department of information science & engineering viii … syllabus.pdf · information science...

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

Information Science and Engineering

DEPARTMENT OF INFORMATION SCIENCE & ENGINEERING

VIII SEMESTER

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

Subject Code Name of the Subject

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

Subject Code Name of the Subject

IS8PE421 Artificial Neural Networks

IS8PE422 Software Architecture and Design Pattern

IS8PE423 Wireless Sensor Networks

IS8PE424 Cloud Computing



Syllabus for the Academic Year – 2019 - 2020

Department: Information Science and Engineering Semester: 8th

Subject Name: CRYPTOGRAPHY, NETWORK SECURITY and CYBER LAW

Subject Code: IS8T01 L-T-P-S-C: 4-0-0-0-4

Course Objectives:

Course Outcomes:

Sl. No

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.

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.



UNIT Description Hours

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 be

liable in certain cases, Miscellaneous Provisions.

12



Question paper Pattern:

From each unit, two questions of 20 marks each have to be given. The student has to

answer one full question of his/her choice.

Text Books:

Sl No

Text Book title Author Volume and Year of Edition

1 Cryptography and Network Security

William Stallings Fifth Edition, Prentice

Hall of India, 2005.

2 Cryptography, Network Security and

Cyber Laws

Ber nard Menezes Cengage Learning, 2010

Edition

Reference Book:

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,

ravindra kumar

Cengage learning.





Subject Name: BIG DATA AND ANALYTICS

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

Course Objectives:

Course Outcomes:

Sl. No

Course Objectives

1 Understand the Big Data Platform and its Use cases.

2 Introduce students the concept and challenge of big data.

3 Provide HDFS Concepts and Interfacing with HDFS.

4

Teach students in applying skills and tools to manage and

analyze the big data.

Course Outcome

Descriptions

CO1

Identify the characteristics of datasets and compare the trivial data and big

data for various applications.

CO2

Demonstrate an open source software framework called Hadoop and

supported tool to empower any meaningful conversation on big data and

analytics.

CO3

Compare and Contrast different Hadoop supporting tools with traditional

tool.

CO4

How Big Data can be analyzed to extract knowledge and apply tools for

big data analytics.




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.

8

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.

8

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.

8

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.

7

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.

8






Text Books:

Sl No


1 Big Data: Black Book: Dt Editorial Services Dreamtech Press,

Edition 2016

(Chapter 1).

2 Big Data and Analytics Seema Acharya,

Subhashini

Chellappan

Infosys Limited,

Publication:Wiley

India Private

Limited,1st Edition

2015.

Reference Book:

Sl No


1 Hadoop in Practice Alex Holmes Manning

Publications Co.,

September 2014, 2nd

Edition.

2 Programming Pig Alan Gates O’Reilly, Kindle

Publication.

3 Programming Hive, , Dean Wampler O’Reilly, Kindle

Publication





Subject Name: INFORMATION RETRIEVAL

Subject Code: IS8PE311 L-T-P-S-C: 3-0-0-0-3

Course Objectives:

Course Outcomes:

Sl. No

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.

Course outcome

Descriptions

CO1 Apply information retrieval models.

CO2 Design Web Search Engine.

CO3 Use Link Analysis and apply document text mining techniques.

CO4 Use Hadoop and Map Reduce




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 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


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 Book:

Sl No


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.





Subject Name: SOCIAL NETWORK ANALYSIS


Course Objectives:

Course Outcomes:

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.

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.




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 Book:

Sl No


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

,Yanchun 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 and

Chantal Soulé-Dupuy

IGI Global Snippet,

2009.

6

“The Social Semantic Web”

John G. Breslin,

Alexander Passant

and Stefan Decker

Springer, 2009.





Subject Name: INFORMATION STORAGE AND MANAGEMENT


Course Objectives:

Course Outcomes

Sl. No

Course Objectives

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.

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.




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 Books:

Sl No


1 Information Storage and Management

G. Somasundaram,

Alok Shrivastava

EMC Education

Services, Wiley-

India, Second Edition.



Reference Book:

Sl No


1 Storage Networks Explained Ulf Troppens, Rainer

Erkes 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





Subject Name: COMPUTER VISION AND ROBOTICS


Course Objectives:

Course Outcomes

Sl. No

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

Course outcome

Descriptions

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.




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, Stereopsis: Reconstruction, Human Stereposis, Binocular Fusion,

Using More Cameras, Segmentation by Clustering: What Is Segmentation?,

Human Vision: Grouping and Getstalt, 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 and Examples.

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 and

Jean Ponce

PHI Learning (Indian

Edition), 2009.

Reference Book:

Sl No


1 Computer and Machine Vision –

Theory, Algorithms and Practicalities

E. R. Davies Elsevier (Academic

Press), 4th edition,

2013.





Subject Name: ARTIFICIAL NEURAL NETWORKS


Course Objectives:

Course Outcomes:

Sl. No

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.

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.




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 Book:

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,

YoshuaBengio and

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, by

O’Reilly

Publications, ISBN-

13: 978-

1491925614.





Subject Name: SOFTWARE ARCHITECTURE AND DESIGN PATTERNS


Course Objectives:

Course Outcomes

Sl. No

Course Objectives

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.

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.




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, Ralph

johman, john

vlissides

PEARSON

Publication, 2013.



Reference Book:

Sl No


1 “Pattern Oriented Software

Architecture”

Frank Bachmann,

RegineMeunier,

Hans Rohnert

Volume 1, 1996.

2 "Anti-Patterns: Refactoring Software,

Architectures and Projects in Crisis"

William J Brown et

al.

John Wiley, 1998.





Subject Name: WIRELESS SENSOR NETWORKS


Course Objectives:

Course Outcomes:

Sl. No

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.

Course outcome

Descriptions

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.




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 Books:

Sl No


1 Wireless Sensor Networks – An

Information Processing Approach,

Feng Zhao, Leonidas

Guibas

Elsevier, 2004.

Reference Book:

Sl

No

Text Book title Author Volume and Year

of Edition

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.





Subject Name: CLOUD COMPUTING


Course Objectives:

Course Outcomes:

Sl. No

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 inCloud Computing.

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.




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 Books:

Sl No


1 “Cloud Computing Bible”

Barrie Sosinsky Wiley Publishing

Inc. 2011 (free e-

book available).

Reference Book:

Sl No


1 Cloud Computing and SOA

Convergence in Your Enterprise:

David S. Linthicum A Step-by-Step

Guide (free e-book

available)

2 “Distributed and Cloud Computing –

From Parallel Processing to the Internet

of 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-book

available)

4 Cloud Computing, A Practical Approach Toby Velte, Anthony

Velte, Robert

Elsenpeter

(free e-book

available)





Subject Name: PROJECT WORK PHASE – II

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

Course Outcomes:

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 the students.

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,

Course outcome

Descriptions

CO1

Design a suitable system according to the problem stated in project work

phase – I.

CO2

Implement the design using necessary algorithms and tools.

CO3

Test the performance of the system with suitable data.



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 stipulated

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

without fail.





Subject Name: TECHNICAL SEMINAR

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

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.



Description

I

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.

department of information science & engineering viii … syllabus.pdf · information science...

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