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COURSE STRUCTURE&

SYLLABUS

For

MCA – SEM - IV

(Applicable for batches admitted from 2018-2019)

Department of Master of Computer Applications (MCA)

QIS COLLEGE OF ENGINEERING AND TECHNOLOGY::ONGOLE

(AUTONOMOUS) (Approved by AICTE, Permanent Affiliated to JNTU Kakinada & UGC Recognized)

(An ISO 9001-2008 Certified & NBA Accredited Institution)

QIS COLLEGE OF ENGINEERING AND TECHNOLOGY::ONGOLE (AUTONOMOUS)

(Approved by AICTE, Permanent Affiliated to JNTU Kakinada & UGC Recognized)



2

IV Semester

Course

Code Course Title

Hours per Week Total

Contact

Hours

Credits Category Year Sem

I to

VI

Sem

Type Lecture Tutorial Practical

18F09001 Software Engineering 3 0 0 3 3 Basic Computer Applications II II IV Regular

18F01009 Advanced Java & Web Technologies 3 0 0 3 3 Basic Computer Applications II II IV Regular

18F13003 Fundamentals of Data Sciences 3 0 0 3 3 Basic Computer Applications II II IV Regular

18F02002 ELECTIVE-II : A) Parallel and Distributed

Algorithms 3 0 0 3 3

Basic Computer Applications II II IV Elective

18F11002 ELECTIVE-II: B) Machine Learning Basic Computer Applications II II IV Elective

18F11003 ELECTIVE-II: C) Image Processing Basic Computer Applications II II IV Elective

18F14003 ELECTIVE-III: A) Next Generation Networks

3 0 0 3 3

Basic Computer Applications II II IV Elective

18F14004 ELECTIVE-III: B) Cryptography & Network

Security Basic Computer Applications II II IV Elective

18F08001 ELECTIVE-III: C) Computer Graphics Basic Computer Applications II II IV Elective

18F01010 Advanced Java & Web Technologies Lab 0 0 2 2 2 Basic Computer Applications II II IV Regular

18F13004 Fundamentals of Data Sciences Lab 0 0 2 2 2 Basic Computer Applications II II IV Regular

18A03001 IPR and Patents 0 0 2 2 2 H & SS including MC II II IV Regular

18T06002 Technical Skill Development-II 0 0 2 2 2 Basic Computer Applications II II IV Regular

18T03001 CRT-Technical 2 0 0 2 0 CRT-Audit course II II IV Regular

25 23

Honors Course - IV 1 0 0 1 3 Basic Computer Applications II II IV Optional





3

SOFTWARE ENGINEERING (18F09001)

II Year II Semester L P C

3 0 3

Course Objectives:

Basic knowledge and understanding of the analysis and design of complex systems.

Ability to apply software engineering principles and techniques.

Ability to develop, maintain and evaluate large-scale software systems.

To produce efficient, reliable, robust and cost-effective software solutions.

Ability to perform independent research and analysis.

To communicate and coordinate competently by listening, speaking, reading and

writing english for technical and general purposes.

Ability to work as an effective member or leader of software engineering teams.

To manage time, processes and resources effectively by prioritising competing

demands to achieve personal and team goals Identify and analyzes the common threats

in each domain.

Ability to understand and meet ethical standards and legal responsibilities

Course Outcomes:

1. Understanding of basic S/W Engineering methods and practices, & their appropriate

application; software process models such as the waterfall models

2. Understanding of the role of project management including planning, scheduling, risk

management, etc., software requirements and the SRS document.

3. Understanding of different software architectural styles, implementation issues such as

modularity and coding standards.

4. Understanding of approaches to verification and validation including static analysis,

and reviews, software testing approaches such as unit testing and integration testing.

5. Understanding of software evolution and related issues such as version management,

quality control and how to ensure good quality software

Unit I:

Introduction to Software Engineering: The evolving role of software, Changing Nature of

Software, Software myths.

A Generic view of process: Software engineering- A layered technology, a process

Framework, The Capability Maturity Model Integration (CMMI), Process patterns, process

assessment, personal and team process models.

Process models: The waterfall model, Incremental process models, Evolutionary process

Models, The Unified process.

Software Requirements: Functional and non-functional requirements, User requirements,

System requirements, Interface specification, the software requirements document.

4

Unit II:

Requirements engineering process: Feasibility studies, Requirements elicitation and

analysis, Requirements validation, Requirements management.

System models: Context Models, Behavioral models, Data models, Object models,

structured methods.

Unit III:

Design Engineering: Design process and Design quality, Design concepts, the design model.

Creating an architectural design: software architecture, Data design, Architectural styles and

patterns, Architectural Design.

Object-Oriented Design: Objects and object classes, An Object-Oriented design process,

Design evolution.

Performing User interface design: Golden rules, User interface analysis and design, interface

analysis, interface design steps, Design evaluation.

Unit IV:

Testing Strategies: A strategic approach to software testing, test strategies for conventional

software, Black-Box and White-Box testing, Validation testing, System testing, the art of

Debugging.

Product metrics: Software Quality, Metrics for Analysis Model, Metrics for Design Model,

Metrics for source code, Metrics for testing, Metrics for maintenance.

Metrics for Process and Products: Software Measurement, Metrics for software quality.

Unit V:

Risk management: Reactive vs Proactive Risk strategies, software risks, Risk identification,

Risk projection, Risk refinement, RMMM, RMMM Plan.

Quality Management: Quality concepts, Software quality assurance, Software Reviews,

Formal technical reviews, Statistical Software quality Assurance, Software reliability, The ISO

9000 quality standards.

Text Books:

1. Software Engineering, A practitioner’s Approach- Roger S. Pressman, 6th

edition.McGrawHill International Edition.

2. Software Engineering- Sommerville, 7th edition, Pearson education.

Reference Books:

1. Software Engineering- K.K. Agarwal & Yogesh Singh,New Age International

Publishers

2. Software Engineering, an Engineering approach- James F. Peters, Witold Pedrycz, John

Wiely.

3. Systems Analysis and Design- Shely Cashman Rosenblatt,Thomson Publications.

4. Software Engineering principles and practice- Waman S Jawadekar, The McGraw-Hill





5

ADVANCED JAVA & WEB TECHNOLOGIES (18F01009)


3 0 3

Course Objectives:

• To understand the concepts of HyperText Markup Language and Cascading Style

Sheets.

• To learn JavaScript for creating dynamic websites.

• To acquire knowledge on creation of software components using JAVA Beans.

• To learn Server-Side Programming using Servlets and Java Server Pages.

• To learn the creation of pure Dynamic Web Application using JDBC.

Course Outcomes:

At the end of the course the student will

1. Develop component-based Java software using JavaBeans.

2. Develop server side programs in the form of Servlets

3. Develop server side programs using JSP

4. Understanding of data access from the databases using Java, Servlet, and JSP.

5. Create and analyze static and dynamic web pages and identify its elements and

attributes, adding Cascading Styles sheets, build web applications using PHP.

Unit I:

Review of Java Fundamentals: The Java Environment, OOPS concepts, Wrapper classes &

classes-Vector, ArrayList, LinkedList, Stack, HashSet, HashMap, HashTable, PriorityQueue,

TreeSet, TreeMap, LinkedHashMap.

Unit II:

Java Beans: Introduction to Java Beans, Advantages of Java Beans, BDK, Introspection,

Using Bound properties, Bean Info Interface, Constrained properties, Persistence, Customizers,

Java Beans API.

UnitIII:

Introduction to Servlets: Lifecycle of a Servlet, JSDK, The Servlet API,The javax.servlet

Package, Reading Servlet parameters, Reading Initialization Parameters, The

javax.servlet.HTTP package, Handling, Http Request &responses, Using Cookies, Session

Tracking, Security Issues.

UnitIV:

Introduction to JSP: The Problem with Servelets, The Anatomy of a JSP Page, JSP

Processing, JSP Application Design with MVC.

Setting Up the JSP Environment: Installing the Java Software Development Kit, Tomcat

Server & Testing Tomcat.

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JSP Application Development: Generating Dynamic Content, Using Scripting Elements,

Implicit JSP Objects, Conditional Processing – Displaying Values, Using an Expression to Set

an Attribute, Declaring Variables and Methods, Error Handling and Debugging, Sharing Data

Between JSP Pages, Requests, and Users, Passing Control and Data Between Pages – Sharing

Session and Application Data. Database Access: Database Programming using JDBC,

Studying Javax.sql.*package. Accessing a Database from a JSP Page

Unit V:

HTML tags, Lists, Tables, Images, forms, Frames. Cascading style sheets.

Introduction to Java script. Objects in Java Script. Dynamic HTML with Java Script

PHP Programming: Introducing PHP: Creating PHP script, Running PHP script. Working

with variables and constants: Using variables, Using constants, Data types, Operators.

Controlling program flow: Conditional statements, Control, statements, Arrays, functions.

Working with forms and Databases such as MYSQL, Oracle, SQL Server.

Text Books:

1. Internet and World Wide Web: How to program,6/e, Dietel, Dietel,Pearson.

2. The Complete Reference Java2, 8/e, Patrick Naughton, Herbert Schildt, TMH.

3. Java Server Faces, Hans Bergstan, O’reilly.

4. Programming the World Wide Web, Robet W Sebesta, 7ed, Pearson.

5. Web Technologies, Uttam K Roy, Oxford

6. The Web Warrior Guide to Web Programming, Bai, Ekedahl, Farrelll, Gosselin, Zak,

Karparhi, Maclntyre, Morrissey, Cengage

Reference Books:

1. Web Programming, building internet applications, 2/e, Chris Bates, Wiley Dreamtech

2. Programming world wide web, Sebesta, PEA

3. Web Tehnologies, 2/e, Godbole, kahate, TMH

4. An Introduction to web Design , Programming ,Wang,Thomson

5. Web Technologies, HTML< JavaScript, PHP, Java, JSP, XML and AJAX, Black book,

Dream Tech.

6. An Introduction to Web Design, Programming, Paul S Wang, Sanda S Katila, Cengage





7

FUNDAMENTALS OF DATA SCIENCES (18F13003)


3 0 3

Course Objectives:

To understand the data analysis techniques

To understand the concepts behind the descriptive analytics and predictive analytics

of data

To familiarize with Big Data and its sources

To familiarize data analysis using R programming

To understand the different visualization techniques in data analysis

Course outcomes:

1. Understand the data analysis techniques and

2. Understand the concepts behind the descriptive analytics and predictive analytics of

data

3. Discuss with Big Data and its sources

4. Explain data analysis using R programming

5. Analyze the different visualization techniques in data analysis

Unit 1:

Introduction to Data Analysis - Evolution of Analytic scalability, analytic processes and

tools, Analysis vs reporting - Modern data analytic tools. Statistical concepts: Sampling

distributions, re-sampling, statistical inference, prediction error.

Unit 2:

Predictive Analytics – Regression, Decision Tree, Neural Networks. Dimensionality

Reduction - Principal component analysis

Unit 3:

Descriptive Analytics - Mining Frequent itemsets - Market based model – Association and

Sequential Rule Mining - Clustering Techniques – Hierarchical – K- Means

UNIT 4:

Introduction to Big data framework - Fundamental concepts of Big Data management and

analytics - Current challenges and trends in Big Data Acquisition

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Unit 5:

Data Analysis Using R - Introduction to R, R Graphical User Interfaces, Data Import and

Export, Attribute and Data Types, Descriptive Statistics, Exploratory Data Analysis,

Visualization Before Analysis, Dirty Data, Visualizing a Single Variable, Examining Multiple

Variables, Data Exploration Versus Presentation, Statistical Methods for Evaluation

Popular Big Data Techniques and tools- Map Reduce paradigm and the Hadoop system-

Applications Social Media Analytics Recommender Systems- Fraud Detection.

Text Books:

1. EMC Education Services, Data Science and Big Data Analytics: Discovering,

Analyzing, Visualizing and Presenting Data. John Wiley & Sons, 2015.

2. Jaiwei Han, Micheline Kamber, “Data Mining Concepts and Techniques”, Elsevier,

2006.

3. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer, 2007.

References Books:

1. Bart Baesens," Analytics in a Big Data World: The Essential Guide to Data Science

and its Business Intelligence and Analytic Trends”, John Wiley & Sons, 2013

Challenges and Future Prospects, Springer, 2014.

2. Michael Minelli, Michele Chambers, AmbigaDhiraj , “Big Data, Big Analytics:

Emerging Min Chen, Shiwen Mao, Yin Zhang, Victor CM Leung ,Big Data: Related

Technologies,





9

ELECTIVE-II: A) PARALLEL AND DISTRIBUTED ALGORITHMS (18F02002)


3 0 3

Course Objectives:

To acquaint with the basic concepts of parallel and distributed computing.

To acquaint with the general principles of parallel and distributed algorithms and their

time complexity.

Course Outcomes:

1. Understand basic principles of parallel computing

2. Understand basic principles of distributed computing

3. Discuss basic principles parallel and distributed algorithms and

4. Analyze their time complexity.

5. Analyze basic principles and possibilities of algorithm parallelization.

Unit 1:

The Idea of Parallelism: A Parallelised version of the Sieve of Eratosthenes, PRAM Model of

Parallel Computation, Pointer Jumping and Divide & Conquer: Useful Techniques for

Parallelization, PRAM Algorithms: Parallel Reduction, Prefix Sums, List Ranking, Preorder

Tree Traversal, Merging Two Sorted Lists, Graph Coloring, Reducing the Number of

Processors and Brent's Theorem, Dichotomoy of Parallel Computing Platforms, Cost of

Communication

Unit II:

Programmer's view of modern multi-core processors, The role of compilers and writing

efficient serial programs, Parallel Complexity: The P-Complete Class, Mapping and

Scheduling, Elementary Parallel Algorithms, Sorting

Unit III:

Parallel Programming Languages: Shared Memory Parallel Programming using OpenMP,

Writing efficient openMP programs, Dictionary Operations: Parallel Search, Graph

Algorithms, Matrix Multiplication

Unit IV:

Distributed Algorithms: models and complexity measures, Safety, liveness, termination,

logical time and event ordering, Global state and snapshot algorithms, Mutual exclusion and

Clock Synchronization, Distributed Graph algorithms

Unit V:

Distributed Memory Parallel Programming: Cover MPI programming basics with simple

programs and most useful directives; Demonstrate Parallel Monte Carlo

10

Text Books:

1. Michael J Quinn, Parallel Computing, TMH

2. Joseph Jaja, An Introduction to Parallel Algorithms, Addison Wesley

3. MukeshSinghal and Niranjan G. Shivaratri, Advanced Concepts in Operating Systems,

TMH

4. AnanthGrama, Anshul Gupta, George Karypis, Vipin Kumar, Introduction to Parallel

Computing, Pearson





11

ELECTIVE-II: B) MACHINE LEARNING (18F11002)


3 0 3

Course Objectives:

Be able to formulate machine learning problems corresponding to different

applications.

Understand a range of machine learning algorithms along with their strengths and

weaknesses.

Understand the basic theory underlying machine learning.

Be able to apply machine learning algorithms to solve problems of moderate

complexity.

Course Outcomes:

1. Understanding of the fundamental issues and challenges of machine learning: data,

model selection, model complexity, etc.

2. Understand the basic building blocks and general principles that allow one to design

machine learning algorithms

3. Understanding of the strengths and weaknesses of many popular machine learning

approaches.

4. Appreciate the underlying mathematical relationships within and across Machine

Learning algorithms and the paradigms of supervised and un-supervised learning.

5. Design and implement various machine learning algorithms in a range of real-world

applications.

Unit I:

Introduction to linear algebra and probability theory basics, Linear Regression, Multivariate

Regression, Euclidean Distance Classifier, Mahalanobis Classifier

Unit II:

Bayesian Classification: Naive Bayes, Parameter Estimation (ML, MAP), Sequential Pattern

Classification. Evaluation and Model Selection: ROC Curves, Evaluation Measures,

Significance tests.

Unit III:

Non-parametric Methods: k-Nearest Neighbours, Parzen Window, Discriminative Learning

models: Logistic Regression, Perceptrons, Artificial Neural Networks, Support Vector

Machines.

Unit IV:

Dimensionality Reduction: Principal Component Analysis, Fischer's Discriminant Analysis.

Decision Trees: Splitting Criteria, CART.

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Unit V:

Ensemble Methods: Boosting, Bagging, Random Forests, clustering: Partitional, Hierarchical,

density based clustering.

Text Books:

1. Pattern Classification by Duda and Hart. John Wiley publication

2. Pattern Recognition and Machine Learning, Christopher Bishop, Springer 2006.

Reference Books:

1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by

TrevorHastie, Robert Tibshirani, Jerome Friedman, Springer.

2. Learning From Data, Yaser S. Abu-Mostafa, Hsuan-Tien Lin, Malik Magdon-

Ismail,AML Book.

3. Introduction to Machine Learning by EthemAlpaydin, The MIT Press.

4. Machine Learning: An Algorithmic Perspective by Stephen Marsland, Chapman

andHall/CRC.

5. Machine Learning by Tom M. Mitchel, McGraw-Hill publication

6. Building Machine Learning Systems with Python by Willi Richert, Luis Pedro Coelho,

Packt Publishing

7. https://www.cse.iitm.ac.in/course_details.php?arg=MTIz





13

ELECTIVE-II: C) IMAGE PROCESSING (18F11003)


3 0 3

Course Objectives:

At the end of the course the learner is expected:

1. To know about image fundamentals and mathematical transforms necessary for image

processing.

2. To gather knowledge about image enhancement techniques

3. To know about image restoration procedures.

4. To learn the image compression procedures.

5. To study the image segmentation and representation techniques.

Course Outcomes:

1. Analyze images in the frequency domain using various transforms.

2. Evaluate the techniques for image enhancement and image restoration.

3. Categorize various compression techniques.

4. Interpret Image compression standards.

5. Interpret image segmentation and representation techniques.

Unit I - Digital Image Fundamentals

Overview of Digital Image Processing – Fields that use Digital image processing –

Fundamental steps in Digital Image Processing – Components of an Image Processing System

– Elements of visual perception – Background on MATLAB and the Image Processing Toolbox

- The MATLAB Working Environment

Unit II - Image Representation & Transformations

Digital Image Representation - Reading Images - Displaying Images - Writing Images –Image

Types - Array Indexing - Intensity Transformations and Spatial Filtering - Intensity

Transformation Functions - Histogram Processing and Function Plotting - The 2-D Discrete

Fourier Transform - Computing and Visualizing the 2-D DFT in MATLAB - Filtering in the

Frequency Domain - Properties of 2D Fourier Transform

Unit III - Image Enhancement:

Image Enhancement in spatial domain: Histogram Equalization – Enhancement using

Arithmetic / Logic Operations – Spatial Filtering – Smoothing & Sharpening Spatial Filters.

Image Enhancement in Frequency domain: Filtering in the frequency domain – Smoothing &

Sharpening

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Unit IV - Image Compression:

Fundamentals – Image Compression models – Lossless Compression: Variable Length Coding

– LZW Coding – Bit plane Coding – predictive coding – Lossy Compression: Transform

coding – Wavelet coding – Basics of Image Compression Standards – JPEG standards – MPEG

standards

Unit V - Image Segmentation & Representation

Edge Detection – Thresholding – Region based Segmentation – Chain codes – Polynomial

approximation – Boundary Segments – Case study using MATLAB.

Text Books:

1. Rafael C Gonzalez, Richard E Woods, 2nd Edition - Digital Image Processing –

Pearson Education - 2003.

2. Rafael C Gonzalez, Richard E Woods, Steven Eddins , 2nd Edition - Digital Image

Processing using MATLAB – Pearson Education - 2003.

References Books:

1. Jain A.K., "Fundamentals of Digital Image Processing", Pearson education

2. William K Pratt, "Digital Image Processing", John Willey 2001

3. Millman Sonka, Vaclav Hlavac, Roger Boyle, Broos/Colic, "Image Processing

Analysis and Machine Vision" - Thompson Learning, 1999.

4. Chanda S., Dutta Majumdar - "Digital Image Processing and Applications", Prentice

Hall of India, 2000.





15

ELECTIVE-III: A) NEXT GENERATION NETWORKS (18F14003)


3 0 3

Course Objectives:

Understand the core technologies, theories, and dilemmas that face next generation

network engineers in this field.

Understand best practices about how to design, deploy, and troubleshoot next

generation networks.

Utilize multivendor, vendor neutral (bare metal), and commercial equipment (such as

Arista, Brocade, Dell, HP, Pica8, and Raspberry Pi) to prepare for real-world

scenarios in industry.

Course Outcomes:

Students will able to

1. Explain various NGN blocks and services

2. Discuss various types IP networks

3. Explain architecture and technology options for Multi-Service Networks

4. Explain frame and cell based MPLS Services and platforms

5. Summarize applications of Next Generation Networks

UNIT I:

Introduction to next generation networks: Communicating in the new Era, New Era of

Networking, Technologies influencing change, IP Everywhere, Optical fiber anywhere,

wireless access, building blocks for NGN, IP Networks, VOIP, Multi service Flexible

Networks architecture. VPNs, Optical Networks, Wire line and Wireless Networks, NGN

Services, Network Infrastructure convergence, services convergence, from technology push to

service pull.

UNIT II:

IP Networks: IP past, present and future, IP influence and confluence, IP version 4, I. P. Version

6, IP Network convergence, LAN Technologies, IP Routing, LAN Switching, WAN’s, WAN

Technologies and Topologies. Wireless IP LANS, Mobility Networks, Global IP Networks,

Global capacity, Globally Resilient IP, Internet – A Network of Networks. Beyond IP,

Technology Brief – IP Networks, Business Drivers, Success factors, Applications and Service

Value.

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UNIT III:

Muti service Networks: Origin of multi service ATM, Next Generation Multi service

Networks, Next Generation Multi service ATM switching, Multi protocol Label switching,

Networks,

UNIT IV:

Frame Based MPLS, Cell based MPLS, MPLS services and their benefits, multi service

provisioning platforms (MSPP) and Multi service switching platform (MSSP)

UNIT V:

NGN Applications: Internet connectivity, e-commerce, call center, third party application

service provision, UMTS, WAP, WiMAX, integrated billing, security and directory enable

networks.

Reference Books:

1. Next Generation Networks Services, Technologies and Strategies, Neill

Wilkinson,Wiley.

2. Next Generation Network Services, Robet Wood, Pearson

3. Next Generation Telecommunications Network, Parliament office of Science and

Technology (Postnote). Dec 2007, No. 296 Ref. www.parliament.uk

4. Mobile Next Generation Networks Huber, JF IEEE Multimedia Vol. 11, Issue I Jan-

March 2004.

5. Next Generation Network (NGN) Service, J.C. Crimi, A Telecoolia Technologies white

paper refer www.telecodia.com

http://www.parliament.uk/





17

ELECTIVE-III: B) CRYPTOGRAPHY & NETWORK SECURITY (18F14004)


3 0 3

Course objectives:

To know about various encryption techniques.

To understand the concept of Public key cryptography.

To study about message authentication and hash functions

To impart knowledge on Network security

Course Outcomes:

1. classify the symmetric encryption techniques

2. Illustrate various Public key cryptographic techniques

3. Evaluate the authentication and hash algorithms.

4. Discuss authentication applications

5. Summarize the intrusion detection and its solutions to overcome the attacks and basic

concepts of system level security

Unit- I:

Introduction: Security Attacks (Interruption, Interception, Modification and Fabrication),

Security Services (Confidentiality, Authentication, Integrity, on-repudiation, access Control

and Availability) and Mechanisms, A model for Internetwork security, Internet Standards and

RFCs, Buffer overflow & format string vulnerabilities, TCP session hijacking, ARP attacks,

route table modification, UDP hijacking, and man-in-the-middle attacks.

Unit –II:

Conventional Encryption:

Conventional Encryption Principles, Conventional encryption algorithms, cipher block modes

of operation, location of encryption devices, key distribution Approaches of Message

Authentication, Secure Hash Functions and HMAC.

Unit –III:

Public key: Public key cryptography principles, public key cryptography algorithms, digital

signatures, digital Certificates, Certificate Authority and key management Kerberos, X.509

Directory Authentication Service.

Unit-IV:

IP Security:IP Security Overview, IP Security Architecture, Authentication Header,

Encapsulating Security Payload, Combining Security Associations and Key Management.

18

Web Security:

Web Security Requirements, Secure Socket Layer (SSL) and Transport Layer Security (TLS),

Secure Electronic Transaction (SET).

Email Privacy: Pretty Good Privacy (PGP) and S/MIME.

Unit-V:

SNMP: Basic concepts of SNMP, SNMPv1 Community facility and SNMPv3, Intruders,

Viruses and related threats.

Fire walls: Firewall Design principles, Trusted Systems, Intrusion Detection Systems.

Text Books:

1. Network Security Essentials: Applications and Standards, William Stallings, PEA.

2. Hack Proofing your Network, Russell, Kaminsky, Forest Puppy,Wiley Dreamtech

Reference Books:

1. Network Security & Cryptography, Bernard Menezes,Cengage,2010

2. Fundamentals of Network Security, Eric Maiwald, Dream Tech.

3. Network Security: Private Communication in a Public World,Kaufman, Perlman,

PEA/PHI.

4. Principles of Information Security, Whitman, Thomson.

5. Cryptography and Network Security, 3/e, Stallings, PHI/PEA.

6. Network Security: The complete reference, Robert Bragg, Mark Rhodes, TMH

7. Introduction to Cryptography, Buchmann.





19

ELECTIVE-III: C) COMPUTER GRAPHICS (18F08001)


3 0 3

Course Objectives:

This course is designed to provide a comprehensive introduction to computer graphics

leading to the ability to understand contemporary terminology, progress, issues, and

trends.

A thorough introduction to computer graphics techniques, focusing on 3D modeling,

image synthesis, and rendering.

Topics cover: geometric transformations, geometric algorithms, software systems

(OpenGL, shades), 3D object models (surface, volume and implicit), visible surface

algorithms, image synthesis.

Shading and mapping, ray tracing, radiosity, global illumination, Monte Carlo path

tracing, photon mapping, and anti-aliasing.

The interdisciplinary nature of computer graphics is emphasized in the wide variety of

examples and applications.

Course Outcomes:

1. Understand the basics of computer graphics, different graphics systems and

applications of computer graphics.

2. Discuss various algorithms for scan conversion and filling of basic objects and their

comparative analysis.

3. Use of geometric transformations on graphics objects and their application in

composite form.

4. Extract scene with different clipping methods and its transformation to graphics

display device.

5. Explore projections and visible surface detection techniques for display of 3D scene

on 2D screen, Render projected objects to naturalize the scene in 2D view and use of

illumination models for this.

Unit -I:

Introduction: Application areas of Computer Graphics, overview of graphics systems, video-

display devices, raster-scan systems, random scan systems, graphics monitors and work

stations and input devices.

Output primitives: Points and lines, line drawing algorithms, mid-point circle and ellipse

algorithms. Filled area primitives: Scan line polygon fill algorithm, boundary-fill and flood-fill

algorithms.

20

Unit-II:

2-D geometrical transforms: Translation, scaling, rotation, reflection and shear

transformations, matrix representations and homogeneous coordinates, composite transforms,

transformations between coordinate systems.

2-D viewing: The viewing pipeline, viewing coordinate reference frame, window to view-port

coordinate transformation, viewing functions, Cohen-Sutherland and Cyrus-beck line clipping

algorithms, Sutherland –Hodgeman polygon clipping algorithm

Unit- III:

3-D object representation: Polygon surfaces, quadric surfaces, spline representation, Hermite

curve, Bezier curve and B-Spline curves, Bezier and B-Spline surfaces. Basic illumination

models, polygon rendering methods. 3-D Geometric transformations: Translation, rotation,

scaling, reflection and shear transformations, composite transformations.

Unit- IV:

3-D viewing : Viewing pipeline, viewing coordinates, view volume and general projection

transforms and clipping Visible surface detection methods: Classification, back-face detection,

depth-buffer, scan-line, depth sorting, BSP-tree methods, area sub-division and octree methods

Unit-V:

Computer animation: Design of animation sequence, general computer animation functions,

raster animation, computer animation languages, key frame systems, motion specifications.

Text Books:

1. Computer Graphics C version, Donald Hearn, M.Pauline Baker, Pearson

2. Computer Graphics Principles & practice, 2/e, Foley, VanDam, Feiner, Hughes,

Pearson.

Reference Books:

1. Computer Graphics, Donald Hearn and M.Pauline Baker, 2/E, PHI

2. Computer Graphics, Zhigand xiang, Roy Plastock, Schaum’s outlines, 2/E, TMH

3. Procedural elements for Computer Graphics, David F Rogers, 2/E, TMH

4. Principles of Interactive Computer Graphics, Neuman , Sproul, TMH.

5. Principles of Computer Graphics, Shalini Govil, Pai, 2005, Springer.

6. Computer Graphics, Steven Harrington, TMH

7. Computer Graphics, Shirley, Marschner, Cengage

8. Computer Graphics, Rajesh Maurya, Wiley, india

9. Computer Graphics Pradeep Bhatiya, IK intentional





21

ADVANCED JAVA & WEB TECHNOLOGIES LAB (18F01010)


0 2 2

Course Objectives:

1. familiar with client server architecture

2. able to develop a web application using java technologies

3. To create fully functional website/web application using Servlets

4. To create fully functional website/web application using JSP

5. To create fully functional website/web application using PHP

Course Outcomes:

1. Students are able to develop a dynamic webpage by the use of java script and DHTML

2. Students will be able to createand test a simple bean . 3. Students will be able to connect a java program to a DBMS and perform insert, update

and delete operations on DBMS table. ·

4. Students will be able to write a server side java application called Servlet to catch form

data sent from client, process it and store it on database. ·

5. Students will be able to write a server side java application called JSP to catch form

data sent from client and store it on database.

List of Experiments:

1. Write a Java Program Vectors, Linked List, HashTable

2. Write a Program for generation of a servlet to display a small message?

3. Write a Servlet Program to retrieve data from text file?

4. Develop a Servlet to validate user name and password with the data stored in Servlet

configuration file. Display authorized user if she/he is authorized else display

unauthorized user

5. Write a JSP Program to display a message?

6. Write a program in JSP by using for loop?

7. Write a Program for including our html program into JSP.

8. Create a small application with the usage of JAVA Beans, JSP and HTML?

9. Write a Java program for data base retrieval from data base

10. Write a JSP program for data base retrieval from data base?

11. Write a Servlet programming with cookies for userid and password?

12. Write a program to create cookies in JSP

13. Create a simple webpage using HTML

14. Add a Cascading Style sheet for designing the web page

15. Design a dynamic web page with validation using JavaScript

16. Design a dynamic web page using php script





22

FUNDAMENTALS OF DATA SCIENCES LAB (18F13004)


0 2 2

Course Objectives:

To know the basic data analytics concepts and its types.

To learn the basics of Business analytics (BA)

To study on fundamentals of R Programming

To perform different data set operations

To apply distribution methods and graphical representation of data sets.

To learn the basics of hypothesis testing concepts

Course Outcomes:

Students will familiar with,

1. The basic data analytics concepts and its types.

2. The basics of Business analytics (BA)

3. The fundamentals of R Programming

4. The different data set operations

5. The data distribution methods and hypothesis testing concepts

6. The graphical representation of data sets.

List of Experiments:

1. Study on different types of analytics (i.e. Descriptive analytics, Predictive analytics,

Prescriptive analytics) and its applications.

2. Study of Business Analytics and Optimization, The value of Business Analytics and

Optimization to Business organization, the impact of Business Analytics and

Optimization on diverse industries.

3. Study on R Programming concepts

4. Study basic Commands on R Platform

5. Experiments on Installing the R platform.

6. Experiments on loading the dataset using R Program

7. Experiments on summarizing the dataset using R Program

8. Experiments on visualizing the dataset using R Program

9. Experiments on Combining Data sets in R Program

23

10. Experiments on data distribution

11. Study on hypothesis Testing

12. Experiments on graphical analysis

Text Book:

1. Beginning R - The Statistical Programming Language, Dr. Mark Gardener, Wrox

Programmer.

Reference Books:

1. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and

Presenting Data, EMC Education Services (Editor), ISBN: 978-1-118-87613-8

2. The Art of Data Science Paperback, Roger Peng, Elizabeth Matsui.





24

INTELLECTUAL PROPERTY RIGHTS AND PATENTS (18A03001)


0 2 2

Course Objectives:

Students in this course will be able to

Get a holistic understanding of the complexities involved in the process of attributing

intellectual property rights to people.

Learn the legalities of intellectual property to avoid plagiarism and other IPR relates

crimes like copyright infringements, etc.

To encourage students at all levels to develop patentable technologies and to provide

financial assistance from the Institute to the extent possible.

Course Outcomes:

1. To facilitate the transfer of knowledge and technology to intending users to promote

utilization of such resources for benefit of the society.

2. To provide an administrative system to determine the commercial significance of

discoveries and developments and to assist in bringing these into public use.

3. To provide for a equitable distribution of economic gains resulting from new

intellectual property among the developer, author, or inventor (the originator), the

Institute, and, where applicable, the sponsor and to provide incentives to originators in

the form of personal development, professional recognition, and financial

compensation.

4. To safeguard, review and manage the intellectual property so that it may receive

adequate and appropriate legal protection against unauthorized use.

5. To create awareness on IPR through conducting seminars, conferences, invited talks

and lectures, and training programs among the academic community.

Unit 1:

Introduction to Intellectual Property Law – The Evolutionary Past - The IPR Tool Kit- Para -

Legal Tasks in Intellectual Property Law – Ethical obligations in Para Legal Tasks in

Intellectual Property Law - Introduction to Cyber Law– Innovations and Inventions Trade

related Intellectual Property Right

Unit 2:

Introduction to Trade mark – Trade mark Registration Process – Post registration procedures –

Trade mark maintenance - Transfer of Rights -Inter parts Proceeding – Infringement - Dilution

Ownership of Trade mark– Likelihood of confusion - Trademarks claims – Trademarks

Litigations – International Trade mark Law

25

Unit 3:

Introduction to Copyrights – – Principles of Copyright Principles -The subjects Matter of Copy

right – The Rights Afforded by Copyright Law –Copy right Ownership, Transfer and duration

– Right to prepare Derivative works – Rights of Distribution – Rights of Perform the work

Unit 4:

Publicity Copyright Formalities and Registrations - Limitations - Copyright disputes and

International Copyright Law – Semiconductor Chip Protection Act

Unit 5:

Introduction to Trade Secret – Maintaining Trade Secret – Physical Security –Employee

Limitation - Employee confidentiality agreement - Trade Secret Law - Unfair Competition –

Trade Secret Litigation – Breach of Contract – Applying State Law

Text Books:

1. Deborah E.Bouchoux: “Intellectual Property”. Cengage learning , New Delhi

2. Kompal Bansal & Parishit Bansal "Fundamentals of IPR for Engineers", BS

Publications (Press)

3. Cyber Law. Texts & Cases, South-Western’s Special Topics Collections

4. Prabhuddha Ganguli: ‘ Intellectual Property Rights” Tata Mc-Graw – Hill, New Delhi

Reference Books:

1. Richard Stim: "Intellectual Property", Cengage Learning, New Delhi.

2. R. Radha Krishnan, S. Balasubramanian: "Intellectual Property Rights", Excel Books.

New Delhi.





26

TECHNICAL SKILL DEVELOPMENT-II (18T06002)


0 2 2

Course Objective:

A computer programmer must develop good programs; most important skills are five

that are necessary for developing effective computer programs:

• Define and analyze the problem (problem solving).

• Develop an algorithm (solution) for solving the problem (critical thinking).

• Code the computer program that implements the algorithm (technical).

• Test the program to make sure it accurately addresses the problem (debugging).

• Write the specifications for the program (writing).

Course Outcomes:

1. Students will be able to understand different problem easily.

2. Student will be able to bring an effective solution for the problem.

3. Students will be able to find right way solving the problem programmatically.

4. Students will be able to understand previously developed program and debug it.

5. Students will be able to prepare a write-up to their developed code.

6. Students will be able to understand all Java API’s.

7. Students will be able to understand all Java compilers thoroughly.

8. Students will be able to do simple project level development using Java language.

By developing these skills you can have a great foundation for becoming a computer

programmer.

Problem Solving

Define Problems

Interviewing people.

Reading available information.

Using existing systems.

Understand The Problems.

Develop an Effective Solution. Critical Thinking

Discovering the solution.

Abstract solution/idea.

Analyzing the idea.

Converting idea into a design pattern.

Create the program. Technical

Writing code using programming language.

Development.

Database development.

Web development.

27

Debugging

Testing and debugging.

Compile time.

Runtime. Writing

By the program (comment line)

By external resources (web pages or etc.,)

Week 1 & 2

Briefing Session

Briefly revising the all java programming topics which are covered in the previous session.

Week 3

Assessment Session

Conduction of any test (Quiz or Brainstorming) to understand learner skill level.

Week 4, 5 & 6

Problem Solving

Various mathematical, programmatical & tricky solutions to solve various problems.

Week 7 & 8

Critical Thinking

Finding programmatic solution for general or behavioural problems, partially or completely.

Week 9, 10, 11 & 12

Technical

All java Programming concepts including which are not usually covered in any course curriculum.

Week 13 & 14

Debugging

All java programming related syntactical and logical errors and their handling (compile time and

runtime).

Week 15

Technical Writing

Creating documentation for the code we developed.

Week 16

Skill Testing

Developing a simple (and/or) sample solution to a problem and demonstrating it.

Test Books:

1. JAVA: How to program, 8/e, Dietal , Dietal, PHI.

2. Introduction of programming with JAVA, S. Dean, TMH.

3. Introduction to Java programming, 6/e, Y. Daniel Liang, Pearson.

4. Core Java 2, Vol 1(Vol 2) Fundamentals(Advanced), 7/e, Cay. S. Horstmann, Gary

Cornell, Pearson.

5. Big Java2,3/e, Cay. S. Horstmann, Wiley.

6. Object Oriented Programming through Java, P. Radha Krishna, University Press.

7. JAVA & Object Orientation an Introduction, 2/e, John Hunt, Springer.





28

CRT-TECHNICAL (18T03001)


2 0 0





29

HONORS – IV COURSES

S.No. Honors Course Title Online

Source Organizers URL Link

1 Honors-IV AI programming with Python UDACITY UDACITY https://in.udacity.com/course/ai-

programming-python-nanodegree--nd089

2 Honors-IV Blockchain Basics COURSERA

Bina Ramamurthy,

Teaching Professor

Computer Science and

Engineering Department,

University at Buffalo, The

State University of New

York

https://www.coursera.org/learn/blockchain-

basics

3 Honors-IV Full Stack Web Developer UDACITY UDACITY https://in.udacity.com/course/full-stack-web-

developer-nanodegree--nd004

for mca sem - iv - mca - r18 - syllabus.pdf · to manage time, processes and resources effectively...

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