processing for Medical Applications by Geoff Dougherty, Cambridge university press, 2009. ISBN-13: 978-0521860857.

3. Paul Suetens, "Fundamentals of Medical Imaging", Second Edition, Cambridge University Press, 2009.

2. H. K. Huang , “PACS and Imaging Informatics: Basic Principles and Applications”, 2010 3. Oleg S. Pianykh, “Digital Imaging and Communications in Medicine (DICOM): A Practical Introduction and Survival Guide”, Springer, 2012, ISBN-13: 978-3642108495

4. Handbook of Medical Imaging, Processing and Analysis, Academic Press, ISBN 0-12-077790-8 (PDF Book)

5. Digital Image Processing, S. Jayaraman, S. Esakkirajan, T. Veerakumar, McGraw Hill Education, 2009

ESE (Weight) 50

·         Understand OSI security architecture and classical encryption techniques.

·         Understand various block cipher and stream cipher models.

·         Describe the principles of public key cryptosystems, hash functions and digital signature.

·         To describe and analyze security protocols using Scyther tool.

·         Understand Cloud Computing Architecture and Security Issues in Cloud

·   Understand the Methods of Conventional Encryption.

·   Know when to use different Cryptographic techniques.

·   Understand the strengths and weaknesses of various encryption mechanisms.

·   Analyze security of security protocols using Scyther tool

Services, Mechanisms and attacks-the OSI security architecture-Network security model-Classical Encryption techniques: Symmetric cipher model, substitution techniques, transposition techniques, steganography). Number Theory: Groups, Rings, Fields.

Data Encryption Standard-Block cipher principles-block cipher modes of operation-Advanced Encryption Standard (AES)-Triple DES-Blowfish-RC5 algorithm. Public key Cryptography: Principles of public key cryptosystems-The RSA algorithm-Key management – Diffie Hellman Key exchange.

Authentication requirement – Authentication function – MAC – Hash function – Security of hash function and MAC –MD5 – SHA – HMAC – CMAC – Digital signature and authentication protocols – DSS – Schnorr

What is Cloud computing?: Cloud computing Defined-The SPI Framework for Cloud computing, The Cloud Service Delivery Model, Cloud Deployment Models; Infrastructure Security: The Network Level, The Host Level and Application Level; Data Storage and Security: Aspects of Data Security, data Security Mitigation, Provider Data and its Security

Scyther : Domain Analysis- Protocol Specification, Agent Model, Communication Model, Threat Model, Cryptographic Primitives, Security Requirements; Security Properties- Security properties as Claim Events.

[2] Tim Mather, Subra Kumaraswamy and Shahed Latif, Cloud Security and Privacy, O’Reily, 2010

[3]  Cremers C.J.F, Scyther :Ssemantics and Verification of Security Protocols, https://pure.tue.nl/ws/files/2425555/200612074.pdf

[2] Ronald L. Kyutz and Rusell Dean Vines-Cloud Security, Wiley India Pvt. Ltd.,2012. 

ESE Weightage 50%

• Learn to use Cloud Services for the research.
• Implement Virtualization and task Scheduling algorithms.
• Apply Map-Reduce concept to applications.
• Learn to develop scalable applications using AWS features for research.

Roots of Cloud Computing, Layers and Types of Clouds, Desired Features of a Cloud, Cloud Infrastructure Management, Infrastructure as a Service Providers, Platform as a Service Providers, Challenges and Risks.

An Introduction, The Evolution of SaaS, The Challenges of SaaS Paradigm, Approaching the SaaS Integration Enigma, SaaS Integration Products and Platforms, SaaS Integration Services, SaaS Integration Appliances, The Enterprise Cloud Computing Paradigm - Issues for Enterprise Applications on the Cloud.

Virtual Machines Provisioning and Migration Services - Virtual Machines Provisioning and Manageability, Virtual Machine Migration Services, On the Management of Virtual Machines for Cloud Infrastructures - The Anatomy of Cloud Infrastructures, Distributed Management of Virtual Infrastructures, Enhancing Cloud Computing Environments Using a Cluster as a Service.

Aneka—Integration of Private and Public Clouds – Introduction - Technologies and Tools for Cloud Computing - Aneka Cloud Platform, Hybrid Cloud Implementation, CometCloud: An Autonomic Cloud Engine, T-Systems’ Cloud-Based Solutions for Business Applications, Workflow Engine for Clouds - Workflow Management Systems and Clouds, Architecture of Workflow Management Systems.

An Architecture for Federated Cloud Computing, SLA Management in Cloud Computing: A Service Provider’s Perspective, Performance Prediction for HPC on Clouds. Best Practices in Architecting Cloud Applications in the AWS Cloud.

ESE  weightage - 50%

• To develop an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages.
• To understand the study of phonological, morphological and syntactic processing.
• To understand research problems and solve specific research problem in the field.

• analyze a problem, and identify and define the computing requirements appropriate to its solution.
• design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs.
• apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices.

Overview: Origins and challenges of NLP-Language and Grammar-Processing Indian Languages - NLP Applications-Information Retrieval. Language Modeling: Various Grammar- based Language Models-Statistical Language Model.

Word Level Analysis: Regular Expressions-Finite-State Automata-Morphological Parsing-Spelling Error Detection and correction-Words and Word classes-Part-of Speech Tagging. Syntactic Analysis: Context-free Grammar-Constituency- Parsing-Probabilistic Parsing.

Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure.

Natural Language Generation: Architecture of NLG Systems- Generation Tasks and Representations- Application of NLG. Machine Translation: Problems in Machine Translation- Characteristics of Indian Languages- Machine Translation Approaches-Translation involving Indian Languages.

Information Retrieval: Design features of Information Retrieval Systems-Classical, Non-classical, Alternative Models of Information Retrieval – valuation Lexical Resources: World Net-Frame Net- Stemmers-POS Tagger- Research Corpora.

ESE Weightage - 50%

• Introduce research culture among the budding researchers.
• Provide strong foundation on analysis, synthesis and comprehension of research thoughts.
• Build a pool of technically and scientifically qualified manpower to create a strong scientific community.
• Impart sound knowledge on computer based research tools.
• Motivate and orient youngsters to do research with proper baseline and ethical values.

Completion of dissertation as per the format

Pre-submission 25

Adjudication 100

Viva 50