curriculum of vii-viii semester

SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY- TUMAKURU (A constituent College of Siddhartha Academy of Higher Education, Tumakuru)

Information Science and Engineering

CURRICULUM OF VII-VIII SEMESTER

ACADEMIC YEAR 2020 – 21

Department: Information Science and Engineering

Semester: 7

Sl. No Subject Code Name of the Subject L T P S C

1 IS7T01 Mobile Application Development 4 0 0 0 4

2 CS7T02 Web Technologies 3 2 0 0 4

3 IS7T03 File Structures 3 0 0 1 4

4 CS7PE4XY /

IS7PE4XY Professional Elective - II 3 0 0 0 3

5 CS7PE5XY /

IS7PE5XY Professional Elective - III 3 0 0 0 3

6 IS7L01 Machine Learning Laboratory 0 0 3 0 1.5

7 CS7L02 Web Technology Laboratory 0 0 3 0 1.5

8 CS7PW01 Project Work Phase - I 0 8 0 0 4

Total 16 10 6 1 25

Professional Elective – II Credits: 3-0-0-0-3

Subject Code Name of the Subject

CS7PE411 Software Testing

IS7PE412 Natural Language Processing

IS7PE413 Multimedia Computing

IS7PE414 Network Analysis and Management

Professional Elective – III Credits: 3-0-0-0-3


IS7PE521 Distributed Computing Systems

IS7PE522 Soft and Evolutionary Computing

CS7PE523 Agile Technologies

IS7PE524 Software Project Management



Syllabus for the Academic Year – 2020 - 2021

Department: Information Science and Engineering Semester: 7

Subject Name: MOBILE APPLICATION DEVELOPMENT

Subject Code: IS7T01 L-T-P-S-C: 4-0-0-0-4

Course Objectives:

Sl. No Course Objectives

1 Learn the basic environment of an Android app.

2

Illustrate the user interface and app functionality.

3 Interpret the techniques of app data access and persistence.

4 Appraise the testing and publishing of an app.

UNIT Description Hours

I

Mobility and Android: Introduction, Mobility Panorama, Mobile

Platforms, App Development Approaches, Android Overview.

Getting Started with Android: Introduction, Setting up Development

Environment, Saying Hello to Android, Traversing an Android App

Project Structure, Logical Components of an Android App, Android Tool

Repository, Installing and Running App Devices. Learning with an

Application - 3CheersCable: Introduction, 3CheersCable App, Mobile

App Development Challenges, Tenets of a Winning App.

8

II

App User Interface: Introduction, Activity, UI Resources, UI Elements

and Events, Interaction among Activities, Fragments, Action Bar. App Functionality - Beyond UI: Introduction, Threads, AsyncTask,

Service, Notifications, Intents and Intent Resolution, Broadcast Receivers, Telephony and SMS.

12

III

App Data - Persistence and Access: Introduction, Flat Files, Shared Preferences, Relational Data, Data Sharing Across Apps, Enterprise Data. Graphics and Animation: Introduction, Android Graphics, Android Animation.

12

IV

Multimedia: Introduction, Audio, Video and Images, Playback 185, Capture and Storage. Location Services and Maps: Introduction, Google Play Services, Location Services, Maps.

10

V

Sensors: Introduction, Sensors in Android, Android Sensor

Framework, Motion Sensors, Position Sensors, Environment Sensors.

Testing Android Apps: Introduction, Testing Android App Components, App Testing Landscape Overview. Publishing Apps:

Introduction, Groundwork, Configuring, Packaging, Distributing.

10



Text Book:

Sl No Title Author Volume and Year

of Edition

1 Composing Mobile Apps: Learn|

Explore|Apply using Android

Anubhav Pradhan,

Anil V. Deshpande

Wiley, First Edition-

2014

Reference Books:

Sl No Title Author Volume and Year

of Edition

1 Android Application Development

All in one for Dummies

Barry Burd Edition: 1st Edition

2 Teach Yourself Android

Application Development in 24

Hours

1st Edition,

Publication SAMS

Course Outcomes:

Course

outcome Descriptions

CO1 Able to understand various approaches and technologies for app

development.

CO2 Capable of understanding, designing app user interface and

implementing app functionality.

CO3 Able to develop location services using device sensors while building

android apps.

CO4 Capable of validating, packaging and moving apps to market.





Subject Name: WEB TECHNOLOGIES

Subject Code: CS7T02 L-T-P-S-C: 3-2-0-0-4

Course Objectives:

Sl. No

Course Objectives

1 Illustrate the Semantic Structure and Compose different tags using XHTML.

2 Define and use user-defined tags for creating a XML framework.

3 Design Client-Side programs using JavaScript.

4 Design Server Side programs using PHP and to infer PHP‟s capabilities to

access database.


I

INTRODUCTION to XHTML Origins and Evolution of HTML and XHTML, Basic Syntax, Standard HTML Document Structure, Basic Text Markup, Images, Hypertext Links, Lists, Tables, Forms, The audio Element, The video Element, Organization Elements, The time Element, Syntactic Differences between HTML and XHTML.

8

II

Introduction to XML Introduction, Uses of XML, The Syntax of XML, XML Document Structure, Namespaces, XML Schemas, Displaying Raw XML Documents, Displaying XML Documents with CSS, XSLT Style Sheets, XML Processors, Web Services.

8

III

The Basics of JavaScript

Overview of JavaScript, Object Orientation and JavaScript, General Syntactic Characteristics, Primitives, Operations, and Expressions, Screen Output and Keyboard Input, Control Statements, Object Creation and Modification, Arrays, Functions , An Example , Constructors , Pattern Matching Using Regular Expressions, Another Example, Errors in Scripts.

8

IV

JavaScript and HTML Documents The JavaScript Execution Environment, The Document Object Model, Element Access in JavaScript, Events and Event Handling, Handling Events from Body Elements, Handling Events from Button Elements, Handling Events from Text Box and Password Elements, The DOM 2 Event Model, The canvas Element, The navigator Object, DOM Tree Traversal and Modification.

8



V

Introduction to PHP Origins and Uses of PHP, Overview of PHP, General Syntactic

Characteristics, Primitives, Operations, and Expressions, Output, Control Statements, Arrays, Functions, Pattern Matching, Form Handling, Cookies, Session Tracking, Database Access with PHP and MySQL.

7

Text Book:

Sl No

Title Author Volume and Year of

Edition

1 Programming the World Wide

Web

Robert W. Sebesta 8th Edition, Pearson

Education, 2015

ISBN-13: 978-0-13-

377598-3

Reference Books:

Sl No


Edition

1

Professional JavaScript for Web Developers

Nicholas C Zakas 3rd Edition,

Wrox/Wiley India, 2012,

ISBN-13:

9788126535088

2

Fundamentals of Web

Development

Randy Connolly,

Ricardo Hoar

Pearson Education,

Inc., 2016, ISBN:

9789332575271, ISSN:

9332575274

3 Open Source Web Development

with LAMP

James Lee and

Brent Ware

Pearson Education,

Inc., 2002, ISBN-13: 978-

0201770612

Course Outcomes:

Course


CO1 Develop Web Pages, Host web site and Deploy web based applications

using various web technologies.

CO2 Design topic – specific markup languages and understand web services.

CO3 Implement, Invoke and develop server side objects using PHP to generate

and display the content dynamically.

CO4 Understand, Implement and develop client side objects using JavaScript

and to achieve an interactive web page.





Subject Name: FILE STRUCTURES


Course Objectives:


1 Understand the techniques for organization and manipulation of

data in secondary storage.

2

Understand and analyze the high-level file structures tools which include

indexing, co-sequential processing, B trees, B+ trees, Hashing and Extendable Hashing.

3 Select the appropriate data structure and file structure for proper File

organization.

4 Build the structure, retrieving selected data, updating and maintaining

the appropriate structure.


I

Fundamental File Structure Concepts, Managing Files of Records: File Structures: The Heart of the file structure Design, A Short History of File Structure Design, A Conceptual Toolkit; Fundamental File Operations: Physical Files and Logical Files, Opening Files, Closing Files, Reading and Writing, Seeking, UNIX file System Commands. Storage as Hierarchy, A journey of a Byte, Buffer Management, Field and Record Organization, Using Classes to Manipulate Buffers, Using Inheritance for Record Buffer Classes, Managing Fixed Length, Fixed Field Buffers, An Object-Oriented Class for Record Files, Record Access, More about Record Structures, Encapsulating Record Operations in a Single Class, File Access and File Organization.

8

II

Organization of Files for Performance, Indexing: Data Compression, Reclaiming Space in files, Internal Sorting and Binary Searching, Key sorting; what is an Index? A Simple Index for Entry- Sequenced File, Template Classes in C++, Object-Oriented support for Indexed, Entry-Sequenced Files of Data Objects, Indexes that are too large to hold in Memory, Indexing to provide access by Multiple keys, Retrieval Using Combinations of Secondary Keys, Improving the Secondary Index structure, Inverted Lists, Selective indexes, Binding.

7

III

Co-sequential Processing and the Sorting of Large Files: A Model for Implementing Consequential Processes, Application of the Model to a General Ledger Program, Extension of the Model to include

Multi-way Merging, A Second Look at Sorting in Memory.

7



IV

Multi-Level Indexing and B-Trees, Indexed Sequential File Access

and Prefix B +TREES:

Introduction; Indexing with Binary Search Trees; Multi-Level Indexing; B- Trees; Example of Creating a B-Tree, An Object-Oriented Representation of B-Trees, B-Tree Methods; Nomenclature, Formal Definition of B-Tree Properties, Worst-case Search Depth, Deletion, Merging and Redistribution during insertion; A Way to improve storage utilization, B* Trees, Buffering of pages; Virtual B-Trees; Variable-length Records and keys. Indexed Sequential Access, Maintaining a Sequence Set, Adding a Simple Index to the Sequence Set, The Content of the Index: Separators Instead of Keys, The Simple Prefix B+ Tree and its maintenance, Index Set Block Size, Internal Structure of Index Set Blocks: A Variable-order B- Tree, Loading a Simple Prefix B+ Trees, B+ Trees, B+ Trees and Simple Prefix B+ Trees in Perspective.

9

V

Hashing, Extendible Hashing:

Introduction, A Simple Hashing Algorithm, Hashing Functions and Record Distribution, How much Extra Memory should be used?, Collision resolution by progressive overflow, Buckets, Making deletions, Other collision resolution techniques, Patterns of record access. How Extendible Hashing Works, Implementation, Deletion, Extendible Hashing Performance, Alternative Approaches.

8

Text Book:

Sl No


Edition

1

File Structures-An Object Oriented Approach with C++

Michael J. Folk, Bill

Zoellick, Greg

Riccardi

3rd Edition, Pearson,

Reference Books:

Sl No


Edition

1

File Structures Using C++ K.R. Venugopal,

K.G. Srinivas, P.M.

Krishnaraj

Tata McGraw-Hill, 2017

2 The C++ Programming

Language

Bjarne Stroustrup 3rd Edition, Pearson, 2018

Course Outcomes:

Course


CO1 Describe the Concept of File Structures and File Organization

Techniques.

CO2 Apply the Concepts of File Structures in the Data Storage and

Manipulation Techniques.



CO3 Analyze and Choose field structures and record structures to different

applications.

CO4 Use high level file structure to provide solution building, retrieving and

updating files.



Subject Name: SOFTWARE TESTING

Subject Code: CS7PE411 L-T-P-S-C: 3-0-0-0-3

Course Objectives:


1 Learn the design of test cases.

2 Investigate the reason for bugs and analyze the principles in software testing to prevent and remove bugs.

3 Learn to apply software testing techniques in commercial environment.

4 Expose how to plan a test project, design test cases and data, conduct testing operations, manage software problems and defects and generate a testing report.


I

A Perspective on Testing: Basic definitions, Test cases, Insights from

a Venn diagram, Identifying test cases, Error and fault taxonomies,

Levels of testing.(1.1 to 1.6) Examples: Generalized pseudo code, The

triangle problem, The NextDate function, The commission problem,

The SATM (Simple Automatic Teller Machine) problem, The currency

converter, Saturn windshield wiper (2.1 to 2.7).

8

II

Boundary Value Testing: Boundary value analysis, Robustness testing, Worst-case testing, Special value testing, Examples, Random testing (5.1 to 5.6) Equivalence Class Testing: Equivalence test cases for the triangle problem, NextDate function, and the commission problem, Guidelines and observations 6.4 to 6.8). Decision Table-Based Testing: Decision tables, Test cases for the triangle problem, Next Date function, and the commission problem, Guidelines and observations (7.1 to 7.5 and 7.7).

8

III

Path Testing, Data Flow Testing and Life Cycle - Based Testing Path Testing: DD paths, Test coverage metrics, Basis path testing,

guidelines and observations (8.1 to 8.4). Data Flow Testing: Definition-Use testing, Slice-based testing (9.1 and 9.2) Life Cycle - Based Testing: Traditional Waterfall Testing, Testing in Iterative Life Cycles, Agile Testing, Agile Model–Driven Development (11.1 to 11.4).

7



IV

Integration Testing: Decomposition-Based Integration, Call Graph–

Based Integration, Path-Based Integration, Example: integration

NextDate (13.1 to 13.4), System Testing: Threads, Basis Concepts for

Requirements Specification, Model-Based Threads, Use Case–Based

Threads, Long versus Short Use Cases (14.1 to 14.5).

8

V

Test and Analysis Activities within a Software Process: The quality process, Planning and monitoring, Quality goals, Dependability properties, Analysis, Testing, Improving the process, Organizational factors (4.1 to 4.8). Fault- Based Testing: Overview, Assumptions in fault-based testing, Mutation analysis, Fault-based adequacy criteria, Variations on mutation analysis 16.1 to 16.5). Test Execution: Overview, From test case specifications to test cases, Scaffolding, Generic versus specific scaffolding, Test oracles, Self-checks as

oracles, Capture and replay (17.1 to 17.7).

8

Text Books:

Sl

No Title Author

Volume and Year of

Edition

1 Software Testing: A Craftsman’s Approach

Paul C. Jorgensen 4th Edition, Auerbach Publications, 2013, ISBN-10: 1466560681

2 Software Testing and Analysis : process, Principles and Techniques

Mauro Pezze, Michal Young

1st Edition, Wiley India, 2008, ISBN-13: 9788126517732 ISBN-10: 8126517735

Reference Books:

Sl

No Title Author

Volume and Year of

Edition

1

Foundations of Software Testing Aditya P Mathur 1st Edition, Pearson Education, 2012, ISBN: 8131707954, 9788131707951

2 Software testing Principles and Practices

Srinivasan Desikan, Gopalaswamy Ramesh

2nd Edition, Pearson Education, 2007, ISBN: 9788177581218

3

The Craft of Software Testing Brian Marrick 1st Edition, MARICK, 1995 ISBN: 813171571X, 9788131715710

Course Outcomes:

Course outcome

Descriptions

https://isbnsearch.org/isbn/9788126517732

https://isbnsearch.org/isbn/8126517735



CO1 Clear understanding and knowledge of the foundations, techniques in the area of software testing and its practice in the industry.

CO2 Compare and pick out the right type of software testing process for any given real world problem.

CO3 Able to plan a test project, design test cases, conduct testing operations, manage software problems and defects.

CO4 Implement various test processes for quality improvement.



Subject Name: NATURAL LANGUAGE PROCESSING

Subject Code: IS7PE412 L-T-P-S-C: 3-0-0-0-3

Course Objectives:


1 Learn the techniques in natural language processing.

2 Be familiar with the natural language generation.

3 Be exposed to Text Mining.

4 Understand the information retrieval techniques


I

Overview and language modeling: 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.

7

II

Word level and syntactic analysis: 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.

7



III

Extracting Relations from Text: From Word Sequences to Dependency Paths: Introduction, Subsequence Kernels for Relation Extraction, A Dependency- Path Kernel for Relation Extraction and Experimental Evaluation. Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles: Introduction, Domain Knowledge and Knowledge Roles, Frame Semantics and Semantic Role Labeling, Learning to Annotate Cases with Knowledge Roles and Evaluations. A Case Study in Natural Language Based Web Search:

In Fact System Overview, the GlobalSecurity.org Experience.

8

IV

iSTART: Evaluation of Feedback Systems, Textual Signatures: Identifying Text-Types Using Latent Semantic Analysisto Measure the Cohesion of Text Structures: Introduction, Cohesion, Coh-Metrix, Approaches to Analyzing Texts, Latent Semantic Analysis,

Predictions, Results of Experiments. Automatic Document Separation: A Combination of Probabilistic Classification and

Finite-State Sequence Modeling: Introduction, Related Work, Data Preparation, Document Separation as a Sequence Mapping Problem,

Results. Evolving Explanatory Novel Patterns for Semantically-Based Text Mining: Related Work, A Semantically Guided Model for Effective Text Mining.

8

V

Information Retrieval and Lexical Resources: 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.

9

Text Books:

Sl No


Edition

1 Natural Language Processing and Information Retrieval

Tanveer Siddiqui, U.S. Tiwary

Oxford University Press, 2008

2 Natural Language Processing and Text Mining

Anne Kao and Stephen R.Poteet

(Eds)

Springer-Verlag London Limited 2007.

Reference Books:

Sl No


Edition

1

Speech and Language Processing: An introduction to Natural Language Processing, Computational Linguistics and Speech Recognition

Daniel Jurafsky and James H Martin

2nd Edition, Prentice Hall, 2008.

2 Natural Language

Understanding

James Allen 2ndEdition, Benjamin / Cummings publishing company, 1995



3 Information Storage and Retrieval systems

Gerald J. Kowalski and Mark.T.

Maybury

Kluwer academic Publishers, 2000.

Course Outcomes:

Course outcome

Descriptions

CO1 Understand and analyze the natural language text.

CO2 Generate the natural language and apply word level and syntactic level

analysis.

CO3 Make use of Text mining and generate report.

CO4 Apply information retrieval techniques.





Subject Name: MULTIMEDIA COMPUTING

Subject Code: IS7PE413 L-T-P-S-C: 3-0–0-0-3

Course Objectives:


1 Learn and understand technical aspect of Multimedia Systems.

2 Understand the standards available for different audio, video and text applications.

3 Design and develop various Multimedia Systems applicable in

realtime.

4 Learn various multimedia authoring systems.


I

Introduction, Media and Data Streams, Audio Technology: Multimedia Elements; Multimedia Applications; Multimedia Systems Architecture; Evolving Technologies for Multimedia Systems; Defining Objects for Multimedia Systems; Multimedia Data Interface Standards; The need for Data Compression; Multimedia Databases. Media: Perception Media, Representation Media, Presentation Media, Storage Media, Transmission Media, Information Exchange Media, Presentation Spaces & Values, and Presentation Dimensions; Key Properties of a Multimedia System: Discrete & Continuous Media, Independence Media, Computer Controlled Systems, Integration; Characterizing Data Streams: Asynchronous Transmission Mode, Synchronous Transmission Mode, Isochronous Transmission Mode; Characterizing Continuous Media Data Streams. Sound: Frequency, Amplitude, Sound Perception and Psychoacoustics; Audio

Representation on Computers; Three Dimensional Sound Projection; Music and MIDI Standards; Speech Signals; Speech Output; Speech Input; Speech Transmission.

8

II

Graphics and Images, Video Technology, Computer-Based Animation: Capturing Graphics and Images Computer Assisted Graphics and Image Processing; Reconstructing Images; Graphics and Image Output Options. Basics; Television Systems; Digitalization of Video Signals; Digital Television; Basic Concepts; Specification of Animations; Methods of Controlling Animation; Display of Animation; Transmission of Animation; Virtual Reality Modeling Language.

8



III

Data Compression 1: Storage Space; Coding Requirements; Source, Entropy, and Hybrid

Coding; Basic Compression Techniques; JPEG: Image Preparation, Lossy Sequential DCT-based Mode, Expanded Lossy DCT-based Mode, Lossless Mode, Hierarchical Mode. Data Compression 2: H.261 (Px64) and H.263: Image Preparation, Coding Algorithms, Data Stream, H.263+ and H.263L; MPEG: Video Encoding, Audio Coding, Data Stream, MPEG-2, MPEG-4, MPEG-7; Fractal Compression.

8

IV

Optical Storage Media: History of Optical Storage; Basic Technology; Video Discs and Other WORMs; Compact Disc Digital Audio; Compact Disc Read Only Memory; CD-ROM Extended Architecture; Further CD-ROM-Based Developments; Compact Disc Recordable; Compact Disc Magneto-Optical; Compact Disc Read/Write; Digital Versatile Disc. DATA AND

FILE FORMAT STANDARDS: Rich-Text Format; TIFF File Format; Resource Interchange File Format (RIFF); MIDI File Format; JPEG DIB File Format for Still and Motion Images; AVI Indeo File Format; MPEG Standards; TWAIN.

7

V

Content Analysis: Simple Vs. Complex Features; Analysis of Individual Images; Analysis

of Image Sequences; Audio Analysis; Applications. MULTIMEDIA

APPLICATION DESIGN: Multimedia Application Classes; Types of Multimedia Systems; Virtual Reality Design; Components of Multimedia Systems; Organizing Multimedia Databases; Application Workflow Design Issues; Distributed Application Design Issues.

8

Text Books:

Sl

No Title Author Volume and Year of Edition

1

Multimedia

Fundamentals

Ralf Steinmetz, Klara

Narstedt:

Vol 1-Media Coding andContent

Processing, 2nd Edition, Pearson

Education, 2003.Chapters 2,3,4,

5, 6, 7, 8, 9).

2 Multimedia Systems

Design

Prabhat K. Andleigh,

Kiran Thakrar

PHI, 2003. (Chapters 1, 3,7)

Reference Books:

Sl No

Title Author Volume and Year of Edition

1

Multimedia

Communication

Systems: Techniques,

Standards, and

Networks

K.R Rao, Zoran S.

Bojkovic and

Dragorad A.

Milovanovic

Pearson Education, 2002

2

Multimedia

Information

Networking

Nalin K Sharad: PHI, 2002.



Course Outcomes:

Course outcome

Descriptions

CO1 Know the challenges of handling media files.

CO2 Able to use different compression techniques to reduce the storage space

for media files.

CO3 Use different techniques to ensure better quality of audio and video

stream.

CO4 Understand the need for different types of file formats for audio and video

data.



Subject Name: NETWORK ANALYSIS AND MANAGEMENT


Course Objectives:


1 Learn network devices functions and configurations hub, switch, tap and

routers.

2 Be familiar with network Security Devices.

3 Be exposed to network services.

4 Understand and analyze application performance.


I

A System Approach to Network Design and Requirement Analysis: Introduction-Network Service and Service based networks- Systems and services- characterizing the services. Requirement Analysis: Concepts – Background–User Requirements- Application Requirements- Host Requirements-Network Requirements – Requirement Analysis: Guidelines – Requirements gathering and listing- Developing service metrics to measure performance – Characterizing behavior- developing performance threshold – Distinguish between service performance levels. Requirement Analysis: Practice –Template, table and maps –simplifying the

requirement analysis process –case study.

8



II

Flow Analysis: Concepts, Guidelines and Practice : Background-

Flows- Data sources and sinks- Flow models- Flow boundaries- Flow

distributions- Flow specifications- Applying the flow model-

Establishing flow boundaries-Applying flow distributions- Combining

flow models, boundaries and distributions- Developing flow

specifications-prioritizing flow- simplifying flow analysis process –

examples of applying flow specs- case study.

8

III

Logical Design: Choices, Interconnection Mechanisms, Network

Management and Security: Background- Establishing design goals- Developing criteria for technology evolution- Making technology choices for design-case study- Shared Medium- Switching and Routing: Comparison and contrast- Switching- Routing-Hybrid Routing/Switching Mechanisms – Applying Interconnection Mechanism to Design – Integrating Network management and security

into the Design- Defining Network Management- Designing with manageable resources- Network Management Architecture- Security- Security mechanism- Examples- Network Management and security plans- Case study.

8

IV

Network design: physical, addressing and routing: Introduction-

Evaluating cable plant design options – Network equipment placement-

diagramming the physical design- diagramming the worksheet – case

study. Introduction to Addressing and routing- establishing routing

flow in the design environments- manipulating routing flows-

developing addressing strategies- developing a routing strategy- case

study.

8

V

Network Management and SNMP Protocol Model: Network and

System management, Network management system platform; Current

SNMP Broadband and TMN management, Network management

standards. SNMPV1, SNMPV2 system architecture, SNMPV2, structure

of management information. SNMPV2 – MIB – SNMPV2 protocol,

SNMPV3- Architecture, Application, MIB, security user based security

model, access control RMON.

7

Text Books:

Sl No


Edition

1 “Practical Computer Network

Analysis and Design”

James.D.McCabe 1st Edition,Morgan

Kaufaman, 1997.

2 “Network Management –

Principles & Practice”

Mani Subramanian 2nd Edition Prentice

Hall, 2012.

Reference Books:

Sl

No Title Author

Volume and Year

of Edition



1 Fundamentals of Computer Network Analysis and Engineering: Basic Approaches for Solving Problems in the Networked Computing Environment

J.Radz, Universe, 2005.

2 Networks: An Introduction

Mark Newman Kindle Edition, 2010.

3 Wireshark 101: Essential Skills for Network Analysis

Laura Chappel

and Gerald

Combs

Kindle Edition, 2013.

4 SNMP, SNMP2, SNMP3 and RMON1 and 2

William Stallings

Pearson Education, 2004.

5 Network Management DawSudira Sonali Publications, 2004.

Course Outcomes:

Course outcome

Descriptions

CO1 Explain the key concepts and algorithms in complex network analysis.

CO2 Apply a range of techniques for characterizing network structure.

CO3 Discuss methodologies for analyzing networks of different fields.

CO4 Describe network management protocol models.





Subject Name: DISTRIBUTED COMPUTING SYSTEMS


Course Objectives:


1

Explain distributed system, their characteristics, challenges and system models.

2

Describe IPC mechanisms to communicate between distributed objects.

3

Illustrate the operating system support and File Service architecture in a distributed system.

4

Analyze the fundamental concepts, algorithms related to synchronization.


I

Characterization of Distributed Systems:

Characterization of Distributed Systems: Introduction, Examples of DS,

Resource sharing and the Web, Challenges. System Models: Architectural

Models, Fundamental Models.

8

II

Inter Process Communication and Distributed Objects and RMI:

Inter Process Communication: Introduction, API for Internet Protocols,

External Data Representation and Marshalling, Client – Server

Communication, Group Communication .Distributed Objects and RMI:

Introduction, Communication between Distributed Objects, RPC, Events and

Notifications.

8

III

Operating System Support and Distributed File Systems:

Operating System Support: Introduction, the OS layer, Protection, Processes

and Threads, Communication and Invocation, Operating system architecture.

Distributed File Systems: Introduction, File Service architecture, Sun Network file systems.

7

IV

Time and Global States and Coordination and Agreement:

Time and Global States: Introduction, Clocks, events and process status,

Synchronizing physical clocks, Logical time and logical clocks, Global states.

Coordination and Agreement: Introduction, Distributed mutual exclusion,

elections.

8



V

Distributed Transactions:

Distributed Transactions: Introduction, Flat and nested distributed

transactions, Atomic commit protocols, Concurrency control in distributed

transactions, distributed deadlocks.

8

Text Book:

Sl No

Title

Author

Volume and Year of Edition

1 Distributed Systems – Concepts and

Design,

George Coulouris,

Jean Dollimore and

Tim Kindberg

5th Edition, Pearson

Publications, 2009.

Reference Books:

Sl

No

Title

Author

Volume and Year

of Edition

1 Distributed Operating Systems Andrew S Tanenbaum 3 rd edition, Pearson

publication, 2007.

2 Distributed Computing: Principles,

Algorithms andSystems,

Ajay D. Kshemkalyani and

MukeshSinghal,

Cambridge

University Press,

2008.

3 “Distributed Computing” Sunita Mahajan, Seema Shan

Oxford University Press, 2015.

Course Outcomes:

Course


CO1 Explain the characteristic of a distributed system along with its and design Challenges.

CO2 Illustrate the mechanism of IPC between distributed objects.

CO3 Describe the distributed file service architecture and the important characteristics of SUN NFS.

CO4 Discuss concurrency control algorithms applied in distributed transactions.





Subject Name: SOFT AND EVOLUTIONARY COMPUTING


Course Objectives:


1

Familiarize with the basic concept of soft computing and intelligent systems.

2 Compare with various intelligent systems.

3 Analyze the various soft computing techniques.

4

Understand the concepts of fuzzy logic and other machine intelligence applications.


I

Introduction to soft computing: ANN, FS,GA, SI, ES, Comparing among

intelligent systems ANN: introduction, biological inspiration, BNN&ANN,

classification, first Generation NN, perceptron, illustrative problems

Text Book 1: Chapter1: 1.1-1.8, Chapter2: 2.1-2.6

8

II

Adaline, Medaline, ANN: (2nd generation), introduction, BPN, KNN, HNN,

BAM, RBF, SVM and illustrative problems

Text Book 1: Chapter2: 3.1,3.2,3.3,3.6,3.7,3.10,3.11

8

III

Fuzzy logic: introduction, human learning ability, undecidability, probability

theory, classical set and fuzzy set, fuzzy set operations, fuzzy relations, fuzzy

compositions, natural language and fuzzy interpretations, structure of fuzzy

inference system, illustrative problems

Text Book 1: Chapter 5

9

IV

Introduction to GA, GA, procedures, working of GA, GA applications,

applicability, evolutionary programming, working of EP, GA based Machine

learning classifier system, illustrative problems

Text Book 1: Chapter 7

7

V

Swarm Intelligent system: Introduction, Background of SI, Ant colony

system Working of ACO, Particle swarm Intelligence (PSO).

Text Book 1: 8.1-8.4, 8.7

7



Text Book:

Sl No

Title

Author


1 Soft computing N. P Padhy and S P

Simon

Oxford University

Press 2015

Reference Book:

Sl

No

Title

Author

Volume and Year

of Edition

1 Principles of Soft Computing Shivanandam, Deepa

S. N

Wiley India, ISBN

13: 2011

Course Outcomes:

Course


CO1 Understand soft computing techniques.

CO2 Apply the learned techniques to solve realistic problems.

CO3 Differentiate soft computing with hard computing techniques.

CO4 Hybridize the Neural Network and fuzzy logicto form aNeuro-fuzzy Networks.



Subject Name: AGILE TECHNOLOGIES


Course Objectives:


1 Understand the background and driving forces for taking an Agile approach to software development.

2 Understand the business value of adopting Agile approaches and the Agile development practices.



3 Learn design principles, refactoring, automated build tools, version control and continuous integration.

4 Understand testing activities within an Agile project.


I

Fundamentals of Agile:

The Genesis of Agile, Introduction and background, Agile Manifesto and

Principles, Overview of Scrum, Extreme Programming, Feature Driven

development, Lean Software Development, Agile project management, Design

and development practices in Agile projects, Continuous Integration,

Refactoring, Pair Programming, Simple Design.

8

II

Agile Scrum Framework:

Introduction to Scrum, Project phases, Agile Estimation, Planning game,

Product backlog, Sprint backlog, Iteration planning, User Stories,

Characteristics and content of user stories, Project velocity, Burn down chart,

Sprint planning and retrospective, Daily scrum, Scrum roles – Product Owner,

Scrum Master, Scrum Team, Key challenges to implementing Agile

Development and Project management Frameworks.

8

III

Agile Software Design and Development:

Agile design practices, Difference between Agile and Traditional Approach,

Role of design Principles including Single Responsibility Principle, Open

Closed Principle, Liskov Substitution Principle, Dependency Inversion

Principle in Agile Design, Interface Segregation Principles, Refactoring

Techniques, Automated build tools, Version control.

7

IV

Agile Testing:

Agile Testing, How is Agile Testing Different, Ten Principles for Agile

Testers, Agile Testing Quadrants, Test-Driven Development(TDD), TDD

Lifecycle, Acceptance tests, Managing testing cycle, Exploratory testing, Risk

based testing, Regression tests, Why Automation, Tools to support the Agile

tester.

8

V

Industry Trends:

Agile Marketing, Challenges in Enterprise adoption of Agile methods, Agile

ALM, Roles in an Agile project, Agile applicability framework, Agile in

Distributed teams, Challenges in Agile, Agile methodology with cloud

computing, Balancing Agility with Discipline, Agile rapid application

development technologies.

8

Text Books:

Sl No


1 Agile Software Development with Scrum.

KenSchawber, Mike Beedle

1st Edition, Prentice Hall, 2001, ISBN: 0130676349,

9780130676344

2 Agile Testing: A Practical Guide for Testers and Agile Teams.

Lisa Crispin, Janet Gregory

1st Edition, Pearson Education,2010, ISBN: 9788131730683

https://www.google.co.in/search?hl=en&gbpv=1&dq=Agile+Testing:+A+Practical+Guide+for+Testers+and+Agile+Teams.&printsec=frontcover&q=inpublisher:%22Pearson+Education%22&tbm=bks&sa=X&ved=2ahUKEwi0746iutbrAhWSzDgGHWa9CugQmxMoADAIegQIAxAC&sxsrf=ALeKk00oLdS5XPFtIWxDWRUxEbRkivV93g:1599461462168

https://www.google.co.in/search?hl=en&gbpv=1&dq=Agile+Testing:+A+Practical+Guide+for+Testers+and+Agile+Teams.&printsec=frontcover&q=inpublisher:%22Pearson+Education%22&tbm=bks&sa=X&ved=2ahUKEwi0746iutbrAhWSzDgGHWa9CugQmxMoADAIegQIAxAC&sxsrf=ALeKk00oLdS5XPFtIWxDWRUxEbRkivV93g:1599461462168



3 Agile Software Development, Principles, Patterns and Practices.

Robert C. Martin 1st Edition, Pearson, 2011, ISBN:9780132760584, 0132760584

Reference Books:

Sl No

Title Author(s) Edition, Publisher, Year, ISBN

1 Agile Software Development: The Cooperative Game

Alistair Cockburn 2nd Edition, Addison Wesley,2006, ISBN:9780321630070, 0321630076

2 User Stories Applied: For Agile Software

Mike Cohn 1st Edition, Addison Wesley,2004, ISBN:9780321205681,

0321205685

Course Outcomes:

Course outcome

Descriptions

CO1 Interpret the business values of adopting Agile approaches to Software Development.

CO2 Apply agile development practices, design principles and refactoring to achieve agility.

CO3 Deploy automated build tools, version control and continuous integration.

CO4 Perform various testing activities within an Agile project.





Subject Name: SOFTWARE PROJECT MANAGEMENT


Course Objectives:


1 Understand the Software Project Planning and Evaluation techniques to manage projects at each stage of the software development life cycle (SDLC).

2 Learn the concepts of activity planning and risk management principles.

3 Manage software projects and control software deliverables.

4

Develop skills to manage the various phases involved in project management and

people management, to deliver successful software projects that support

organization strategic goals.


I

Project Evaluation and Project Planning:

Importance of Software Project Management – Activities Methodologies –

Categorization of Software Projects – Setting objectives – Management

Principles – Management Control – Project portfolio Management – Cost

benefit evaluation technology – Risk evaluation – Strategic program

Management – Stepwise Project Planning.

8

II

Project Life Cycle and Effort Estimation:

Software process and Process Models – Choice of Process models - mental

delivery – Rapid Application development – Agile methods – Extreme

Programming – SCRUM – Managing interactive processes – Basics of

Software estimation – Effort and Cost estimation techniques – COSMIC Full

function points - COCOMO II A Parametric Productivity Model - Staffing

Pattern.

8

III

Activity Planning and Risk Management:

Objectives of Activity planning – Project schedules – Activities – Sequencing

and scheduling – Network Planning models – Forward Pass & Backward Pass

techniques – Critical path (CRM) method – Risk identification – Assessment –

Monitoring – PERT technique – Monte Carlo simulation – Resource

Allocation – Creation of critical patterns – Cost schedules.

8



IV

Project Management and Control:

Framework for Management and control – Collection of data Project

termination – Visualizing progress – Cost monitoring – Earned Value

Analysis-Project tracking–Change control-Software Configuration

Management – Managing contracts – Contract Management.

7

V

Staffing in Software Projects:

Managing people – Organizational behavior – Best methods of staff selection – Motivation – The Oldham-Hackman job characteristic model – Ethical and

Programmed concerns – Working in teams – Decision making – Team

structures – Virtual teams – Communications genres – Communication plans.

8

Text Book:

Sl

No

Title

Author

Volume and Year

of Edition

1 Software Project Management Bob Hughes, Mike

Cotterell andRajib

Mall:

Fifth Edition, Tata

McGraw Hill,New

Delhi, 2012.

Reference Books:

Sl No

Title

Author


1 “Effective Software Project Management”

Robert K. Wysocki Wiley Publication, 2011.

2 “Software Project Management” Walker Royce Addison-Wesley,

1998.

3 “Managing Global Software Projects” Gopalaswamy

Ramesh

McGraw Hill

Education (India),

FourteenthReprint 2013.

Course Outcomes:

Course outcome

Descriptions

CO1 Analyze Project Management principles, project management concepts, and framework and software effort estimation techniques.

CO2 Estimate the risks involved in various project activities.

CO3 Define the checkpoints, project reporting structure, project progress and tracking mechanisms using project management principles.

CO4 Plan staff selection process and the issues related to people management.





Subject Name: BLOCKCHAIN TECHNOLOGY


Course Objectives:


1 Understand the working of Blockchain systems.

2 Design, build, and deploy smart contracts and distributed applications.

3 Integrate ideas from Blockchain technology into their own projects.

4 Evaluate security, privacy, and efficiency of a given Blockchain system.


I

Introduction:

Background and history, what is Blockchain? Characteristics of Blockchain,

Do we really need a Blockchain? Applicability of Blockchain, Pros and cons

of Blockchain compared to centralized system.

8

II

Cryptography requirements for Blockchain Hash functions, Asymmetric key

cryptography -Encryption, Decryption, Digital Signature (ECDSA),

Blockchain Architecture - Transaction, Transaction types - UTXO, world state,

Addresses and Address derivation, Private key storage, Ledgers, Merkel trees,

Blocks, Chaining Blocks.

8

III

Blockchain Categorization - Permissonless, Permissioned, Forking issues in

Permissonless Blockchain, Applications considerations for Permissonless /

Permissioned Blockchain, smart contracts, Synchronous and Asynchronous

distributed system, Crash faults, Byzantine faults, Distributed Consensus

mechanism-properties.

8

IV

Consensus for synchronous system, OM and SM synchronous consensus

algorithms, PBFT, Consensus algorithms based on Proof of Work, Proof of

Stake, Proof of Elapsed Time, Round Robin, Proof of Knowledge, History of

Money, Dawn of Bitcoin, Crypto currency.

8

V

Blockchain platforms architecture, transaction-structure, life cycle, block creation,

smart contract, Bitcoin (BTC), Ethereum, Hyper Ledger, CORDA. 7

Reference Papers:

1. Andreas M Antonopoulos, Mastering Bitcoin, O’Reilly Media publications, First Edition,

ISBN: 978-1-449-37404-4, Dec. 2014. (only theory part must be taught, coding not

included).

2. Nancy A. Lynch, Distributed Algorithms, Elsevier, 2013.



3. Dylan Yaga, Peter Mell, Nik Roby Karen Scarfone , Blockchain Technology Overview-

NISTIR 8202.

4. Karl Wüst, Arthur Gervais, "Do you need a Blockchain?”

https://eprint.iacr.org/2017/375.pdf.

5. Ferguson, Niels, Schneier, Bruce Schneier , Practical Cryptography

6. Castro, M. and Liskov, B. 1999. Practical Byzantine Fault Tolerance. Proc. Usenix OSDI,

Berkeley, California.

7. Satoshi Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System,

https://bitcoin.org/bitcoin.pdf.

8. Elli Androulaki, Artem Barger, Vita Bortnikov, Christian Cachin, Kon-stantinos Christidis,

Angelo De Caro, David Enyeart, Christopher Ferris, Gennady Laventman, Yacov

Manevich, et al. Hyperledger fabric: A distributed operating system for permissioned

blockchains. ArXiv, preprint arXiv:1801.10228, 2018.

9. Gavin, Wood, ETHEREUM: A Secure Decentralized Generalized Transaction Ledger,

https://ethereum.github.io/yellowpaper/paper.pdf.

10. Richard Gendal Brown, The Corda Platform: An Introduction,

https://www.corda.net/content/corda-platform-whitepaper.pdf.

Course Outcomes:

Course outcome

Descriptions

CO1 Describe the working of block chain technology and architectures.

CO2 Analyze different smart contracts and distributed applications.

CO3 Compare Synchronous and Asynchronous distributed system.

CO4 Analyze the design principles of Bitcoin and Ethereum.



Subject Name: MACHINE LEARNING LABORATORY

Subject Code: IS7L01 L-T-P-S-C: 0-0-3-0-1.5

Course Objectives:

Sl. No

Course Objectives

1

Make use of Data sets in implementing the machine learning algorithms

https://eprint.iacr.org/2017/375.pdf

https://bitcoin.org/bitcoin.pdf



2

Implement the machine learning concepts and algorithms in any suitable language of choice.

3

Understand and present the key algorithms and theory that form the core of machine learning.

4

Discuss how the learning performance varies with the number of training examples presented.

Lab

cycles Description

I

Description (If any):

1. The programs can be implemented in either JAVA or Python.

2. For Problems 1 to 6 and 10, programs are to be developed without using the built-in

classes or APIs ofJava/Python.

3. Data sets can be taken from standard repositories

(https://archive.ics.uci.edu/ml/datasets.html) or constructed by the students.

1. Implement and demonstrate the FIND-S algorithm for finding the most specific

hypothesis based on a given set of training data samples. Read the training data

from a .CSVfile.

2. For a given set of training data examples stored in a .CSV file, implement and

demonstrate the Candidate-Elimination algorithm to output a description of the

set of all hypotheses consistent with the training examples.

3. Write a program to demonstrate the working of the decision tree based ID3

algorithm. Use an appropriate dataset for building the decision tree and

apply this knowledge to classify a new sample.

II

4. Build an Artificial Neural Network by implementing the Backpropagation

algorithm and test the same using appropriate datasets.

5. Write a program to implement the naïve Bayesian classifier for a sample training

data set stored as a .CSV file. Compute the accuracy of the classifier, considering

few test datasets.

6. Assuming a set of documents that need to be classified, use the naïve Bayesian

Classifier model to perform this task. Built-in Java classes/API can

beusedtowritetheprogram.Calculatetheaccuracy,precision,andrecallfor

your data set.

https://archive.ics.uci.edu/ml/datasets.html



III

7. Write a program to construct a Bayesian network considering medical data. Use

this model to demonstrate the diagnosis of heart patients using standard Heart

Disease Data Set. You can use Java/Python ML libraryclasses/API.

8. Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same

data set for clustering using k-Means algorithm. Compare the results of these two

algorithms and comment on the quality of clustering. You can add Java/Python ML

library classes/API in the program.

9. Write a program to implement k-Nearest Neighbour algorithm to classify the iris

data set. Print both correct and wrong predictions. Java/Python ML library classes

can be used for this problem.

Implement the non-parametric Locally Weighted Regression algorithm in order to fit

data points. Select appropriate data set for your experiment and draw graphs.

Pattern for practical exam conduction:

Course Outcomes:

Course outcome

Descriptions

CO1 Understand the implementation procedures for the machine learning algorithms.

CO2 Design Java/Python programs for various Learning algorithms.

CO3 Apply appropriate data sets to the Machine Learning algorithms.

CO4 Identify and apply Machine Learning algorithms to solve real world problems.



Subject Name: WEB TECHNOLOGY LABORATORY

Subject Code: CS7L02 L-T-P-S-C: 0-0-3-0-1.5

a. Experiment Distribution:

For laboratories having only one part: Students are allowed to pick one experiment from

the lot with equal opportunity.

For laboratories having PART A and PART B: Students are allowed to pick one

experiment from PART A and one experiment from PART B, with equal opportunity.

b. Change of experiment is allowed only once and 20% of the maximum marks to be

deducted.



Course Objectives:


1 Design and develop static and dynamic web pages.

2 Know different Framework like PHP and JavaScript.

3

Familiarize with Client-Side Programming, Server-Side Programming.

4 Learn Database Connectivity to web applications.

Lab cycles

Description

I

1. Develop and demonstrate a HTML document that illustrates the use of external

style sheet, ordered list, table, borders, padding, color, and the tag.

2. Write a JavaScript to design a simple calculator to perform the following

operations: sum, product, difference and quotient.

3. Write a JavaScript that calculates the squares and cubes of the numbers from 0 to

10 and outputs XHTML text that displays the resulting values in an XHTML table

format.

4. Write a JavaScript code that displays text “TEXT-GROWING” with increasing

font size in the interval of 100ms in RED COLOR, when the font size reaches 50pt

it displays “TEXT-SHRINKING” in BLUE color. Then the font size decreases

to5pt.

5. Develop and demonstrate a XHTML file that includes JavaScript script that uses

functions for the following problems:

a. Parameter: A string b. Output: The position in the string of the left-most vowel

c. Parameter: A number d. Output: The number with its digits in the reverse order

6. Create a XHTML form with Name, Address Line2 and E-mail text fields. On

Submitting, store the values in MYSQL table. Retrieve and display the data based

on Name.



II

7. Write a PHP program to keep track of the number of visitors visiting the web page

and to display this count of visitors, with proper headings.

8. Write a PHP program to display a digital clock which displays the current time of

the server.

9. Write the PHP programs to do the following:

a. Find the transpose of a matrix b. Multiplication of two matrices c. Addition of

two matrices (Note: Students have to execute either a & c or b & c).

10. Write a PHP program to sort the student records based on USN which are stored in

the database.

III

Practice Programs:

1. Design an XHTML that uses CSS to illustrate the usage of: hover and: focus

pseudo classes.

2. Design an XHTML that uses CSS to illustrate the usage of Font and Color

properties and Text Decoration Elements.

3. Design an XHTML that uses CSS to test External Style Sheets.

4. Design an XHTML that uses CSS to illustrate usage of borders, margin, padding

and Background images.

5. Design an XML document to store information about a student in an engineering

college SSIT. The information must include USN, Name, and Name of the College,

Branch, Year of Joining, and email id. Make up sample data for 3 students. Create

a CSS style sheet and use it to display the document.

Pattern for practical exam conduction:

a. Experiment Distribution:

For laboratories having only one part: Students are allowed to pick one experiment from

the lot with equal opportunity.

For laboratories having PART A and PART B: Students are allowed to pick one

experiment from PART A and one experiment from PART B, with equal opportunity.

b. Change of experiment is allowed only once and 20% of the maximum marks to be

deducted.



Course Outcomes:

Course outcome

Descriptions

CO1 Design and develop dynamic web pages with good aesthetic sense of designing and latest technical know-how's.

CO2 Embed JavaScript and PHP into XHTML.

CO3 Apply Web Application Terminologies, Internet Tools and other web services.

CO4 Developing the Website and storing and retrieving the data dynamically.





Subject Name: PROJECT WORK PHASE – I

Subject Code: CS7PW01 L-T-P-S-C: 0-8-0-0-4

Description

Scheme of Evaluation

1. Students shall carry out a detailed survey on the Area and the Topic on

which they are interested to do the Project work. Students are expected to

prepare documentation and submit three different Synopses to the

Evaluation Committee.

2. Evaluation Committee will review the synopsis and suggest suitable area

for project. If project proposals are not to the expected standards or outdates

then recommend the students resubmit the refined synopsis.

3. Students are expected to give a detailed presentation on the Topic approved

and justify the panel members to start their project work.

4. Presentation consists of Basic Overview of the Project which includes

Introduction, Literature Survey, Problem Statement, Motivation,

Objectives, Requirement Analysis and Specification, Features of Existing

and Proposed System and Algorithms Selected.

Evaluation Scheme - I (50% percent of CIE):

Continuous evaluation will be done by respective Project Guides based on the

Regularity, Technical Knowledge and Competence, Programming Skills,

Communication Skills, Demonstration skills, Collaborative Learning and

Documentation Skills of the students.

Evaluation Scheme - II (50% percent of CIE):

Students are evaluated by the team of faculty members based on the

Presentation, Technical Competence, Slides Preparation, Team Working

Abilities, Questionnaires and overall Performance in the Seminar-1 and

Seminar-2 of Project Phase-I.

Course Outcomes:

Course Outcome

Descriptions

CO1 Identify the problem in the specified area by a literature survey.

CO2 Analyze the problem and identify the different modules/algorithms tosolve the problems.

CO3 Choose the platform to solve the selected problem.

CO4 Document and present the proposed plan of the project development.



Department: Information Science and Engineering

Semester: 8

Sl. No. Subject

Code Name of the Subject L T P S C

1 IS8T01 Cryptography, Network Security

and Cyber Law 4 0 0 0 4

2 CS8T02 Big Data and Analytics 3 0 0 1 4

3 IS8PE3XY Professional Elective - IV 3 0 0 0 3

4 IS8PE4XY Professional Elective – V 3 0 0 0 3

5 CS8PW02 Project Work Phase-II 2 4 12 0 10

6 CS8TS01 Technical Seminar 0 0 0 1 1

Total 15 4 12 2 25

Professional Elective – IV Credits: 3-0-0-0-3


IS8PE311 Information Retrieval

IS8PE312 Social Network Analysis

IS8PE313 Information Storage and Management

IS8PE314 Computer Vision and Robotics

Professional Elective – V Credits: 3-0-0-0-3


IS8PE421 Artificial Neural Networks

IS8PE422 Software Architecture and Design Pattern

IS8PE423 Wireless Sensor Networks

IS8PE424 Cloud Computing



Syllabus for the Academic Year – 2020-2021


Subject Name: CRYPTOGRAPHY, NETWORK SECURITY and CYBER LAW


Course Objectives:


1 Understand the fundamentals of cryptography and network security

2 Illustrate the key management issues and solutions.

3 Familiarize the cryptography and very essential algorithms.

4 Understand the concepts of cyber security and introduces cyber laws and ethics to be followed.


I

Introduction, Symmetric Ciphers: Introduction: The OSI Security Architecture, Security Attacks, Security Services, Security Mechanisms, A Model for Network

Security. Classical Encryption Techniques: Symmetric Cipher

Model, Substitution Techniques, Transposition Techniques,

Steganography. Block Cipher and the Data Encryption Standard:

Block Cipher principles, The Data Encryption Standard, The Strength of DES, Block Cipher Operation: Multiple Encryption and triple DES, Electronic Code Book, Cipher Block Chaining Mode, Cipher Feedback Mode, Output Feedback Mode, Counter Mode.

10

II

Number Theory and Public Key Cryptosystem:

Number Theory: Prime Numbers, Format's and Euler's Theorems,

Testing for Primality. Public-Key Cryptography and RSA: Principles of Public- Key Cryptosystems, The RSA Algorithm. Diffie-Hellman Key Exchange. Cryptographic Data Integrity Algorithms: Cryptographic Hash Functions, Applications of Cryptographic hash functions, Two simple hash Functions, Secure Hash Algorithm. Digital Signatures: Digital Signatures, Digital Signature Standard.

10

III

Key Management, Transport-Level Security: Key Management and Distribution: Symmetric Key distribution using symmetric encryption, Symmetric Key distribution using Asymmetric encryption, Distribution of public keys, X.509 Certificates, Kerberos. Transport level security: Web Security considerations, Secure Sockets Layer and Transport Layer Security.

10



IV

Internet Security, System Security: Electronic Mail Security: Pretty Good Privacy. IP Security: Overview, IP

Security Policy. Intruders: Intruders, Intrusion detection. Malicious Software: Types of Malicious Software Viruses. Firewalls: The need for Firewalls, Firewall Characteristics, Types of Firewalls.

10

V

Internet Law and Cyber Crimes: Internet and Need for Cyber Law, Modes of Regulation of internet, Types of Cyber Terror Capability, Net Neutrality, Types of Cyber Crimes, India and the Cyber Law, Cyber Crimes and ‘The Information Technology Act’, 2000, Internet Censorship, Cyber Crimes and Enforcement Agencies. IT act aim and objectives, Scope of the act, Major Concepts, Important provisions, Attribution, acknowledgement, and dispatch of electronic records, Secure electronic records and secure digital signatures, Regulation of certifying authorities: Appointment of Controller and Other officers, Digital Signature

certificates, Duties of Subscribers, Penalties and adjudication, The cyber regulations appellate tribunal, Offences, Network service providers not to beliable in certain cases, Miscellaneous Provisions.

12

Text Books:

Sl No

Title

Author


1 Cryptography and Network Security

William Stallings Fifth Edition, Prentice Hall of India, 2005.

2 Cryptography, Network Security andCyber Laws

Ber nard Menezes Cengage Learning, 2010 Edition

Reference Books:

Sl No


1 Network Security: Private

Communication in a Public

World,

Charlie Kaufman,

Radia Perlman,

Mike Speciner,

Second Edition,

Pearson,Education

Asia, 2002.

2 Cryptography and Network

Security

AtulKahate Tata McGrawHill,

2003

3 Cyber Security and Cyber Laws Alfred Basta,

Nadine Basta, Mary

Brown,

Ravindrakumar

Cengage Learning.



Course Outcomes:

Course Outcome

Descriptions

CO1

Define the security principles and understand the working of typical

symmetric and asymmetric ciphers.

CO2

Analyze and use cryptographic data integrity algorithms and user authentication protocols.

CO3

Apply effective cryptographic techniques for information security and

other applications.

CO4

Interpret the structure, mechanics and evolution of the internet in the

context of cyber-crimes and cyber laws.



Subject Name: BIG DATA AND ANALYTICS

Subject Code: CS8T02 L-T-P-S-C: 3-0-0-1-4

Course Objectives:

Sl. No

Course Objectives

1 To optimize business decisions and create competitive advantage with Big Data analytics.

2 To explore the fundamental concepts of big data analytics.

3 To understand the applications using Map Reduce Concepts.

4 To introduce programming tools PIG & HIVE in Hadoop echo system.


I

Getting an Overview of Big Data:

What is Big Data? History of Data Management-Evolution of Big Data,

Structuring Big Data-Types of Data, Elements of Data, Advantages of

Big Data Analytics Introducing Technologies for Handling Big Data

Distributed and Parallel Computing for Big Data, Introducing Hadoop,

Cloud Computing and Big Data: Cloud Delivery Models, Cloud

Services for Big Data, Cloud Providers in Big Data Market, In-Memory

Computing Technology for Big Data.

10



II

Big Data Analytics and Technology Landscape:

Where do we Begin? What is Big Data Analytics? What Big Data

Analytics Isn’t? Why this Sudden Hype Around Big Data Analytics?

Classification of Analytics, Greatest Challenges that Prevent

Businesses from Capitalizing on Big Data, Top Challenges Facing Big

Data, Why is Big Data Analytics Important? What Kind of

Technologies are we looking Toward to Help Meet the Challenges

Posed by Big Data? Data Science, Data Scientist...Your New Best

Friend!!! , Terminologies Used in Big Data Environments, Basically

Available Soft State Eventual Consistency (BASE) , Few Top Analytics

Tools.NoSQL (Not Only SQL) , Hadoop.

10

III

Introduction to Hadoop and MongoDB:

Introducing Hadoop, Why Hadoop? Why not RDBMS? RDBMS versus

Hadoop, Distributed Computing Challenges ,History of Hadoop ,

Hadoop Overview, Use Case of Hadoop ,Hadoop Distributors ,HDFS

(Hadoop Distributed File System),Processing Data with Hadoop,

Managing Resources and Applications with Hadoop YARN (Yet another

Resource Negotiator),Interacting with Hadoop Ecosystem .

Introduction to MongoDB: What is and Why MongoDB? Terms used in

RDBMS and MongoDB, Data types in MongoDB, MongoDB Query

language.

10

IV

Introduction to Cassandra and MAPREDUCE: Apache Cassandra, features, CQL data types, CQLSH, key spaces, CRUD, collections, TTL, using a counter, ALTER commands, import and export, query system tables. MAPREDUCE Programming: Mapper, Reducer, Combiner, Partitioner, Searching, Sorting, Compression.

11

V

Introduction to Hive and Pig: What is Hive? , Hive Architecture, Hive Data Types, Hive File Format, Hive Query Language (HQL), RCFile Implementation, SerDe, and User-defined Function (UDF). What is Pig? The Anatomy of Pig, Pig on Hadoop , Pig Philosophy, Use Case for Pig: ETL Processing, Pig Latin Overview , Data Types in Pig ,Running Pig, Execution Modes of Pig ,HDFS Commands ,Relational Operators, Eval Function, Complex Data Types ,Piggy Bank, User- Defined Functions (UDF) ,Parameter Substitution , Diagnostic Operator , Word Count Example using Pig ,When to use Pig? When not to use Pig? Pig at Yahoo!, Pig versus Hive.

11

Text Books:

Sl No

Title Author(s) Edition, Publisher, Year, ISBN

1 Big Data: Black Book

Dt Editorial Services 1st Edition, Dream tech Press, 2016, ISBN -13:9789351197577

2 Big Data and Analytics

Seema Acharya, Subhashini Chellappan

1st Edition, Wiley India 2015, ISBN:978-81-265-5478-2



Reference Books:

Sl No


1 Hadoop in Practice Alex Holmes 2nd Edition, Manning

Publications Co., January 2015, ISBN-13:978-9351197423

2 Programming Pig Alan Gates 2nd Edition, O'Reilly Media,

2017, ISBN-978-1-491-93709-9

3 Programming Hive Dean Wampler 1st Edition, O'Reilly Media, 2012,

ISBN:978-1-449-31933-5

Course Outcomes:

Course Outcome

Descriptions

CO1 Identify the characteristics of datasets and compare the trivial data and big data for various applications.

CO2 Demonstrate the concept to interact with Big data using open source software called Hadoop and supporting tools.

CO3 Comparative study of traditional tools with Big data programming tools.

CO4 Outline the concepts of analytics in Big data.



Subject Name: INFORMATION RETRIEVAL


Course Objectives:


1 Learn the information retrieval situations for text and hyper media.

2 Understand how to store, and retrieve information from www using semantic approaches.

3 Familiar with the usage of data/file structures in building computational

search engines.

4

Analyze the performance of information retrieval using advanced techniques such as classification, clustering, and filtering over multimedia.




I

Introduction: Introduction -History of IR- Components of IR - Issues –Open source Search engine Frameworks - The impact of the web on IR - The role of artificial intelligence (AI) in IR – IR Versus Web Search - Components of a Search engine- Characterizing the web.

7

II

Information Retrieval:

Boolean and vector-space retrieval models- Term weighting - TF-IDF weighting- cosine similarity – Preprocessing - Inverted indices - efficient processing with sparse vectors – Language Model based IR - Probabilistic IR – Latent Semantic Indexing - Relevance feedback and query expansion.

7

III

Web Search Engine – Introduction and Crawling:

Web search overview, web structure, the user, paid placement, search engine optimization/ spam. Web size measurement - search engine optimization/spam– Web Search Architectures - crawling - meta-crawlers- Focused Crawling - web indexes –- Near-duplicate detection- Index Compression - XML retrieval.

8

IV

iSTA0052T: Evaluation of Feedback Systems, Textual Signatures: Identifying Text-Types Using Latent Semantic Analysisto Measure the Cohesion of Text Structures: Introduction, Cohesion, Coh-Metrix, Approaches to Analyzing Texts, Latent Semantic Analysis, Predictions, Results of Experiments. Automatic Document Separation: A Combination of Probabilistic Classification and Finite-State Sequence Modeling: Introduction, Related Work, Data Preparation, Document Separation as a Sequence Mapping Problem, Results. Evolving Explanatory Novel Patterns for Semantically-Based Text Mining: Related Work, A Semantically GuidedModel for Effective Text Mining.

8

V

Information Retrieval and Lexical Resources: 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.

9

Text Books:

Sl No


Edition

1 “Natural Language Processing and Information Retrieval”

Tanveer Siddiqui,

U.S. Tiwary,

OxfordUniversity Press, 2008.

2 “Natural Language Processing and Text Mining”

Anne Kao and Stephen R. Poteet (Eds),

Springer-Verlag London Limited 2007

Reference Books:



Sl No


Edition

1

“Speech and Language Processing: An introduction to Natural Language Processing, Computational Linguistics and Speech Recognition”

Daniel Jurafsky and James H Martin,

2nd Edition, Prentice Hall, 2008.

2 “Natural Language Understanding”

James Allen 2ndEdition, Benjamin /Cummings publishing company,1995.

3 “Information Storage and Retrieval systems”

Gerald J. Kowalski and Mark. T. Maybury

Kluwer academic Publishers, 2000.

Course Outcomes:

Course Outcome

Descriptions

CO1 Apply information retrieval models.

CO2 Design Web Search Engine.

CO3 Make use of Link Analysis and apply document text mining techniques.

CO4 Apply Hadoop and Map Reduce techniques.



Subject Name: SOCIAL NETWORK ANALYSIS


Course Objectives:

Sl.

No Course Objectives

1 Understand the concept of semantic web and related applications.

2 Learn knowledge representation using ontology.

3 Understand human behavior in social web and related communities.

4 Learn visualization of social networks.




I

The Semantic Web and Social Networks:

Introduction to Semantic Web: Limitations of current Web – Development of Semantic Web -Emergence of the Social Web – Social Network analysis: Development of Social Network Analysis- Key concepts and measures in network analysis.

8

II

Semantic Technology for Social Network Analysis:

Electronic sources for network analysis: Electronic discussion networks, Blogs and online communities – Web-based networks-Ontology-based knowledge Representation –Resource Description Framework – Web Ontology Language- Modeling and aggregating social network data: State-of-the-art in network data representation - Ontological representation of social individuals –Ontological representation of social relationships - Aggregating and reasoning with social network data.

8

III

Extraction and Mining Communities in Web Social Networks: Detecting communities in social networks – Definition of community – Evaluating communities – Methods for community detection and mining – Applications of community mining algorithms – Tools for detecting communities - social network infrastructures and communities – Decentralized online social networks – Challenges of DOSNs - General Purpose DOSNs.

8

IV

Predicting Human Behavior and Privacy Issues: Understanding and predicting human behavior for social communities – User data management, Inference and Distribution – Enabling new human experiences – The Technologies - Privacy in online social networks – Trust in online environment – Trust models based on subjective logic – Trust network analysis – Trust transitivity analysis – Combining trust and reputation – Trust derivation based on trust comparisons.

8

V

Visualization and Applications of Social Networks: Graph theory – Centrality – Clustering – Node-Edge Diagrams – Matrix representation – Visualizing online social networks, Visualizing social

networks with matrix-based representations – Matrix and Node-Link Diagrams– Hybrid representations – Applications – Cover networks – Community welfare -Collaboration networks – Co-Citation networks.

7

Text Books: NIL

Reference Books:

Sl No

Title Author Volume and Year

of Edition

1

“Social Networks and the Semantic Web”

Peter Mika First Edition, Springer 2007

2

“Handbook of Social Network Technologies and Applications”

BorkoFurht, 1st,Edition, Springer, 2010.



3

“Web Mining and Social Networking – Techniques and applications”

GuandongXu,Yanch

un Zhang and Lin

Li,

First Edition Springer, 2011.

4

“Social information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively”

Dion Goh and Schubert Foo

IGI Global Snippet, 2008

5

“Collaborative and Social Information Retrieval and Access: Techniques for Improved user Modelling”

Max Chevalier, Christine Julien andChantal Soulé-Dupuy

IGI Global Snippet, 2009.

6 “The Social Semantic Web” John G.Breslin,

Alexander Passant and Stefan Decker

Springer, 2009.

Course Outcomes:

Course

Outcome Descriptions

CO1 Develop semantic web related applications.

CO2 Represent knowledge using ontology.

CO3 Predict human behavior in social web and related communities.

CO4 Visualize social networks.



Subject Name: INFORMATION STORAGE AND MANAGEMENT


Course Objectives:

Sl.


1

Learn about the various storage infrastructure components in data center environments

2

Familiarize in making decisions on storage-related Technologies in an increasingly complex IT environment.

3

Understand the storage technologies, architectures, features, and benefits of intelligent storage systems

4

Exposed to block-based, file-based, object-based, unified storage and software-defined storage.




I

Introduction to Information Storage, Data center Environment: Information Storage, Evolution of Storage Architecture Data Center Infrastructure, Virtualization and Cloud computing(1.1 to 1.4), Application, Database Management systems, Host, Connectivity(2.1 to 2.4).

8

II

Data Protection: RAID, Intelligent Storage System:\RAID implementation methods, RAID array components, RAID Techniques, RAID, Levels, RAID impact on Disk Performance, RAID comparison, Hot spares(3.1 to 3.7), Components of an Intelligent Storage System, Storage Provisioning, Types of Intelligent Storage Systems(4.1 to 4.3).

8

III

Fibre Channel Storage Area Networks: Fibre Channel overview, The SAN and its evolution, Components of SAN, FC connectivity, Switched

Fabric Ports, Fibre Channel Architecture, Fabric Services, Switched Fabric Login Types, Zoning, FC SAN Topologies, Virtualization and SAN(5.1 to 5.11).

7

IV

IP SAN, FCoE and NAS: Network Attached Storage iSCSI(internet Small Computer System Interface), FCIP(Fibre Channel over Internet Protocol), FCoE(Fibre Channel over Ethernet)(6.1 to 6.3), General purpose servers versus NAS devices, Benefits of NAS, File Systems and Network File Sharing, Components of NAS, NAS I/O Operation, NAS Implementations, NAS File Sharing Protocols, Factors Affecting NAS Performance, File Level Virtualization(7.1 to 7.9).

8

V

Introduction to Business Continuity and Backup and archive: Information Availability, BC Terminology, BC Planning Life Cycle, failure Analysis, Business Impact Analysis, BC Technology Solutions (9.1 to 9.6), backup Purpose, backup Considerations, backup Granularity, recovery Considerations, Backup Methods, Backup Architecture, Backup and Restore Operations, Backup Topologies, Backup in NAS Environments.(10.1 to 10.9)

8

Text Book:

Sl No


1 Information Storage and Management

G.Somasundaram, Alok Shrivastava

EMC Education

Services, Wiley- India, Second Edition.

Reference Books:

Sl No


1 Storage Networks Explained Ulf Troppens,

RainerErkes and Wolfgang Muller

Wiley India, 2003.



2 Storage Networks, The Complete Reference.

Rebert Spalding Tata McGraw Hill, 2003

3 Storage Area Networks Essentials A

Complete Guide to Understanding and Implementing SANs

Richard Barker and Paul Massiglia

Wiley India, 2002

Course Outcomes:

Course

Outcome

Descriptions

CO1 Understand Storage Area Networks characteristics and Architectures.

CO2 Explain Storage Network Technologies and Virtualization.

CO3 Analyze the Securing and Managing of Storage Infrastructure.

CO4 Configure and Simulate Storage Area Network Technologies.



Subject Name: COMPUTER VISION AND ROBOTICS


Course Objectives:


1 Review image processing techniques for computer vision

2 Explain shape and region analysis.

3 Illustrate Hough Transform and its applications to detect lines, circles,

ellipses.

4 Contrast three-dimensional image analysis techniques, motion analysis

and applications of computer vision algorithms.


I

CAMERAS: Pinhole Cameras, Radiometry – Measuring Light: Light in Space, Light Surfaces, Important Special Cases, Sources, Shadows, And Shading: Qualitative Radiometry, Sources and Their Effects, Local Shading Models, Application: Photometric Stereo, Inter reflections: Global Shading Models, Color: The Physics of Color, Human Color Perception, Representing Color, A

Model for Image Color, Surface Color from Image Color.

8



II

Linear Filters:

Linear Filters and Convolution, Shift Invariant Linear Systems, Spatial

Frequency and Fourier Transforms, Sampling and Aliasing, Filters as

Templates, Edge Detection: Noise, Estimating Derivatives, Detecting

Edges, Texture: Representing Texture, Analysis (and Synthesis) Using

Oriented Pyramids, Application: Synthesis by Sampling Local Models,

Shape from Texture.

7

III

The Geometry of Multiple Views:

Two Views, Stereo sis: Reconstruction, Human Sternposts, Binocular Fusion, Using More Cameras, Segmentation by Clustering: What Is Segmentation?, Human Vision: Grouping and Gestalt, Applications: Shot Boundary Detection and Background Subtraction, Image Segmentation by Clustering Pixels, Segmentation by Graph-Theoretic Clustering,

8

IV

Segmentation by Fitting a Model: The Hough Transform, Fitting

Lines, Fitting Curves, Fitting as a Probabilistic Inference Problem,

Robustness, Segmentation and Fitting Using Probabilistic Methods:

Missing Data Problems, Fitting, and Segmentation, The EM Algorithm

in Practice, Tracking With Linear Dynamic Models: Tracking as an

Abstract Inference Problem, Linear Dynamic Models, Kalman Filtering,

Data Association, Applications andExamples.

8

V

Geometric Camera Models: Elements of Analytical Euclidean

Geometry, Camera Parameters and the Perspective Projection, Affine

Cameras and Affine Projection Equations, Geometric Camera

Calibration: Least-Squares Parameter Estimation, A Linear Approach

to Camera Calibration, Taking Radial Distortion into Account,

Analytical Photogrammetry, An Application: Mobile Robot Localization,

Model- Based Vision: Initial Assumptions, Obtaining Hypotheses by

Pose Consistency, Obtaining Hypotheses by pose

Clustering, Obtaining Hypotheses Using Invariants, Verification,

Application: Registration In Medical Imaging Systems, Curved Surfaces

and Alignment.

8

Text Books:

Sl No


1

Computer Vision – A Modern Approach

David A. Forsyth andJean Ponce

PHI Learning (IndianEdition), 2009.

Reference Books:

Sl

No Title Author

Volume and Year

of Edition

1 Computer and Machine Vision– Theory, Algorithms and Practicalities

E. R. Davies Elsevier(Academic Press), 4thedition, 2013.



Course Outcomes:

Course


CO1 Implement fundamental image processing techniques required for Computer vision.

CO2 Perform shape analysis.

CO3 Implement boundary tracking techniques.

CO4 Apply chain codes and other region descriptors.



Subject Name: ARTIFICIAL NEURAL NETWORKS


Course Objectives:


1 Perceive the basic concepts of ANN, applications and learning techniques.

2 Explain the working of perceptron and multilayer perceptron and

related learning algorithms.

3 Gain essential knowledge on convolution neural networks and

applications.

4 Explore structured probabilistic models for deep learning.


I

Artificial Neural Networks – Introduction and Learning Process-I: What is a Neural Network? Human Brain, Models of a Neuron, Neural Networks Viewed as DG, Feedback, Network Architectures, Error-correction learning, Memory-based learning, Hebbian Learning, Competitive learning, Boltzmann Learning.

7

II

Learning Process-II and Perceptron:

Learning with a teacher, learning without a teacher, Learning tasks,

Memory and adaptation. Statistical Learning Theory, VC dimension,

Probably approximately correct model of learning, Single-Layer

Perceptrons: Adaptive filtering problem, Unconstrained optimization

techniques: Steepest Descent, Newton’s, Gauss-Newton; Linear Least-

Squares Filter, LMS algorithm, Learning curves, Learning rate

annealing techniques, Perceptron and Convergence theorem.

8



III

Multilayer Perceptron and Generalization:\ BP algorithm, Two passes of computation, Sequential and Batch

Modes of training, Stopping Criteria, XOR problem, Heuristics for BP algorithm to perform better, Output representation and Decision rule, Generalization, Universal approximation theorem, Cross-validation.

8

IV

Convolution Networks: Convolution Operation, Motivation, Pooling, Convolution and Pooling as an Infinitely Strong Prior, Variants of the basic convolution function, Structured Outputs, Data types, Efficient Convolution Algorithms, Random or Unsupervised features, The Neuroscientific basis for convolutional networks.

8

V

Structured Probabilistic Models for Deep Learning: The challenge of unstructured modeling, Using graphs to describe model structure: Directed, Undirected, Partition function, Energy-

based models, Factor graphs; Sampling from graphical models, Advantages of structured modeling, learning about dependencies, Inference and approximate inference, The deep learning approach to structured probabilistic models.

8

Text Books: NIL

Reference Books:

Sl No


1 Neural Networks – A Comprehensive Foundation

Simon Haykin 2nd Edition,2005. PHI, (Units I to III).

2 Deep Learning (Adaptive Computation and Machine Learning Series)

Ian Good fellow, YoshuaBengioand Aaron Courville

(3 January 2017), MIT Press, ISBN-13: 978- 0262035613.

3 Introduction to Artificial Neural Networks

Gunjan Goswami 2012 Edition, S.K.Kataria & Sons; ISBN-13: 978-9350142967.

4 Fundamentals of Deep

Learning: Designing Next-Generation Machine Intelligence Algorithms

Nikhil Buduma 2016 Edition,byO’Reilly

Publications, ISBN- 13:9781491925614.

Course Outcomes:

Course Outcome

Descriptions

CO1 Describe basic concepts of neural network, its applications and various

learning models.

CO2 Analyze different Network Architectures, learning tasks, convolutional

networks, and deep learning models.

CO3 Investigate and apply neural networks model and learning techniques

to solve problems related to society and industry.

CO4 Demonstrate a prototype application developed using any NN tools and

APIs.





Subject Name: SOFTWARE ARCHITECTURE AND DESIGN PATTERNS


Course Objectives:

Sl.


1 Learn How to add functionality to designs while minimizing

complexity.

2 Understand the code qualities required to maintain to keep code flexible.

3 To understand the common design patterns.

4 To explore the appropriate patterns for design problems.


I

Introduction: What is a design pattern? Describing design patterns,

the catalog of design pattern, organizing the catalog, how design

patterns solve design problems, how to select a design pattern, how to

use a design pattern. What is object- oriented development? , key

concepts of object oriented design other related concepts, benefits and

drawbacks of the paradigm.

8

II

Analysis a System: overview of the analysis phase, stage 1: gathering the requirements functional requirements specification, defining conceptual classes and relationships, using the knowledge of the domain. Design and implementation, discussions and further reading.

8

III

Design Pattern Catalog: Structural patterns, Adapter, bridge, composite, decorator, facade, flyweight, proxy.

7

IV

Interactive systems and the MVC architecture: Introduction , The MVC architectural pattern, analyzing a simple drawing program , designing the system, designing of the subsystems, getting into implementation , implementing undo operation , drawing incomplete items, adding a new feature , pattern based solutions.

7

V

Designing with Distributed Objects: Client server system, java remote method invocation, implementing an object oriented system on the web (discussions and further reading) a note on input and output, selection statements, loops arrays.

8

Text Books:



Sl No


1 Object-oriented analysis, design and

implementation

Brahma Dathan,

Sarnathrammath,

Universities Press,

2013.

2 Design patterns, erich gamma Richard helan,

Ralphjohman,johnvl

issides

PEARSON

Publication, 2013.

Reference Books:

Sl No


1

“Pattern Oriented Software

Architecture”

Frank Bachmann,

Regine Meunier,

Hans Rohnert

Volume 1, 1996.

2

"Anti-Patterns: Refactoring

Software, Architectures and Projects

in Crisis"

William J Brown et

al.

John Wiley, 1998.

Course Outcomes:

Course Outcome

Descriptions

CO1 Design and implement codes with higher performance and lower

complexity.

CO2 Aware of code qualities needed to keep code flexible.

CO3 Experience core design principles and be able to assess the quality of a

design with respect to these principles.

CO4 Capable of applying these principles in the design of object oriented

systems.


Department: Information Science and Engineering Semester: 8th

Subject Name: WIRELESS SENSOR NETWORKS


Course Objectives:


1 Understand the basic WSN technology and supporting protocols, basic

sensor systems and provide a survey of sensor technology.



2 Understand the medium access control protocols and address physical layer issues.

3 Learn key routing protocols for sensor networks and main design issues.

4 Learn transport layer protocols for sensor networks, and design

requirements.


I

Introduction: Unique Constraints and Challenges, Advantages of Sensor Networks - Energy advantage and Detection advantage, Sensor Network Applications - Habitat monitoring, Wildlife conservation through autonomous, non-intrusive sensing, Tracking chemical plumes, Ad hoc, just-in-time deployment mitigating disasters, Smart

Transportation: networked sensors making roads safer and less congested, Collaborative Processing, Key Definitions of Sensor Networks, Canonical Problem: Localization and Tracking: - Tracking Scenario, Problem Formulation - Sensing model, Collaborative localization, Bayesian state estimation.

8

II

Canonical Problem: Localization and Tracking contd..

Distributed Representation and Inference of States, Impact of choice

of representation, Design in Distributed Tracking, Tracking Multiple

Objects, State Space Decomposition, Data association, Sensor Models,

Performance Comparison and Metrics. Networking Sensors: - Key

Assumptions, Medium Access Control - The SMAC Protocol, IEEE

802.15.4 Standard and ZigBee, General Issues.

8

III

Networking Sensors contd..

Geographic-Energy-Aware Routing, Unicast Geographic Routing,

Routing on a Curve, Energy-Minimizing Broadcast, Energy- Aware

Routing to a Region, Attribute-Based Routing – Directed Diffusion,

Rumor Routing, Geographic Hash Tables. Infrastructure

Establishment: - Topology Control, Clustering, Time Synchronization -

Clocks and Communication Delays, Interval Methods, Reference

Broadcasts.

7

IV

Infrastructure Establishment contd.. Localization and Localization Services - Ranging Techniques, Range-Based Localization Algorithms, Other Localization Algorithms, Location Services. Sensor Tasking and Control: - Task-Driven Sensing, Roles of Sensor Nodes and Utilities, Information Based Sensor Tasking - Sensor Selection, IDSQ: Information-Driven Sensor Querying, Cluster Leader Based Protocol, Sensor Tasking in Tracking Relations.

8



V

Sensor Tasking and Control contd.. Joint Routing and Information Aggregation – Moving Center of

Aggregation, Multistep Information- Directed Routing, Sensor Group Management. Sensor Network Platforms and Tools: Sensor Node Hardware – Berkeley Motes, Sensor Network Programming Challenges, Node-Level Software Platforms, Operating system: Tiny OS, Imperative language: nesC, Dataflow style language: Tiny GALS, Node-Level Simulators, The NS-2 Simulator and its Sensor Network Extensions, The Simulator TOSSIM.

8

Text Book:

Sl No


1 Wireless Sensor Networks – An

Information ProcessingApproach,

Feng Zhao,

Leonidas Guibas

Elsevier, 2004.

Reference Books:

Sl No


1 “Protocols and Architectures

for Wireless Sensor Networks”

Holger Karl, Andreas

Willig

John Wiley &

Sons, Inc., 2005.

2

“Ad Hoc Mobile Wireless Networks” Subir Kumar Sarkar,

T G Basavaraju, C

Puttamadappa,

Auerbach

Publications, 2008.

Course Outcomes:

Course


CO1 Identify different issues in wireless sensor networks and its

applications.

CO2 Capable of analyzing the protocols developed for sensor networks.

CO3 Design sensor networks using sensor tasking and controls.

CO4 Understand about various tools used for simulating sensor networks.





Subject Name: CLOUD COMPUTING


Course Objectives:


1 Provide comprehensive view to different aspects of cloud computing like;

service models, Deployment models and challenges.

2 Introduce to cloud virtualization, with different type of virtualization and

capacity planning metrics to clouds.

3 To know the concrete concepts of cloud security and their standards.

4 Contrast how Service oriented Architecture principles is helpful in Cloud

Computing.


I

Examining the value proposition: Cloud Types, The NIST model, The Cloud Cube Model, Deployment models, Service models, Examining the Characteristics of Cloud Computing, Paradigm shift, Benefits of cloud computing, Disadvantages of cloud computing; Assessing the value proposition: Early adopters and new applications, the laws of cloudonomics, cloud computing obstacles, behavioral factors relating to

cloud adoption, measuring cloud computing costs, specifying SLAs.

8

II

Continuation of Examining, the value Proposition: Understanding Cloud Architecture: Exploring the Cloud Computing Stack, Composability, Infrastructure, Platforms, Virtual Appliances, Communication Protocols; Understanding Services and Applications

by Type: Defining IaaS, Defining PaaS, Defining SaaS, Defining IDaaS.

8

III

Understanding Platform:

Using Virtualization Technologies, Load balancing and Virtualization, Understanding Hypervisors; Capacity Planning: Defining Baseline and Metrics, Baseline measurements, System metrics, Load testing, Resource ceilings, Server and instance types, Network Capacity, Scaling.

8

IV

Exploring Cloud Infrastructure:

Securing the Cloud, The security boundary, Security service boundary, Security mapping, Securing Data, Brokered cloud storage access, Storage location and tenancy, Encryption, Auditing and compliance, Establishing Identity and Presence, Identity protocol

standards, Windows Azure identity standards.

7



V

Understanding Services and Applications: Understanding Service Oriented Architecture: Introducing Service

Oriented Architecture, Event-driven SOA or SOA 2.0, The Enterprise Service Bus, Service catalogs, Defining SOA Communications, Business Process Execution Language, Business process modeling, Managing and Monitoring

SOA, SOA management tools , SOA security , The Open Cloud

Consortium, Relating SOA and Cloud Computing.

8

Text Book:

Sl No


1 “Cloud Computing Bible” Barrie Sosinsky Wiley Publishing Inc.

2011 (free e- book available).

Reference Books:

Sl

No

Title

Author


1 Cloud Computing and SOA Convergence in YourEnterprise: David S.

Linthicum

A Step-by-Step

Guide(freee-bookavailable)

2 “Distributed and Cloud

Computing – From Parallel

Processing to the Internetof

Things”

Kai Hwang,

Geoffrey

C. Fox, and Jack

J. Dongarra,

Morgan Kaufman

Publishers,2012.

3 Enterprise Cloud Computing

Technology Architecture Applications

Gautam Shroff (free e-bookavailable)

4 Cloud Computing, A Practical

Approach

Toby Velte,

Anthony

Velte,Robert

Elsenpeter

(free e-book

available)

Course Outcomes:

Course Outcome

Descriptions

CO1 Define Cloud computing and characteristics and various types of cloud

services.

CO2 Describe benefits and drawbacks of Cloud computing.

CO3 Explain various types of virtualization and capacity planning metrics.

CO4 Discuss Cloud Security and various challenges, SOA and various issues.





Subject Name: PROJECT WORK PHASE – II

Subject Code: CS8PW02 L-T-P-S-C: 2-4-12-0-10

Description

Scheme of Evaluation

1. Students shall present on the System Design Phase which includes System

Architecture, High Level Design, Low Level Design, System Models, System

Modules, Implementation Tools used and Algorithms used and implemented.

2. Final seminar on the complete project is presented by thestudents.

Project Phase - II Demonstration

Students have to demonstrate the working model of the Project to their respective

guides.

Evaluation Scheme-I (50% percent of CIE):

Continuous evaluation will be done by respective Project Guides based on the

Regularity, Technical Knowledge and Competence, Programming Skills,

Communication Skills, Demonstration skills, Collaborative Learning and

Documentation Skills of the students.

Evaluation Scheme II (50% percent of CIE):

Students are evaluated by the team of faculty members based on the

Presentation, Technical Competence, Slides Preparation, Team Working Abilities,

Questionnaires and overall Performance in the Seminar-1 and Seminar-2 of

Project Phase-I.

Students are required to meet their respective project guides on a stipulatedday

once in a week and update their progress and get signature from the guides

without fail.

Course Outcomes:

Course Outcome

Descriptions



CO1 Design a suitable system to solve the problem identified in project work

phase I and plan to work as a team.

CO2 Implement the design using necessary algorithms and by incorporating the necessary suggestions, if any.

CO3 Test the performance of the system with suitable data and demonstrate the project.

CO4 Document the project work and present the work carried out to the

audience.





Subject Name: TECHNICAL SEMINAR

Subject Code: CS8TS01 L-T-P-S-C: 0-0-0-1-1

Description

Guidelines for preparing Technical Seminar

1. Selection of topic/area:

Select a paper according to the specialization of students. Papers from

any other approved journals can also be selected.

2. Approval to the selected topic:

After selecting the paper, get approval from the concerned faculty in charge.

3. Study of topic:

Students are requested to acquire a thorough knowledge on the subject by referring back papers and reference books (These may be included as references at the end of the paper) on the corresponding area.

4. Seminar:

Final seminar is presented by the students through slides.

Course Outcomes:

Course Outcome

Descriptions

CO1 Survey the changes in the technologies relevant to the topic selected.

CO2

Discuss the technology and interpret the impact on the society, environment and domain.

CO3 Compile report of the study and present to the audience.