Department of COMPUTER SCIENCE

Syllabus for
Master of Computer Applications
Academic Year  (2020)

 
1 Semester - 2020 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA131 DIGITAL LOGIC AND COMPUTER ORGANISATION - 4 4 100
MCA132 RELATIONAL DATABASE MANAGEMENT SYSTEMS - 4 4 100
MCA133 OPERATING SYSTEM - 4 4 100
MCA134 PROBABILITY AND STATISTICS - 4 4 100
MCA135 RESEARCH METHODOLOGY - 2 2 50
MCA171 PROGRAMMING IN C AND DATA STRUCTURES - 7 5 150
MCA172 WEBSTACK DEVELOPMENT - 7 5 150
2 Semester - 2020 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA231 COMPUTER NETWORKS - 4 04 100
MCA241A DATA MINING - 4 4 100
MCA241B INTERNET OF THINGS - 4 04 100
MCA241C ARTIFICIAL INTELLIGENCE - 4 04 100
MCA241D USER INTERFACE DESIGN - 4 04 100
MCA241E DIGITAL IMAGE PROCESSING - 4 04 100
MCA271 MICROPROCESSOR AND ALP - 6 05 150
MCA272 JAVA PROGRAMMING - 7 05 150
MCA273 PYTHON PROGRAMMING - 4 3 100
MCA274 SOFTWARE ENGINEERING PROJECT - 6 4 100
MCA281 SEMINAR - 1 01 50
3 Semester - 2019 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA331 COMPUTER NETWORKS - 4 04 100
MCA332 SOFTWARE ENGINEERING - 4 4 100
MCA333 OPERATING SYSTEMS - 4 4 100
MCA371 JAVA PROGRAMMING - 7 5 150
MCA372 UNIX PROGRAMMING - 6 4 100
MCA381 RDBMS PROJECT LAB - 4 2 100
MCA382 RESEARCH - PROBLEM IDENTIFICATION - 2 1 50
4 Semester - 2019 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA431 MACHINE LEARNING - 4 4 100
MCA441A NETWORK SECURITY - 4 04 100
MCA441B OOAD WITH UML - 4 4 100
MCA441C WEB ENGINEERING - 4 04 100
MCA441D WIRELESS AND MOBILE NETWORKS - 4 04 100
MCA441E DATA ANALYTICS - 4 4 100
MCA441F DIGITAL MARKETING - 4 04 100
MCA471 MOBILE APPLICATION - 7 5 150
MCA472A DIGITAL IMAGE PROCESSING - 6 5 150
MCA472B SOFTWARE QUALITY AND TESTING - 6 5 150
MCA472C DATA MINING - 6 5 150
MCA472D NoSQL - 6 5 150
MCA472E USER INTERFACE_USER EXPERIENCE DESIGN UI_UX - 6 5 150
MCA472F LINUX ADMINISTRATION - 6 5 150
MCA481 IOT PROJECT - 4 02 100
MCA482 RESEARCH - DATA COLLECTION AND IMPLEMENTATION - 4 2 50
MCA483 SEMINAR - 2 1 50
5 Semester - 2018 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA531 CLOUD COMPUTING - 4 4 100
MCA532 ARTIFICIAL INTELLIGENCE - 4 4 100
MCA541A SOFTWARE ARCHITECTURE - 4 4 100
MCA541B WIRELESS AND MOBILE NETWORKS - 4 04 100
MCA541C PARALLEL COMPUTING WITH OPEN CL - 4 4 100
MCA541D MACHINE LEARNING - 4 4 100
MCA541E EMBEDDED PROGRAMMING AND RTOS - 4 4 100
MCA541F NEURAL NETWORK - 4 4 100
MCA541G STORAGE AREA NETWORK - 4 4 100
MCA542A INFORMATION RETRIEVAL AND WEB MINING - 4 4 100
MCA542B DATABASE ADMINISTRATION - 4 4 100
MCA542C DATA ANALYTICS - 4 4 100
MCA542D PRINCIPLES OF USER INTERFACE DESIGN - 4 4 100
MCA542E SOFT COMPUTING - 4 4 100
MCA542F AGENT BASED COMPUTING - 4 4 100
MCA542G DISTRIBUTED SYSTEMS - 4 4 100
MCA551 CLOUD COMPUTING LAB - 4 2 100
MCA581 COMPUTER NETWORKS PROJECT - 4 2 100
MCA582 SPECIALIZATION PROJECT - 4 2 100
MCA583 RESEARCH - MODELING / IMPLEMENTATION - 4 2 50
6 Semester - 2018 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA681 INDUSTRY PROJECT - 2 10 300
MCA682 RESEARCH PUBLICATION - 4 2 100
      

    

Department Overview:
Department of Computer Science of CHRIST (Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation?s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.
Mission Statement:
Vision The Department of Computer Science endeavors to imbibe the vision of the University ?Excellence and Service?. The department is committed to this philosophy which pervades every aspect and functioning of the department. Mission ?To develop IT professionals with ethical and human values?. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their career. The department also moulds the students to
Introduction to Program:
Master of Computer Applications is a Two year post graduate programme spread over four semesters. This programme strives to shape the students into outstanding computer professionals for the challenging opportunities in IT industry. It enables students to evolve from the stereo type thinking to better achievers and prepares them to scale the global standards. Curriculum incorporates the state of the art areas of IT industry to provide opportunity for extended study in an area of specialization.
Program Objective:
Programme Objective ? To strengthen the concept of Computer Science and applications for career growth and employability. ? To provide multidisciplinary and application oriented programme. ? To inculcate in students professional and ethical attitude, team work and effective communication skills. ? Students are encouraged to implement independent projects of their own choice and to use latest tools. Programme Outcomes PO1: Computational Knowledge : Apply knowledge of computing fundamentals, computing specialisation, mathematics, and domain knowledge appropriate for the computing specialisation to the abstraction and conceptualisation of computing models from defined problems and requirements. PO2: Problem Analysis: Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines. PO3: Design/Development of Solutions: Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations. PO4: Conduct Investigations of complex computing problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions. PO5: Modern Tool usage: Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations. PO6: Professional Ethics: Understand and commit to professional ethics and cyber regulations, responsibilities, and norms of professional computing practices. PO7: Life-long learning: Recognise the need, and have the ability, to engage in independent learning for continual development as a computing professional. PO8: Project management and finance: Demonstrate knowledge and understanding of the computing and management principles and apply these to one?s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments. PO9: Communication Efficacy: Communicate effectively with the computing community, and with society at large, about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions. PO10: Societal and Environmental Concern: Understand and assess societal, environmental, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practices. PO11: Individual and Team work: Function effectively as an individual and as a member or leader in diverse teams and in multidisciplinary environments. PO12: Innovation and Enterpreneurship: Identify a timely opportunity and using innovation to pursue that opportunity to create value and wealth for the betterment of the individual and society at large.
Assesment Pattern

CIA: 50%

ESE: 50%

Examination And Assesments

CIA: 50%

ESE: 50%

MCA131 - DIGITAL LOGIC AND COMPUTER ORGANISATION (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To enable the students to learn the basic functions, principles and fundamental aspects of computer architecture and design in terms of digital logic elements and circuits, central processing unit and memory unit.

Course Outcome

CO1:  Understand different number system, binary codes and digital logic elements

CO2: Acquaint with elementary postulates of Boolean algebra and methods for simplifying Boolean expressions

CO3: Illustrate the procedures for the analysis and design of sequential and combinational circuits

CO4: Demonstrate the basic structure and operation of the processing unit and get familiarize with different types of memory systems

Unit-1
Teaching Hours:12
Number System and Binary Coding
 

Number system- Decimal number system- Binary number system- octal number system- hexadecimal number system- number system conversion- number representation- unsigned representation – signed number representation-1’s complement – 2’s complement- 9’s complement – 10’s complement- binary arithmetic operation- binary addition- binary subtraction- Binary multiplication- binary division- Binary codes- BCD and Gray code

Unit-1
Teaching Hours:12
Digital Logic Elements
 

Introduction- Boolean algebra- Boolean operators- truth table- laws of Boolean algebra- De Morgan’s Law- Logic gates- Description of logic gates- Universal properties- Simplification of logic functions- Realization using NAND and NOR  gate

Self learning: Implementation using simulator

Unit-2
Teaching Hours:12
Combinational circuits
 

Logic expression- minterm - maxterm- SOP - POS expression- minimization techniques- Karnaugh Map Combinational circuits- Half Adder – Full adder- Half subtractor-Full subtractor- Binary adder-Binary subtractor-Binary adder subtractor-BCD adder – Realization using NAND gates -Binary multiplier- Encoder- Decoder- Multiplexer- Demultiplexer-BCD to seven segment display

Unit-3
Teaching Hours:12
Sequential Circuits (FF?s with Timing Diagram)
 

Sequential Circuit Definitions - Latches- Clock - Types of Clock – positive - Negative edge triggered -  Flip-Flops- SR Flip Flop – D Flip Flop – JK Flip Flop -Edge Triggered Flip Flop- T Flip-Flop - Master-Slave JK Flip-Flop-Timing diagram.

Text Books And Reference Books:

[1] Donald P Leach, Albert Paul Malvino, Goutam Saha, Digital Principles and Applications, 8th Edition, Tata Mc Graw-Hill, 2018

[2] William Stallings Computer Organisation and Architecture, 10th edition, Pearson,2016

Essential Reading / Recommended Reading

[1] Mano, Morris M and Kime Charles R., Logic and Computer Design Fundamentals, Pearson education, 2nd edition, 2015.

[2] Bartee, Thomas C, Digital Computer Fundamentals, Tata Mc Graw-Hill, 6th edition, 2016.

[3] William Stallings, Computer Architecture and Organization, PHI, Eigth  Edition, 2016.

[4] David A. Patterson and John L.Hennessey, Computer Organization and Design, Morgan Kauffman / Elsevier, Fifth edition, 2016.

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA132 - RELATIONAL DATABASE MANAGEMENT SYSTEMS (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To provide a strong foundation for database application design and development by introducing the fundamentals of database technology

Course Outcome

CO1: Understand the basic concepts of database systems, database transactions and related database facilities like concurrency control, data object locking and protocols

CO2: Analyze the database requirements and determine the entities involved in the system and their relationships to one another

CO3: Develop the logical design of the database using data modeling techniques

CO4: Create a relational database and use data manipulation language to query, update and manage a database

Unit-1
Teaching Hours:12
Introduction to Database system concepts, file structures, conceptual Modeling Database system concepts and architecture
 

Data models, schemas and instances, DBMS architecture and data independence, Database languages and interfaces, database system environment, Classification of DBMS.

Disk storage, basic file structures and hashing     

Secondary storage devices, buffering of blocks, Placing File Records on Disk Operations on Files, Files of Unordered Records, Files of Ordered Records hashing techniques.

Data modelling using ER model

Entities, attributes and relationships, Different types of attributes, E- R Diagrams, Specialization and generalization, constraints and characteristics of specialization and generalization, Relationship types of degree higher than two.

Unit-2
Teaching Hours:12
Relational Data Model and Database design,ER and EER to Relational Mapping ,Database Design
 

Relational Data Model and Database design

Relational Model Concepts, Relational Model Constraints and Relational Database Schemas, Update Operations, Transactions, and Dealing with Constraint Violations.

ER and EER to Relational Mapping

Relational database design using ER to Relational Mapping, Mapping EER Model concepts to relations.

Database Design

Informal design guidelines for Relation schemes, Functional dependencies, Normal forms based on primary keys, General definitions of second and third normal forms.

Unit-3
Teaching Hours:12
Advanced normalization concepts and SQL,Basic SQL
 

Boyce – Code normal form, multi-valued dependencies and fourth normal form, Join dependencies and fifth normal form.

Basic SQL      

SQL Data Definition and Data Types, Specifying Constraints in SQL, Basic Retrieval Queries in SQL, INSERT, DELETE, and UPDATE Statements in SQL, Additional features of SQL.

Text Books And Reference Books:

[1] Elmasri & Navathe, Fundamentals of Database Systems, Addison-Wesley, 6th Edition, 2010.

Essential Reading / Recommended Reading

[1] Korth F. Henry and Silberschatz Abraham, Database System Concepts, McGraw Hill, 6th Edition, 2010.

[2] O’neil Patric, O’neil Elizabeth ,Database Principles, Programming and Performance, Argon Kaufmann Publishers, 2nd Edition, 2002.

[3] Ramakrishnan and Gehrke, Database Management System, McGraw-Hill, 3rd  Edition, 2003

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA133 - OPERATING SYSTEM (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To acquire the fundamental knowledge of the operating system architecture and components.

Course Outcome

CO1: Demonstrate the fundamental principles of operating system, system structure, system calls, programs and system boot.

CO2: Evaluate the process scheduling, Thread scheduling, scheduling criteria, critical section problems to calculate the processing time effectively.

CO3: Implement deadlock system and multiple memory management strategies.

CO4: Apply the appropriate file system for overall management of any operating system.

CO5: Analyze the file management concepts using LINUX.

Unit-1
Teaching Hours:12
Fundamentals
 

Operating system definition, Computer system organization, structure, architecture and operations, process and storage management, Protection and security, Distributed systems, Special purpose systems, Computing Environments, Linux Operating Systems.  System structure: operating system services, user interface, system calls, system programs, OS design, Implementation and structure, virtual machines, system boot

OS structure and system calls can be demonstrated using Linux.

Unit-2
Teaching Hours:12
Process Scheduling
 

Process concepts, scheduling, operations on processes, Inter process communication, Examples of IPC systems, Communication in client server systems, Threads, Multi threading models, threading issues, Basic concepts, scheduling criteria, scheduling algorithms, Thread scheduling, Multiple-processor scheduling.

IPC, Threads, Scheduling algorithms can be demonstrated using Linux.

Unit-3
Teaching Hours:12
Process Coordination
 

Critical section problems, Peterson solution, Introduction to semaphores, classic problems of synchronization, Monitors, synchronization examples, atomic transaction, System model, deadlock characterization, methods for handling deadlock, deadlock prevention, avoidance, detection and recovery from deadlock.

Process synchronization and deadlock concepts can be demonstrated using Linux.

Text Books And Reference Books:

[1] Silberschatz, P.B. Galvin, G. Gadne, Operating System Concepts, Wiley-India, 9th Edition, 2015.

[2] Robert Love, Linux System Programming, O’Reilly, 2014.

 

Essential Reading / Recommended Reading

[1]William Stallings, Operating Systems: Internals and Design Principles, Pearson, 7th Edition, 2013.

Evaluation Pattern

 CIA: 50%

ESE: 50%


 

MCA134 - PROBABILITY AND STATISTICS (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The main aim of this course is to provide the grounding knowledge of statistical methods for data analytics. Data summarization, probability, random variables with properties and distribution functions were included. Sampling distributions and their applications in hypothesis testing advanced statistical methods like ANOVA and correlation and regression analysis were included.

Course Outcome

After completion of this course, students are able to

 CO1: Understand how to summarize and present the data using exploratory data analysis

CO2: Demonstrate the distribution functions of data and important characteristics

CO3: Infer the sampling distributions and their applications in hypothesis testing

CO4: Identify the relationship between the variables and modeling the same

Unit-1
Teaching Hours:10
Exploratory Data Analysis
 

Definition of Statistics, applications, data types and measurements, graphical representation of data using histogram, line diagram, bar diagram, time series plots; measures of central tendency and dispersion; coefficient of skewness and kurtosis and their practical importance.

Unit-2
Teaching Hours:15
Probability and Random Variables
 

Random experiment, sample space and events. Definitions of probability, addition and multiplication rules of probability, conditional probability and some numerical examples; Random variables: Definition, types of random variables, pmf and pdf of random variables; Mathematical expectation: mean, variance, covariance, mgf and cgf of a random variable(s); Probability distributions: Binomial, Poisson and Normal distributions with their important characteristics.

Unit-3
Teaching Hours:10
Sampling Distributions
 

Concepts of population, sample, parameter, statistic, and sampling distribution of a statistics; Sampling distribution of standard statistics like, sample mean, variance, proportions etc. t, F and Chi- square distributions with statistical properties

 

Text Books And Reference Books:

  1. Gupta S.C & Kapoor V.K, Fundamentals of Mathematical statistics, SultanChand & sons, 2009. 
  2. Douglas C Montgomery, George C Runger, Applied Statistics and Probability for Engineers, Wiley student edition, 2004.
Essential Reading / Recommended Reading

  1.  Freund J.E, Mathematical statistics, Prentice hall, 2001.
  2. Levine, David M; Berenson, L Mark; Stephen, David, Statistics for Managers Using Microsoft Excel, 2nd ed, PHI, New Delhi (2012)
Evaluation Pattern

CIA: 50%

ESE: 50%

MCA135 - RESEARCH METHODOLOGY (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course is intended to assist students in planning and carrying out research. The students are exposed to the principles, procedures and techniques of implementing a research project. The course starts with an introduction to research and leads through the various methodologies involved in the research process. It focus on finding out the research gap from the literature using computer technology,introduces basic statistics required for research and report the research outcomes scientifically with emphasis on research ethics.

Course Outcome

CO1: Understand the essense of research and the necessity of defining a research problem.

CO2: Apply research methods and methodology including research design, data analysis, and interpretation.

CO3: Create scientific reports according to specified standards.

Unit-1
Teaching Hours:8
RESEARCH METHODOLOGY
 

Defining research problem:Selecting the problem, Necessity of defining the problem ,Techniques involved in defining a problem- Ethics in Research.

Unit-2
Teaching Hours:8
RESEARCH DESIGN
 

Principles of experimental design,Working with Literature: Importance, finding literature, Using your resources, Managing the literature, Keep track of references,Using the literature, Literature review,On-line Searching: Database ,SCIFinder, Scopus, Science Direct ,Searching research articles , Citation Index ,Impact Factor ,H-index.

Unit-3
Teaching Hours:7
RESEARCH DATA
 

Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation. 

Text Books And Reference Books:

[1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014.

[2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005. 

Essential Reading / Recommended Reading

[1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4thed. SAGE Publications, 2014.

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010. 

Evaluation Pattern

CIA - 50%

ESE - 50%

MCA171 - PROGRAMMING IN C AND DATA STRUCTURES (2020 Batch)

Total Teaching Hours for Semester:105
No of Lecture Hours/Week:7
Max Marks:150
Credits:5

Course Objectives/Course Description

 

To prepare students to create programs to solve real world problems and also to design appropriate data structure to improve its efficiency.

Course Outcome

CO1: Able to understand problem solving through C Programming

CO2: Able to demonstrate basic data structures

CO3: Design and develop modular programs using relevant data structure operations to improve the efficiency of the programs

Unit-1
Teaching Hours:21
PROGRAMMING CONCEPTS
 

Introduction to C-Identifiers and keywords-Data types and constants-Variables - Operators and expressions-Data input and output-Control statements

Self-learning

Applications and Features of C

Lab Exercise:

1.   1. Implementation of the various Data Types with modifiers and type conversion in C

2.  2.Implementation of various Control structures in C

 

3.  3.Implementation of Operators

Unit-2
Teaching Hours:21
FUNCTIONS
 

Arrays and Strings-Functions and structures-Recursions- Storage Classes-Command line arguments-Macro Processor-Pointers in C- Pointer to functions

Self-learning

 

Various built-in functions in C

Lab Exercise:

1. Implementation of functions

2. Demonstration of string operation

 3. Demonstration of command line arguments

 

Unit-3
Teaching Hours:21
INTRODUCTION TO DATA STRUCTURES
 

Introduction to data structures-Complexity analysis-performance measures for data structures-Arrays and Structures- Abstract Data Type(ADT), Array representation and ADT, Dynamically Allocated Arrays- Sparse matrix- Stacks-ADT-Stacks using dynamic arrays - Applications –Queue-ADT- Circular queue- Priority queue

Self-learning

Multidimensional array and  Evaluation of expressions

Lab Exercise:

1. Implementation of Arrays

2. Implementation Stacks.

3. Implementation Queues.

Text Books And Reference Books:

[1] Forouzon A Behrouz , Gilberg F Richard ,A Structured Programming Approach usingC- 3rd Illustrated Edition, 2009

[2] Aaron M. TenenbaumYedidyahLangsam ,Moshe J. Augenstein, Data Structures Using C,  Pearson India,2019

Essential Reading / Recommended Reading

[1] Horowitz Sahni Anderson-Freed, Fundamental of Data Structures in C, Universities Press, Reprint 2009

[2] YashwantKanetkar, Data Structures Through C, 9th Edition, BPB Publication 2010.

[3] Tremblay J.P and Sorenson P.G: An Introduction to Data Structures with Applications, Tata McGraw-Hill 2nd Edition, 2002,

[4] Programming With C, Programming With C, McGraw Hill Education, Chennai: 2019.

[5] Dey, Pradip, Manas, Ghosh. and Thareja, Reema, Computer Programming and Data Structures , Oxford University Press; 2009

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA172 - WEBSTACK DEVELOPMENT (2020 Batch)

Total Teaching Hours for Semester:105
No of Lecture Hours/Week:7
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course is designed to introduce the students to advanced technologies which can help realization of complex Web applications. Students examine advanced topics in Hyper Text Markup Language, Cascade Style Sheet and JavaScript for interactive web applications that use rich user interfaces and also understand the server-side web technologies for dynamic web applications and creating modern web applications using MEAN and FULL Stack.

On completion of this course, a student will be familiar with client server architecture and able to develop a web application using advanced technologies and cultivate good web programming style and discipline by solving the real world scenarios

Course Outcome

CO1: Apply JavaScript, HTML5, and CSS3 effectively to create interactive and dynamic websites

CO2: Describe the main technologies and methods currently used in creating advanced web applications

CO3: Design websites using appropriate security principles, focusing specifically on the vulnerabilities inherent in common web implementations

CO4: Create modern web applications using MEAN&FULL stack 

Unit-1
Teaching Hours:21
OVERVIEW OF WEB TECHNOLOGIES AND HTML5
 

Internet and web Technologies- Client/Server model -Web Search Engine-Web Crawling-Web Indexing-Search Engine Optimization and Limitations-Web Services –Collective Intelligence –Mobile Web –Features of Web 3.0-HTML vs HTML5-Exploring Editors and Browsers Supported by HTML5-New Elements-HTML5 Semantics-Migration from HTML to HTML5-Canvas-HTML Media

Self-learning

Introduction to CSS3-CSS2 vs CSS3

Lab Exercise:

1. Develop static pages for a given scenario using HTML

2. Creating Web Animation with audio using HTML5 & CSS3

3. Demonstrate Geolocation and Canvas using HTML5

 

Unit-2
Teaching Hours:21
XML AND AJAX
 

XML AND AJAX XML-Documents and Vocabularies-Versions and Declaration -Namespaces JavaScript and XML: Ajax-DOM based XML processing Event-oriented Parsing: SAX-Transforming XML Documents-Selecting XML Data:XPATH-Template based Transformations: XSLT-Displaying XML Documents in Browsers - Evolution of AJAX -Web applications with AJAX -AJAX Framework

Lab Exercise:

1.      Write an XML file and validate the file using Document Type Definition (DTD)

2.      Demonstrate DOM parser

3.      Demonstrate SAX parser

Unit-3
Teaching Hours:21
CLIENT SIDE SCRIPTING
 

JavaScript Implementation - Use Javascript to interact with some of the new HTML5 apis -Create and modify Javascript objects- JS Forms - Events and Event handling-JS Navigator-JS Cookies-Introduction to JSON-JSON vs XML-JSON Objects-Importance of Angular JS in web-Angular Expression and Directives

Lab Exercise:

1.      Write a JavaScript program to demonstrate Form Validation and Event Handling

2.      Create a web application using AngularJS with Forms.

3.      Implement web application using AJAX with JSON

Text Books And Reference Books:

 1. Internet and World Wide Web:How to Program,  Paul Deitel , Harvey Deitel & Abbey Deitel , Pearson Education, Fifth edition,2018

2. HTML 5 Black Book (Covers CSS3, JavaScript, XML, XHTML, AJAX, PHP, jQuery), DT Editorial Services, Dreamtech Press,Second Edition,2016

Essential Reading / Recommended Reading

 1. The Full Stack Developer: Your Essential Guide to the Everyday Skills Expected of a Modern Full Stack Web Developer,Chris Northwood,Apress Publications,First edition,2018 

2. Mastering HTML, CSS & Javascript Web Publishing, Laura Lemay, Rafe Colburn & Jennifer Kyrnin, BPB Publications, First edition,2016

3. Mastering MongoDB 3.x, Alex Giamas Packt Publishing Limited,First Edition,2017

Web Resources:

1.      www.w3cschools.com

2.      http://www.php.net/docs.php

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA231 - COMPUTER NETWORKS (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

To study about network components, topologies, network models, protocols and algorithms.

Course Outcome

CO1: Comprehend knowledge about network architecture and its functionality

CO2: Evaluate different techniques / algorithms of standard network models

CO3: Analyze network protocols for data transmission in various types of networks

CO4: Design solution to real time problems related to network security and compression

Unit-1
Teaching Hours:12
The Physical Layer
 

Wireless Transmission, Brief introduction about bluetooth and wimax. Multiplexing: Frequency Division Multiplexing, Wavelength Division Multiplexing, Time Division Multiplexing; Switching: Circuit Switching, Message Switching, Packet Switching; Ethernet cabling, Manchester encoding, Differential Manchester Coding.

Self Learning:

Network Hardware: LAN, MAN, WAN, Wireless Network, Guided Transmission media: Magnetic Media, Twisted Pair, Coaxial Cable, Fiber Optics

Unit-1
Teaching Hours:12
Introduction, The Physical Layer
 

Introduction

Uses of Computer Networks, Internetworks; Network Software: Protocol hierarchies, Design issues for the layers, Connection Oriented and Connection less Services, Service Primitives; Reference Models: OSI, TCP/IP, Comparison of OSI and TCP reference models. 

Unit-2
Teaching Hours:13
The Medium Access Control Sublayer
 

The Channel Allocation problem, Multiple access protocols: ALOHA, Pure ALOHA, Slotted ALOHA, Carrier Sense Multiple Access protocols, Persistent and Non persistent CSMA, CSMA with collision detection, Collision-Free protocols: Bit map protocol, Binary countdown; Limited Contention protocols; Brief introduction to IEEE 802 standards; Ethernet MAC address, Brief introduction to Wireless LAN's, Bluetooth: Architecture, Applications, Protocol stack, Radio Layer, Bluetooth based layer, Frame structure; High-Speed LAN's, Satellite Networks.

Unit-2
Teaching Hours:13
The Data Link Layer, The Medium Access Control Sublayer
 

The Data Link Layer

Data Link layer design issues, Error Detection and Correction, Elementary Data Link protocols: Unrestricted simplex protocol, Simplex stop-and-wait protocol, Simplex protocol for a noisy channel; Sliding Window protocols: One-bit sliding window protocol, Protocol using Go back N, Example Data link protocol: Higher Level Data Link Control, Data link layer in the internet.

Unit-3
Teaching Hours:12
The Network Layer
 

Network layer design issues, Routing Algorithms: Optimality principle, Shortest Path Routing, Flooding, Distance Vector Routing, Link State Routing, Hierarchical Routing, Broadcast Routing, Multicast Routing; Congestion Control Algorithms: Congestion Prevention Policies, Jitter Control, Techniques for achieving good quality of service, Congestion control for multicasting; Internetworking, The Network layer in the Internet.

Text Books And Reference Books:

[1]  Andrew S Tanenbaum ,Computer Networks, PHI publications, 5th Edition, 2012.

[2]   Forouzan, Behrouz A., Mosharraf Firouz., Computer Networks A Top-Down Approach, TaTa McGraw Hill publications, First Edition, 2012.

Essential Reading / Recommended Reading

[1]   Stallings, William, Data & Computer Communications, Pearson Education Asia, 6th Edition, 2001.

 

[2]   Prakash C. Gupta, Data communications and Computer Networks, 1st Edition, 5th Reprint, PHI, 2009.

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA241A - DATA MINING (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

This course helps to preprocess and analyze data, choose relevant models and algorithms for respective applications and to develop research interest towards advances in data mining.

Course Outcome

 CO1: Understand different types of data to be mined

CO2: Categorize the scenario for applying different data mining techniques

CO3: Evaluate different models used for classification and Clustering

CO4: Focus towards research and innovation

Unit-1
Teaching Hours:12
Introduction and Data Preprocessing
 

 

Data Mining – Kinds of data to be mined – Kinds of patterns to be mined – Technologies – Targeted Applications - Major Issues in Data Mining – Data Objects and Attribute Types – Measuring Data similarity and dissimilarity - Data Cleaning –Data Integration - Data Reduction – Data Transformation – Data Discretization

Unit-2
Teaching Hours:12
MINING FREQUENT PATTERNS AND ADVANCED PATTERN MINING
 

 

Basic Concepts – Frequent Itemset Mining Methods – Pattern Evaluation Methods – Pattern Mining in Multilevel, Multidimensional space – Constraint-Based Frequent Pattern Mining – Mining Compressed or Approximate Patterns – Pattern Exploration and Application

Unit-3
Teaching Hours:12
CLASSIFICATION TECHNIQUES
 

 

Basic Concepts – Decision Tree Induction – Bayes Classification Methods – Rule-Based Classification – Model Evaluation and Selection – Techniques to Improve Classification Accuracy – Bayesian Belief Networks – Classification by Backpropagation – Support Vector Machines

Text Books And Reference Books:

1. Data Mining Concept and Techniques, Jiawei Han, Micheline Kamber, Jian Pie, Morgan and Kaufmann Publisher, Third Edition,2012

              2. Data Mining Techniques, Arun K Pujari, Second Edition, Universities Press India Pvt. Ltd.2010

Essential Reading / Recommended Reading

1.         Data Mining and Predictive Analytics Daniel T. Larose, Chantal D. Larose (Wiley Series on Methods and Applications in Data Mining), WileyPublications,

2.         Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Mark A. Hall, Morgan and Kaufmann Publisher, Third Edition,2014

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA241B - INTERNET OF THINGS (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

The explosive growth of the “Internet of Things” is changing our world and the rapid growth of IoT components is allowing people to innovate new designs and products at home.  Wireless Sensor Networks form the basis of the Internet of Things. To latch on to the applications in the field of IoT of the recent times, this course provides a deeper understanding of the underlying concepts of IoT and Wireless Sensor Networks.

Course Outcome

CO1: Understand the concepts of IoT and IoT enabling technologies

CO2: Gain knowledge on IoT programming and able to develop IoT applications

CO3: Identify different issues in wireless ad hoc and sensor networks

CO4: To develop an understanding of sensor network architectures from a design and performance perspective

CO5: To understand the layered approach in sensor networks and WSN protocols

Unit-1
Teaching Hours:12
INTRODUCTION TO IoT
 

Introduction to IoT - Definition and Characteristics, Physical Design Things- Protocols, Logical Design- Functional Blocks, Communication Models- Communication APIs- Introduction to measure the physical quantities, IoT Enabling Technologies - Wireless Sensor Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded System- IoT Levels and Deployment Templates.

Unit-2
Teaching Hours:12
IoT PROGRAMMING
 

Introduction to Smart Systems using IoT - IoT Design Methodology- IoT Boards (Rasberry Pi, Arduino) and IDE - Case Study: Weather Monitoring- Logical Design using Python, Data types & Data Structures- Control Flow, Functions- Modules- Packages, File Handling - Date/Time Operations, Classes- Python Packages of Interest for IoT.

Unit-3
Teaching Hours:12
IoT APPLICATIONS
 

Home Automation – Smart Cities- Environment, Energy- Retail, Logistics- Agriculture, Industry- Health and Lifestyle- IoT and M2M.

Text Books And Reference Books:

[1]   ArshdeepBahga and Vijay Madisetti, Internet of Things: Hands-on Approach, Hyderabad University Press, 2015.

[2]   KazemSohraby, Daniel Minoli and TaiebZnati, Wireless Sensor Networks: Technology. Protocols and Application, Wiley Publications, 2010.

[3]   WaltenegusDargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, AJohn Wiley and Sons Ltd., 2010.

Essential Reading / Recommended Reading

[1]    Edgar Callaway, Wireless Sensor Networks: Architecture and Protocols, Auerbach Publications, 2003.

[2]  Michael Miller, The Internet of Things, Pearson Education, 2015.

[3]    Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor Networks, John Wiley & Sons Inc., 2005.

[4]   ErdalÇayırcıandChunmingRong, Security in Wireless Ad Hoc and Sensor Networks, John Wiley and Sons, 2009.

[5]   Carlos De MoraisCordeiro and Dharma PrakashAgrawal, Ad Hoc and Sensor Networks: Theory and Applications, World Scientific Publishing, 2011.

[6]  WaltenegusDargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks Theory and Practice, John Wiley and Sons, 2010

[7]   Adrian Perrig and J. D. Tygar, Secure Broadcast Communication: In Wired and Wireless Networks, Springer, 2006.

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA241C - ARTIFICIAL INTELLIGENCE (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

This course aims at developing an understanding about the issues involved in defining and simulating perception, identifying the problems where AI is required and the different methods available, to compare and contrast different AI techniques available,  to define and explain learning algorithms and to provide the student additional experience in the analysis and evaluation of complicated systems.

Course Outcome

 

CO1: Express the modern view of AI and its foundation

CO2: Illustrate Search Strategies with algorithms and Problems

CO3: Implement Propositional logic and apply inference rules

CO4: Apply suitable techniques for NLP and Game Playing

 

Unit-1
Teaching Hours:12
INTRODUCTION
 

Introduction to AI, The Foundations of AI, AI Technique -Tic-Tac-Toe. Problem characteristics, Production system characteristics, Production systems: 8-puzzle problem. Searching: Uniformed search strategies – Breadth first search, depth first search.

Unit-2
Teaching Hours:12
LOCAL SEARCH ALGORITHMS
 

Generate and Test, Hill climbing, simulated annealing search, Constraint satisfaction problems, Greedy best first search, A* search, AO* search. Toy problems

Unit-3
Teaching Hours:12
KNOWLEDGE REPRESENTATION
 

First order logic. Inference in first order logic, propositional Vs. first order inference, unification & lifts, Clausal form conversion, Forward chaining, Backward chaining, Resolution.

SELF LEARNING

Propositional logic - syntax & semantics

Text Books And Reference Books:

[1] E. Rich and K. Knight, Artificial Intelligence, 3rd  Edition. New york: TMH, 2019.

[2] S. Russell and P. Norvig, Artificial Intelligence A Modern Approach, 3rd  Edition. Pearson Education, 2019.

Essential Reading / Recommended Reading

[1] Eugene Charniak and Drew McDermott, Introduction to Artificial Intelligence, 2ndEdition. Singapore: Pearson Education, 2005.

[2] George F Luger, Artificial Intelligence Structures and Strategies for Complex ProblemSolving, 4th Edition. Singapore: Pearson Education, 2008, ISBN-13  9780321545893

[3] N.L. Nilsson, Artificial Intelligence: A New Synthesis, 1st Edition. USA: MorganKaufmann, 2000.

[4] Introduction to artificial intelligence by Patterson, ISBN-13: 978-0134771007

 

Web Resources:

 

1.     https://ai.google/education/

 

2.     https://intellipaat.com/blog/tutorial/artificial-intelligence-tutorial/

 

3.     https://www.javatpoint.com/artificial-intelligence-tutorial

 

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA241D - USER INTERFACE DESIGN (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

The objective of this course is: for students to learn how to design, prototype and evaluate user interfaces to effectively browse and search systems by examining what research has uncovered, what developers have produced, and how people perform information tasks.

Course Outcome

CO1: Describe design principles

CO2: Demonstrate impactful visual design and color concepts

CO3: Apply design principles and skills for design prototype

CO4: Design an intuitive design for software products

Unit-1
Teaching Hours:12
Introduction and Overview
 

Usability of interactive systems: Usability Goals and Measures, Usability Motivations, Universal Usability, Goals for our Design Profession. Guidelines, Principles, and Theories of Design.

Unit-2
Teaching Hours:12
UI Design Process and Interaction styles
 

Design process introduction, designing to address a problem w/o solution ideas, designing for a known solution direction, designing to iterate on/improve an existing solution, common elements, usability engineering and task-centered approaches, use cases, personas, tasks and scenarios, intro to design centered approaches, design centered methods and when they work best.

Direct manipulation and virtual environments-Introduction- Examples of direct Manipulation, discussion of Direct Manipulation, 3D interfaces, teleoperation, Virtual and Augmented Reality. Menu Selection,Form Fill-in,and Dialog Boxes- Introduction- Task related menu organization, single menus, combinations of multiple menus, content organization, fast movement through menus, Data entry with Menus, audio menus and menus for small displays.

Unit-3
Teaching Hours:12
Psychology and human Factors for User interface Design
 

Fitt’s Law, Short and long term memory, attention, perception and visualization, hierarchy, mistakes, errors and slipsm, conceptual models, the gulf executionand the gulf of evaluation, design principles: visibility, feedback, mappings, constraints, interacting beyond individuals (social psychology), high-level models:distributed cognition, activity theory, situated action, assignment video: interface critiques.

Text Books And Reference Books:

[1] Ben Shneiderman, Designing the User Interface, Pearson Education, 6th Edition, 2018

[2] Wilber O Galitz, An Introduction to GUI Design Principles and Techniques, John- Wiley &Sons, 2007

[3] Andrew Faulkner, Conrad Chavez, Adobe Photoshop CC Classroom in a Book, The official training work book from Adobe,2018.

Essential Reading / Recommended Reading

[1] Jeff Johnson, Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Rules , Morgan Kaufmann, 1st Edition, 2010.

[2] Alan J Dix et al, Human-Computer Interaction, Pearson, 2009.

 

Web Resources:

1.https://www.interaction-design.org

2.https://www.sketch.com/

3.https://www.invisionapp.com/studio

4.https://www.adobe.com/in/products/photoshop.html

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA241E - DIGITAL IMAGE PROCESSING (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

This course provides comprehensive understanding of theory and algorithms that are widely used in Digital image processing. Student gets hands-on experience to implement programs in Matlab to process images.

Course Outcome

CO1: Understand the theoretical background of Image processing

CO2: Apply image enhancement, restoration, compression and segmentation in both frequency and spatial domain.

CO3: Represent and recognize objects through patterns in application.

Unit-1
Teaching Hours:12
INTRODUCTION AND DIGITAL IMAGE FUNDAMENTALS
 

The origins of Digital Image Processing, Fundamental Steps in Image Processing, Elements of Digital Image Processing System, Image Sampling and Quantization, Basic relationships: Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations.

Unit-2
Teaching Hours:12
IMAGE ENHANCEMENT
 

Spatial Domain :Gray Level Transformations - Histogram Processing - Histogram equalization, Histogram specification - Basics of Spatial Filters - Smoothening and Sharpening Spatial Filters. Frequency Domain : Introduction to Fourier Transform and the frequency Domain - Smoothing and Sharpening - Frequency Domain Filters - Homomorphic Filtering

Unit-3
Teaching Hours:12
IMAGE RESTORATION AND IMAGE COMPRESSION
 

A model of The Image Degradation / Restoration Process - Noise Models, Restoration in the presence of Noise - Periodic Noise Reduction by Frequency Domain Filtering.  Image Compression models: Huffman coding - Run length coding - LZW coding.

Text Books And Reference Books:

[1] R. C. Gonzalez & R. E. Woods, Digital Image Processing, PearsonEducation, 4th Edition, 2018.

[2] A.K. Jain, Fundamental of Digital Image Processing, PHI, 4th Edition, 2011.

Essential Reading / Recommended Reading

[1] Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, Digital Image Processing Using MATLAB, PHI, 2nd Edition, 2009.

[2] M. A. Joshi, Digital Image Processing: An algorithmic approach, PHI, 2nd Edition, 2009.

[3] B. Chanda, D. Dutta Majumdar, Digital Image Processing and analysis, PHI, 1st Edition, 2011.

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA271 - MICROPROCESSOR AND ALP (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:05

Course Objectives/Course Description

 

To help students to understand the basics of 8085 microprocessor-based systems and assembly language programming. This Course also gives the introduction to 8051 microcontroller.

Course Outcome

CO1: Identify the basic elements and functions of microprocessor and describe the architecture of microprocessor and its peripheral devices

CO2: Understand the basic concept of microcontroller

CO3: Demonstrate fundamental understanding on the operation between the microprocessor and its interfacing devices

CO4: Apply the programming techniques in developing the assembly language program for microprocessor application

Unit-1
Teaching Hours:18
Microprocessor 8085,8085 Machine cycles and bus Timings
 

Microprocessor 8085         

Introduction to Microprocessor 8085 –Signals -Address Bus, Data Bus, Control & status signals, Power supply and Frequency signals, Externally initiated signals, serial I/O ports 

8085 Machine cycles and bus Timings     

Opcode Fetch Machine cycle, Memory Read, Memory Write, I/O Read and I/O Write Machine cycles, Calculation of execution time for a program with examples

1

Unit-1
Teaching Hours:18
Lab Exercises
 

     [1] Write a program to add N one byte numbe

     [2] Write a program to interchange N one bytes of data.

     [3] Write a program to check whether the 4th bit of a number is zero or one.  Display FF if 1 otherwise display 00. 

     [4] Write a program to find the first 10 terms of a Fibonacci sequence

     [5] Write a program to find sum of first 10 terms of odd and even series.

Unit-2
Teaching Hours:18
Architecture of 8085 MPU
 

Block Diagrams, Registers, Flags, ALU, Timing and Control Unit, Instruction Decoder, Serial I/O Control, Stack, PC, Address/Data Buffers

Unit-2
Teaching Hours:18
Lab Exercises
 

[6] Write a program to check whether a byte belongs to the 2-out-of-5codes. Display FF if it is a 2-out-of- 5 code otherwise00.(Number is 2-out-of-5 code if the left most three bits are zero and in the  remaining five bits  there are exactly two 1’s) 

 [7] Write a program to perform linear search over a set of N numbers.  Display FF and its position if found otherwise 00. 

[8] Write a program to add two 32 - bit binary numbers. 

[9] Write a program to add two 32 - bit BCD numbers. 

[10] Write a program to subtract a 16 - bit number from another 16 - bit number.

Unit-3
Teaching Hours:18
Introduction to 8085 programming
 

The 8085 programming model, Instruction Classification, Data Format and storage, 8085 instruction Set Addressing Modes, Data Transfer Operations, Arithmetic Operations, Logic Operations, Branch Operations, Programming Techniques, Writing simple programs.

Unit-3
Teaching Hours:18
Lab Exercises
 

[11] Write a program to subtract a 16 - bit BCD number from another 16 – bit BCD number.

[12] Write a program to multiply two 8 - bit number.  

[13] Write a program to divide a 16 - bit number by an 8 - bit numbers. 

[14] Write a program to find the largest and smallest of N numbers. 

[15] rite a program to sort the numbers in ascending and in descending and in descending order using bubble sort.

Text Books And Reference Books:

[1] Ramesh.S.Goankar ,Microprocessor Architecture, Programming & Applications With 8085, 5th Edition – Penram International – 2013. ISBN 81-87972-09-2.

Essential Reading / Recommended Reading

[1] Hall.D.V., Microprocessor and Digital System, McGraw Hill Publishing Company, 2nd Edition, 2008.

[2] Charles M Gilmore, Pal Ajit, Microprocessor Principles and Applications, Tata McGraw Hill, 2nd Edition, 2009.

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA272 - JAVA PROGRAMMING (2020 Batch)

Total Teaching Hours for Semester:105
No of Lecture Hours/Week:7
Max Marks:150
Credits:05

Course Objectives/Course Description

 

This course will help the learner to gain a sound knowledge in object oriented principles, GUI application design with data base, enterprise application design with java beans and Servlets.

Course Outcome

CO1: Understanding and applying the principles and practice of object oriented programming in the construction of robust maintainable programs

CO2: Competence in the use of Java Programming Language in the development of small to medium sized applications that demonstrate  professionally  acceptable  coding  and  performance  standards 

CO3: To prepare the students to address the challenging requirements coming from the enterprise applications

Unit-1
Teaching Hours:21
Introduction to Object Oriented Programming (OOP) and Classes Introduction to Object Oriented Programing(OOP)
 

Introduction to Object Oriented Programing(OOP)

Object-Oriented Programming(OOP) Principles- The Evolution of Java- Buzzwords of Java - Class Fundamentals - Declaring Objects - Introducing Methods - Overloading methods – Constructors - Parameterized Constructors - this Keyword

 

Class features

Garbage Collection - the finalize () Method - Introducing Access Control - Understanding static - Introducing nested and inner classes - String class - String Buffer Class - Command Line Arguments

Unit-1
Teaching Hours:21
Lab Exercises
 

Self Learning Topics

Data types, Keywords, Operators, Control Statements and Arrays

 

Lab 1

Identify a domain of your choice, list out ten entities in the domain. For each entity, identify minimum 10 attributes and assign the data type for each attribute with proper justification.

Lab 2

Select any one entity from the domain, design the class for that entity and implement various operators in the class.

Lab 3

Implement the concept of control statements and Arrays in the class 

Lab4

Implement the concept of class, data members, member functions and accessspecifiers.

Lab5

Implement the concept of function overloading & Constructoroverloading.

Lab6

Implement the static keyword – static variable, static block, static function and staticclass

Lab7

Implement String and String Bufferclasses.

Lab8

Implement this keyword and command line arguments.

Unit-2
Teaching Hours:21
Inheritance in Java, Interfaces and Packages, Exception Handling in Java
 

Inheritance in Java

Inheritance Basics - Multilevel Hierarchy- Using super - Method overriding - Dynamic Method Dispatch- Abstract keyword- Using final with inheritance - the Object Class.

Interfaces and Packages

Inheritance in java with Interfaces – Defining Interfaces - Implementing Interfaces - Extending Interfaces- Creating Packages - CLASSPATH variable - Access protection  -  Importing Packages - Interfaces in a Package.

Exception Handling in Java

try-catch-finally mechanism - throw statement - throws statement - Built-in-Exceptions – CustomExceptions.

Unit-2
Teaching Hours:21
Lab Exercises
 

Lab 9

Implement the concept of inheritance, super, abstract and final keywords injava.

Lab10

Implement package and interface keywords injava

Lab11

Implement Exception Handing in java

Unit-3
Teaching Hours:21
Multithreading, Generics and The Collections Framework
 

Multithreading         

Java Thread Model - Life cycle of a Thread - Java Thread Priorities - Runnable interface and Thread Class- Thread Synchronization – Inter Thread Communication.

Generics

Generics Concept - General Form of a Generic Class – Bounded Types – Generic Class Hierarchy - Generic Interfaces – Restrictions in Generics

The Collections Framework

The Collections Overview – Collection Interface – List Interface – Set Interface – SortedSet Interface – Queue Interface -  ArrayList Class – LinkedList Class – HashSet Class – Using an Iterator – The For Each Statement

Unit-3
Teaching Hours:21
Lab Exercises
 

Lab 12

Implement multithreading – Thread class, Runnable interface, thread synchronization and thread communication.

Lab 13

Implement generic concept – generic class and generic interface

Lab 14

Implement collections – collection Interfaces and collection classes

Text Books And Reference Books:

[1] Schildt Herbert, Java: The Complete Reference, Tata McGraw-Hill, 10th Edition, 2017.

Essential Reading / Recommended Reading

[1]  Java How to Program, Paul Deitel, Pearson Education Asia, 11th Edition,2017.

[2]  Core Java Volume 1 Fundamentals, Cay S Horstmann, Prentice Hall, 11th Edition,2018.

 

Web Resources:

    [1] www.w3cschools.com

    [2] https://www.javatpoint.com/

    [3] http://stackoverflow.com/

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA273 - PYTHON PROGRAMMING (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course covers programming paradigms brought in by Python with a focus on Regular Expressions, List and Dictionaries. It explores the various modules and libraries to cover the landscape of Python programming.

Course Outcome

CO1: Demonstrate the use of the built ‐in objects of Python

CO2: Demonstrate significant experience with the Python program development environment.

CO3: Understand and implement the basic methods of python modules like NumPy, Matplotlib

Unit-1
Teaching Hours:12
INTRODUCTION TO PYTHON DATA STRUCTURES
 

Underlying mechanism of Module Execution- Sequences, Mapping and Sets- Dictionaries- Functions - Lists and Mutability- Problem Solving Using Lists and Functions. Custom and built-in modules.

Lab Exercises:

1. Implement python objects

2Implement lambda functions and custom modules  

Unit-2
Teaching Hours:12
OBJECT ORIENTED PROGRAMMING USING PYTHON AND REGULAR EXPRESSIONS
 

Classes: Classes and Instances-Inheritance—Polymorphism- Abstract classes-Exceptional Handling- Regular Expressions using “re” module.

Lab Exercises:

3. Implement Polymorphism

4. Implement “re” module

Unit-3
Teaching Hours:12
USING NUMPY, PANDAS AND MATPLOTLIB
 

Computation on NumPy-Aggregations-Computation on Arrays-Comparisons, Masks and Boolean Arrays-Fancy Indexing-Sorting Arrays-Structured Data: NumPy’s Structured Array. Introduction to Pandas Objects-Data indexing and Selection-Operating on Data in Pandas-Handling Missing Data-Hierarchical Indexing. Basic functions of Matplotlib-Simple Line Plot, Scatter Plot.

Lab Exercises:

5. Implement “Numpy” and “Matplotlib” modules to plot line and scatterplot

6. Implement Pandas to demonstrate data handling and indexing

Text Books And Reference Books:

[1] Zhang.Y ,An Introduction to Python and Computer Programming, Springer Publications,2016

[2] Wesely J.Chun,Core Python Application Programming ,Prentice Hall,third edition 2015.

Essential Reading / Recommended Reading

[1] Mark Lutz ,Programming Python, O’Reily Media,Inc, 2013

              [2] T.R.Padmanabhan, Programming with Python,Springer Publications,2016

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA274 - SOFTWARE ENGINEERING PROJECT (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To enable the students to understand the concepts software engineering.  To prepare the students to develop the skills necessary to handle software projects. To make the students aware of the importance of software engineering principles in designing software projects.

Course Outcome

CO1: Understand the importance of the stages in the software life cycle and the various process models

CO2: Design software by applying the software engineering principles

CO3: Develop the quality software using efficient project management

Unit-1
Teaching Hours:10
Process models, Understanding Requirements and Design Concepts
 

A generic process model – Prescriptive Process Models – The waterfall Model, Incremental Model, Evolutionary Process Model, Concurrent Model, Component based Development, The formal Methods Model. Requirements Engineering - Design concepts – Abstraction, Architecture, Patterns, Separation of  concerns, Modularity, information hiding, Functional Independence, refinement, Aspects, Refactoring, Object Oriented design concepts Design classes,  The design Model – Data Design elements, Architectural Design elements, Interface Design Elements, Component-Level Design elements, Deployments level Design elements.

Unit-2
Teaching Hours:10
Component Level Design, User Interface Design
 

What is a component – An Object-Oriented View, The Traditional View, A Process-Related View, Designing class based components – Basic Design Principles, Component-level Design guidelines, Cohesion, Coupling,  Functional design at the Component level,  designing traditional components – Graphical design notation, Tabular Design Notation, Program Design Language, Component based development- Domain Engineering, Component qualification, Adaptation, and Composition, Analysis and Design for reuse, classifying and retrieving components. The golden rules- Place the User in Control, Reduce the User's Memory load, Make the interface Consistent, Interface Analysis and Design models, The Process, Interface  Analysis User Analysis, Task Analysis, Analysis of Display Content, Analysis of the Work Environment, Interface design steps – Applying Interface Design steps, User Interface design patterns, Design Issues.

Unit-3
Teaching Hours:10
Quality Management, Testing
 

Software Quality, Garvin's Quality Dimensions, McCall's Quality Factors, ISO 9126 Quality Factors, Targeted Quality factors, Transition to a Quantitative view, Achieving software quality- Software Engineering Methods, Project Management Techniques, Quality Control, Quality Assurance. Software testing fundamentals, internal and external view of testing, White-box testing, Basic path testing -  Flow graph notation, Independent program path, Deriving test cases, Graph matrices-, , control structure testing – Condition testing, Data flow testing, loop testing-, Black- box testing – Graph- based Testing Methods, Equivalence Partitioning, Boundary Value Analysis, Orthogonal Array Testing, Model Based Testing, Testing for specialized environments, Architectures, and Applications – Testing GUIs, Testing of Client-Server Architectures, Testing Documentation and Help facilities, testing for Real-Time Systems, Patterns for software testing.

Text Books And Reference Books:

[1] Pressman S Roger, Software Engineering -  A Practitioner’s Approach, Mc Graw Hill, 7th edition, 2014.

Essential Reading / Recommended Reading

[1] Sommerville, Ian, Software Engineering, Addison Wesley, 9th edition, 2010.

Evaluation Pattern

CIA: 50%

ESE: 50%

 

MCA281 - SEMINAR (2020 Batch)

Total Teaching Hours for Semester:15
No of Lecture Hours/Week:1
Max Marks:50
Credits:01

Course Objectives/Course Description

 

The course is designed to enhance the soft skills and technical understanding of the students.

Course Outcome

CO1: Understand new and latest trends in Computer Science

CO2: Demonstrate the professional presentation abilities

CO3: Apply the acquired knowledge in their Research

Unit-1
Teaching Hours:30
Seminar
 

Students will be giving presentations on any advanced concepts and technologies in Computer Science and submit the report.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

-

MCA331 - COMPUTER NETWORKS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

To study about network components, topologies, network models, protocols and algorithms.

Course Outcome

CO1: Comprehend knowledge about network architecture and its functionality

CO2: Evaluate different techniques / algorithms of standard network models

CO3: Analyze network protocols for data transmission in various types of networks

CO4: Design solution to real time problems related to network security and compression

Unit-1
Teaching Hours:12
Introduction,The Physical Layer
 

Introduction

Uses of Computer Networks, Internetworks; Network Software: Protocol hierarchies, Design issues for the layers, Connection Oriented and Connection less Services, Service Primitives; Reference Models: OSI, TCP/IP, Comparison of OSI and TCP reference models. 

Unit-1
Teaching Hours:12
The Physical Layer
 

Wireless Transmission, Brief introduction about bluetooth and wimax. Multiplexing: Frequency Division Multiplexing, Wavelength Division Multiplexing, Time Division Multiplexing; Switching: Circuit Switching, Message Switching, Packet Switching; Ethernet cabling, Manchester encoding, Differential Manchester Coding.

Self Learning:

Network Hardware: LAN, MAN, WAN, Wireless Network, Guided Transmission media: Magnetic Media, Twisted Pair, Coaxial Cable, Fiber Optics

Unit-2
Teaching Hours:13
The Data Link Layer, The Medium Access Control Sublayer
 

The Data Link Layer

Data Link layer design issues, Error Detection and Correction, Elementary Data Link protocols: Unrestricted simplex protocol, Simplex stop-and-wait protocol, Simplex protocol for a noisy channel; Sliding Window protocols: One-bit sliding window protocol, Protocol using Go back N, Example Data link protocol: Higher Level Data Link Control, Data link layer in the internet.

Unit-2
Teaching Hours:13
The Medium Access Control Sublayer
 

The Channel Allocation problem, Multiple access protocols: ALOHA, Pure ALOHA, Slotted ALOHA, Carrier Sense Multiple Access protocols, Persistent and Non persistent CSMA, CSMA with collision detection, Collision-Free protocols: Bit map protocol, Binary countdown; Limited Contention protocols; Brief introduction to IEEE 802 standards; Ethernet MAC address, Brief introduction to Wireless LAN's, Bluetooth: Architecture, Applications, Protocol stack, Radio Layer, Bluetooth based layer, Frame structure; High-Speed LAN's, Satellite Networks.

 

Unit-3
Teaching Hours:12
The Network Layer
 

Network layer design issues, Routing Algorithms: Optimality principle, Shortest Path Routing, Flooding, Distance Vector Routing, Link State Routing, Hierarchical Routing, Broadcast Routing, Multicast Routing; Congestion Control Algorithms: Congestion Prevention Policies, Jitter Control, Techniques for achieving good quality of service, Congestion control for multicasting; Internetworking, The Network layer in the Internet.

Text Books And Reference Books:

[1]  Andrew S Tanenbaum ,Computer Networks, PHI publications, 5th Edition, 2012.

[2]   Forouzan, Behrouz A., Mosharraf Firouz., Computer Networks A Top-Down Approach, TaTa McGraw Hill publications, First Edition, 2012.

Essential Reading / Recommended Reading

[1]     Stallings, William, Data & Computer Communications, Pearson Education Asia, 6th Edition, 2001.

 

[2]   Prakash C. Gupta, Data communications and Computer Networks, 1st Edition, 5th Reprint, PHI, 2009.

Evaluation Pattern

CIA:50%

ESE:50%

MCA332 - SOFTWARE ENGINEERING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To provide the students to understand the concepts software engineering.  To prepare the students to develop the skills necessary to handle software projects. To make the students aware of the importance of software engineering principles in designing software projects.

Course Outcome

CO1: Understand the importance of the stages in the software life cycle and the various process models.

CO2: Design software by applying the software engineering principles.

CO3: Develop the quality software using efficient project management

Unit-1
Teaching Hours:12
Process models, Understanding Requirements
 

A generic process model – Defining a framework activity, identifying a Task Set, Process Patterns, Process Assessment and improvement, Prescriptive Process Models – The waterfall Model, Incremental Model, Evolutionary Process Model, Concurrent Model,  Component based Development, The formal Methods Model. Requirements Engineering, Establishing the groundwork – Identifying Stakeholders, Recognizing multiple viewpoints, Working toward Collaboration, Asking the first questions, Eliciting requirements – Collaborative requirement gathering, Quality function Deployments, Usage Scenario Elicitation Work Products, Developing use cases, building the requirements model – Elements of the requirements Model, Analysis pattern, Negotiating requirements, validating requirements.

Unit-2
Teaching Hours:12
Design Concepts
 

The design within the context of Software Engineering,  The design process – Software quality guidelines and attributes, The evolution of software design, Design concepts – Abstraction, Architecture, Patterns, Separation of  concerns, Modularity, information hiding, Functional Independence, refinement, Aspects, Refactoring, Object Oriented design concepts Design classes,  The design Model – Data Design elements, Architectural Design elements, Interface Design Elements, Component-Level Design elements, Deployments level Design elements. Software architecture – What is architecture, Why is Architecture important, Architectural descriptions, Architectural Decisions, Architectural style – Brief taxonomy of Architectural  styles,  Architectural Patterns, Organization and refinement.

Unit-3
Teaching Hours:12
Component Level Design, User Interface Design
 

What is a component – An Object-Oriented View, The Traditional View, A Process-Related View, Designing class based components – Basic Design Principles, Component-level Design guidelines, Cohesion, Coupling,  Functional design at the Component level,  designing traditional components – Graphical design notation, Tabular Design Notation, Program Design Language, Component based development- Domain Engineering, Component qualification, Adaptation, and Composition, Analysis and Design for reuse, classifying and retrieving components. The golden rules- Place the User in Control, Reduce the User's Memory load, Make the interface Consistent, Interface Analysis and Design models, The Process, Interface  Analysis User Analysis, Task Analysis, Analysis of Display Content, Analysis of the Work Environment, Interface design steps – Applying Interface Design steps, User Interface design patterns, Design Issues.

Text Books And Reference Books:

[1] Pressman S Roger, Software Engineering -  A Practitioner’s Approach, Mc Graw Hill, 7th edition, 2014.

Essential Reading / Recommended Reading

[1] Sommerville, Ian, Software Engineering, Addison Wesley, 9th edition, 2010.

Evaluation Pattern

 

CIA (Weightage)

ESE (Weightage)

50%

50%

 

 

 
 

 

MCA333 - OPERATING SYSTEMS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

To acquire the fundamental knowledge of the operating system architecture and components.

Course Outcome

CO1: Demonstrate the fundamental principles of operating system, system structure, system calls, programs and system boot

CO2: Evaluate the process scheduling, Thread scheduling, scheduling criteria, critical section problems to calculate the processing time effectively

CO3: Implement deadlock system and multiple memory management strategies

CO4: Apply the appropriate file system for overall management of any operating system

 

CO5: Analyze the file management concepts using LINUX

 

Unit-1
Teaching Hours:12
Fundamentals
 

Operating system definition, Computer system organization, structure, architecture and operations, process and storage management, Protection and security, Distributed systems, Special purpose systems, Computing Environments, Linux Operating Systems. System  structure: operating system services, user interface, system calls, system programs, OS design, Implementation and structure, virtual machines, systemboot

OS structure and system calls can be demonstrated using Linux.

Unit-2
Teaching Hours:12
Process Scheduling
 

Process concepts, scheduling, operations on processes, Inter process communication, Examples of IPC systems, Communication in client server systems, Threads, Multi threading models, threading issues, Basic concepts, scheduling criteria, scheduling algorithms, Thread scheduling, Multiple-processor scheduling.

IPC, Threads, Scheduling algorithms can be demonstrated using Linux.

 

Unit-3
Teaching Hours:12
Process Coordination
 

Critical section problems, Peterson solution, Introduction to semaphores, classic problems of synchronization, Monitors, synchronization examples, atomic transaction, System model, deadlock characterization, methods for handling deadlock, deadlock prevention, avoidance, detection and recovery from deadlock.

Process synchronization and deadlock concepts can be demonstrated using Linux.

Text Books And Reference Books:

[1]   Silberschatz, P.B. Galvin, G. Gadne, Operating System Concepts, Wiley-India, 9th Edition, 2015.

[2]  Robert Love, Linux System Programming, O’Reilly,2014.

Essential Reading / Recommended Reading

[1]William Stallings, Operating Systems: Internals and Design Principles, Pearson, 7th Edition, 2013.

Evaluation Pattern

50%CIA + 50% ESE

MCA371 - JAVA PROGRAMMING (2019 Batch)

Total Teaching Hours for Semester:105
No of Lecture Hours/Week:7
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course will help the learner to gain a sound knowledge in object oriented principles, GUI application design with data base, enterprise application design with java beans and Servlets.

Course Outcome

CO1: Understanding and applying the principles and practice of object oriented programming in the construction of robust maintainable programs

CO2: Competence in the use of Java Programming Language in the development of small to medium sized applications that demonstrate  professionally  acceptable  coding  and  performance  standards 

CO3: To prepare the students to address the challenging requirements coming from the enterprise applications

Unit-1
Teaching Hours:21
Introduction to Object Oriented Programming (OOP) and Classes Introduction to Object Oriented Programing(OOP)
 

Object-Oriented Programming(OOP) Principles- The Evolution of Java- Buzzwords of Java - Class Fundamentals - Declaring Objects - Introducing Methods - Overloading methods – Constructors - Parameterized Constructors - this Keyword

Class features

Garbage Collection - the finalize () Method - Introducing Access Control - Understanding static - Introducing nested and inner classes - String class - String Buffer Class - Command Line Arguments

Unit-1
Teaching Hours:21
LAB
 

Data types, Keywords, Operators, Control Statements and Arrays

Lab 1

Identify a domain of your choice, list out ten entities in the domain. For each entity, identify minimum 10 attributes and assign the data type for each attribute with proper justification.

Lab 2

Select any one entity from the domain, design the class for that entity and implement various operators in the class.

Lab 3

Implement the concept of control statements and Arrays in the class 

Lab4

Implement the concept of class, data members, member functions and accessspecifiers.

Lab5

Implement the concept of function overloading & Constructoroverloading.

Lab6

Implement the static keyword – static variable, static block, static function and staticclass

Lab7

Implement String and String Bufferclasses.

Lab8

Implement this keyword and command line arguments.

Unit-2
Teaching Hours:21
Inheritance in Java, Interfaces and Packages, Exception Handling in Java
 

Inheritance in Java

Inheritance Basics - Multilevel Hierarchy- Using super - Method overriding - Dynamic Method Dispatch- Abstract keyword- Using final with inheritance - the Object Class.

Interfaces and Packages

Inheritance in java with Interfaces – Defining Interfaces - Implementing Interfaces - Extending Interfaces- Creating Packages - CLASSPATH variable - Access protection  -  Importing Packages - Interfaces in a Package.

Exception Handling in Java

try-catch-finally mechanism - throw statement - throws statement - Built-in-Exceptions – CustomExceptions.

Unit-2
Teaching Hours:21
LAB
 

Lab 9

Implement the concept of inheritance, super, abstract and final keywords injava.

Lab10

Implement package and interface keywords injava

Lab11

Implement Exception Handing in java

Unit-3
Teaching Hours:21
Multithreading, Generics and The Collections Framework
 

Java Thread Model - Life cycle of a Thread - Java Thread Priorities - Runnable interface and Thread Class- Thread Synchronization – Inter Thread Communication. 

Generics 

Generics Concept - General Form of a Generic Class – Bounded Types – Generic Class Hierarchy - Generic Interfaces – Restrictions in Generics

The Collections Framework

The Collections Overview – Collection Interface – List Interface – Set Interface – SortedSet Interface – Queue Interface - ArrayList Class – LinkedList Class – HashSet Class – Using an Iterator – The For Each Statement

Unit-3
Teaching Hours:21
LAB
 

Lab 12

Implement multithreading – Thread class, Runnable interface, thread synchronization and thread communication.

Lab13

Implement generic concept – generic class and genericinterface

Lab14

Implement collections – collection Interfaces and collection classes

Text Books And Reference Books:

[1] Schildt Herbert, Java: The Complete Reference, Tata McGraw-Hill, 10th Edition, 2017.

Essential Reading / Recommended Reading

[1]  Java How to Program, Paul Deitel, Pearson Education Asia, 11th Edition,2017.

[2]  Core Java Volume 1 Fundamentals, Cay S Horstmann, Prentice Hall, 11th Edition,2018.

Evaluation Pattern

CIA 50%

ESE 50%

MCA372 - UNIX PROGRAMMING (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The course provides comprehensive understanding of the system calls for File System, Process Management, Inter Process Communication, Memory Management and IO System. The lab also enables to develop shell scripts, socket programming applications in a multi process Unix programming environment

Course Outcome

CO1: Understand Unix Operating System Programming environment

CO2: Develop shell script for UNIX system administration

CO3: Apply system calls for file system, process creation and management and Inter Process Communication

CO4: Develop system software with process and thread synchronization

Unit-1
Teaching Hours:18
UNIX Shell Programming
 

Types of shells – Features of Bourne, C and Korn Shells. Redirection: The three standard files - /dev/null and /dev/tty: Two special Files – Shell variables - Shell Keywords - Positional parameters - Passing command line arguments. Arithmetic in shell scripts - Read and Echo - Control Structures - if-then-fi - if-then-else-fi - Nested if - Case control structure – Loops - while-until –for - break and continue. Shell meta characters - Exporting variables - User defined Functions.

Lab Exercises:

1. Write a shell script to print prime numbers for a given range using functions.

2. Write a shell script to find the list of students who stayed more than eight hours in the campus. Input file contains Student Id, Time In and Time Out in (HR:MIN:SEC) format.

3. Write a shell script to merge two tables with multiple fields with a unique key for both the files.

Unit-2
Teaching Hours:18
Shell Commands and Filters
 

Directory related commands: pwd, mkdir, cd, rmdir - File manipulation commands: cat, cp, rm, mv, more, spell and ispell, cmp, comm, diff - File compression commands: gzip, gunzip, tar, zip, unzip - Listing Directory Attributes, File Ownership, File Permissions - chmod: Changing File Permissions, Directory Permissions - Changing File Ownership, Creating Hard links, Symbolic Links, ln, umask, find, pr: Paginating Files, head: Displaying the Beginning of a File, tail: Displaying the End of a File, cut: Slitting a File Vertically, sed: stream editor, paste: Pasting Files, sort: Ordering a File, uniq: Locate Repeated and Non repeated Lines, tr: Translating Characters, wc: word count and awk for preprocessing input data. Filters for regular expressions: grep: Searching for a Pattern, and egrep. Communicating with other users: mail, wall, send, mesg, ftp.

Lab Exercises:

4. Demonstrate open, read, write and close system calls.

5. Demonstrate dup, link, unlink and lseek system calls

6. Write a program to create multiple processes as per given process hierarchy graph. Parent process should be alive till any of the child process is alive.

Unit-3
Teaching Hours:18
Process Management
 

Memory Layout of a C Program. Shared Libraries – Memory allocation – environment variables – Process identifiers –fork, wait, waitpid system call – exec and system function – Process accounting – User identification – Process scheduling.

Lab Exercises:

7. Demonstrate exec family of system calls and report file descriptor behavior in fork and exec family system calls.

8. Demonstrate two-way data transfer between processes using pipes.

9. Synchronization between parent and child process for transferring data using signals.

Text Books And Reference Books:

[1] Sumitabha Das, “Unix Concepts and Applications”, 4th Edition, New Delhi: Tata McGraw Hill, 2011  

    [2] Richard Stevens, “Advanced programming in the UNIX environment”, Addison Wesley, Third edition, 2017.

Essential Reading / Recommended Reading

[1] Bach M.J,, “The Design of the Unix Operating System”, Pearson, First Edition, 2015.

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA381 - RDBMS PROJECT LAB (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

At the end of the semester the students should develop the working project using RDBMS concepts.

Course Outcome

CO1: Understand the practical concepts and the technical issues related to the development of RDBMS project and identify the problem.

CO2: Analyze the problem, identify the solution, various front end and backend tools required for the project and apply them as per the requirement. 

CO3: Create a working project that satisfies the need of the end user. 

CO4: Develop communication skills, ethics and leadership qualities as an individual and as a leader.

Unit-1
Teaching Hours:60
RDBMS Project
 

Students will carry out a group project in any area involving RDBMS concepts.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CSA 50%

ESE 50%

MCA382 - RESEARCH - PROBLEM IDENTIFICATION (2019 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

Course Description :

There is only CIA for this course. Students should do a thorough literature review in their research area. They should give a presentation and submit a document containing the following:

  • Introduction to topic, existing scenario and applications.

  • Literature review (Minimum 25 references).

  • Existing Model and Methodology.

  • Concrete problem statement definition.

Course Outcome

Course Outcomes: 

CO1 : Able to produce commercially valuable intellectual property.

CO2 : Able to produce new products/processes/methods/model/Framework.

Unit-1
Teaching Hours:30
MCA382 - Research - Problem Identification
 

Week 1 - Discussion and Identification of Research Domain (Updations)

Week 2 - Identification of Research Gap / OBJECTIVES OF RESEARCH

Week 3 - Research Design Phase - I

Week 4 - Research Design Phase - II

Week 5 - Research Design Phase - III

Week 6 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 7 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 8 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 9 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 10 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 11 - Implementation Phase - I

Week 12 - Implementation Phase - I (a)

Week 13 - Implementation Phase - I (b)

Week 14 - Implementation Phase - I (c)

Week 15 - Implementation Phase - I (d)

Text Books And Reference Books:

RESEARCH

Essential Reading / Recommended Reading

RESEARCH

Evaluation Pattern

CIA

MCA431 - MACHINE LEARNING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

The objective of this course is to provide introduction to the principles and design of machine learning algorithms. The course is aimed at providing foundations for conceptual aspects of machine learning algorithms along with their applications to solve real world problems.

Course Outcome

CO1: Understand basic principles of machine learning techniques

CO2: Formulate machine learning problems and their solutions

CO3: Apply machine learning algorithms to solve real world problems

Unit-1
Teaching Hours:12
INTRODUCTION
 

Machine Learning-Examples of Machine Applications-Learning Associations-Classification- Regression-Unsupervised Learning-Reinforcement Learning. Supervised Learning: Learning class from examples- Probably Approach Correct(PAC) Learning-Noise-Learning Multiple classes. Regression-Model Selection and Generalization.

Introduction to Parametric methods-Maximum Likelihood Estimation: Bernoulli Density- Multinomial Density-Gaussian Density, Nonparametric Density Estimation: Histogram Estimator-Kernel Estimator-K-Nearest Neighbour Estimator

Unit-2
Teaching Hours:12
DIMENSIONALITY REDUCTION
 

Introduction- Subset Selection-Principal Component Analysis, Feature Embedding-Factor Analysis-Singular Value Decomposition-Multidimensional Scaling- Linear Discriminant Analysis- Bayesian Decision Theory

Unit-3
Teaching Hours:12
SUPERVISED LEARNING ? I
 

Linear Discrimination: Introduction- Generalizing the Linear Model-Geometry of the Linear Discriminant- Pairwise Separation-Gradient Descent-Logistic Discrimination.

Kernel Machines

Introduction- optical separating hyperplane- v-SVM, kernel tricks- vertical kernel- vertical kernel- defining kernel- multiclass kernel machines- one-class kernel machines

Text Books And Reference Books:

 [1] E. Alpaydin, Introduction to Machine Learning, 3rd Edition, MIT Press, 2014.

Essential Reading / Recommended Reading

[1]  C.M. Bishop, Pattern Recognition and Machine Learning, Springer,2016.

[2]  T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, 2nd Edition,2009.

[3]  K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press,2012.

Evaluation Pattern

 

CIA 50%

ESE 50%

MCA441A - NETWORK SECURITY (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

To make the students learn the principles and practices of Cryptography, Network Security and to enable the students understand the various methods of encryption and authentication and help them identify the application of these techniques for providing Network and System Security.

Course Outcome

CO1: Understand the principles and practices of Cryptography and Network Security.

CO2: Appreciate the role played by Cryptographic techniques in enhancing Network and system Security.

CO3: Identify and explain the concepts, protocols and technologies associated with a secure communication across the Network and the Internet.

CO4: Discuss the objectives of authentication and access control methods and describe how the available methods are implemented in the defense of a network.

Unit-1
Teaching Hours:13
Concepts of Security & Classical Encryption Techniques
 

Introduction, The need for security, Security Approaches, Security Attacks, Security Services, Security Mechanisms, A Model for Network Security. Symmetric Cipher Models – Substitution techniques, Transposition techniques, Steganography, Block Cipher Operation, Electronic Code Book, Cipher Block Chaining, Block Cipher Principles, The Data Encryption Standard, A DES Example, The Strength of DES, Evaluation criteria for AES, AES Cipher.

Unit-2
Teaching Hours:12
Public Key Cryptography and Cryptographic Hash Functions
 

Introduction To Number Theory, Modular Arithmetic, Prime Numbers, Euler’s Totient Function, Principles of Public Key Cryptosystems, The RSA Algorithm, Other Public key cryptosystems, Diffie Hellman Key Exchange.

Applications of Cryptographic Hash Functions, Two Simple Hash Functions, Hash Functions Based on Cipher Block Chaining, MD5 Message Digest Algorithm, Secure Hash Algorithm SHA 512.

Unit-3
Teaching Hours:11
Message Authentication Codes and Digital Signatures
 

Message Authentication Requirements –Message Authentication Functions –Requirements for Security of MACs, MACs Based on Hash Functions, HMAC, MACs Based on Block Ciphers, Data Authentication Algorithm.

Digital Signatures, Elgamal Digital Signature Scheme, Schnorr Digital Signature Scheme, Digital Signature Standard.

Text Books And Reference Books:

[1] William Stallings, Cryptography and Network Security, Prentice Hall, 5th Edition, 2010. 

Essential Reading / Recommended Reading

[1] Atul Kahate, Cryptography and Network Security, Tata McGraw-Hills, 8th Reprint, 2006.

[2] Brijendra Singh, Network Security and Management, PHI, 3rd Edition, 2013

[3] Eric Maiwald, Information Security Series, Fundamental of Network security, Dreamtech press 2004.

Evaluation Pattern

CIA: 50%

 

ESE: 50%

MCA441B - OOAD WITH UML (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Object Oriented Analysis and Design Using UML course provides instruction and practical experience focusing on the effective use of object-oriented technologies and the judicious use of software modeling as applied to a software development process.

Course Outcome

CO 1: Understand the object-oriented life cycle.

CO 2: Identify the objects, relationships, services, and attributes through UML.

CO 3: Apply object-oriented design process  and architectural modeling

 

Unit-1
Teaching Hours:12
The Object Model
 

The evolution of object model, Elements of object model, applying the object model, Foundations of the object model.

Unit-1
Teaching Hours:12
Complexity
 

The inherent complexity of software, The Structure of complex systems, Bringing order to chaos, on designing complex systems, Categories of analysis and Design methods.

Unit-2
Teaching Hours:13
Classification
 

The importance of proper classification, Identifying classes and objects, Key abstraction and mechanisms, A problem of classification.

Unit-2
Teaching Hours:13
Classes and Objects
 

The nature of an object, Relationship among objects, the nature of a class, Relationship among classes, The interplay of classes and objects, On building quality classes and objects, invoking a method.  

Unit-3
Teaching Hours:12
Notation
 

Basic Behavioural Modelling, Basic elements, class diagram, object, state Transition diagram, Interactions, Use Case Diagrams, Activity, module and process diagrams.

Text Books And Reference Books:

 

[1] Grady Booch, Object-Oriented Analysis And Design With Applications, Pearson Education, 3rd edition, 2009.

Essential Reading / Recommended Reading

[1] Mahesh P. Matha, Object-Oriented analysis and Design Using UML, PHI,3rd  reprint, 2012.

[2] Grady Booch, James Rumbaugh and Ivar Jacobson, The Unified Modeling Languages User Guide, Addison Wesley, 4th Edition, Reprint 2000.

[3] Mike O’Docherty, Object Oriented Analysis and Design Understanding system development with UML2.0, John Wiley and Sons, 1st Edition, 2005.

Evaluation Pattern

CIA - 50%

ESE - 50%

MCA441C - WEB ENGINEERING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

The World Wide Web has become a major delivery platform for information resources. Many applications continue to be developed in an ad-hoc way, contributing to problems of usability, maintainability, quality and reliability. This Course Description: examines systematic, disciplined and quantifiable approaches to developing of high-quality, reliable and usable web applications.

 

Course Outcome

CO1: Demonstrate the principles, architecture and model for requirements engineering

CO2: Design appropriate testing and security methods for the web applications

CO3: Build architecture for client server based applications

CO4: Determine the specifics for semantic web application and appropriate tools

Unit-1
Teaching Hours:12
Requirements Engineering and Modeling
 

RE Fundamentals and Specifics - Principles for RE - Adapting RE Methods - Modeling Fundamentals and Specifics - Modeling Requirements - Content Modeling - Hypertext Modeling - Presentation Modeling - Customization Modeling.

Unit-2
Teaching Hours:12
Web Application Architectures and Design
 

Fundamentals and Specifics – Components - Layered Architectures - Data-aspect Architectures - Evolutionary Perspective - Presentation Design - Interaction Design - Functional Design - Outlook

Unit-3
Teaching Hours:12
Testing, Operation and Maintenance
 

Fundamentals and Specifics of Testing - Test Approaches and Schemes - Test Methods andTechniques - Test Automation – Challenges in Launch of a Web Application - Promoting a Web Application - Content Management - Usage Analysis

Text Books And Reference Books:

[1] Gerti Kappel , Web Engineering: The Discipline of Systematic Development of Web Applications, John Wiley, 2006.

Essential Reading / Recommended Reading

[1] Diane Cerra, Unleashing Web 2.0: From Concepts to Creativity, Elsevier, 2009.

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA441D - WIRELESS AND MOBILE NETWORKS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

The goal is to make students familiar with the basic concepts and structure of modern wireless and mobile communication networks.

Course Outcome

CO1: Analyze the trends, strengths, problems and limitations of current wireless networking mechanisms for mobile communications.

CO2: Understand and identify the GSM, GPRS, CDMA, LTE, Bluetooth software model for mobile computing.

CO3: Investigate the characteristics and limitations of mobile hardware devices including their user-interface modalities.

CO4: Analyze and critically evaluate the performance of different networks and algorithms for mobile communication.

CO5: Design novel mechanisms and systems for supporting mobile communications including wireless communication architectures, wireless communication protocols, wireless security mechanisms.

Unit-1
Teaching Hours:12
Wireless Telecommunications Systems and Networks, Evolution and Deployment of Cellular Telephone Systems
 

Wireless Telecommunications Systems and Networks

History and Evolution of Wireless Radio Systems, Development of Modern Telecommunications Infrastructure, Overview of Existing Network Infrastructure, Wireless Network Applications: Wireless Markets

Unit-1
Teaching Hours:12
Evolution and Deployment of Cellular Telephone Systems
 

Different Generations of Wireless Cellular Networks, 1G Cellular Systems, 2G Cellular Systems, 2.5G Cellular Systems, 3G Cellular Systems, 4G Cellular Systems and Beyond, Wireless Standards Organizations

Unit-2
Teaching Hours:12
Common Cellular System Components,Wireless Network Architecture and Operation
 

Common Cellular System Components

Common Cellular Network Components, Hardware and Software Views of the Cellular Network, 3G Cellular System Components, Cellular Component Identification, Call establishment.

Unit-2
Teaching Hours:12
Wireless Network Architecture and Operation
 

The Cellular Concept, Cell Fundamentals, Capacity Expansion Techniques, Mobility Management, Wireless Network Security.

Unit-3
Teaching Hours:13
GSM and TDMA Technology
 

Introduction to GSM and TDMA, GSM Network and System Architecture, GSM Channel Concept, GSM Identities, GSM System Operations, GSM Infrastructure Communications.

Text Books And Reference Books:

[1] Gary J Mullett. Wireless Telecommunications Systems and Networks, Clifton Park (N.Y.) : Thomson Delmar Learning, cop.2008

Essential Reading / Recommended Reading

[1] Raj Kamal, Mobile Computing, Oxford University Press, 2012.

[2] Stallings William, Wireless Communications and Networks, Pearson Education Asia, 2nd Edition, 2009.

[3] Theodore S Rappaport, Wireless Communications: Principles and Practice, Pearson Education Asia, 2nd Edition, 2009.

[4] Jochen Schiller, Mobile Communication, Addison-Wesley, 2nd Edition, 2011.

 

Evaluation Pattern

CIA:50%

ESE:50%

MCA441E - DATA ANALYTICS (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course Description:

Data Science is the latest buzz word in the modern era of cloud and big data in academic research and corporate world. Data Science experts must acquire analytical skill set for pursuing research and generating new knowledge in the business process. Data Analytics course delivers various techniques to discover new and hidden knowledge from the data set. This course provides insight into the complete research process in phases as research methodology, data exploration, modeling, evaluation and visualization. R programming, Python programming, MATLAB and Excel are the suggestive tools for implementation.

Course Objectives:

To prepare data for analysis.

To identify suitable models for respective applications.

To understand the visualization models for interpreting results.

Course Outcome

Upon successful completion of the course the student will be able to:

CO1: Identify various type of data used for analysis

CO2: Apply various supervised and unsupervised algorithms to real world problems

CO3: Interpret the results of developed models using different visualization techniques

Unit-1
Teaching Hours:12
Data, Relations and preprocessing
 

Data Analytics - Data Mining - Knowledge Discovery - Data and Relations - Data preprocessing - Data Visualization.

Unit-2
Teaching Hours:12
Correlation and Regression
 

Correlation: Linear Correlation, Correlation and Causality, Chi-Square Test - Regression: Linear Regression - Linear Regression with Nonlinear Substitution - Robust Regression - Neural Networks - Radial Basis Function Networks - Cross Validation - Feature Selection - Forecasting: Finite State Machines - Recurrent Models - Autoregressive Models.

Unit-3
Teaching Hours:12
Association Rule Mining and Classification
 

Mining Frequent item-sets - Market based model - Apriori Algorithm - FP growth algorithm, Handling large data sets in Main memory - Classification Criteria - Naive Bayes Classifier - Linear Discriminant Analysis - Support Vector Machine - Nearest Neighbor Classifier - Learning Vector Quantization - Decision Trees - Neural Networks.

Text Books And Reference Books:
  1. Runkler, Thomas. A, "Data Analytics: Models and Algorithms for Intelligent Data Analysis", Springer, 2012.
  2. Michael Berthhold, David J. Hand, "Intelligent Data Analysis - An Introduction", 2nd Edition, Springer Publications, 2002.
Essential Reading / Recommended Reading
  1. Jiawei Han, Micheline Kamber, Jian Pie, "Data Mining Concept and Techniques", Morgan and Kaufmann Publisher, Third Edition, 2012.
Evaluation Pattern

CIA - 50%

ESE - 50%

MCA441F - DIGITAL MARKETING (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:04

Course Objectives/Course Description

 

To enable the learners to create a structured digital marketing plan and prioritize the strategic options for boosting customer acquisition, conversion and retention. To facilitate the use of digital technologies as a tool and potential imperative for competitive advantage.

Course Outcome

CO1: Explain emerging trends in digital marketing and critically assess the use of digital marketing tools

CO2: Implement SEO techniques, social media marketing and web analytics for business success

CO3: Demonstrate cognitive knowledge of the skills required in conducting research on digital market opportunities

Unit-1
Teaching Hours:12
GOING DIGITAL - THE EVOLUTION OF MARKETING
 

The changing face of advertising, The technology behind digital marketing, Need of digital marketing strategy, business and digital marketing, Defining the digital marketing strategy , Understanding the digital consumer, Mind the Ps – Place, Price, Product and Promotion.

Unit-2
Teaching Hours:12
THE SEARCH FOR SUCCESS
 

 

Search: the online marketer’s holy grail, About the engines, Optimizing the site for the engines, Advertising on the search engines, Black Hat - the darker side of search, Universal search – more opportunities to rank.

Website intelligence and return on investment - Measuring the way to digital marketing success, Information measuring, Measuring what’s important to you, Harness the power of online data, and watch the ROI take off.

Unit-3
Teaching Hours:12
SEARCH ENGINE OPTIMIZATION
 

 

Introduction to SEO, On-Page SEO, Off-Page SEO, Local SEO, Steps involved: On-Page SEO, Site structure, Good site structure, Creating a sitemap, Conducting keyword research, Optimizing your site content, Link building process, White Hat vs Black Hat SEO, SEO tools and helpful sites.

Text Books And Reference Books:

[1] Damian Ryan & Calvin Jones, Understanding Digital Marketing: Marketing Strategies for Engaging the Digital Generation, Kogan Page Limited, Fourth Edition,2016

[2] Shivani Karwal, Digital Marketing Handbook: A Guide to Search Engine Optimization, Pay Per Click Marketing, Email Marketing, Social Media Marketing and Content Marketing, CreateSpace Independent Publishing Platform,2015.

Essential Reading / Recommended Reading

[1] Ian Dodson, The Art of Digital Marketing: The Definitive Guide to CreatingStrategic, Targeted, and Measurable Online Campaigns, Wiley, First Edition, 2016 

[2] Deepak  Bansal, A Complete Guide To Search Engine Optimization, B.R. Publishing Corporation, First Edition,2009 

[3] Justin Cutroni, Google Analytics: Understanding Visitor Behavior, Shroff, First Edition, 2010

 

Web Resources:

[1]    https://www.digitalvidya.com/blog/learn-digital-marketing-guide/

[2]    https://www.searchenginejournal.com/seo-guide/

[3]    https://moz.com/blog/absolute-beginners-guide-to-google-analytics

Evaluation Pattern
CIA: 50%
ESE: 50% 

MCA471 - MOBILE APPLICATION (2019 Batch)

Total Teaching Hours for Semester:105
No of Lecture Hours/Week:7
Max Marks:150
Credits:5

Course Objectives/Course Description

 

Course is concerned with development of Mobile Applications, Course is designed to quickly get speed with writing apps for Android devices. It exposes students to a wide range of concepts and technical skills

Course Outcome

CO1: Understand the basic concepts of Mobile application development

CO2: Design and develop user interfaces for the Android platforms

CO3: Apply Java programming concepts to Android application development

CO4: Demonstrate advanced Java programming competency by developing a maintainable and efficient cloud based mobile application.

Unit-1
Teaching Hours:21
INTRODUCTION
 

Brief History of embedded device programming-History of Mobile apps-App development trends of past years-Introduction to Android-get to know the required tools-Web APP-Mobile app-API-IDE concept-Why Android, Features of android-Android software Stack-Android System Architecture- Android Core building blocks.

Introduction to Android Studio-Building first Android App-Layouts and resources for UI-Text and Scrolling views.   

LAB:

1. Installation of Android Studio and Hello World

2. Layout Editors

3. Input Controlss.

Unit-2
Teaching Hours:21
ACTIVITY AND INTENTS
 

Introduction to Activity and Intent, Activity life cycle and state, Implicit and Explicit Intents, The Android Studio debugger, App testing and Android support library. Understanding the views, components, understanding screen, screen orientation, Button, clickable images, Input controls, Menus and pickers, usernavigation.

 

LAB:

 4. Activity and Intents- Implicit and Explicit and camera

 5. Input controls

 6. Menu and pickers

Unit-3
Teaching Hours:21
WORKING WITH BACKGROUND
 

Background Tasks-AsyncTask and AsyncTaskloader, Internet connection-Broadcast receiver- Services-Alarms and Schedulers -Notifications-Alarms- Delightful user experience- Drawables, styles and themes-Material Design- resources for adaptive layouts- UI Testing- Recycler View

LAB:

7. User navigation – Recyclerview

8. MediaController

9. Fragments

10. AsyncTask and AsyncTaskloader

Text Books And Reference Books:

[1] Android programming for beginners, John Horton, Packt-BirminghamMumbai-2nd edition,2018 ISBN – 978-1-78953-850-2

[2] Android Programming: The Big Nerd ranch Guide (4th edition), Bill Philips, Chris Stewart, Kristin Masrsicano, 2019,ISBN :-100136590071

Essential Reading / Recommended Reading

[1] Head First Android Development: A Brain-Friendly guide, Dawn Griffiths and David Griffiths, O’Reilly , 2ndedition-2019

[2] Practical Android: 14 Complete Projects on Advanced Techniques and Approaches, Mark Wickham, APRESS, ISBN-13978-1-4842-3332

Evaluation Pattern

CIA 50%

ESE 50%

MCA472A - DIGITAL IMAGE PROCESSING (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course provides comprehensive understanding of theory and algorithms that are widely used in Digital image processing. Student gets hands-on experience to implement programs in Matlab to process images.

Course Outcome

CO1: Understand the theoretical background of Image processing

CO2: Apply image enhancement, restoration, compression and segmentation in both frequency and spatial domain.

CO3: Represent and recognize objects through patterns in application.

Unit-1
Teaching Hours:18
INTRODUCTION AND FUNDAMENTALS OF DIGITAL IMAGE
 

The origins of Digital Image Processing, Fundamental Steps in Image Processing, Elements of Digital Image Processing System, Image Sampling and Quantization, Basic relationships: Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations.

Unit-1
Teaching Hours:18
LAB EXERCISES
 

1.        Introduction, Installation, General Commands

2.        Practicing Image related Commands, Matrices

Unit-2
Teaching Hours:18
IMAGE ENHANCEMENT
 

Spatial Domain :Gray Level Transformations - Histogram Processing - Histogram equalization, Histogram specification - Basics of Spatial Filters - Smoothening and Sharpening Spatial Filters. Frequency Domain : Introduction to Fourier Transform and the frequency Domain - Smoothing and Sharpening - Frequency Domain Filters - Homomorphic Filtering

Unit-2
Teaching Hours:18
LAB EXERCISES
 

3. Functions and general Programs

4. Program to perform Resize and Rotation of images using different methods.

Unit-3
Teaching Hours:18
LAB EXERCISES
 

5. Program to implement basic Enhancement techniques

6. Program to implement Contrast stretching

Unit-3
Teaching Hours:18
IMAGE RESTORATION AND IMAGE COMPRESSION
 

A model of The Image Degradation / Restoration Process - Noise Models, Restoration in the presence of Noise - Periodic Noise Reduction by Frequency Domain Filtering. Image Compression models: Huffman coding - Run length coding - LZW coding.

Text Books And Reference Books:

1. Digital Image Processing,R. C. Gonzalez & R. E. Woods, PearsonEducation, 4th Edition, 2018.

2. Fundamental of Digital Image Processing, A.K. Jain, PHI, 4th Edition, 2011.

Essential Reading / Recommended Reading

1.Digital Image Processing Using MATLAB, Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, PHI, 2nd Edition, 2009.

2.Digital Image Processing: An algorithmic approach, M. A. Joshi, PHI, 2nd Edition, 2009.

3. Digital Image Processing and analysis, B. Chanda, D. Dutta Majumdar, PHI, 1st Edition, 2011. 

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA472B - SOFTWARE QUALITY AND TESTING (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course introduces the basics of quality assurance and testing of a software product. It helps to identify and describe the design concepts for system testing and execution and illustrate the software quality assurance, metrics and defect prevention techniques.

Course Outcome

CO1: Understand fundamental concepts of Software Quality and Testing.

CO2: Ability to test code, artifact better and apply different Quality Tools.

CO3: Create effective test plan and detailed test cases.

Unit-1
Teaching Hours:18
Introduction to Software Quality, Framework and Quality Standards
 

Quality: professional view- software quality- total quality management- The defect prevention process- process maturity framework and quality standards (CMM , SPR Assessment, Malcolm Bridge, ISO 9000, Six Sigma)

Lab Exercises:

[1] Implement checklist for Design Review for RDBMS Projects.

Unit-2
Teaching Hours:18
Fundamentals in Measurement Theory
 

Levels of measurement some basic measures- reliability and validity Software quality metrics Product Quality Metrics- in-process quality process- example of Metrics Program –Motorola,  HP.

Lab Exercises: 

[2] Review few projects to check for Non Compliances of currently developed DBMS projects by the students in the UG Course in the department. Based on the checklist create Design Review Report.

Unit-3
Teaching Hours:18
Seven Basic Quality Tools
 

Ishikawas’ seven basic tools- checklist- pareto diagram- histogram- run chart- scatter diagram - control chart- cause and effect diagram-Defect Removal Effectiveness- defect removal effectiveness and quality planning

Lab Exercises: 

[3] Implement the following Quality Tools for a hypothetical project.

           Pareto Diagram, Histogram, Run chart, Scatter Diagram, Control Chart

[4] Create a root cause analysis for any current social issue.

Text Books And Reference Books:

[1]    Stepen H Kan, “Metrics and Models in Software Quality Engineering”, 2nd Edition ,reprint 2016.

[2]   William Perry, "Effective Methods for Software Testing", John Wiley, 2009.

 

Essential Reading / Recommended Reading

[1]   Glenford J. Myers ,“The Art of Software Testing” ,John Wiley and Sons publications,2014.

Evaluation Pattern

CIA-50%

ESE-50%  

MCA472C - DATA MINING (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course helps to preprocess and analyze data, choose relevant models and algorithms for respective applications and to develop research interest towards advances in data mining.

 

Course Outcome

CO1: Understand different types of data to be mined

CO2: Categorize the scenario for applying different data mining techniques

CO3: Evaluate different models used for classification and Clustering

CO4: Focus towards research and innovation

Unit-1
Teaching Hours:18
Introduction and Data Preprocessing
 

Data Mining – Kinds of data to be mined – Kinds of patterns to be mined – Technologies – Targeted Applications - Major Issues in Data Mining – Data Objects and Attribute Types – Measuring Data similarity and dissimilarity - Data Cleaning –Data Integration - Data Reduction – Data Transformation – Data Discretization

 

Lab Exercises:

1. Identify a dataset, Preprocess the dataset set using normalization techniques

 

2. Explore data reduction techniques

Unit-2
Teaching Hours:18
MINING FREQUENT PATTERNS AND ADVANCED PATTERN MINING
 

Basic Concepts – Frequent Itemset Mining Methods – Pattern Evaluation Methods – Pattern Mining in Multilevel, Multidimensional space – Constraint-Based Frequent Pattern Mining – Mining Compressed or Approximate Patterns – Pattern Exploration and Application

Lab Exercises:

3. Identify frequent itemsets using Apriori Algorithm

4.  Generate FP Tree for a transaction dataset

Unit-3
Teaching Hours:18
CLASSIFICATION TECHNIQUES
 

Basic Concepts – Decision Tree Induction – Bayes Classification Methods – Rule-Based Classification – Model Evaluation and Selection – Techniques to Improve Classification Accuracy – Bayesian Belief Networks – Classification by Backpropagation – Support Vector Machines

 

Lab Exercises:

5. Construct Decision Tree for a dataset and identify the order of attributes 

6. Apply Bayes Classification

Text Books And Reference Books:

[1] Data Mining Concept and Techniques, Jiawei Han, Micheline Kamber, Jian Pie, Morgan and Kaufmann Publisher, Third Edition,2012

[2] Data Mining Techniques, Arun K Pujari, Second Edition, Universities Press India Pvt. Ltd.2010

Essential Reading / Recommended Reading

[1] Data Mining and Predictive Analytics Daniel T. Larose, Chantal D. Larose (Wiley Series on Methods and Applications in Data Mining), WileyPublications

[2] Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Mark A. Hall, Morgan and Kaufmann Publisher, Third Edition,2014

Evaluation Pattern

Evaluation Pattern (Theory)

 

CIA (Weightage)

ESE (Weightage)

50%

50%

 

MCA472D - NoSQL (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course helps to understand the essential concepts that act as the building blocks for many of the NoSQL products. It starts from the fundamentals of NoSQL and graduates to advanced concepts of architecture, storage internals and indexing.

 

Course Outcome

CO 1: Demonstrate the concepts related to NoSQL databases

CO 2: Analyze the working of architecture and internals of NoSQL Databases

CO 3: Design NoSQL database applications using the methods of storing, accessing, querying, ordering and indexing

 

Unit-1
Teaching Hours:18
Introduction to NoSQL
 

Definition and Introduction-Sorted Ordered Column-Oriented Stores- Key/Value Stores- Document Databases-Graph Databases- Examining Two Simple Examples-Location Preferences Store-Car Make and Model Database.

 

 Lab Content 

 1.        NO SQL CRUD OPERATIONS

 2.        NO SQL AGGREGATE FUNCTIONS

Unit-2
Teaching Hours:18
Interacting with NoSQL
 

If NoSQL Then What-Language Bindings for NoSQL Data Stores-Performing Crud Operations- Creating Records-Accessing Data-Updating and Deleting Data.

Lab Content 

     1.        LANGUAGE BINDINGS

   2.        CREATING NOSQL APPLICATIONS

Unit-3
Teaching Hours:18
NoSQL Storage Architecture
 

Working with Column-Oriented Databases-HBase Distributed Storage Architecture-Document Store Internals-Understanding Key/Value Stores in Memcached And Redis-Eventually Consistent Non-Relational Databases-Neo4J StorageArchitecture.

 

Lab Content 

    1.        ACCESSING DATASTORE

  2.       IMPLEMENTING STORAGE ARCHITECTURE

Text Books And Reference Books:

[1]   Shashank Tiwari, Professional NoSQL, Wrox Press, Wiley,2011.

[2]   Gaurav Vaish, Getting Started with NoSQL, Packt Publishing,2013.

 

Essential Reading / Recommended Reading

[1]   Pramod Sadalage and Martin Fowler, NoSQL Distilled, Addison-Wesley Professional,2012.

[2]   Dan McCreary and Ann Kelly, Making Sense of NoSQL, Manning Publications,2013.

 

Web Resources:

[1]   https://www.mongodb.com

[2]   https://achouettz.firebaseapp.com ›professional-NoSQL-by-Shashank-tiwari

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA472E - USER INTERFACE_USER EXPERIENCE DESIGN UI_UX (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

The objective of this course is for students to learn how to design, prototype and evaluate user interfaces to effectively browse and search systems by examining what research has uncovered, what developers have produced, and how people perform information tasks.

Course Outcome

CO1: Describe design principles

CO2: Demonstrate impactful visual design and color concepts

CO3: Apply design principles and skills for design prototype

CO4: Design an intuitive design for software products.

Unit-1
Teaching Hours:18
Introduction and Overview
 

 

Usability of interactive systems: Usability Goals and Measures, Usability Motivations, Universal Usability, Goals for our Design Profession. Guidelines, Principles, and Theories of Design.

Lab Exercises:

1. Identifying design problems / understanding the problem.

a.    Task/ Scenario evaluation #1

b.    Task/Scenario evaluation #2

2. Intro to UI Design: UI design process

a.    Test your knowledge #1

b.    Test your knowledge #2

Unit-2
Teaching Hours:18
UI Design Process and Interaction styles
 

Design process introduction, designing to address a problem w/o solution ideas, designing for a known solution direction, designing to iterate on/improve an existing solution, common elements, usability engineering and task-centered approaches, use cases, personas, tasks and scenarios, intro to design centered approaches, design centered methods and when they work best.

Direct manipulation and virtual environments-Introduction- Examples of direct Manipulation, discussion of Direct Manipulation, 3D interfaces, teleoperation, Virtual and Augmented Reality. Menu Selection,Form Fill-in,and Dialog Boxes- Introduction- Task related menu organization, single menus, combinations of multiple menus, content organization, fast movement through menus, Data entry with Menus, audio menus and menus for small displays.

Lab Exercises:

3.  Psychology and Human factor in designing #1

4.  Psychology and Human factor in designing #2

Unit-3
Teaching Hours:18
Psychology and human Factors for User interface Design
 

Fitt’s Law, Short and long term memory, attention, perception and visualization, hierarchy, mistakes, errors and slipsm, conceptual models, the gulf executionand the gulf of evaluation, design principles: visibility, feedback, mappings, constraints, interacting beyond individuals (social psychology), high-level models:distributed cognition, activity theory, situated action, assignment video: interface critiques.

Lab Exercises:

5. Designing for problem #1

 

6. Designing for a problem #2

Text Books And Reference Books:

[1] Ben Shneiderman, Designing the User Interface, Pearson Education, 6th Edition,2018.

    [2] Wilber O Galitz, An Introduction to GUI Design Principles and Techniques, John- Wiley &Sons, 2007.

    [3] Andrew Faulkner, C onrad Chavez, Adobe Photoshop CC Classroom in a Book, The official training work book from Adobe, 2018.

Essential Reading / Recommended Reading

[1] Jeff Johnson, Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Rules , Morgan Kaufmann, 1st Edition, 2010.

    [2] Alan J Dix et al, Human-Computer Interaction, Pearson, 2009.

    [3] Steve KrugDon't Make Me Think!: A Common Sense Approach to Web Usability, Pearson, second edition.

    [4] Don Norman, The Design of Everyday Things: Revised and Expanded Edition, 2013, Basic Books, Perseus books group.

Evaluation Pattern

CIA: 50%

ESE: 50%

MCA472F - LINUX ADMINISTRATION (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course provides a practical introduction to Linux system Administration. It helps students gain knowledge and skills required for the role of Linux system administrator.

Course Outcome

CO1: Acquired concepts of LINUX Operating System, its kernel and different subsystems of kernel

CO2: Demonstrate the systematic approach for configure the Linux environment

CO3: Create customized partitions using LVM

CO4: Managing and Configuring the various Servers

Unit-1
Teaching Hours:18
Installation and System Administration Overview
 

Introduction- Installation- Linux Architecture- Duties of the System Administrator- Superusers and the Root Login- Sharing Superuser Privileges with Others- Boot Process- Kernel- System Initialization- GRUB(Modify the system boot loader)- GUI- CLI(Access a shell prompt and issue commands with correct syntax- Use input &output redirection (>, >>, |, 2>, etc.)- create and edit text files, delete, copy, and move files and directories- Introduction to Bash Shell- Basic Commands, Editors, Man Pages- Boot systems into different targets manually & automatically

Lab Exercise

1. Breaking the ROOT password and NMCLI configuration

2. Special File Permissions and Access Control Lists

 

Unit-2
Teaching Hours:18
Linux File System and Security
 

Filesystem Types-Conventional Directory Structure-Mounting a File System-The /etc/fstab File- Special Files (Device Files)-Inodes-Hard File Links-Soft File Links-Creating New File Systems with mkfs-File Permissions-Directory Permissions-Octal Representation-Changing Permissions- Setting Default Permissions-Access Control Lists (ACLs)-The getfacl and setfacl commands- Special Permision(SUID,SGID,Stickybit)

Lab Exercise

1. Process related commands

2. Scheduling process using at and crontab

 

Unit-3
Teaching Hours:18
Managing Users, Groups & controlling process
 

Setting      Policies-User       File       Management-The /etc/passwd file-The /etc/shadow file- The /etc/group file-The /etc/gshadow file-Adding Users-Modifying User Accounts-Deleting User Accounts-Working with Groups-Setting User Environments-Characteristics of Processes-Parent- Child Relationship-Examining Running Processes-Background Processes-Controlling Processes- Signaling Processes-Killing Processes-Automating Processes-cron and crontab-at and batch

Lab Exercise

1. LVM Partitions and Extending LVM

2. Swap Partitions

 

Text Books And Reference Books:

[1]    Mastering Linux Administration, Paul Cobbaut, First Edition, Samurai Media Limited,2016.

    [2]    Linux Administration: A Beginners Guide, Sixth Edition (Network Pro Library), Wale Soynika, McGraw-Hill Education, 2012.

Essential Reading / Recommended Reading

[1]     Collings Terry and Wall Kurt, Red Hat Linux Networking & System Administration, Wiley Indian, 3rd Edition, reprint 2009.

    [2]     Petersen Richard, The Complete Reference: Fedora 7 & Red Hat Enterprise Linux, Tata McGraw Hill Edition, 2007.

Evaluation Pattern

CIA-50%

ESE-50%

MCA481 - IOT PROJECT (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:02

Course Objectives/Course Description

 

-

Course Outcome

CO1:Understand the state of art of IoT  and identify  a socially and environmentally relevant requirement that can be achieved  using IoT technology 

CO2:Analyze and  design suitable data and knowledge management schemes and select feasible devices.

CO3:Develop relevant  IoT applications as a team with the understanding of real world IoT requirements, design constraints and security issues.

Unit-1
Teaching Hours:60
IOT Project
 

The Internet of Things Lab serves as an exciting multidisciplinary learning and research “sandbox” as well as a thought-leadership and innovation showcase to explore, experience, and extend cutting-edge technologies and use-cases. Students will work on variety of emerging devices and technologies (involving smart sensing, pervasive connectivity, virtual interfaces and ubiquitous computing), and their potential applications in consumer, retail, healthcare and industrial contexts.

Students should be divided into batches, each batch containing not more than 3 students.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA482 - RESEARCH - DATA COLLECTION AND IMPLEMENTATION (2019 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Research in Computer Science : Research inclusive curriculum is an initiative by the Department of Computer Science through which regular curriculum of Post Graduate Computer Science courses is augmented with formal research.

This research inclusive curriculum is designed with two main objectives:

  • Inculcating research culture among the post graduate students.
  • Enhancing employability skills of students by providing necessary research foundation.

Course Outcome

CO1: Literature survey of existing data sets or any primary data sets in the respective area.

CO2: Gather the datasets from various sources (like visiting websites, universities, person, creating individually, etc.

CO3: Steps in pre-processing / Proposed model design / framework design

Unit-1
Teaching Hours:60
RESEARCH - DATA COLLECTION AND IMPLEMENTATION
 

There is only CIA for this course. Students should carry out the following tasks:

  • Literature survey of existing data sets or any primary data sets in the respective area 
  • Gather the datasets from various sources (like visiting websites, universities, person, creating individually, etc.
  • Steps in pre-processing 

Note: In case the research problem involves no dataset or minimal dataset, then the guide allots marks based on the research work that is done during the semester.

Week 1 - Discussion and Identification of Research Domain (Updations)

Week 2 - Identification of Research Gap / OBJECTIVES OF RESEARCH

Week 3 - Research Design Phase - I

Week 4 - Research Design Phase - II

Week 5 - Research Design Phase - III

Week 6 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 7 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 8 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 9 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 10 - Methods of Data Collection - PROCESSING - ANALYSIS OF DATA

Week 11 - Implementation Phase - I

Week 12 - Implementation Phase - I (a)

Week 13 - Implementation Phase - I (b)

Week 14 - Implementation Phase - I (c)

Week 15 - Implementation Phase - I (d)

Text Books And Reference Books:

RESEARCH - ARTICLES (SPRINGER, IEEE, ACM, ELSEVIER, INDERSCIENCE, .......)

Essential Reading / Recommended Reading

RESEARCH - ARTICLES (SPRINGER, IEEE, ACM, ELSEVIER, INDERSCIENCE, .......)

Evaluation Pattern

Evaluation Rubrics

S.No

Criteria for Evaluation

Marks

1

Submission of document

35

2

Presentation

10

3

Attendance

5

MCA483 - SEMINAR (2019 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description