Assessment of each paper

·       Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·       End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

This course focuses on learning emerging issues related to Cloud computing technology. The objectives are:

• Ability to understand various basic concepts related to Cloud Computing technologies
• To demonstrate the architecture and concept of different cloud models: IaaS, PaaS, SaaS
• Ability to analyse Big Data analysis tools and techniques
• To apply the underlying principle of Cloud Virtualization, Cloud Storage, Data Management and Data Visualization.
• To derive different Cloud Programming Platforms and Tools
• To implement various application development and deployment using cloud platforms such as Google app Engine and Amazon Web Services (AWS)
• To apply the basic concepts of MapReduce programming and design models on Cloud.

Upon successful completion of this course the students should be able to:

• Develop and deploy cloud application using popular cloud platforms,
• Design and develop highly scalable cloud-based applications by creating and configuring virtual machines on the cloud and building private cloud.
• Explain and identify the techniques of big data analysis in cloud.
• Compare, contrast, and evaluate the key trade-offs between multiple approaches to cloud system design, and Identify appropriate design choices when solving real-world cloud computing problems.
• Write comprehensive case studies analysing and contrasting different cloud computing solutions.
• Make recommendations on cloud computing solutions for an enterprise. 

Cloud Computing – History of Cloud Computing – Cloud Architecture – Cloud Storage – Why Cloud Computing Matters – Advantages of Cloud Computing – Disadvantages of Cloud Computing – Companies in the Cloud Today – Cloud Services- Web-Based Application – Pros and Cons of Cloud Service Development.

Types of Cloud Service Development – Infrastructure / Hardware as a Service-Software as a Service – Platform as a Service, Web Services – On-Demand Computing. Migrating into a Cloud , Types of Clouds-Amazon Ec2 – Google App Engine – Microsoft Azure, IBM Clouds.

Characteristics of Virtualized Environment – Taxonomy of Virtualization Techniques–Virtualization and Cloud Computing – Pros and Cons of Virtualization – Technology Examples: Xen, VMware, Hyper-V. Virtual Machines Provisioning and Manageability–Virtual Machine Migration Services – Provisioning in the Cloud Context.

Workflow Management Systems and Clouds- Architecture of Workflow Management Systems – Utilizing Clouds for Workflow Execution, Data-Intensive Computing– Technologies for Data-Intensive Computing – Storage Systems – Programming Platforms- Aneka MapReduce Programming, Major MapReduce Implementations for the Cloud.

Case Study:  Business and Consumer Applications: CRM and ERP, Social Networking, Multiplayer Online Gaming, Technologies for Data Security in Cloud Computing. Cloud Computing and Data Security Risk- The Cloud, Digital Identity, and Data Security–Content Level Security, Data Privacy and Security Issues, HPC in the Cloud: Performance related Issues.

Text BookS:

1. Mastering Cloud Computing - Rajkumar Buyya, Vecchiola, Selvi, McGraw Hill, 2013.

2. “Cloud Computing – A Practical Apprach”, Anthony Velte, Toby Velte and Robert Elsenpeter, McGraw Hill, 2010.

3. Cloud Computing – Principles and Paradigms, Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, Wiley 2011.

Reference Books:

1. “Grids, Clouds and Virtualization”, Massimo Cafaro and Giovanni Aloisio, Springer 2011.

2. “Cloud Computing – Web Based Applications That Change the Way You Work and Collaborate Online”, Michael Miller, Que Publishing, August 2008.

IEEE Transactions Referred

(i)           Rajkumar Buyya, “Introduction to Cloud Computing”, IEEE Transactions on Cloud Computing, Vol.1, No. 1, pp. 1-19, 2013.

(ii)      K Hwant, X Bai, Li, Chen and Wu, “Cloud Performance Modeling with Benchmark Evaluation of Elastic Scaling Strategies”, IEEE Transactions on Parallel and Distributed Systems, Vol.27, Issue.1, pp.130-143, 2016.

(iii)    Z Xiao, W Song and Qi Chen, “Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment”, IEEE Transactions on Parallel and Distributed Systems, Vol.24, Issue.6, pp.1107-1117, 2013.

(iv)       X Fei and S Lu, “A Dataflow-Based Scientific Workflow Composition Framework”, IEEE Transactions On Services Computing, Vol.5, Issue.1, pp.45-58, 2012.

(v)       Q  Wang, C Wang, K Ren and Li, “Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing”, IEEE Transactions on Parallel and Distributed Systems, Vol. 22, No.5, 2011.

(vi)     Sugam Sharma , “Evolution of as-a-Service Era in Cloud”, Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa, USA. Cornell University Library: http://arxiv.org/ftp/arxiv/papers/1507/1507.00939.pdf

• To have a thorough understanding of the basic structure and operation of a digital computer.
• To discuss in detail the operation of the instruction level parallelism.
• To study in detail the different instruction level parallelism in software approach.
• To study the different ways of communicating with I/O devices and standard I/O interfaces.
• To analyze multiprocessor architectures and thread level parallelism.

Measuring and Reporting performance - Quantitative principles of computer Design - Classifying
instruction set Architecture - Memory addressing - Addressing modes - Type and size of operands -
Operations in the instruction set - Operands and operations for media and signal processing -
Instructions for control flow - Encoding an instruction set - Example Architecture - MIPS and
TM32.

Pipelining and Hazards - Concepts of ILP - Dynamic scheduling - Dynamic Hardware prediction -
Multiple issues - Hardware based speculation - Limitations of ILP - Case studies: lP6
Microarchitecture

Compiler techniques for exposing ILP - Static branch prediction - Static multiple issues: VLIW -
Advanced compiler support - Hardware support for exposing parallelism - Hardware Vs software
speculation. Mechanism - IA 64 and Itanium Processor.

Cache performance - Reducing cache miss penalty and miss rate - Reducing hit time - Main
memory and performance - Memory technology. Types of storage devices - Buses - RAID -
Reliability, availability and dependability - I/O performance measures - Designing I/O system.

Symmetric and distributed shared memory architectures - Performance issues - Synchronization -
Models of memory consistency - Multithreading-Case Study.

1. John L. Hennessey and David A. Patterson," Computer Architecture: A Quantitative Approach", Fifth Edition, Morgan Kaufmann, 2012.

REFERENCE BOOKS

1. D. Sima, T. Fountain and P. Kacsuk, " Advanced Computer Architectures: A Design Space
Approach", Pearson, 2002.
2. Kai Hwang "Advanced computer architecture Parallelism Scalability Programmability"
Tata Mcgraw Hill Edition 2001.
3. Vincent P.Heuring, Harry F.Jordan, “Computer System Design and Architecture”, Addison

 2. Kai Hwang "Advanced computer architecture Parallelism Scalability Programmability" Tata Mcgraw Hill Edition 2015.

3.Stallings, William. Computer organization and architecture: designing for performance. Pearson Education India, 2015.

4. Vincent P.Heuring, Harry F.Jordan, “Computer System Design and Architecture”, Addison Wesley, 2nd Edition 2008.

EXTERNAL - 50

COURSE DESCRIPTION:

 To provide an in-depth knowledge in problem solving techniques, data structures and the various algorithms paradigms.

Scope and Objective: 

• To learn the systematic way of solving problems.
• To understand the different methods of organizing large amounts of data.
• To efficiently implement the different data structures.
• To efficiently implement solutions for specific problems.

• Understand the properties of advanced data structures.
• Design algorithms and employ appropriate advanced data structures for solving computing problems efficiently.
• Analyze and compare the efficiency of algorithms.
• Design and implement efficient algorithms for solving computing problems in a high-level object-oriented programming language.
• Solve problems independently.
• Think critically for improvement in solutions.

Growth of Functions: Asymptotic notations; Standard

notations and common functions; Recurrences and Solution of Recurrence equationsThe

substitution method, The recurrence – tree method, The master method; Amortized Analysis:

Aggregate, Accounting and Potential Methods.

Graph Algorithms: Bellman Ford Algorithm; Single source shortest paths in a DAG;Algorithm for sparse graphs; Flow networks and Ford Fulkerson method; Maximum bipartite matching. 

Polynomials and the FFT: Representation of polynomials; The DFT and FFT; Efficient implementation of FFT.

Elementary notions; GCD; Modular Arithmetic; Solving modular linear equations; The Chinese remainder theorem; Powers of an element; RSA

cryptosystem; Primality testing; Integer factorization.

Naïve string Matching; Rabin Karpalgorithm; String matching with finite automata; KnuthMorrisPratt algorithm; Boyer – Moore algorithms.

Probabilistic and Randomized Algorithms: Probabilistic algorithms; Randomizing deterministic algorithms, Monte Carlo and Las Vegas algorithms; Probabilistic numeric algorithms.

Case Study: Comparison of Algorithm Design Strategies based on CPU, Memory, Disk and Network usages.

1. T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd Edition, The MIT Press, 2014.
2. Kenneth A. Berman, Jerome L. Paul: “Algorithms”, Cengage Learning, 2013.

REFERENCE BOOKS

1. Horowitz, Sahni, Rajasekaran, “Computer Algorithms”, University press 2008
2. Tanenbaum A.S., Langram Y, Augestien M.J., ”Data Structures using Java”, Prentice Hall of India, 2009
3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd edition, Pearson Education, 2012.
4. Aho, Hopcroft, Ullman, “Data Structures and Algorithms”, Pearson Education, 2009.

Proceedings of the 35th International Convention MIPRO : IEEE Conference publications

         a. Library assignment

         b. Presentation

CIA 2: 50 mark

        MSE

CIA 3: 20 mark

        a. Test

        b.  Review of IEEE papers on Optimization algorithms

1.                  COURSE DESCRIPTION:

The concept of processes is at the heart of software and systems engineering. Software process models integrate software engineering methods and techniques and are the basis for managing large-scale software and IT projects. High product quality routinely results from high process quality.

Software process management deals with getting and maintaining control over processes and their evolution. Becoming acquainted with existing software process models is not enough, though. It is important to understand how to select, define, manage, deploy, evaluate, and systematically evolve software process models so that they suitably address the problems, applications, and environments to which they are applied. Providing basic knowledge for these important tasks is the main goal of this textbook.

Courtesy: Software Process definition and Management by Authors: Münch, J., Armbrust, O., Kowalczyk, M., Soto, M.

2.                  Scope and Objective:

The course aims to provide an in-depth knowledge in at understanding the software process management methods. They include..

•     To study the needs of Software maturity framework

•     To understand Managing software organizations

•     To make the students to get familiarized with Software standards, inspections with defined and             optimized processes.

· Understand and practice the Software maturity framework and its application in delivering successful IT projects.

· Evaluate a project to develop the scope of work, provide accurate cost estimates and to plan the various activities.

· Understand the need of Software configuration management tools that identify the factors that put into baselines.

· Identify the different software standards, software inspections, configuration management and its application in delivering successful IT projects.

· Monitor the progress of a project and to assess the risk of slippage, revising targets or counteract drift.

· Distinguish between the different types of project and follow the stages needed to negotiate an appropriate contract.

Software Improvement, process maturity level, people in the optimizing level, need for the optimizing process.

Managing software organizations: commitment discipline, the management system, establishing a project management system. The Project plan: project planning principles, contents, size measures, estimating, productivity factors, scheduling, project tracking, the developing plan,planning models, final Considerations.

Software configuration management: Need for configuration management, software product nomenclature, basic configuration management function, baselines, configuration management responsibilities, need for automated tools, software quality management.

Software standards: definitions, reasons, benefits, examples of major standards, establishing software standard, standards versus guidelines.

Software inspections : Types of reviews, objectives, basic inspection principles, the conduct of inspections, inspection training, reports and tracking, other considerations, initiating and inspection program, future directions.

Software configuration management : the Software configuration management plan, Software configuration management questioners, scm support functions, the requirement phase, design control, the implementation phase, operational data, the test phase, scm for tools, configuration accounting, the software configurations audit.

Data gathering and analysis: The principles of data gathering, data gathering process, software measures, data analysis, other considerations. Managing software quality: The quality management paradigm, quality motivation, quality goals, quality plans, tracking and controlling software quality.

Defect Prevention: Defect prevention not a idea, the principles of SDP, process changes for defect prevention, defect prevention consideration, management role.

Automating the software process: The need for software automation, What to automate? Development environments, organizational plans to automate, technology transitions,productivity. Case Study.

1. Introduction to the Personal Software Process by Watts S. Humphrey, published by Pearson Education 2012.

2. Software Change Management: Case Studies and Practical Advice by Donald J. Reifer Pearson Education 2012.

3. Managing the software process by Watts S. Humphrey, published by Pearson Education 2013.

REFERENCE BOOKS

1. Software Process Definition and Management Jürgen Münch Ove Armbrust Martin

Kowalczyk Martín Soto - May 27, 2012 Springer Science & Business Media - Publisher

2. Software Process Modeling Silvia T. Acuna Natalia Juristo- January 27, 2006 Springer Science & Business Media - Publisher

3. A Discipline to Software Engineering by Watts S. Humphrey Pearson Education 2008.

4. Software Project Management in Practice by Pankaj Jalote, Pearson Education 2010.

5. Software Project Management Readings and Cases by Chris Kemerer 2010.

ONLINE REFERENCE (MOOC and NPTEL)

1.      https://www.futurelearn.com/courses/business-process-management

2.      https://www.edx.org/course/enterprise-software-lifecycle-management-mephix-mephi001x

3.      https://www.coursera.org/learn/process-improvement

4.      https://www.coursera.org/learn/software-processes-and-agile-practices

5.      http://nptel.ac.in/courses/106101061/36#

6.      http://nptel.ac.in/courses/106105087/pdf/m13L35.pdf

JOURNALS AND RESEARCH PAPERS (IEEE)

1.      Software Engineering Journal IEEE Xplore Digital Library

2.      Process Management Tools IEEE Xplore Digital Library

3.      IEEE Transactions on Engineering Management IEEE Xplore Digital Library

4.      CMMI Guided Process Improvement for DevOps Projects: An Exploratory Case Study

5.      Convergence analysis of ISO/IEC 12207 and CMMI-DEV: A systematic literature review

6.      Mapping between the Guide of IT Solution Contract and CMMI Models: A Qualitative Analysis

7.      Business goals monitoring and control measures in CMMI

8.      Industrial Experience with Open Source Software Process Management

9.      Identifying Software Process Management Challenges: Survey of Practitioners in a Large Global IT Company

10.  The design of software process management and evaluation system in small and medium software enterprises

11.  A Case Study on Usage of a Software Process Management Tool in China

Research Paper draft making (Moodle submissions):

Good Grade (75%-90%): Submitting well in advance (before deadline) in the appropriate/                                                            instructed format.

                                    Should have also reverted back for the rough draft before actual submission for                                         feedback.

                                    Plagiarism should be less than 20% in the submitted draft. Pictures should also                                          not to be copied from internet and to have good resolution.

Average Grade (55-75): One day late submission. Feedback taken but not implemented in the work.

                                    Plagiarism more than 40% with few copied figures and also partially following                                          the instructed format.

Poor Grade (25-55):    More than 2 days late submissions. Not reverting for any feedback. Plagiarism                                           more than 50% without following the format.

CIA I Component II

Quiz: MCQ/ True or False/ Matching (Moodle based)

Good Grade (75%-90%): First attempt with all most all correct answers and finishing with in stipulated                                       time.

Average Grade (55-75):          First attempt with more than 30% wrong answers.

Poor Grade (25-55):    Could not attend for the scheduled test and appeared in a rescheduled one.                                                Failing to answer at least more than 50% of questions.

CIA III Component I

Assignment: Every student is given a specific unique problem/case related to software process management

Good Grade (75%-90%): Report submission within said deadline.

                                    Following the format instructed with less than 20% plagiarism.

                                    Showing the utilization of research papers/ library books as proof of concept.

                                    Flavor of management principles followed in report generation.

Average Grade (55-75):          Report submission after a day of deadline.

                                    Following the format partially less than 35% plagiarism.

                                    Not showing the utilization of research papers/ library books as proof of concept.

Poor Grade (25-55):    Submissions after 2 days of deadline.

                                    Not following the instructed format and plagiarized content more than 50%.

                                    Copied contents from the fellow mates.

CIA III Component II

Written Test: On research topics related to subject syllabus.

Good Grade (75%-90%): Relevant answers with appropriate figures and content answering all the                                               questions. 

Average Grade (55-75):          Answering only an 80% of the questions with the contents related to it.

Poor Grade (25-55):    Not attempting on the scheduled date of test.

                                    Answering the paper with only 50% of questions.

                                    Poor figures and lack of continuity in answers.                                                                             

·         To study the image fundamentals and mathematical transforms necessary for image processing.

·         To study the image enhancement techniques

·         To study image restoration procedures.

·         To study the image compression procedures.

·         To study the image segmentation and representation techniques.

·         To study pattern recognition and interpretation.

·     Students will be able to do the following:

·         Explain and analyze the steps of image formation, sampling, quantization and representation digitally.

·         Outline and examine how images are processed by discrete, linear, time-invariant systems.

·         Summarize how images are perceived by humans  and how color is represented .

·         Point out how image information can be modeled analytically  and compare transform-domain representation of images (Fourier, DCT, Haar, WHT)

·         Outline how images are enhanced to improve subjective perception and ability to differentiate about restoration of images based on the knowledge acquisition system.

·         Ability to summarize about how images are analyzed to extract features of interest

·         Ability to summarize the principles of image compression 

Image formation, Image transforms – Fourier transforms, Walsh, Hadamard, Discrete cosine, Hotelling transforms.

Histogram modification techniques - Image smoothening - Image Sharpening - Image Restoration - Degradation Model – Noise models - Spatial filtering – Frequency domain filtering.

Compression Models - Elements of information theory - Error free Compression -Image segmentation –Detection of discontinuities – Region based segmentation - Morphology.

Representation schemes- Boundary descriptors- Regional descriptors - Relational Descriptors

Patterns and pattern classes - Decision-Theoretic methods - Structural methods-Case studies

1.      Gonzalez.R.C & Woods. R.E., “Digital Image Processing”, 3rd Edition, Pearson Education, Indian edition published  by Dorling Kindersely India Pvt. Ltd. Copyright © 2009, Third impression 2011.

2.      Gonzalez.R.C & Woods. R.E., “Digital Image Processing using MATLAB”, 2nd Edition, McGraw Hill Education (India) Pvt Ltd  2011 (Asia)

3.      Madan, “ An Introduction to MATLAB for Behavioural Researchers”, Sage Publications, 2014

4.   David A Forsyth & Jean ponce “Computer Vision: A Modern Approach” 2nd Edition, Pearson Education India 2015

Anil K.Jain, “Fundamentals of Digital image Processing”, Prentice Hall of India, 2011.

External 50 Marks

● To increase the knowledge of advanced paradigms of algorithm design.
● To make the students learn an object oriented way of solving problems.
● To Enhance students’ capability of selecting the best and efficient way for encoding problems.

 ·         Assess and to make critical judgment on the choices of algorithms for modern computer systems. 

1. Implementation of Dictionary using Binary Search trees.
2. Implementation of Sorting Techniques like Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort and Heap Sort and compare their performances.
3. Implementation of Shortest Path Algorithm (Bellman Ford).

1. Design, develop, and write a program to implement a Monte Carlo algorithm to test the primality of a given integer and determine its performance.
2. Design, develop, and write a program to solve string matching problem using naïve approach, the KMP and Robin Karp algorithm. Compare their performances.

1. Modeling and simulation Cloud computing environments, including Data Centers, Hosts and Cloudlets and perform VM provisioning using CloudSim: Design a host with two CPU cores, which receives request for hosting two VMs, such that each one requires two cores and plans to host four tasks units. More specifically, tasks t1, t2, t3 and t4 to be hosted in VM1, while t5, t6, t7, and t8 to be hosted in VM2. Implement spaceshared allocation policy and timeshared allocation policy. Compare the results.

1. Implement MapReduce concept for
   A. Strassen’s Matrix Multiplication for a huge matrix.
   B. Computing the average number of citation index a researcher has according to age among some 1 billion journal articles.
   Consider a network of entities and relationships between them. It is required to calculate a state of each entity on the basis of properties of the other entities in its neighborhood. This state can represent a distance to other nodes, indication that there is a neighbor with the certain properties, characteristic of neighborhood density and so on. A network is stored as a set of nodes and each node contains a list of adjacent node IDs. Mapper emits messages for each node using ID of the adjacent node as a key. Reducer must recompute state and rewrite node with the new state. Implement this scenario.

1. Implementation of a Parallel Search Algorithm.
2. Case study of Load Balancing – Static & Dynamic.
3. Case study of Job Sequencing & collision prevention.

1. T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd Edition, The MIT Press, 2014.
2. Kenneth A. Berman, Jerome L. Paul: “Algorithms”, Cengage Learning, 2013.

Record : 10mark

MSE : 20 mark

ESE : 50 mark

·         To study the image fundamentals and mathematical transforms necessary for image processing.

·         To study the image enhancement techniques

·         To study image restoration procedures.

·         To study the image compression procedures.

·         To study the image segmentation and representation techniques. 

·         To study pattern recognition and interpretation.

 Students will be able to do the following:

·         Explain and analyze the steps of image formation, sampling, quantization and representation digitally.

·         Outline and examine how images are processed by discrete, linear, time-invariant systems.

·         Summarize how images are perceived by humans  and how color is represented .

·         Point out how image information can be modeled analytically  and compare transform-domain representation of images (Fourier, DCT, Haar, WHT)

·         Outline how images are enhanced to improve subjective perception and ability to differentiate about restoration of images based on the knowledge acquisition system.

·         Ability to summarize about how images are analyzed to extract features of interest

·         Ability to summarize the principles of image compression

1. Display of Grayscale Images,

1. Implementation of  various transforms and their use.
2. Implementation of  Histogram Equalization,  Non-linear Filtering.

1. Implementation of   Edge detection using Operators,  2-D DFT and DCT.
2. Implementation of   Filtering in frequency domain.

1. Implementation of   Segmentation using  various transform.

1. Implementation of  various Morphological algorithms.
2. Implementation of IEEE/ACM paper in Digital image processing area.

1. Rafael C. Gonzalez, Richard E. Woods, Steven Eddins, “Digital Image Processing using MATLAB”, Pearson Education, Inc., 2004.

2. Gonzalez.R.C & Woods. R.E., “Digital Image Processing using MATLAB”, 2nd Edition, McGraw Hill Education (India) Pvt Ltd  2011 (Asia)

3. Madan, “ An Introduction to MATLAB for Behavioural Researchers”, Sage Publications, 2014

External 25

The course will broadly cover the following aspects:

•    Teaching skills

•    Laboratory skills and other professional activities

•    Research methodology

During the seminar session each student is expected to prepare and present a topic on engineering/ technology, it is designed to:

·     Review and increase their understanding of the specific topics tested.

·     Improve their ability to communicate that understanding to the grader.

·     Increase the effectiveness with which they use the limited examination time.

Students are encouraged to use various teaching aids such as over head projectors, power point presentation and demonstrative models. This will enable them to gain confidence in facing the placement interviews and intended to increase the score they earn on the upcoming exam above what they would otherwise earn.

This course is specially designed for the students of higher degree. It aims to train and equip the students towards acquiring competence in teaching, laboratory skills, research methodologies and other professional activities including ethics in the respective academic disciplines.

 EVALUATION SCHEME

Component InstructorsWeightage

TeachingLecture materials      7.5

Lecture presentation     10

Laboratory and

Professional activitiesReports     7.5

Viva/presentation     10

ResearchProposal     2.5

Viva/presentation     2.5

ComprehensiveTest/ viva     10

Total      50

HeadoftheDepartmentwillassignasuitableinstructor/faculty membertoeachstudent.Studentsandfacultymemberscoveringabroadareawillbegroupedin apanel consistingof 4-5students and 4-5 facultymembers.

Withinone weekafter registration,thestudentshouldplanthedetailsofthetopicsof lectures,laboratory experiments,developmentalactivitiesandbroadtopicofresearchetcin consultationwiththeassignedinstructor/faculty.Thestudenthastosubmittwocopiesof the written outlineof thetotal work to theinstructor within one week.

Ina particulardiscipline,Instructorsbelongingtothe broadareaswillform the paneland willnominateoneof themasthepanelcoordinator.Thecoordinatortogether withthe instructors willdrawacompleteplanoflecturestobedelivered by allstudentsinasemester.Eachstudent willpresent3-4lectures,whichwillbeattendedby allotherstudentsandInstructors.These lectureswillbeevenly distributedovertheentiresemester.Thecoordinatorwillannouncethe schedule forthe entiresemester and fixsuitablemeetingtime in the week.

Eachstudentwillalsoprepare onepresentationabouthisfindingsonthebroadtopicof research.Thefinal reporthastobesubmitted inthe formof acompleteresearchproposal.The Referencesandthebibliography shouldbecitedinastandardformat.Theresearchproposal shouldcontaina)Topic ofresearchb)Backgroundandcurrentstatusoftheresearchworkinthe areaasevidentfromtheliteraturereviewc)Scopeoftheproposed workd)Methodology e) References and bibliography.

Areportcoveringlaboratory experiments,developmentalactivitiesandcodeof professional conduct andethics in discipline has to besubmitted byindividual student.

Thepanelwilljointly evaluateallthecomponentsofthecoursethroughoutthesemester and the mid semestergradewillbe announced bythe respectiveinstructor to his student.

Acomprehensiveviva/testwill be conductedatthe endofthesemesterjointly,wherever feasibleby allthepanelsinaparticularacademicdiscipline/department,inwhichintegrationof knowledge attained through various courseswillbetested and evaluated.

Wherever  necessary  and  feasible,  the  panel  coordinator  in  consultation  with  the concerned  groupmayalsoseekparticipationofthefacultymembersfromothergroupsin lectures andcomprehensive viva.

Midsemesterreportandfinalevaluationreportshouldbesubmittedinthe9thweekand 15thweek of thesemesterrespectively. Theseshould contain the followingsections:

• Section(A):Lecturenotesalong withtwoquestionpapers each of180minduration,one quizpaper (CIA-I) of 120mindurationonthe topicsof lectures.The questionpaper shouldtest concepts,analyticalabilitiesandgraspof the subject. Solutionsof questionsalsoshouldbe provided. All thesewill constitute lecturematerial.
• Section (B):Laboratoryexperiments reports andprofessional workreport.

• Section (C): Research proposal with detailed references andbibliographyin a standard format.

Wherever necessary,respective HeadoftheDepartmentscouldbeapproachedby Instructors/panelcoordinatorsforsmoothoperationofthecourse.Speciallecturesdealing with professional ethics in thediscipline mayalso be arranged bythegroup fromtime to time.

EVALUATION SCHEME

·      To understand the concepts of internetwork.

·      To study the functions of different layers.

·      To introduce IEEE standards employed in computer networking.

              ·      To make the students to get familiarized with different protocols and network components.

·         Explain how communication works in data networks and the Internet.

·         Recognize the different internetworking devices and their functions.

·         Explain the role of protocols in networking.

·         Analyze the services and features of the various layers of data networks.

·         Design, calculate, and apply subnet masks and addresses to fulfill networking requirements.

·         Analyze the features and operations of various application layer protocols such as ARP, DHCP, ICMP, HTTP, DNS, SNMP and SMTP.

              ·         Analyze the security issues in the networks. 

Building a Network, Requirements, Perspectives, Scalable Connectivity, Cost-Effective Resource sharing, Support for Common Services, Manageability, Protocol layering, Performance, Bandwidth and Latency, Delay X Bandwidth Product, Perspectives on Connecting, Classes of Links, Reliable Transmission, Stop-and-Wait , Sliding Window, Concurrent Logical Channels.    

Switching and Bridging, Datagram’s, Virtual Circuit Switching, Source Routing, Bridges and LAN Switches, Basic Internetworking (IP), Service Model, Global Addresses, Datagram Forwarding in IP, subnetting and classless addressing, Address Translation(ARP), Host Configuration(DHCP), Error Reporting(ICMP), Virtual Networks and Tunnels.

Network as a Graph, Distance Vector (RIP), Link State (OSPF), Metrics, The Global Internet, Routing Areas, Routing among Autonomous systems (BGP), IP Version 6(IPv6), Mobility and Mobile IP.

Simple Demultiplexer (UDP), Reliable Byte Stream(TCP), End-to-End Issues, Segment Format, Connecting Establishment and Termination, Sliding Window Revisited, Triggering Transmission, Adaptive Retransmission, Record Boundaries, TCP Extensions, Queuing Disciplines, FIFO, Fair Queuing, TCP  Congestion Control, Additive Increase/Multiplicative Decrease, Slow Start, Fast Retransmit and Fast Recovery.

Congestion-Avoidance Mechanisms, DEC bit, Random Early Detection (RED), Source-Based Congestion Avoidance. The Domain Name System(DNS),Electronic Mail(SMTP,POP,IMAP,MIME),World Wide Web(HTTP),Network Management(SNMP).

1.  Larry Peterson and Bruce S Davis “Computer Networks: A System Approach” 5th Edition, Elsevier -2014

2.  Douglas E Comer, “Internetworking with TCP/IP, Principles, Protocols and Architecture” 6th Edition, PHI – 2014

REFERENCE BOOKS

1. Uyless Black “Computer Networks, Protocols, Standards and Interfaces” 2nd Edition – PHI

2. Behrouz A Forouzan “TCP /IP Protocol Suite” 4th Edition – Tata McGraw-Hill

3. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education 4th edition, 2012.

4. Larry L.Peterson and Brule S.Davie, “Computer Networks – A System Approach” MarGankangmann – Harcourt Asia, Fifth Edition, 2011.

5. William Stallings, “SNMP, SNMP V2, SNMPV3, RMON 1 and 2”, Pearson 2006

6.J.F Kurose and K.W. Ross, “Computer Networking –A top –down approach featuring the internet”, Pearson, 2012.

7.William Stallings, “Data & Computer Communication”, 6th Edition, Pearson Education, 2007.

8.Mani Subramanian, “Network Management: Principles and Practice”, Addison Wesley, 2000.

External 50 Marks

1. To learn the fundamentals of data models and to conceptualize and depict a database system

using ER diagram.

2. To understand the internal storage structures using different file and indexing techniques

which will help in physical DB design.

3. To know the fundamental concepts of transaction processing- concurrency control

techniques and recovery procedure.

4. To have an introductory knowledge about the emerging trends in the area of distributed DBOO

 DB- Data mining and Data Warehousing and XML.

2.     Create conceptual and logical database designs for a business information problem.

3.     Build a database management system that satisfies relational theory and provides users with business queries, business forms, and business reports.

4.     Understand the core terms, concepts, and tools of relational database management systems, distributed database, parallel database, spatial database, multimedia database and emerging technologies.

5.     Work in teams and utilize effective group techniques to manage a complex project.

 6.     Understand research issues in this field.

Distributed Databases Vs. Conventional Databases-Architecture-Fragmentation-Query Processing-Transaction Processing-Concurrency Control-Recovery.

Introduction to Object Oriented Databases-Approaches-Modeling and Design-Persistence-Query Languages-Transaction-Concurrency-Multiversion Locks-Recovery

Enhanced Data Models-Client/ Server Model-Data warehousing and Data mining-Web database-Mobile databases-Introduction to BigData.

ER Model-Normalization-Security-Integrity-Consistency-Database Tuning-Optimization and Research Issues-Design of Temporal Databases-Spatial Databases

Rules-Knowledge bases-Active and Deductive Databases-Parallel Databases-Multimedia Databases-Image Databases-Text Databases-Case Studies-Research Issues-IEEE/ACM journal/Proceedings study

1. R. Elmasri and S.B. Navathe, “Fundamentals of Database Systems”, 6th Edition, Addison

Wesley, 2010

2. Abraham Silberschatz, Henry. F. Korth, S.Sudharsan, “Database System Concepts”, 6th

Edition. Tata McGraw Hill, 2010

3. Carlos Coronel & Steven Morris, “Database Systems: Design, Implementation, &

Management”, February 4, 2014

4. Stefano Ceri & Giuesppe Pelagatti, Distributed Databases - Principles and Systems, McGraw

Hill Book Company, 2008.

5. C.J.Date, “An Introduction to Database system”, Pearson Education, 7th Edition, 2006

REFERENCE BOOKS

1. Raghu Ramakrishnan & Johannes Gehrke, “Database Management Systems”, 3rd Edition,

TMH, 2003

2. Philip M. Lewis, Arthur Bernstein, Michael Kifer, “Databases and Transaction Processing:

An Application-Oriented Approach”, Addison-Wesley, 2002Jim Buyens, Step by Step Web

Database Development, PHI, 2002.

Thomas Connolly, Carolyn Begg, Database Systems: A Practical Apprach to Design, Implementation and Management

External 50 Marks

• To study the concepts of data science process.
• To study various algorithms and span filters.
• To study about logistic regression and approaches to Social research.
• To study the concepts of data visualization and data journalism.
• To learn about data engineering and ethics of data scientist

Big Data and Data Science Hype, Getting Past the Hype, A Data Science Profile, Thought Experiment: Meta-Definition. Statistical Thinking in the Age of Big Data, Exploratory Data Analysis, The Data Science Process.

Machine Learning Algorithms, Three Basic Algorithms, Exercise: Basic Machine Learning Algorithms, Thought Experiment: Automated Statistician, Naive Bayes, Comparing Naive Bayes to k-NN, Jake’s Exercise: Naive Bayes for Article Classification.

Classifiers, M6D Logistic Regression Case Study, Kyle Teague and GetGlue Timestamps, Financial Modeling, The Kaggle Model, Feature Selection David Huffaker: Google’s Hybrid Approach to Social Research.

Data Visualization History, Data Science and Risk, Data Visualization at Square, Social Network Analysis at Morning Analytics. Social Network Analysis, Terminology from Social Networks, Data Journalism. Epidemiology: Madigan’s Background, Modern Academic Statistics. Medical Literature and Observational Studies.

MapReduce, Word Frequency Problem, Pregel, Economic Interlude, Case Studies - Hadoop: Starting with Hadoop, Data Science, Next-Gen Data Scientists, Ethical Data Scientist.

1.      Cathy O'Neil, Rachel Schutt, “Doing Data Science: Straight Talk from the Frontline”, O'Reilly Media, 2013.

Reference Books:

1.      John Hopcroft and R Kannan, “Foundations of Data Science”, Microsoft Research, 2014.  

2.      Jeffrey Stanton, “An Introduction to Data Science”, Syraccuse University, 2012.

3.      Calvin.Andrus, Jon Cook, Suresh Sood, “Data Science: An Introduction”, Wikibooks, 2014.

External 50 Marks

●      To orient the student to make an informed choice from the large number of alternative methods and experimental designs available.

●      To enable the student to present a good research proposal.

●      To familiarize the student with the nature of research and scientific writing

●      To empower the student with the knowledge and skills they need to undertake a research project, to present a conference paper and to write a scientific article.

       ●     To know how to apply the basic aspects of the research process in order to plan and execute a research project. To plan a research project on a                 topic relevant to  subject area of interest and write a draft research proposal for this research project

       ●     To effectively use the library and its resources in gathering information related to the learners' research project. To find two international journal                 articles relevant to their topic of research;  two national journal articles relevant to their topic of research;  two newspaper articles relevant to                   their topic of research;  two dissertations relevant to their topic of research

        ●     To know how to perform basic operations with Excel spreadsheats. To draw graphs and diagrams using Excel and insert these graphs and                            diagrams into Word

        ●     To use SPSS to conduct statistical analyses. Summarise and describe data by computing descriptive statistics, compiling frequency tables and                    drawing graphs; determine relationships between variables by computing correlations and regression equations;  compare means of populations                by using t-tests, ANOVA and post hoc comparisons;  determine the effects of more than one factor and correction of co-variables by interpreting ANOVA and ANCOVA tables;  determine assumptions of statistical analyses; compute non-parametric statistics and interpret test statistics;  assess the construct validity and reliability of a questionnaire/test;  design an experiment and do sampling

           ●     To be able to use SPSS to perform multivariate statistical analyses, including canonical analysis, multiple regression analysis, logistic                                 regression analysis, exploratory factor analysis, one-way analysis of variance, and multivariate analysis of variance.  OR To be able to use                         EViews and STATA for simple regression and descriptive data analysis. Select the correct statistical techniques for a specific design, study                           population, apply SPSS to perform the analyses, apply guidelines to assess effects, report results correctly in tables, and interpret results                           correctly.  OR Use a predetermined dataset and perform basic regression analysis and descriptive statistics and print and interpret these

                 To be able to present, review and publish scientific articles. To evaluate the quality of scientific articles according to specific criteria and write a                    research proposal according to the guidelines

 ●              To be able to prepare for and present a conference paper/poster at a national/international conference.

An Introduction Meaning of Research , Objectives of Research , Motivation in Research , Types of Research , Research approaches ,Research Method versus Methodology ,Research and Scientific Method, Importance of Knowing How Research is Done

, Research Process, Criteria of Good Research, problem Encountered by Researchers in India .

Defining the Research Problem: Definition of Research Problem, Selecting the Problem, Necessity of Defining the Problem Technique Involved in Defining a Problem        

Measurement in Research, Measurement Scales, Sources of Error in Measurement, Tests of Sound Measurement, Technique of Developing Measurement Tools, Scaling, Meaning of Scaling, Scale Classification Bases, Important Scaling Techniques, Scale Construction Techniques. 

Processing Operations, Some Problems in Processing, Elements /Types of Analysis, Statistics in Research, Measures of Central Tendency, Measures of Dispersion Measures of Asymmetry (Skewness), Measures of Relationship, Partial Correlation, Association in case of Attributes, Other Measures.                 

The role of algorithm in computing –Insertion sort–Analyzing and designing algorithms –growth of functions–introduction to NP –completeness 

Analysis of variance(ANOVA), basic principles , technique, setting up  analysis of variance table, short cut method for one- way ANOVA, coding method, two-way-ANOVA, ANOVA in Latin-Square-Design, Analysis of Co-variance(ANOCOVA), technique, assumption in ANOCOVA. 

Need for Sampling, Some Fundamental Definitions, Central Limit Theorem, Sampling Theorem, Sandler’s A-test, Concept of Standard Error, Estimation, Estimating the Population Mean, Estimating the Population Proportion, Sample size and its Determination, Determination of Sample Size through the Approach, Based on Precision Rate and Confidence Level, Determination of Sample Size through the Approach, Based on Bayesian Statistics.

Meaning of Interpretation, Technique of Interpretation: Precaution in Interpretation, 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 Report, Case study . 

Montgomery, Douglas C., Design and Analysis of Experiments, Willey India, 2007 

Montgomery, Douglas C. & Runger, George C. ,Applied Statistics & Probability for Engineers, Wiley India , 2010

 Charlie Catlett, Wolfgang Gentzsch, Lucio Grandinetti, Gerhard Joubert, and José Luis Vasquez-Poletti, Cloud computing and big data , Published/Distributed:Amsterdam : Washington, DC : IOS Press, [2013]

External 50 Marks

• To understand various protocols in internetworking.
• To understand IEEE standards in various protocols and technologies.
• To make students to familiarize with protocols, security and optimization techniques

• TCP/IP suite like ICMP Protocol, TFTP, NNTP, Proxy Server, Application Firewall, Web browsers, ARP, DHCP, ICMP, DNS and SNMP.
• Performance Evaluation of TCP and UDP over Wireless Ad-hoc Networks with varying traffic loads.
• Prevention of ARP spoofing: A probe packet based technique.
• Security techniques over media streaming over peer-to-peer networks.
• Various techniques in optimization of bandwidth consumption, request for unauthorized access, signal-to-noise ratio, download channel capacity, packet delivery ratio and inter-packet delay.

External 25

To study Data Defition, Control and Manipulation languages.

To maintain database.

To understand database design contraints.

Use of PL/SQL , trigger and cursor in database design.

1.      Study of all SQL commands

2.      Implementation of  PL/SQL Programs.

3.      Implementation of  Cursor, Trigger.

4.      Implement the inventory control system with a reorder level

5.      Develop a package for a bank to maintain its customer details

6.      Develop a package for the payroll of a company

7.      Implementation of IEEE/ACM paper

8.      Implementation of Data Science Application Problems

9.       Learning SPSS tool to implement research based concepts

2. Philip M. Lewis, Arthur Bernstein, Michael Kifer, “Databases and Transaction Processing: An Application-Oriented Approach”, Addison-Wesley, 2002Jim Buyens, Step by Step Web Database Development, PHI, 2002.

External 25

Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:

• Review and increasetheir understandingof thespecific topics tested.
• Improvetheir abilityto communicate that understandingto thegrader.
• Increasetheeffectiveness with which theyusethelimited examinationtime.

 students towards acquiring competence in teaching, laboratoryskills, research methodologies and otherprofessional activities includingethics in the respective academicdisciplines.

The course will broadly cover the following aspects:

• Teachingskills
• Laboratoryskills andother professional activities
• Research methodology

Notices pertaining to this course will be displayed on the respective departmental notice boards by the panel coordinator/instructor.Students may also check the exam notice board for notices issued by the exam division.

MAKEUPPOLICY:  All students are required to attend all the lectures and presentations in the panel. Participation and cooperation will also be taken into account in the final evaluation. Requests for makeup should normally be avoided. However,in genuine cases,panel will decide action on a case by case basis.

NOTE:Seminar shall be presented in the department in presence of a committee (Batch of Teachers)constituted by HOD.The seminar marks are to be awarded by the committee. Students shall submit the seminar report in the prescribed Standard format.

The course aims to develop the skills of the students in the areas of pure Mathematics problems and hence developing a strong logical concept. This paper will extend student’s mathematical maturity and ability to deal with abstraction and to introduce most of the basic terminologies used in computer science courses and application of ideas to solve practical problems. It paper will be very helpful for them to consider it as a prerequisite in order to pursue a career in research and development.

At the end of the course the students would be capable of mathematically formulating certain practical problems in terms of combinatorics and physically interpret the results. Also students would be capable of developing and understanding algebraic structures, Algorithms skills.

The objective of this paper is to:

·          help the students in understanding the fundamental subject of Number Theory.

·                                  have knowledge of the concepts needed to test the logic of a program.

                be exposed to concepts and properties of algebraic structures

Propositional logic – Logical connectives – Truth tables –  Normal forms ( conjunctive and disjunctive) - Predicate logic - Universal  and  existential quantifiers - Proof techniques –– Mathematical Induction.

Divisibility, gcd, prime numbers, fundamental theorem of arithmetic, Congruences, Fermat's theorem, Euler function, primality testing, solution of congruences, Chinese remainder theorem, Wilson’s theorem.

Groups, Definition and examples, Cyclic groups,  Permutation group (S_n and D_n),  Subgroups, Cosets, Lagrange’s Theorem .

Fundamental principles of counting, pigeonhole principle, principle of inclusion and exclusion, Solving Linear Recurrence Relations, Divide-and-Conquer Algorithms and Recurrence Relations, generating functions, Solve Recurrence Relations  using Generating Functions

Relations, Representation of Relations, equivalence relations and partitions, partial order, lattices and Boolean algebra.

1. David M. Burton, “Elementary Number Theory”, Tata McGraw – Hill Publishing, 2007
2. Ivan Niven,  S. Zuckerman, L. Montgomery, “An Introduction to The Theory of Numbers”, John Wiley & Sons (ASIA) Pte. Ltd. Singapore. 2006.

3. I. N. Herstein, “Topics in Algebra”, John Wiley & Sons Inc., 1975.
4. Michael Artin, “Algebra”, Prentice – Hall of India Private Limited, 2000.
5. J.P. Tremblay and R. Manohar, “Discrete Mathematical Structures with Applications to Computer Science”, TMH, 1997.
6. Kenneth H. Rosen, “Discrete Mathematics and its Applications”, Fifth Edition, TMH, 2003.
7. R.P. Grimaldi, “Discrete and Combinatorial Mathematics”, 5^th Edition, Pearson Publication, New Delhi 2009.
8. M. K. Venkataraman, N. Sridharan and N.Chandrasekaran, “Discrete Mathematics”, The National Publishing Company, 2003.

2. R.P. Grimaldi, “Discrete and Combinatorial Mathematics”, 5^th Edition, Pearson Publication, New Delhi 2009.

End Semester Examination (ESE) :

The ESE is conducted for 100 marks of 3 hours duration.

The syllabus for this paper is divided into FIVE units and each unit carries equal weightage in terms of marks distribution.

Question paper pattern is as follows.

Two full questions with either or choice, will be drawn from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a question. The emphasis on the questions is broadly based on the following criteria:

50 % - To test the objectiveness of the concept

30 % - To test the analytical skill of the concept

20 % - To test the application skill of the concept

 Mid Semester Examination (MSE) :

            The MSE is conducted for 50 marks of 2 hours duration.

               Question paper pattern; Five out of Six questions have to be answered. Each question carries 10 marks.

Cyber Security is defined as the body of technologies, processes and practices designed to protect networks, computers, programs and data from attack, damage or unauthorized access. Similar to other forms of security, Cyber Security requires coordinated effort throughout an information system.  This course will provide a comprehensive overview of the different facets of Cyber Security.  In addition, the course will detail into specifics of Cyber Security for all parties who may be involved keeping view of Global and Indian Legal environment. 

After learning the course for a semester, the student will be aware of the important cyber laws in the Information Technology Act (ITA) 2000 and ITA 2008 with knowledge in the areas of Cyber-attacks and Cyber-crimes happening in and around the world. The student would also get a clear idea on some of the cases with their analytical studies in Hacking and its related fields.

Security Fundamentals, Social Media and Cyber Security Security Fundamentals - Social Media –IT Act- CNCI – Legalities

Cyber Attack and Cyber Services Vulnerabilities - Phishing - Online Attacks. – Cyber Attacks - Cyber Threats - Denial of Service Vulnerabilities  - Server Hardening  

Risk Management and Assessment - Risk Management Process - Threat Determination Process - Risk Assessment - Risk Management Lifecycle – Vulnerabilities, Security Policy Management - Security Policies  - Coverage Matrix, Business Continuity Planning - Disaster Types - Disaster Recovery Plan - Business Continuity Planning - Business Continuity Planning Process.

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Architectural Integration: Security Zones - Devices viz Routers, Firewalls, DMZ Host, Extenuating Circumstances viz. Business-to-Business, Exceptions to Policy, Special Services and Protocols, Configuration Management - Certification and Accreditation

Authentication and Cryptography: Authentication - Cryptosystems - Certificate Services Securing Communications: Securing Services - Transport – Wireless - Steganography and NTFS Data Streams, Intrusion Detection and Prevention Systems: Intrusion - Defense in Depth - IDS/IPS  -  IDS/IPS Weakness and Forensic Analysis, Cyber Evolution: Cyber Organization - Cyber Future

TEXT BOOKS:   

1. Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, Cyber Security Policy Guidebook, Wiley; 1 edition , 2012,  ISBN-10: 1118027809 
2. Dan Shoemaker and Wm. Arthur Conklin, Cybersecurity: The Essential Body Of Knowledge,   Delmar Cengage Learning; 1 edition (May 17, 2011) ,ISBN-10: 1435481690
3. Jason Andress, The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice, Syngress; 1 edition (June 24, 2011) ,  ISBN-10: 1597496537

4. Stallings, “Cryptography & Network Security - Principles & Practice”, Prentice Hall, 3rd Edition 2002. 
5. Bruce, Schneier, “Applied Cryptography”, 2nd Edition, Toha Wiley & Sons, 2007. 
6. Man Young Rhee, “Internet Security”, Wiley, 2003. 
7. Pfleeger & Pfleeger, “Security in Computing”, Pearson Education, 3rd Edition, 2003.  

REFERENCES:

1. Information Technology Act 2008 Online 2. IT Act 2000.

• To study the concepts of different approaches in bioinformatics.
• To study various database and networks.
• To study about search engines and visualization.
• To understand the concepts of statistics, data mining, pattern matching.

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.

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.        

Statistical concepts: Microarrays, Imperfect Data, Randomness, Data Analysis. Clustering and Classification, Data Mining, Methods, Selection and Sampling, Preprocessing and Cleaning, Transformation and Reduction, Data Mining Methods, Pattern Recognition and Discovery, Machine Learning, Text Mining, Pattern Matching: Dot Matrix analysis, Substitution matrices, Dynamic Programming, Word methods, Bayesian methods.

Drug Discovery, components, process, Perspectives, Numeric considerations, Algorithms, Hardware, Issues, Protein structure, Ab Initio Methods, Heuristic methods, Systems Biology, Tools, Case Studies : Collaboration and Communications, Standards, Security, Intellectual property.

TEXT BOOKS

1. Bryan Bergeron, “Bio Informatics Computing”, 1st Edition, PHI Learning, 2010. (Reprint 2015).

REFERENCE BOOKS

1. Zoe Lacroix and Terence Critchlow, “Bio Informatics - Managing scientific Data”, Morgan Kaufmann, 2005.

2. T.K. Affward, D.J. Parry Smith, “Introduction to Bio Informatics”, Pearson Education, 2001.

3. Pierre Baldi, Soren Brunak, “Bio Informatics – The Machine Learning Approach”, 2nd Edition, First East West Press, 2003.

4. Introduction to Bio Informatics, Attwood, Smith, Longman, 1999.

·       Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·       End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I  :   Mid Semester Examination (Theory)                : 25 marks            

CIA II:  Assignments                                                          : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work              : 10 marks

    Attendance                                                                            : 05 marks

            Total                                                                                       : 50 marks

• To understand the concepts of machine learning
• To appreciate supervised and unsupervised learning and their applications
• To understand the theoretical and practical aspects of Probabilistic Graphical Models
• To appreciate the concepts and algorithms of reinforcement learning
• To learn aspects of computational learning theory

Machine Learning - Machine Learning Foundations –Overview – applications - Types of machine learning - basic concepts in machine learning Examples of Machine Learning -Applications – Linear Models for Regression - Linear Basis Function Models - The Bias-Variance Decomposition - Bayesian Linear Regression - Bayesian Model Comparison

Linear Models for Classification - Discriminant Functions -Probabilistic Generative Models -Probabilistic Discriminative Models - Bayesian Logistic Regression. Decision Trees – Classification Trees- Regression Trees - Pruning. Neural Networks -Feed-forward Network Functions - Error Backpropagation - Regularization - Mixture Density and Bayesian Neural Networks - Kernel Methods - Dual Representations - Radial Basis Function Networks. Ensemble methods- Bagging- Boosting.                            

Clustering- K-means - EM - Mixtures of Gaussians - The EM Algorithm in General -Model selection for latent variable models - high-dimensional spaces -- The Curse of Dimensionality –Dimensionality Reduction - Factor analysis - Principal Component Analysis - Probabilistic PCA- Independent components analysis

Directed Graphical Models - Bayesian Networks - Exploiting Independence Properties – From Distributions to Graphs -Examples -Markov Random Fields - Inference in Graphical Models – Learning –Naive Bayes classifiers-Markov Models – Hidden Markov Models – Inference – Learning- Generalization – Undirected graphical models- Markov random fields- Conditional independence properties - Parameterization of MRFs - Examples - Learning - Conditional random fields (CRFs) - Structural SVMs

Sampling – Basic sampling methods – Monte Carlo. Reinforcement Learning - K-Armed Bandit - Elements - Model-Based Learning - Value Iteration - Policy Iteration. Temporal Difference Learning- Exploration Strategies- Deterministic and Non-deterministic Rewards and Actions- Eligibility Traces- Generalization- Partially Observable States- The Setting- Example. Semi - Supervised Learning. Computational Learning Theory - Mistake bound analysis, sample complexity analysis, VC dimension. Occam learning, accuracy and confidence boosting

1. Christopher Bishop, “Pattern Recognition and Machine Learning” Springer, 2006
2. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012
3. Ethem Alpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005
4. Hastie, Tibshirani, Friedman, “The Elements of Statistical Learning” (2nd ed)., Springer, 2008
5. Stephen Marsland, “Machine Learning –An Algorithmic Perspective”, CRC Press, 2009

REFERENCES

1.      Tom Mitchell, "Machine Learning", McGraw-Hill, 1997.

Journal Paper

ESE Marks : 50%

During these minar session each student is expected to prepare and presentatopicon engineering/ technology, itis designed to: 

• Review and increasetheir understandingof thespecific topics tested.
• Improvetheir abilityto communicate that understandingto thegrader.
• Increasetheeffectiveness with which theyusethelimited examinationtime.

The course will broadly cover the following aspects:

• Teachingskills
• Laboratoryskills andother professional activities
• Research methodology

¨      Presentation assessed by Panel Members

¨      Guide

¨      Mid semester Project Report

Internships are short-term work experiences that will allow  a student to observe and participate in professional work environments and explore how his interests relate to possible careers. They are important learning opportunities trough industry exposure and practices.   More specifically, doing internships is beneficial because they provide the opportunity to:

• Get an inside view of an industry and organization/company
• Gain valuable skills and knowledge
• Make professional connections and enhance student's network
• Get experience in a field to allow the student  to make a career transition

1.The student shall undergo an Internship for30 days  starting from the end of 2nd semester examination and completing it during the initial period of 3rd semester.

2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of  the students

3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise.

4. The Internship shall be completed by the end of  2nd semesters.

5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance.

6. Students can also undergo internships arranged by the department during vacation.

7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.

8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor  and a presentation on the internship given to department constituted panel.

The objectives of this course are to: 

introduce students to the techniques of operations research in mining operations 

provide students with basic skills and knowledge of operations research and its application in mineral industry 

introduce students to practical application of operations research in big mining projects

· Explain the meaning of operations research

· Know the various techniques of operations research;

· Apply the techniques used in operations research to solve real life problem in mining industry

· Select an optimum solution with profit maximization;

· Have complete understand of the significant role operation research play in mining project completion at every stage of the mines (Skills)

 · Use operations research to solve transportation problems during the allocation of trucks to excavators  formulate operation research models to solve real life problem o proficiently allocating scarce resources to optimize and maximize profit o eliminate customers / clients waiting period for service delivery

· Turn real life problems into formulation of models to be solve by linear programming etc.

· Determine critical path analysis to solve real life project scheduling time and timely delivery

 · Use critical path analysis and programming evaluation production and review techniques for timely project scheduling and completion and

· Conduct literature search on the internet in the use of operation research techniques in mining projects execution and completion.

Poisson Process – Markovian Queues – Single and Multi-server Models – Little’s formula – Machine Interference Model – Steady State analysis – Self Service Queue. 

Non- Markovian Queues – Pollaczek Khintchine Formula – Queues in Series – Open Queueing Networks –Closed Queueing networks. 

Discrete Even Simulation – Monte – Carlo Simulation – Stochastic Simulation – Applications to Queueing  systems.  

Formulation – Graphical solution – Simplex method – Two phase method Transportation and Assignment Problems. 

Lagrange multipliers – Equality constraints – Inequality constraints – Kuhn – Tucker conditions – Quadratic Programming. 

1. Winston.W.L. “Operations Research”, Fourth Edition, Thomson – Brooks/Cole, 2003.

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

REFERENCES  

1.Robertazzi. T.G. “Computer Networks and Systems – Queuing Theory and Performance Evaluation”, Third Edition, Springer, 2002 Reprint.

Journal Papers 

ESE Marks : 50 %

Objective of this course is to encourage students to do research oriented project.

v

§  Continuous Internal Assessment:100 Marks

¨      Presentation assessed by Panel Members

¨      Assessment by Guide

§  Dissertation (Exclusive assessment of Project Report): 100 Marks

§  End Semester Examination:100 Marks

¨      Viva Voce

¨      Demonstration

¨      Project Report

External 100