CHRIST (Deemed to University), Bangalore

DEPARTMENT OF COMPUTER SCIENCE

School of Business and Management

Syllabus for
Master of Computer Applications
Academic Year  (2023)

 
1 Semester - 2023 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA131 MATHEMATICAL FOUNDATION FOR COMPUTER SCIENCE Core Courses 3 2 50
MCA132 PROBLEM SOLVING USING C Core Courses 3 2 50
MCA133 RESEARCH METHODOLOGY Skill Enhancement Courses 3 2 50
MCA134 COMPUTER ORGANIZATION AND DESIGN Core Courses 4 3 100
MCA135 ADVANCED DATABASE TECHNOLOGIES Core Courses 4 3 100
MCA171 PYTHON PROGRAMMING Core Courses 6 4 100
MCA172 WEB STACK DEVELOPMENT Core Courses 7 4 150
2 Semester - 2023 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA231 SOFTWARE ENGINEERING Core Courses 3 2 50
MCA232 APPLIED STATISTICS USING R Core Courses 4 3 100
MCA233 OPERATING SYSTEM Core Courses 4 3 100
MCA251 SOFTWARE PROJECT DEVELOPMENT LAB -PHASE I Core Courses 3 1 50
MCA271 DATA STRUCTURES AND ALGORITHMS Core Courses 8 4 150
MCA272 PROGRAMMING USING JAVA Core Courses 8 5 150
3 Semester - 2023 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA331 DATA COMMUNICATION AND CRYPTOGRAPHY Core Courses 4 3 100
MCA332 DATA MINING Core Courses 4 3 100
MCA333A ACCOUNTING AND FINANCE MANAGEMENT Discipline Specific Elective Courses 3 2 50
MCA333B ECONOMETRICS Discipline Specific Elective Courses 3 2 50
MCA333C COMPUTATIONAL SOCIAL SCIENCE Discipline Specific Elective Courses 3 2 50
MCA333D COGNITIVE PSYCHOLOGY Discipline Specific Elective Courses 2 2 100
MCA351 SOFTWARE PROJECT DEVELOPMENT LAB -PHASE II Core Courses 3 1 50
MCA371 MOBILE APPLICATION DEVELOPMENT Core Courses 8 4 150
MCA372A ADVANCED PYTHON PROGRAMMING Discipline Specific Elective Courses 7 4 150
MCA372B VISUAL PROGRAMMING (.NET) Discipline Specific Elective Courses 7 4 150
MCA372C ASSEMBLY LANGUAGE PROGRAMMING USING 8086 Discipline Specific Elective Courses 7 4 150
MCA372D GO LANG Discipline Specific Elective Courses 7 4 150
4 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA441A TEXT ANALYTICS Discipline Specific Elective Courses 4 3 100
MCA441B DATA ENGINEERING AND KNOWLEDGE REPRESENTATION Discipline Specific Elective Courses 4 3 100
MCA441C EMBEDDED SYSTEMS AND INTERFACING Discipline Specific Elective Courses 4 3 100
MCA471 MOBILE APPLICATIONS Core Courses 7 4 150
MCA472 MACHINE LEARNING Core Courses 7 4 150
MCA473A BIG DATA ANALYTICS Discipline Specific Elective Courses 8 4 150
MCA473B NATURAL LANGUAGE PROCESSING Discipline Specific Elective Courses 8 4 150
MCA473C IOT SYSTEM DESIGN AND DEVELOPMENT Discipline Specific Elective Courses 8 4 150
MCA481 SEMINAR Core Courses 3 2 50
5 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA571 CLOUD COMPUTING Core Courses 8 4 150
MCA572A IMAGE ANALYTICS Discipline Specific Elective Courses 8 4 150
MCA572B NEURAL NETWORKS AND DEEP LEARNING Discipline Specific Elective Courses 8 4 150
MCA572C SYSTEM SIMULATION FOR IOT AND SENSOR NETWORKS Discipline Specific Elective Courses 8 4 150
MCA573A QUANTUM MACHINE LEARNING Discipline Specific Elective Courses 8 4 150
MCA573B COMPUTER VISION Discipline Specific Elective Courses 8 4 150
MCA573C IOT DATA ANALYTICS Discipline Specific Elective Courses 8 4 150
MCA581 SPECIALIZATION PROJECT Core Courses 6 2 100
6 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA681 INDUSTRY PROJECT Core Courses 30 12 300
    

    

Introduction to Program:

Master of Computer Applications is a Two year post graduate programme spread over six Trimesters. 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.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

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: 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 Entrepreneurship: 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

Continuous Internal Assessment: 50% Weightage

End Semester Examination: 50% Weightage

MCA131 - MATHEMATICAL FOUNDATION FOR COMPUTER SCIENCE (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course aims to provide fundamental knowledge of mathematical foundations for Computer Science.

Course Outcome

CO1: Understand the concepts of Discrete theory, relations and functions used in Computer Science

CO2: Understand the Propositional Logic, and Algebraic structure concepts used in Computer science

CO3: Understand and Apply Finite State Automata and Turing Machines with Computer related problems

Unit-1
Teaching Hours:6
DISCRETE THEORY, RELATIONS AND FUNCTIONS
 

Introduction -Elementary theory of sets-Set rules and Set Combinations-Relations-Functions- Discrete Numeric Functions-Addition of Numeric Functions-Multiplication of numeric functions-Multiplication with Scalar Factor to Numeric Function.

Unit-2
Teaching Hours:6
PROPOSITIONAL LOGIC
 

Introduction to Logic-Symbolization of Statements-Equivalence of Formula-Propositional Logic-Theory of Inference-Predicate Logic-Inference Theory of Predicate Logic

Unit-3
Teaching Hours:6
ALGEBRAIC STRUCTURE
 

Introduction-Groups-Semi Groups-Complexes-Product Semi Groups-Permutation Groups-Order of a Group-Sub Groups-Cyclic Groups

Unit-4
Teaching Hours:6
INTRODUCTION TO LANGUAGES AND FINITE AUTOMATA
 

Basic Concepts of Automata Theory-Deterministic Finite State Automata (DFA) - Non-deterministic Finite State Automata (NDFA) - Conversion of NDFA to DFA

Unit-5
Teaching Hours:6
TURING MACHINES
 

Introduction-Basic Features of a Turing Machine-Language of a Turing Machine-General Problems of a Turing Machine.

Text Books And Reference Books:

[1] Y.N Singh, “Mathematical Foundation of computer science”, New Age International Publishers, New Delhi,2005

[2] Kenneth H Rosen, “Discrete Mathematics and its Applications”, Tata McGraw Hill, 2016.

Essential Reading / Recommended Reading

[1] John C Martin, "Introduction to Languages and the Theory of Computation", Tata McGraw Hill, 2015.

[2] Donald F. Stanat and David F. McAllister, “Discrete mathematics in Computer Science”.

Evaluation Pattern

CIA          ESE

50%        50%

MCA132 - PROBLEM SOLVING USING C (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:2

Course Objectives/Course Description

 

To provide extensive knowledge of C programming language to the students. It helps in developing the ability to solve computational problems through programs. Lab component is included to give hands-on experience to the students

Course Outcome

CO1: Understand different features of C language

CO2: Analyse real life problem statements to enhance problem solving skills

CO3: Apply the features of C language to develop applications targeting to the industry needs.

Unit-1
Teaching Hours:6
C CONTROL STRUCTURES
 

Tokens in C, data types and keywords - Decision control structures - Loop control structure.

Unit-2
Teaching Hours:6
FUNCTIONS AND POINTERS
 

Functions - Library functions - Function definitions - Prototype - Scope - Storage classes -Call by value - Pointers variable - Definition and initialization - Pointer operators - Calling function by reference - const qualifier with pointers - sizeof operator - Pointer arithmetic - Pointers to functions - Recursion - Recursion and stack.

Unit-3
Teaching Hours:6
ARRAYS AND STRINGS
 

Arrays - Definition - Initialization - 2D arrays - Memory map of 2D arrays - Pointers and 2D arrays -  Passing Arrays to functions - Strings - Characters - Character handling library - String I/O - Pointers and strings

Unit-4
Teaching Hours:6
STRUCTURES, UNIONS, ENUMS
 

Structure definitions - Initializing structures - Accessing structure members - Array of structures - Pointers to structures - Using structures with functions - Self referential structures -  typedef – Unions, enums

Unit-5
Teaching Hours:6
FILE HANDLING AND PREPROCESSORS
 

File processing - Data hierarchy - File and streams - File operations - Sequential-Access file - Random-Access file  - Preprocessors - symbolic constants and macros - File inclusion - Conditional compilation

Lab Exercises:

1.Implement a sample case study: e.g., Bank transaction processing system, Hospital appointment system, Hotel booking system, etc

Text Books And Reference Books:

[1] P. J. Deitel, H. M. Deitel, C: How to Program, Pearson Prentice Hall, 9th Edition, 2021. 

[2] Byron Gottfried, Programming with C, McGraw Hill, 4th Edition, 2018.

Essential Reading / Recommended Reading

[1] Herbert Schildt, The Complete Reference C, Mc Graw Hill, 4th Edition, 2000. 

[2] Brian W. Kernighan, Dennis M. Ritchie, The C Programming Language, Pearson, 2nd Edition, 2012.

Evaluation Pattern

CIA         ESE

50%       50%

MCA133 - RESEARCH METHODOLOGY (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:2

Course Objectives/Course Description

 

This course starts with an introduction to the basic concepts in research and leads through the various methodologies involved in the research process. It focuses on finding out the research gap from the literature and encourages lateral, strategic, and creative thinking. This course also introduces computer technology and basic statistics required for conducting research and reporting the research outcomes scientifically, with emphasis on research ethics.

Course Outcome

CO1: Understand the essence of research and the necessity of defining a research problem

CO2: Apply research methods and methodologies including research design, data collection, data analysis, and interpretation

CO3: Create scientific reports according to specified standards

Unit-1
Teaching Hours:6
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:6
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-SCI Finder- Scopus- Science Direct-Searching research articles- Citation Index -Impact Factor -H-index.

Unit-3
Teaching Hours:6
RESEARCH DATA
 

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

Unit-4
Teaching Hours:6
SCIENTIFIC WRITING
 

Scientific Writing: Significance- Steps- Layout- Types- Mechanics and Precautions- Paper writing for international journals- Writing scientific report.

Unit-5
Teaching Hours:6
REPORT WRITING
 

Latex: Introduction-Text-Tables- Figures- Equations- Citations- Referencing and Templates (IEEE style).

Text Books And Reference Books:

[1] C. R. Kothari, Research Methodology Methods and Techniques, 4th Edition, New Age International Publishers, 2019.

[2] Zina O’Leary, The Essential Guide of Doing Research, 3rd Edition, SAGE Publications Ltd, 2017.

Essential Reading / Recommended Reading

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

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 4th Edition, SAGE Publications Ltd, 2014.

Evaluation Pattern

CIA

ESE

50%

50%

MCA134 - COMPUTER ORGANIZATION AND DESIGN (2023 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course begins with an introduction to organizational Basic building block diagram of a digital computer system. As the course progresses each major block ranging from Processor to I/O will be discussed in their full architectural detail. The course talks primarily about Computer Organization and Architecture issues, Architecture of a typical Processor, Memory Organization, I/O devices and their interface and System Bus organization etc.

Course Outcome

CO1: Understand and analyze computer architecture and organization, computer arithmetic, and CPU design

CO2: Compare the design issues in terms of speed, technology, cost and performance

CO3: Identify the performance of various classes of Memories, build large memories using small memories for better performance and analyze arithmetic for ALU implementation

Unit-1
Teaching Hours:9
BASICS OF DIGITAL ELECTRONICS AND MICRO OPERATIONS
 

Basics Of Digital Electronics: Multiplexers and De multiplexers, Decoder and Encoder, Registers., shift registers, Introduction to combinational circuit, introduction to sequential circuits

Register Transfer and Micro Operations: Register Transfer Language and Register Transfer, Bus and Memory Transfer, Logic Micro Operations, Shift Micro Operations, Design of arithmetic logic unit., arithmetic microoperations

Unit-2
Teaching Hours:9
COMPUTER ARITHMETIC
 

Data representation: signed number representation, fixed and floating point representations, character representation. Computer arithmetic - integer addition and subtraction, ripple carry adder, carry look-ahead adder, etc. multiplication - shift-and-add, Booth multiplier, carry save multiplier, etc. Division - non-restoring and restoring techniques, floating point arithmetic.

Unit-3
Teaching Hours:9
BASIC PROCESSING MODULE
 

Fundamental concepts – Execution of a complete instruction – Multiple bus organization – Hardwired control – Micro programmed control -Basic concepts – Data hazards – Instruction hazards – Influence on Instruction sets – Data path and control consideration – Superscalar operation

Unit-4
Teaching Hours:9
MEMORY SYSTEM
 

Memory Hierarchy and Processor Vs Memory Speed– Semiconductor RAMs – ROMs – Speed – size and cost – Cache memories – Performance consideration – Virtual memory- Memory Management requirements – Secondary storage

Unit-5
Teaching Hours:9
PARALLEL PROCESSING
 

Introduction to Parallel Processing : Pipelining, Characteristics of multiprocessors, Interconnection Structures, parallel processing

Latest technology and trends in computer architecture : multi-cores processor., next generation processors architecture, microarchitecture, latest processor for smartphone or tablet and desktop

Multiprocessors : Categorization of multiprocessors(SISD,MIMD,SIMD.SPMD), Introduction to GPU

Text Books And Reference Books:

1. Computer Organization – Carl Hamacher, Zvonks Vranesic, SafeaZaky, Vth Edition, McGraw Hill., 2011

2. Computer Systems Architecture – M.Moris Mano, IIIrd Edition, Pearson/PHI,2017

Essential Reading / Recommended Reading

1. Computer Organization and Architecture – William Stallings Sixth Edition, Pearson/PHI,2016

2. Structured Computer Organization – Andrew S. Tanenbaum, 4th Edition PHI/Pearson, 2006

3. Fundamentals or Computer Organization and Design, - Sivaraama Dandamudi Springer Int.  V Edition, 2006

4. Computer Architecture a quantitative approach, John L. Hennessy and David A. Patterson, Fourth Edition Elsevier, 3RD Edition 2012

Evaluation Pattern

CIA         ESE

50%       50%

MCA135 - ADVANCED DATABASE TECHNOLOGIES (2023 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To provide a strong foundation for database design and application development and understand the underlying core database concepts and emerging technologies

Course Outcome

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

CO2: Analyze the database requirements and develop the logical design of the database

CO3: Develop NoSQL database applications using storing, accessing, and querying

Unit-1
Teaching Hours:9
CONCEPTUAL MODELING AND DATABASE DESIGN
 

Using High-Level Conceptual Data Models for Database Design - Entity Types, Entity Sets, Attributes, and Keys - Relationship Types, Relationship Sets, Roles, and Structural Constraints - Weak Entity Types - ER Diagrams, Naming Conventions, and Design Issues - Relationship Types of Degree Higher than Two -  Enhanced Entity Relationship Model - Relational Database Design by ER- and EER-to-Relational Mapping  

Unit-2
Teaching Hours:9
NORMALIZATION, FILE ORGANIZATION, AND INDEXING
 

Design Guidelines for Relation Schemas - Functional Dependencies - Normal Forms Based on Primary Keys - Second and Third Normal Forms - Boyce-Codd Normal Form - Multivalued Dependency and Fourth Normal Form - Join Dependencies and Fifth Normal Form - - File Organization - Organization of Records in Files - Ordered Indices - B+ Tree Index Files - Static Hashing - Bitmap Indices

Unit-3
Teaching Hours:9
TRANSACTION PROCESSING AND DISTRIBUTED DATABASES
 

Transaction - Introduction to transaction processing- transaction and system concept- Desirable properties of transaction- Transaction support in SQL- concurrency control techniques – Two phase Locking techniques for concurrency- Concurrency Control Based on Timestamp Ordering. Recovery Concepts.Distributed databases: Distributed Database concepts- Types - Data Fragmentation- Replication- Allocation Techniques. Overview of Transaction Management - Overview of Concurrency Control and Recovery

Unit-4
Teaching Hours:9
INTRODUCTION TO NOSQL
 

Definition and Introduction-Sorted Ordered Column-Oriented Stores- Key/Value Stores. Interacting with NoSQL, NoSQL Storage Architecture: Working with Column-Oriented Databases-HBase Distributed Storage Architecture, NoSQL Stores: Accessing Data from Column-Oriented Databases Like HBase-Querying Redis Data Stores- Querying in Neo4J

Unit-5
Teaching Hours:9
IMPLEMENT THE FOLLOWING BASED ON A DOMAIN
 

DDL commands, DML commands, TCL commands,  NoSQL CRUD operations,  NoSQL aggregate functions, Data manipulation using CASSANDRA

Text Books And Reference Books:

[1]  Elmasri & Navathe, Fundamentals of Database Systems, Addison-Wesley, 7th Edition, 2021.

[2] Shashank Tiwari, Professional NoSQL, Wrox Press, Wiley, 2021,

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            ESE

50%          50%

MCA171 - PYTHON PROGRAMMING (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:6
Max Marks:100
Credits:4

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: Understand and apply Python Data structures

CO2: Demonstrate Object Oriented Concepts in Python

CO3: Apply NumPy and Pandas for solving real time problems

CO4: Design GUI window with database operations

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

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

Lab Exercises:

1. Demonstrate use of Python data structures

2. Demonstrate Lists  and Dictionary comprehension

3. Demonstrate Custom modules with functions

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:

4. Demonstrate use of object- oriented programming concepts

5. Demonstrate exceptional handling

6. Implement ‘re’ module

Unit-3
Teaching Hours:12
INTRODUCTION TO NUMPY AND PANDAS
 

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.

 

Lab Exercises:

7. Implement NumPy features

8. Demonstrate Pandas with its operations  

9. Apply different types of indexing methods using NumPy and Pandas 

Unit-4
Teaching Hours:12
MATPLOTLIB and GUI PROGRAMMING
 

Basic functions of Matplotlib-Simple Line Plot, Scatter Plot. Introduction to Tkiner module-Root Window-Widgets-Button-Label-Message-Text-Menu-Listboxes-Spinbox-Creating tables.

Lab Exercises:

10. Apply regular expression for form validation.

11. Demonstrate the use of “Matplotlib” modules to plot line and scatter plot

Unit-5
Teaching Hours:12
INTRODUCTION TO DJANGO FRAMEWORK AND DATABASE PROGRAMMING
 

Introduction-Web framework-creating model to add database service- Django administration application.

Basic Database Operations and SQL, Databases and Python, The Python DB-API, Connection Objects Databases and Python: Adapters Examples of Using Database Adapters, A Database Adapter Example Application.

Lab Exercises:

12. Create a web application using Django framework.

13. Establish database connectivity for a GUI application using all the appropriate widgets and demonstrate data manipulation and visualization

Text Books And Reference Books:

[1]   Wesely J.Chun, Core Python Application Programming, Prentice Hall, 3rd Edition, 2019

[2]   Python Tutorial,  Guido Rossum, CreateSpace Independent Publishing Platform, 2018

[3]   Python Programming Fundamentals, Kent D. Lee, Springer Publications, 2nd  Edition, 2015

Essential Reading / Recommended Reading

[1]   Programming Python, Mark Lutz , O’Reily Media Inc., 2019.

[2]   Programming with Python, T. R. Padmanabhan, Springer Publications, 2019.

[3]   Murach's Python Programming (2nd Edition),  Joel MurachMichael Urban, Mike Murach & Associates, Incorporated, 2021

Web Resources:

1.     https://cfm.ehu.es/ricardo/docs/python/Learning_Python.pdf

2.     Python Tutorial

Evaluation Pattern

CIA        ESE

50%       50%

MCA172 - WEB STACK DEVELOPMENT (2023 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:7
Max Marks:150
Credits:4

Course Objectives/Course Description

 

On completion of this course, a student will be familiar with full stack 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: Design websites using appropriate security principles, focusing specifically on the vulnerabilities inherent in common web implementations

CO3: Create modern web applications using MERN

Unit-1
Teaching Hours:15
OVERVIEW OF WEB TECHNOLOGIES AND HTML5
 

Internet - 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-Canvas-HTML Media.

Git-commit-rollback-remote repository- GitHub-merge conflict-CSS specificity rule-Pseudo selectors-media queries-flexbox-responsive web design-transition-Bootstrap 5 responsive grid-Components ( Navbar, tables, heroes, carousel, modal etc.,) - font awesome icons

Lab Exercises:

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.

2. Develop static pages for a given scenario using HTML

3. Demonstrate Geolocation and Canvas using HTML5

Unit-2
Teaching Hours:15
XML AND AJAX
 

XML-Documents and Vocabularies -Versions and Declaration -Namespaces JavaScript and XML: Ajax-DOM based XML processing Event-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 Exercises:

4. Write an XML file and validate the file using XSD

5. Demonstrate XSL with XSD

Unit-3
Teaching Hours:15
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-Async await-JS Navigator-JS Cookies - Introduction to JSON-JSON vs XML-JSON Objects-fetch API

Lab Exercises:

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

7. Implement web application using AJAX with JSON

8. Demonstrate to fetch the information from an XML file (or) JSON with AJAX

Unit-4
Teaching Hours:15
React JS
 

Package Manager (NPM) - ES6- Introduction to React.js - Create React App & React file structure - JSX and Components -passing and destructuring props - React Hooks - Axios - Images and Forms - Conditional Rendering - Routes - Redux

Lab Exercises:

9. Create a web application using React Js with Forms.

10. Develop SPA ( Single Page Application)  with React JS

11. Implement CRUD Operation using React JS.

Unit-5
Teaching Hours:15
Node JS and MYSQL
 

Introduction to Node.js - Express JS - Node mailer - NODE JS WITH MYSQL  - Introduction to MySQL - Performing basic database operation(DML) (Insert, Delete, Update, Select)-Prepared Statement- Uploading Image or File to MySQL- Retrieve Image or File from MySQL - bcrypt hashing

Lab Exercises:

12. Demonstrate Node.js file system module.

13. Implement CRUD operation with MySQL using Node.JS

Text Books And Reference Books:

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

[2] Modern Full-Stack Development: Using TypeScript, React, Node.js, Webpack, and Docker,  Frank Zammetti,  APRES, 1st Edition, 2020

Essential Reading / Recommended Reading

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

[2] Laura Lemay, Rafe Colburn & Jennifer Kyrnin, Mastering HTML, CSS & Javascript Web Publishing, BPB Publications, 1st Edition, 2016.

Web Resources:

[1] www.w3cschools.com

[2] https://fullstackopen.com/en/part1/introduction_to_react

Evaluation Pattern

CIA        ESE

50%       50%

MCA231 - SOFTWARE ENGINEERING (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:2

Course Objectives/Course Description

 

The Course provides solid fundamental knowledge of software engineering concepts to the students and it prepares them to develop the skills necessary to handle software projects. It also enables the students to apply software engineering principles to develop quality software applications.

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:6
PROCESS MODELS, UNDERSTANDING REQUIREMENTS
 

A generic process model – Defining a framework activity, identifying a Task Set, Process - Prescriptive Process Models-Specialized Process Models. Requirements Engineering- Developing use cases, Elements of the requirements Model, Analysis pattern, negotiating requirements, validating requirements-Latest Methodology-RAD, DevOps, Fish Model, SCRUM Agile Modeling- Practicing with Rational Rose / other Open Source for all the phases of SDLC

Unit-2
Teaching Hours:6
DESIGN CONCEPTS
 

The design process-Design concepts – Abstraction, Architecture, Patterns, Separation of concerns, Modularity, information hiding, Functional Independence, refinement, Aspects, Refactoring, Design classes, The design Model – Data Design elements, Architectural Design elements, Interface Design Elements, Component-Level Design elements, Deployment’s level Design elements

Unit-3
Teaching Hours:6
COMPONENT LEVEL DESIGN, USER INTERFACE DESIGN
 

Basic Design Principles, Component-level Design guidelines, Cohesion, Coupling, Functional design at the Component level, Designing traditional components–Component based development-Domain Engineering, Component qualification, Adaptation, and Composition, Analysis and Design for reuse. User Interface Analysis and Design models, COCOMO II Model.

Unit-4
Teaching Hours:6
QUALITY MANAGEMENT, TESTING
 

Software Quality- Software testing fundamentals- internal and external view of testing, White-box testing, Basic path testing - control structure testing - Black- box testing- Strategic Approach to Software Testing-verification and validation, unit testing-Integration Testing-Unit testing in OO context, Integration testing in OO context, validation testing, system testing.

Unit-5
Teaching Hours:6
PROCESS AND PROJECT METRICS
 

Metrics in the process and project domains-Metrics for software quality, The project planning process, Software scope and Feasibility, Resources, software project estimation, Decomposition techniques- DevOps- Empirical estimation models, Software equation.

 

Text Books And Reference Books:

[1]  Pressman S Roger, Software Engineering A Practitioner’s Approach, McGraw Hill International Editions, 8th Edition (Indian Edition), 2019.

[2]  Sommerville, Ian, Software Engineering, Addison Wesley, 9th Edition, 2011.

Essential Reading / Recommended Reading

[1]  Pankaj Jalote, Software Engineering: A Precise Approach, Wiley India, 2010.

[2]  Stephen R. Schach, Software Engineering, Tata McGraw-Hill Publishing Company Limited, 2007.

Web Resources:

[1] www.nptel.ac.in  

Evaluation Pattern

CIA      ESE

50%      50%

MCA232 - APPLIED STATISTICS USING R (2023 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course covers the concept of applied statistics,  probability and R tool in computational perspective. It explore the practical experience of statistics and probability using R programming

Course Outcome

CO1: Understand the applied statistics and probability concepts from a computational perspective

CO2: Creating knowledge on statistics and probability to learn courses like machine learning and deep learning

CO3: Apply the implementation of statistical concepts with R programming

Unit-1
Teaching Hours:9
INTRODUCTION TO R
 

Basic calculation - Getting Help - Installing Packages - Data and programming : Data Types, Data Structures, programming Basics

Lab Exercises:

  1. Perform basic calculations using R data structures(Vector, Matrices, List, Data Frames)
  2. Reshape data structures
Unit-2
Teaching Hours:9
DESCRIPTIVE STATISTICS
 

 Introduction to Statistics and Data, Types of Data -Quantitative Data, Qualitative Data, Data, Multivariate Data etc. Features of Data distributions - Center, Spread, Shape, Symmetry, Skewness and Kurtosis, Stem and Leaf Diagrams, Frequency Distributions and Histogram, Measures of Center - Mean, Median, Mode, Measures of Spread - Range, Variance, Standard Deviation, Interquartile range, Measures of Relative Position: Quartiles, Percentiles.

Plotting - Histogram, Bar plot, Box plot, Scatter Plot, Pie chart.

Lab Exercises:

  1. Calculate descriptive statistics
  2. Visualize Data using plots(Bar, histogram, pie, scatter, Box)

 

Unit-3
Teaching Hours:9
INFERENTIAL STATISTICS
 

Hypothesis Tests in R - One sample t-Test Review and example, Two sample t-Test Review - and example  - Simulation, Simple Linear Regression - Modeling, Least square approach, The lm function - Maximum likelihood Estimation(MLE) Approach, Simulating SLR, Analysis of Varience - One-Way ANOVA, Two-Way ANOVA

Lab Exercises:

  1. Build simple linear regression model
  2. Perform a one-way analysis of variance
  3. Perform a Two-way analysis of variance

 

Unit-4
Teaching Hours:9
PROBABILITY
 

Sample Spaces - Events - Model Assignments - Properties of Probability - Counting Methods - Conditional probability - Independent Events - Bayes' Rule - Random Variables

Lab Exercises:

  1. Demonstrate conditional probability
  2. Demonstrate Bayes' rule
Unit-5
Teaching Hours:9
CASE STUDY
 

Healthcare - Finance - Digital Marketing- Environment-Sports

Lab Exercises:

  1.  Explore all learned statistical concepts using dataset of any domain.
Text Books And Reference Books:

[1]   Applied Statistics with R, David Dalpiaz, 2021.

[2]   Introduction to Probability and Statistics Using R, G. Jay Kerns,  Lulu.com, 2016.

Essential Reading / Recommended Reading

[1]   An introduction to statistical data analysis using R, Basic operations, graphics and modelling using R, Christoph Scherber

[2]   Applied Statistics with R- A Practical Guide for the Life Sciences,Justin C. Touchon, Oxford university press, 2021.

[3]   SimpleR – Using R for Introductory Statistics, John Verzani

[4]   A Handbook of Statistical Analyses Using R, Brian S. Everitt and Torsten Hothorn

[5]   Probability and Statistics with Examples using R,  Siva Athreya, Deepayan Sarkar, and Steve Tanner

 

Web Resources:

1.      https://book.stat420.org/applied_statistics.pdf

2.      https://eleuven.github.io/statthink/ChapCaseStudies.html#physical-strength-and-job-performance

3.      https://wwwuser.gwdg.de/~cscherb1/content/Statistics%20Course%20files/R-Manual%20Goettingen.pdf

  1. https://cbb.sjtu.edu.cn/~mywu/bi217/usingR.pdf
Evaluation Pattern

CIA      ESE

50%      50%

MCA233 - OPERATING SYSTEM (2023 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To understand and appreciate the different functions of Operating Systems

Course Outcome

CO1: Comprehend the fundamentals concepts and building blocks of Operating Systems

CO2: Understand the concepts of processes, threads, files, inter-process communication and memory management

CO3: Appreciate the concepts of processes, threads, files, inter-process communication and memory management

Unit-1
Teaching Hours:9
FUNDAMENTALS AND PROCESS MANAGEMENT
 

Concepts - Operating System Definition – Operating System operations – Kernel Data Structures - Operating System Services - System Calls - Linkers and Loaders – Process Management – Concepts - Process Concept – Kernel Level Data Structures for Process Management - Operations on Process IPC Basics – IPC in Shared-Memory Systems – IPC in Message-Passing Systems – Examples of IPC Systems – Pipe, FIFO, Message Queue

Unit-2
Teaching Hours:9
FILE MANAGEMENT
 

File-System Interface - File Concept – File Operations - Kernel Level Data Structures for File Management - Operations on Files File-System Implementation – File System Structure - File System Operations - Directory Allocation - Allocation Methods – Free Space Management – Kernel Level Data Structures for handing open files.

Unit-3
Teaching Hours:9
THREADS AND SYNCHRONIZATION
 

Multi-Threading – Overview – Multi-Threading Models – Thread Libraries Thread Synchronization – Critical Section – Synchronization Objects

Unit-4
Teaching Hours:9
MEMORY MANAGEMENT
 

Main Memory – Conceptual background – Contiguous Memory Allocation – Paging – Swapping Virtual Memory – Background – Demand Paging – Page Replacement – Thrashing

Unit-5
Teaching Hours:9
OPERATING SYSTEM RELATED COMMANDS
 

Process Related commands – Debugging Commands – process synchronization - shell scripting – file related commands – system calls - Socket Programming

Text Books And Reference Books:

[1]   Abraham Silberschatz, P.B. Galvin, G. Gagne, Operating System Concepts, Wiley, 10th Edition, 2018

[2]   Andrew S Tanenbaum & Herbert Bos, Modern Operating Systems, Pearson, 4th Edition, 2014

Essential Reading / Recommended Reading

[1]   Times New Roman, font size 12, Justified alignment

[2]   Mention the Book Title, Author Name(s), Publisher Name, Edition, Year

[3]   Digital Computer Fundamentals, Floyd, Thomas L, Pearson International, 11th Edition, 2015

 Web Resources:

1.      www.w3cschools.com

2.      https://archive.ics.uci.edu

Evaluation Pattern

CIA      ESE

50%      50%

MCA251 - SOFTWARE PROJECT DEVELOPMENT LAB -PHASE I (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:1

Course Objectives/Course Description

 

To have hands on experience in developing a software project by using various software engineering principles and methods in each of the phases of software development. Ability to translate end-user requirements into system and software requirements  Able to identify and formulate research problem, conduct critical research review based on the domain

Course Outcome

CO1: Understand the concepts of Software Engineering

CO2: Identify the problem in the specified area and Analyze the problem, identify the different modules to solve the problems

CO3: Analyze the research gap and propose the novel methodology for given problem

Unit-1
Teaching Hours:30
PROJECT DEVELOPMENT
 

Each student will be encouraged to develop a project based on the societal and institutional needs.    At the end of the Course the students will be submitting design document / literature review document in the IEEE format.

Option – I :Software Development

  1. Domain Identification, Problem Identification, Requirement Analysis for the specific Problem  - 15 Hours
  2. Preparation of SRS Document, DFD, Design the Modules - 15 Hours

Option – II :Research Project

  1. Domain Identification, Conduct the Critical review on the selected research problem, Identify the Research Gap - 15 Hours
  2. Formulate research questions, Collect the data based on the research questions, Propose Novel methodology to solve the research issues - 15 Hours
Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA      ESE

50%      50%

MCA271 - DATA STRUCTURES AND ALGORITHMS (2023 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

To provide extensive knowledge of data structures and algorithms using C language to the students. It helps in developing the ability to solve computational problems through programs. Lab component is included to give hands-on experience to the students. It includes linked lists, stacks, queues, trees, heaps, hash tables, and graphs

Course Outcome

CO1: Design code involving applications arrays, structures, Pointer, stacks, queues, trees, and graphs

CO2: Understand various techniques for searching, sorting, and hashing

CO3: Implement an appropriate data structure to solve real world problems

Unit-1
Teaching Hours:18
INTRODUCTION TO DATA STRUCTURE
 

Abstract Data Types - Arrays, Limitation of the Array, Records & Pointers-About   Arrays, Records & Pointers; Their   Implementation   in Memory, Using One Dimensional Array& Two Dimensional, About Record & Pointers. Linked List - Concept of Singly Linked List, Operations on Linked List, Inserting and   Removing Nodes from a List, Array Implementation   of Lists, Implementation Over Linked List, Doubly Linked List, Generalized List.

Lab Programs

  1. Implement Matrix manipulation on Arrays
  2. Implement linked list and its operations
Unit-2
Teaching Hours:18
STACK AND QUEUES
 

Stacks- Definition   and Example, Primitive Operations, Stack as an ADT, Implementation   of Stacks as An Array and Linked List, Operations on Stacks, Stack Stored as A Linked List, Arithmetic   Expression, Converting an Expression   from Infix to Postfix.

Queues - Definition   And examples Of Queues, Queues   as An Abstract Data Type, Queues Stored   as a Linked List, Circular Queue, Implementation of Queues as An   Array and Linked List, Operations on Queues, Priority   Queue & Dequeue.

Lab Programs

  1. Application of Stack (convert an infix expression to the postfix form)
  2. Queue Operations using Linked List
Unit-3
Teaching Hours:18
SORTING & SEARCHING
 

Searching - Linear Search, Binary Search, Hashing: hash tables, hash functions, collision resolution‐separate chaining, open addressing‐linear probing, quadratic probing, double hashing – Patter matching: Naïve / KMP

Sorting: Bubble Sort, Insertion Sort, Selection Sort, Merge and Quick sort along with time complexity

Lab Programs

  1. Implementation of Linear and Binary Search
  2. Implementation of Quick / Merge Sort
Unit-4
Teaching Hours:18
TREES
 

Trees- Definition of Trees, Basic Terminology of Trees, Binary Tree, Binary Tree Representation as An Array & Linked List, Application of Trees, Binary Tree Traversal: In-Order, Pre-Order, Post-Order - Threaded Binary Tree, Height Balance Tree, B-Trees, Binary Search Trees, Construction of BST Operations‐ Searching, Insertion and Deletion, AVL Trees, Height of an AVL Tree, Operations – Insertion, Deletion and Searching.

Lab Programs

  1. Implementation of Tree Traversal
  2. Construction of BST and operations
Unit-5
Teaching Hours:18
GRAPHS
 

Graphs: Basic Terminology of Graphs, Implementation    of Graphs as An Arrays& Linked List, Operation on Graphs, Graphs Traversals: Breadth First Search, Depth First Search – Topological Sort – Minimum Spanning Tree: Prims and Kruskals

Lab Programs:

  1. Implementation of Graph Traversal
  2. Program to construct Minimum Spanning Tree
Text Books And Reference Books:

[1] Gilberg, F Richard & Forouzan, A Behrouz, Data Structures A Pseudocode approach with C,Cengage. 2nd Edition, 2008.

[2] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, Introduction to Algorithms, MIT Press, 3rd Edition, 2009

Essential Reading / Recommended Reading

[1] Peter Brass, Advanced Data Structures, Cambridge University Press.

[2] Horowitz Sahni Anderson-Freed, Fundamental of Data Structures in C, Universities Press, Reprint, 2008.

[3] Yashavant Kanetkar , Data Structures Through C, BPB Publications, 2019.

Evaluation Pattern

CIA      ESE

50%      50%

MCA272 - PROGRAMMING USING JAVA (2023 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:5

Course Objectives/Course Description

 

This course will help the learner to gain sound knowledge in object-oriented principles, GUI application design with database, and enterprise application design with Servlets

Course Outcome

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

CO2: Analyze the various societal and environmental problems critically to develop solutions using the features of programming language

CO3: Develop sustainable and innovative solutions for real-time problems

Unit-1
Teaching Hours:18
INTRODUCTION TO OBJECT ORIENTED PROGRAMMING (OOP) AND CLASSES
 

Introduction to Object Oriented Programming (OOP)

Object-Oriented Programming (OOP) Principles- 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.

 Lab Exercises:

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.

2. Implement the concept of class, data members, member functions and access specifiers.

3. Implement the concept of function overloading & Constructor overloading.

Unit-2
Teaching Hours:18
INHERITANCE, INTERFACES & PACKAGES AND MULTITHREADING 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.

Multithreading Java

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

Lab Exercises:

4. Implement String and String Buffer classes.

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

6.Implement the concept of package and interface.

7.Implement the concept of multithreading.

Unit-3
Teaching Hours:18
GENERICS, LAMBDA AND THE COLLECTIONS FRAMEWORK
 

Generics

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

Lambda Expression

Introduction to Lambda expression- Block Lambda Expressions - Generic Functional Interfaces - Passing lambda expressions as arguments - Lambda expressions and exceptions- Lambda expressions and variable capture.

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. Working with maps – The map interfaces, the map classes. Comparators- the collection algorithms

Lab Exercises:

8. Implement the concept of Generics

9. Implement the concept of the lambda expression

10. Implement the concept of a collection framework

Unit-4
Teaching Hours:18
JAVA BEANS AND JDBC
 

JDBC

Introduction to JDBC- Connecting to the database- Basic JDBC Operations – Essential JDBC Classes – JDBC Drivers – JDBC-ODBC Bridge – Connecting to a database with driver manager – JDBC database URL.

JAVA BEANS

Java beans - Advantages of Beans – Introspection- Bound and Constrained Properties – Persistence – Customizers - The JavaBeans API.

JAVA SWING

Swing Basics – Components and Containers – JLabel and ImageIcons- JTextField – Swing Buttons – JTabbedPane – JScrollPane – JList – JComboBox – JTable – Swing Menus.

Lab Exercises:

 11. Implement the concept of JDBC

12. Implement the concept of java beans

13. 13. Implement the concept of java swing

Unit-5
Teaching Hours:18
JAVA SERVLETS & JSP
 

JAVA SERVLETS

Servlets Basics – Life Cycle of a Servlet –A Simple Servlet - The Servlet API – Servlet Interfaces – Generic Servlet Class- HttpServletRequest Interface – HttpServeltResponse

JSP

The JSP development model – component of jsp page – Page directive – Action – scriptlet – JSP expression, JSP Syntax and semantics, JSP in XML.

Lab Exercises:

14. Implement the concept of java servlets

15. Implement the concept of JSP

Text Books And Reference Books:

[1] Schildt Herbert, Java : The Complete Reference, Tata McGraw- Hill, 11 th Edition,2019

[2] The complete reference JSP 2.0, Tata McGraw- Hill, 2nd Edition, Phil Hanna

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

Evaluation Pattern

CIA      ESE

50%      50%

MCA331 - DATA COMMUNICATION AND CRYPTOGRAPHY (2023 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course aims to set the foundation for computer networks and introduce the cryptographic approaches. The course covers the communication process between devices with a standard set of protocols based on the Internet model (TCP/IP). The last two units present the cryptographic approaches used for network security

Course Outcome

CO1: Follow Network Architecture and its functionality

CO2: Evaluate network protocols for data transmission in various types of networks

CO3: Explain the working principle of Algorithms in Cryptography

Unit-1
Teaching Hours:9
DATA COMMUNICATIONS
 

Data Communications - Data Transmission: Concepts and Terminology - Analog and Digital Data Transmission - Transmission Impairments - Transmission Media - Guided Transmission Media - Wireless Transmission - Signal Encoding Techniques - Digital Data - Digital Signals - Digital Data - Analog Signals - Analog Data - Digital Signals - Analog Data - Analog Signals

Unit-2
Teaching Hours:9
DIGITAL DATA COMMUNICATION
 

Digital Data Communication Techniques- Asynchronous and Synchronous Transmission - Types of Errors - Error Detection - Error Correction - Line Configurations - Multiplexing: Frequency - Division Multiplexing - Synchronous Time-Division Multiplexing - Statistical Time-Division Multiplexing - Asymmetric Digital Subscriber Line - Circuit Switching Networks - Circuit Switching Concepts - Packet-Switching Principles

Unit-3
Teaching Hours:9
CONGESTION CONTROL
 

Congestion Control in Data Networks - Congestion Control - Traffic Management - Congestion Control in Packet - Switching Networks - High-Speed LANs: The Emergence of High-Speed LANs - Ethernet - Wireless LANs: IEEE 802.11 Architecture and Services - Internetwork Protocols - Internetwork Protocols: Internet Protocol - IPv6 - Transport Protocols: Connection-Oriented Transport Protocol Mechanisms – TCP - TCP Congestion Control - UDP

Unit-4
Teaching Hours:9
CRYPTOGRAPHY AND CRYPTOSYSTEMS
 

Introduction to Cryptography and Data Security - Stream Ciphers - Block Cipher - The Data Encryption Standard (DES) and Alternatives - The Advanced Encryption Standard (AES) - Introduction to Public-Key Cryptography - The RSA Cryptosystem - Public-Key Cryptosystems Based on the Discrete Logarithm Problem - Elliptic Curve Cryptosystems

Unit-5
Teaching Hours:9
CRYPTOGRAPHIC HASH FUNCTION
 

Digital Signatures - The Digital Signature Algorithm (DSA) - Hash Functions - Message Authentication Codes (MACs) - Principles of Message Authentication Codes - MACs from Hash Functions: HMAC - Key Establishment

Text Books And Reference Books:

[1].  Stallings William, “Data and Computer Communications”, PHI, 9th Edition, 2011.

[2].  Bart Preneel, “Understanding Cryptography”, Springer Heidelberg Dordrecht London New York, 2010.

Essential Reading / Recommended Reading

[1].  Forouzan, Behrouz A., “Data Communications and Networking”, Tata McGrawHill publishing Company Limited, 5th Edition, 2013.

[2].  AtulKahate, “Cryptography and Network Security”, Tata McGraw-Hills, 2010.

[3].  Brijendra Singh, “Network Security and Management”, PHI, 3rd Edition, 2013.

[4].  William Stallings, “Cryptography and Network Security”, Prentice Hall, 6th Edition, 2014.

Web Resources:

  1. https://studyopedia.com/computer-networks/introduction-computer-networks/
  2. https://www.johannes-bauer.com/compsci/ecc/
  3. https://www.tutorialspoint.com/dsl/dsl_adsl_fundamentals.htm

 

Evaluation Pattern

CIA      ESE

50%      50%

MCA332 - DATA MINING (2023 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

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 and different preprocessing techniques

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:9
INTRODUCTION AND PREPROCESSING
 

Data Mining Introduction: An overview of Data Mining – Kinds of data and pattern to be mined –Technologies – Targeted Applications - Major Issues in Data Mining – Data Objects and Attribute Types – Measuring Data Similarity and Dissimilarity

Data Preprocessing: Data Cleaning –Data Integration–Data Reduction–Data Transformation – Data Discretization

Unit-2
Teaching Hours:9
MINING FREQUENT PATTERNS AND ADVANCED PATTERN MINING
 

Basic Concepts – Frequent Itemset Mining Methods – Apriori Algorithm-Generating Association Rules from Frequent Itemsets – 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:9
CLASSIFICATION TECHNIQUES
 

Classification – Model Evaluation and Selection – Techniques to Improve Classification Accuracy – Classification by Backpropagation – Support Vector Machines – Learning from Neighbors.

Unit-4
Teaching Hours:9
CLUSTERING TECHNIQUES
 

Cluster Analysis – Definition – Types of Data in Cluster Analysis, Clustering methods– Partitioning Methods – k-Means– k-Medoids– Hierarchical Methods –Agglomerative versus Divisive Hierarchical Clustering –BIRCH–Density-Based Methods–DBSCAN

Unit-5
Teaching Hours:9
OUTLIER DETECTION and APPLICATIONS
 

Outliers and Outlier Analysis – Clustering-Based Approach – Classification-Based Approach – Mining Complex Data Types – Data Mining Applications.

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]   Series on Methods and Applications in Data Mining), Wiley Publications.

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

 Web Resources:

https://data-flair.training/blogs/data-mining-tutorial/

Evaluation Pattern

CIA      ESE

50%      50%

MCA333A - ACCOUNTING AND FINANCE MANAGEMENT (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:2

Course Objectives/Course Description

 

The main objective of this course is to introduce the basics of the accounting and financial management for the domain specific application development.

Course Outcome

CO1: Understand the basics of Accounting and Financial process

CO2: Demonstrate financial instruments for the application development

Unit-1
Teaching Hours:6
FINANCIAL ACCOUNTING FUNDAMENTALS
 

Introduction to financial accounting - The accounting equation and financial statements - The accounting cycle and adjusting entries - Cash flow statement and financial analysis

Unit-2
Teaching Hours:6
MANAGERIAL ACCOUNTING AND COSTING
 

Introduction to managerial accounting - Cost behavior and cost-volume-profit analysis – Job - costing and process costing-Budgeting and variance analysis

Unit-3
Teaching Hours:6
FINANCIAL MANAGEMENT BASICS
 

Introduction to financial management - Time value of money and discounted cash flows - Risk and return, portfolio theory, and capital asset pricing model (CAPM) - Capital budgeting and financing decisions

Unit-4
Teaching Hours:6
FINANCIAL MARKETS AND INSTRUMENTS
 

Financial markets and intermediaries - Stocks, bonds, and other securities - Derivatives and  - hedging - Investment banking and mergers and acquisitions

Unit-5
Teaching Hours:6
FINANCIAL REPORTING AND ANALYSIS
 

Financial statement analysis - Ratio analysis and benchmarking - Forecasting and valuation models - Corporate governance and ethical considerations

Text Books And Reference Books:
  1. https://www.udemy.com/topic/financial-management/
  2. https://in.coursera.org/learn/financial-accounting-polimi
  3. https://www.edx.org/course/accounting-and-finance
Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA       ESE

50%     50%

MCA333B - ECONOMETRICS (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:2

Course Objectives/Course Description

 

The main objective of this course is to introduce the basics of the econometrics for the domain specific software application development.

Course Outcome

CO1: Understand the basics of Econometrics

CO2: Identify the suitable models for the econometric applications development

Unit-1
Teaching Hours:6
INTRODUCTION TO ECONOMETRICS
 

Overview of econometrics as a field - Theoretical concepts and empirical methods - Types of data and variables in econometrics - Probability and statistical inference

Unit-2
Teaching Hours:6
LINEAR REGRESSION ANALYSIS
 

Simple linear regression model - Multiple linear regression model - Estimation and inference in linear regression - Assumptions and diagnostics in linear regression

Unit-3
Teaching Hours:6
ADVANCED REGRESSION MODELS
 

Nonlinear regression models - Panel data models and fixed effects models - Instrumental variable estimation - Time series models and forecasting

Unit-4
Teaching Hours:6
CAUSAL INFERENCE AND PROGRAM EVALUATION
 

Counterfactual analysis and causality - Experimental and quasi-experimental designs - Regression discontinuity and difference-in-differences - Propensity score matching and sensitivity analysis

Unit-5
Teaching Hours:6
APPLIED ECONOMETRICS AND POLICY ANALYSIS
 

Microeconometric applications (e.g., labor, education, health) - Macroeconometric models and forecasting - Program evaluation and policy impact analysis - Ethics and communication in econometrics research

Text Books And Reference Books:
  1. https://www.udemy.com/course/prerequisites-for-econometric-the-best-course-ever/
  2. https://in.coursera.org/learn/erasmus-econometrics
  3. https://onlinecourses.nptel.ac.in/noc21_hs01/preview
Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA            ESE

50%          50%

MCA333C - COMPUTATIONAL SOCIAL SCIENCE (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:2

Course Objectives/Course Description

 

The main objective of this course is to introduce the basics of the social science domain for social application development.

Course Outcome

CO1: Understand the process of social data analysis

CO2: Identify and use suitable tools for the computational social sciences

Unit-1
Teaching Hours:6
INTRODUCTION TO COMPUTATIONAL SOCIAL SCIENCE
 

Introduction to computational social science and relation to computer science - Theoretical and methodological foundations of CSS - Data types and sources in CSS (e.g., text, network, geospatial data) - Research design and ethical considerations in CSS research

Unit-2
Teaching Hours:6
DATA WRANGLING AND PREPROCESSING
 

Data acquisition and scraping using web APIs and libraries (e.g., Requests, BeautifulSoup) - Data cleaning and validation using regular expressions and string manipulation – Data transformation and normalization using Pandas and Numpy libraries - Exploratory data analysis and visualization using Matplotlib and Seaborn libraries

Unit-3
Teaching Hours:6
MACHINE LEARNING FOR SOCIAL DATA
 

Machine learning algorithms for social data (e.g., classification, clustering, dimensionality reduction) - Model selection and evaluation using cross-validation and hyperparameter tuning - Deep learning models for natural language processing (e.g., word embeddings, Convolutional neural networks) - Social network analysis using graph algorithms (e.g., centrality measures, community detection)

Unit-4
Teaching Hours:6
SOCIAL MEDIA ANALYSIS AND TEXT MINING
 

Collecting and processing social media data using APIs and web scraping – Sentiment - analysis and opinion mining using natural language processing techniques (e.g., lexicons, machine learning) - Topic modeling and clustering using Latent Dirichlet Allocation (LDA) and K-means algorithms - Social media network analysis and visualization using NetworkX and Gephi libraries

Unit-5
Teaching Hours:6
Applications of CSS in Computer Science
 

Social computing and crowdsourcing applications (e.g., recommendation systems, human computation) -Algorithmic fairness and ethical issues in CSS - Human-Computer Interaction (HCI) research using CSS techniques - Future directions and emerging topics in CSS and computer science

Text Books And Reference Books:
  1. https://www.coursera.org/specializations/computational-social-science-ucdavis
Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA            ESE

50%          50%

MCA333D - COGNITIVE PSYCHOLOGY (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:100
Credits:2

Course Objectives/Course Description

 

This course provides an introduction to cognitive psychology, covering its history, theories, and research methods, as well as exploring attention, perception, memory models, encoding, storage, retrieval, language acquisition, comprehension, non-verbal communication, problem solving, decision-making, creativity, and cognitive tools. It also examines the intersection of cognitive psychology and AI, including cognitive models, the role of cognitive psychology in AI development, human-AI interaction, and emerging topics in the field.

Course Outcome

CO1: Understanding of Cognitive Psychology Principles

CO2: Integration of Cognitive Psychology and AI

Unit-1
Teaching Hours:30
INTRODUCTION TO COGNITIVE PSYCHOLOGY
 

Overview of cognitive psychology as a field  (History, Theory and Research) - Theoretical approaches to studying cognition - Basic concepts and methods in cognitive psychology - Attention and perception

Unit-2
Teaching Hours:6
MEMORY AND LEARNING
 

Models of memory and forgetting - Encoding, storage, and retrieval processes - Long-term memory structures and organization - Factors influencing memory performance

Unit-3
Teaching Hours:6
LANGUAGE AND COMMUNICATION
 

Language acquisition and development - Language processing and comprehension - Speech perception and production - Non-verbal communication and gestures

Unit-4
Teaching Hours:6
PROBLEM SOLVING AND DECISION-MAKING
 

Decision making and reasoning - Heuristics and biases in judgment - Creativity and innovation - Problem solving strategies and cognitive tools

Unit-5
Teaching Hours:6
COGNITIVE MODELLING AND AI
 

Cognitive architectures and models (e.g., ACT-R, SOAR, CLARION) - Cognitive psychology in the development of AI and machine learning algorithms - Human-AI interaction and explainability - Future directions and emerging topics in cognitive psychology and computer science

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

ESE 50%

CIA 50%

MCA351 - SOFTWARE PROJECT DEVELOPMENT LAB -PHASE II (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:1

Course Objectives/Course Description

 
  • To inculcate project development skills and various software engineering principles and methods
  • Ability to use recent technologies for the software development
  • To acquire research skills and able to publish research articles

Course Outcome

CO1: Develop the software project based on requirements

CO2: Solve the research issues using novel methodology

CO3: Develop realtime projects / present Paper, publish research articles and Patents

Unit-1
Teaching Hours:30
SOFTWARE PROJECT DEVELOPMENT
 
  • In continuation with Semester II Software Development Lab Phase – I, Students are asked to continue their Software Development / Research Project Development
  • Students are expected to prepare and submit final report on the project in the IEEE format.

 Option – I : Software Development

  1. Develop the Modules, Implementation, Testing - 15 Hours
  2. Review of the Modules, Report Preparation - 15 Hours

 Option – II : Research Project

  1. Implementation of Research Problem, Formulate Research Article - 15 Hours
  2. Present Article in National / International Conference and Publish Article in UGC CARE / WoS/ Scopus / International Journals  - 15 Hours
Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA      ESE

50%      50%

MCA371 - MOBILE APPLICATION DEVELOPMENT (2023 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course will enable students to learn to setup Android Application development environment, create user friendly User Interfaces, handle multiple activity, persistent application development, handle data in cloud, test and deploy the App in the market

Course Outcome

CO1: Understand the basic concepts of Mobile application development

CO2: Design and develop user interfaces for the Android platforms

CO3: Apply Kotlin programming concepts to Android application development

CO4: Deploy mobile app with material design principles

Unit-1
Teaching Hours:18
INTRODUCTION TO ANDROID
 

History of Mobile Apps, Trends in Market-Web App Vs Mobile App-Mobile OS.Introduction to  Android and Kotlin: Kotlin Basics – Classes and Objects- Inheritance- Functions – Extension Functions – First Android App – Anatomy of an Android App - Deploying the app: Running and Debugging app in Android Emulator.

Lab Exercises:

1. Form Creation

2. Activity and Layout demonstration

Unit-2
Teaching Hours:18
LAYOUT NAVIGATION
 

Layouts in Android ConstraintLayout - Displaying lists with RecyclerView Multiple activities and intents - App bar, navigation drawer, and menus Fragments - Navigation in an app - Navigation UI.

 Lab Exercises:

3. Intents

4. User navigation

Unit-3
Teaching Hours:18
ACTIVITY AND FRAGMENT LIFECYCLE
 

Introduction to Activity-Activity Lifecycle – Logging. Fragment: Introduction - Lifecycle- Task and Back Stack. Android App Architecture - View Model -Data Binding – Live Data- Transform Live Data.

 Lab Exercises:

1. Activity Lifecycle

2. Fragment Lifecycle

Unit-4
Teaching Hours:18
SAVING USER DATA
 

Store Data-Room Persistency Library-Asynchronous program-Coroutines-Testing Databases. Introduction to Advanced Binding – Multiple Item View types-Headers -GridLayouts.

 Lab Exercises:

1. Sharedpreference

2. Recyclerview

Unit-5
Teaching Hours:18
ADVANCED RECYCLERVIEW
 

Connect to the Internet-Android Permissions-connect to and from Network Resources – Connect to the Web Services-Display Images. Repository pattern – Work Manager – Work Input/Output – Work Request Constraints. App UI Design: Android Styling – Typography-Material Design- Material Components- Localization.

 Lab Exercises:

1. Work  Manager

2. Material Design

Text Books And Reference Books:

[1] John Horton, Android programming with Kotlin for beginners, Packt-Birmingham, Mumbai, 2nd edition, 2019.

[2] Gardner, B., Sills, B., Stewart, C., Marsicano, K. Android Programming: The Big Nerd Ranch Guide. United Kingdom: Addison Wesley Professional, 4th edition,2022

Essential Reading / Recommended Reading

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

[2]   Mark Wickham, Practical Android: 14 Complete Projects on Advanced Techniques and Approaches, APRESS.

Web Resources:

  1. https://developer.android.com/
  2. https://kotlinlang.org/education/
Evaluation Pattern

CIA        ESE

50%      50%

MCA372A - ADVANCED PYTHON PROGRAMMING (2023 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:7
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course inculcates the theoretical and practical approaches which focus on advanced programming concepts in Python. This course explores data analysis, text analysis, gaming, and web development using python.

Course Outcome

CO1: Create different visualizations using Python

CO2: Design websites using Python IDE frameworks

CO3: Apply Python for Image Processing and Text analysis

CO4: Develop Games using modern tools

Unit-1
Teaching Hours:15
PYTHON FOR DATA VISUALIZATION
 

Making 3D visualizations: Creating 3D bars- Creating 3D histograms – Animating in Matplotlib – Plotting Charts with Images and Maps: Processing images with PIL – Plotting with Images – Plotting data on a map using Basemap

Lab Exercises:

1. Demonstrate Plots with Images and Maps

2. Apply 3D visualization concepts

Unit-2
Teaching Hours:15
PYTHON FOR WEB DEVELOPMENT USING FLASK
 

Basic Application Structure: Initialization – Routes and View functions – Server Startup – The request – response cycle. Templates: The Jinja2 Template Engine – Links – Static Files. 

Web Forms: Form Classes – HTML rendering forms – Form Handling.

Lab Exercises:

3. Design a website using FLASK and perform CRUD operations

4. Demonstrate views and templates in FLASK

Unit-3
Teaching Hours:15
PYTHON FOR IMAGE PROCESSING
 

Image and its Properties-Image types – Data structures for Image analysis -  Filtering – Image Enhancement -Segmentation.

Lab Exercises:

5. Apply Image transformation and Manipulations

6. Use Image Enhancement techniques

Unit-4
Teaching Hours:15
PYTHON FOR TEXT ANALYSIS
 

Processing and understanding text: Text processing and wrangling – Text classifications: Automated Text classifications – Data retrieval – Classification models.

Lab Exercises

7.Find text similarity using Information Retrieval

8. Demonstrate the text analytics process in Social Media like Twitter / Facebook / Instagram

Unit-5
Teaching Hours:15
PYTHON FOR GAME DEVELOPMENT
 

Introducing Pygame- Installing Pygame – Using Pygame – Understanding Events – Opening a display – Using the font module.

Lab Exercises:

9. Build a game using Cocos2D

10. Design a game object with different movements

Text Books And Reference Books:

[1]   Python Data Visualization Cook Book, Igor Mialovanovic, PACKT publications, First Edition, 2013

[2]   Flask Web Development, Miguel Grinberg , O’Reilly Publications, First Edition, 2014          

[3]   Image Processing and Acquisition using Python, Ravishankar Chityala, ‎Sridevi Pudipeddi, CRC Press, Taylor & Francis Group, 2014

[4]   Text Analytics with Python, Dipanjan Sarkar, Apress publications, Second Edition, 2019

[5]   Beginning Game with Python and Pygame, Will McGugan, Apress publications, 2007

Essential Reading / Recommended Reading

[1]    Web Development with Django, Ben Shaw, ‎Saurabh Badhwar, ‎Andrew Bird , PACKT publishing, 2021 

[2]   Python Web Development with Django, Jeff Forcier, Paul Bissex, Wesley Chun,

 

 Web Resources:

 

1.      Python Data Visualization Cookbook (blaqueyard.com)

 

2.      Flask Web Development (coddyschool.com)

 

Evaluation Pattern

CIA   50%

ESE   50%

MCA372B - VISUAL PROGRAMMING (.NET) (2023 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:7
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course is designed to provide the knowledge of .NET Frameworks along with C# programming.

Course Outcome

CO1: Understand .NET architecture and C# programming Language.

CO2: Develop windows based and navigation applications with database.

CO3: Create application development and deployment using ASP.NET

CO4: Apply data sources connection using ADO.NET and managing them

Unit-1
Teaching Hours:15
INTRODUCTION TO .NET
 

.NET Architecture – Common Language Runtime, MSIL, Support of different Languages. Language Interoperability, .NET Framework Classes. Advantages of Managed Code – Strong Data Type Check, Garbage Collection, Security, Performance Improvement. C# as a programming language, Features of C# – Data types, Flow Control – the Main method, Program Structure, Methods, Arrays, Namespaces

Lab Exercises:

1.     Program to find the largest element from an integer array in C#.

2.     Design a window based application with common controls (Text box, Label, button, List box, check box, radio button, combo box, Link label, groupbox, panel, rich textbox, form, picture box, message box)

3.     Program to implement date time picker, month calender and numeric updown.

Unit-2
Teaching Hours:15
WINDOWS APPLICATIONS
 

Understanding Windows Forms Architecture, Windows controls: Common, controls, Containers, Menus and Tool strips, Dialog controls, Data, Reporting. Adding and using windows controls to the form, working of window-based application with database.

Lab Exercises:

1.     Design window-based application with containers, menu strip, status strip and tool strip.

2.     Program to design Dialog controls-font dialog, openfile dialog, save file dialog.

3.     Design a windows-based application to perform CRUD operation into database.

Unit-3
Teaching Hours:15
WINDOWS PRESENTATION FOUNDATION
 

Windows Presentation Foundation Application Fundamentals, Navigation applications / XAML Browser Applications, Binding to a WPF element, Transformations- Render, Skew, Rotate.

Lab Exercises:

  1. Program to implement Transformations Scale and Skew.
  2. Program to implement Transformations Translate and Rotate
Unit-4
Teaching Hours:15
ASP.NET
 

Introduction to Visual Studio .NET – ASP .NET. Difference between ASP and ASP.NET. Creating a Web application using ASP.NET. Components of an ASP.NET User Control, Custom Control, Deploying ASP .NET applications. Master Pages, Themes. Assemblies, Features of Assemblies, Application Domains, Assembly Structure, Assembly manifests, Assemblies and Components.

Lab Exercises:

  1. Program to implement user control and custom control.
  2. Design a web-based application to perform CRUD operation into database
Unit-5
Teaching Hours:15
DATA ACCESS
 

ADO.NET overview. Various data access objects – Connection, Command and DataSet Objects. Binding data to ASP .NET server controls. Accessing data from a database using ADO.NET.

Lab Exercises:

1.     Program to implement various data access objects.

2.     Design a web based application to access data from database using ADO.net.

Text Books And Reference Books:

[1]   Jeff Ferguson, Brian Patterson, Jason Beres ,C# Programming Bible , Wiley Publishing Inc., Reprint 2015.

[2]   Mastering C# and .Net Framework , Marino Posadas, Packt Publishing 2016.

[3]   Asp.net MVC 1.0 website programming: problem - design – solution, Bernadi andnick,2009.

[4]   ASP.NET 4, Unleashed – Stephen Walther, Kevin Hoffman, Nate Dudek, Pearson,2016.

Essential Reading / Recommended Reading
  1. ASP .NET complete reference, Matthew Macdonald,2017
  2. Programming in C#, E Balaguruswamy,2017.
Evaluation Pattern

CIA          ESE

50%         50%

MCA372C - ASSEMBLY LANGUAGE PROGRAMMING USING 8086 (2023 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:7
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course will enable students to Familiarize basic architecture of 8086 microprocessor and Programming 8086 Microprocessor using Assembly Level Language Use Macros and Procedures. The functionalities of stack and interrupts will be addressed including timing and delay.

Course Outcome

CO1: Understand the necessity, features and architecture of 8086

CO2: Apply various addressing modes in 8086 programming

CO3: Develop an ALP using assembler

CO4: Develop and critique ALP using procedures, macros and modular programming approaches

Unit-1
Teaching Hours:15
16-BIT MICROPROCESSOR 8086
 

Salient features of 8086 Microprocessor, architecture of 8086 (Block diagram, signal description), register

organization, concepts of pipelining.  memory segmentation and memory address generation from segment offset address.  Minimum and Maximum Mode operation and diagram

Lab Exercises:

1Addition, subtraction, multiplication and division of 8 bit signed and unsigned numbers.

2.  Addition, subtraction, multiplication and division of 16 bit numbers.

3.  Addition, subtraction, multiplication and division of 32-bit numbers.

Unit-2
Teaching Hours:15
THE ART OF ASSEMBLY LANGUAGE PROGRAMMING
 

Assembly Language Programming Tools Editors -Assembler, Linker, Debugger.  Assembler directives, model of 8086 assembly language programming, programming using assembler.

 Lab Exercises:

 1.     ASCII/ BCD arithmetic and conversion of numbers.

2.     Find valid 2 out of 5 code of a given number.

3.      Copy/exchange block of data (Array of 8 bit, 16 bit) from one location to another with and without overlap.

Unit-3
Teaching Hours:15
8086 INSTRUCTION SET
 

Concept of Machine Language, Instruction format, addressing modes.  Instruction set (Arithmetic, logical, data transfer, bit manipulation, string, program control transfer, process control)

 Lab Exercises:

1. Addition, subtraction, multiplication and division of two array (8 bit, 16 bit) and store result in third array.

2. Find the Maximum/Minimum from given 8/16-bit given array.

3.  Arrange given array in ascending and descending order.

Unit-4
Teaching Hours:15
STACK AND INTERRUPTS
 

Introduction to stack, Stack structure of 8086, Programming for Stack. Interrupts and Interrupt Service routines, Interrupt cycle of 8086, NMI, INTR, Interrupt programming, Timing and Delays.

 Lab Exercises:

 1. Find from the  given array/ byte is palindrome or not.

2. (i) copy string to another location/compare two strings (ii) Reverse string (iii) check palindrome or not (iv) searching a word from given string (vi) Find a character and replace with another character from given string. 

3. Generate Fibonacci series of 8 bit numbers

Unit-5
Teaching Hours:15
PROCEDURE AND MACRO
 

Defining Procedure (Directives used, FAR and NEAR, CALL and RET instructions) - Defining Macros.-Assembly Language Programs using Procedure and Macros-DOS interrupt services.

Introduction to 8051 microcontrollers

 Lab Exercises:

 1.     Compute factorial of given number using near procedure and far procedure.

2.      Copy string to another location using MACRO.

3.       Boolean expression simplification.

Text Books And Reference Books:
  1. 1.     A. K.Ray , K M Bhurchandi, “Advanced Microprocessor & Peripherals”, Tata McGraw Hill,3nd Edition,2013
  2. 2.      Douglas V Hall, “Microprocessor & Interfacing: Programming and Hardware”, Tata McGraw Hill, 2nd Edition,2006.
  3. Yn - cheng Liu and Gibson, G.A., “Microcomputer Systems: The 8086 / 8088Family Architecture, Programming and Design”, Prentice Hall of India, 2nd Edition, 2006.
  4. Badri Ram , ‘’Advanced Microprocessors and Interfacing”’, McGraw Hill, 2014
Essential Reading / Recommended Reading

1.The Intel Microprocessors: 8086/8088, 80186/80188, 80286, 80386, 80486, Pentium, Pentium Pro Processor, Pentium II, Pentium III, Pentium 4, and Core2 with 64-bit Extensions, 8th Edition , Barry B. Brey , Pearson Education , 2011

2. Microprocessors and Interfacing By Douglas V Hall Revised Second Edition, McGraw Hill Publication , 2021

3. The 8088 and 8086 Microprocessors, Programming, Interfacing, Software, Hardware and Applications, Fourth Edition, By Walter A Triebel and Avtar Singh, Pearson Education,2002

 

Web Resources:

1. www.tutorialpoint.com

2. https://www.javatpoint.com/8086-microprocessor

3.https://www.youtube.com/watch?v=clOFKQKdI3k&list=PLc21Sqj4D8SSRpPFZLL6XvS7aGFs3HQ4H

Evaluation Pattern

CIA         ESE

50%       50%

MCA372D - GO LANG (2023 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:7
Max Marks:150
Credits:4

Course Objectives/Course Description

 

Go (or Golang) is an open-source programming language designed to build fast, reliable, and efficient software at scale. Google uses Go specifically for its large networks of servers, and Go also powers much of Google’s own cloud platform. Developers use Go in application development, web development, in operations and infrastructure teams, and much more. It is the language of Cloud Native infrastructure and software development.

Course Outcome

CO1: Apply modern software design patterns utilizing the Go language

CO2: Build grouping of data and functions

CO3: Create concise, efficient, and clean applications using Go

Unit-1
Teaching Hours:15
PROGRAMMING FUNDAMENTALS
 

Why Go? Variables, values & type – introduction to packages, short declaration operator, var keyword, exploring type, zero values, fmt package, creating your own type, conversion, not casting. Control flow – Understanding control flow, loop, conditional.

Lab Exercises:

1. Implement the concept of Variables, values and type.

2. Implement the concept of control flow.

Unit-2
Teaching Hours:15
GROUPING DATA
 

Array. Slice - composite literal, for range, slicing a slice, append to a slice, delete from a slice, make, multi-dimensional slice. Map - introduction, add element & range, delete. Struct – introduction, embedded structs, anonymous structs.

 Lab Exercises:

1. Implement the concept of Array and Slice.

2. Implement the concept of Map and Structs

Unit-3
Teaching Hours:15
FUNCTIONS
 

Introduction, variadic parameter, unfurling a slice, Defer, Panic, Methods, Interfaces & polymorphism, Anonymous function, function expression, returning a function, callback, closure, recursion. Error handling – introduction, checking errors, Printing and logging, Recover, Errors with info.

Lab Exercises:

1. Implement the concept of functions and error handling

2. Implement the concept of interface

Unit-4
Teaching Hours:15
POINTERS AND APPLICATION
 

Pointer – introduction, use, method sets, Passing and Returning Pointers from Functions, Passing by Value vs. Passing by Pointer. Application – JSON marshal and unmarshal, bycrypt. Testing and Benchmarking – introduction, table test, golint, benchmark, coverage. 

 Lab Exercises:

1. Implement the concept of Pointers, call by value and call by function.

2. Implement the concept of JSON marshal and unmarshal. Write its unit test case.

Unit-5
Teaching Hours:15
CONCURRENCY
 

Concurrency vs parallelism, Wait-group, race condition, mutex, atomic. Goroutines, and Channels – introduction, Directional channels, using channels, range, select.

Lab Exercises:

1. Implement the concept of Concurrency.

2. Implement the concept Goroutines and Channels

Text Books And Reference Books:

[1]    Head First Go, Jay McGavren, O′Reilly 2019

[2]   The Go Programming Language, Alan A. A. Donovan, Brian W. Kernighan, 2016, Pearson Education;

[3]   Go in Action, William Kennedy, Brian Ketelsen, Erik St. Martin Manning; 2015

Essential Reading / Recommended Reading

[1]   Introducing Go: Build Reliable, Scalable Programs, Caleb Doxsey,Shroff/O'Reilly; First edition 2016

[2]   Get Programming with Go, Nathan Youngman, Roger Peppé, Manning; 2018

[3]   Hands-on Go Programming,  Sachchidanand Singh, Prithvipal Singh, BPB Publications 2021

 Web Resources:

1.     https://go.dev/doc/

2.     https://developers.google.com/learn/topics/go

Evaluation Pattern

CIA          ESE

50%         50%

MCA441A - TEXT ANALYTICS (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The main aim of this course is to understand and apply a wide range of classification, clustering, estimation and prediction algorithms on Textual data and to perform social network analysis. They can also design a new solution to opinion extraction, sentiment classification and data summarization problems. 

Course Outcome

CO1: Provide an overview of common text mining and social media data analytic activities

CO2: Understand the complexities of processing text and network data from different data sources

CO3: Solve complex real-world problems for sentiment analysis and Recommendation systems.

Unit-1
Teaching Hours:9
INTRODUCTION AND MINING TEXTUAL DATA
 

Introduction to Text Mining - Text Representation- tokenization, stemming, stop words, TF-IDF, Feature Vector Representation, NER,N-gram modeling.

Text Clustering, Text Classification, Topic Modeling-LDA, HDP

Unit-2
Teaching Hours:9
INTRODUCTION TO WEB-MINING
 

Introduction To Web-Mining - inverted indices and boolean queries, plsi, query Optimization, page ranking.Web usage web content mining - essentials of social graphs, social networks, models, Information diffusion in social media.

Unit-3
Teaching Hours:9
INTRODUCTION TO SOCIAL MEDIA ANALYSIS
 

Essentials of Social graphs, Social Networks, Models, Information Diffusion in social media. Analyzing social media:

Behavioral Analytics, Influence and Homophily, Recommendation in social media

Unit-4
Teaching Hours:9
SENTIMENTAL ANALYSIS
 

Sentiment Classification, feature based opinion mining, comparative sentence and relational mining, Opinion spam

Unit-5
Teaching Hours:9
RECENT TRENDS IN TEXT ANALYTICS
 

Recent Trends in Text, Web and Social Media Analytics

Text Books And Reference Books:

[1].BingLiu,“WebDataMining-ExploringHyperlinks,Contents,andUsageData”,Springer,Second Edition, 2011.

[2].RezaZafarani,MohammadAliAbbasiandHuanLiu,“SocialMediaMining-AnIntroduction”, Cambridge University Press, 2014.

[3].Bing Liu, “Sentiment Analysis and Opinion Mining”, Morgan & Claypool Publishers,2012.

Essential Reading / Recommended Reading

[1].NitinIndurkhya,FredJDamerau,“HandbookofNaturalLanguageProcess”,2ndEdition,CRC Press, 2010.

[2].Matthew A. Russell, “Mining the social web”, 2nd edition- O'Reilly Media, 2013

Evaluation Pattern

CIA        ESE

50%      50%

MCA441B - DATA ENGINEERING AND KNOWLEDGE REPRESENTATION (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

To provide a foundational knowledge of data engineering and knowledge representation. To store, retrieve, analyze and design data for various applications. To represent different sorts of knowledge, such as uncertain or incomplete knowledge.

Course Outcome

CO1: store and retrieve data effectively

CO2: analyze the data from different sources

CO3: analyze and design knowledge based systems

Unit-1
Teaching Hours:9
DATA ENGINEERING and DATA MODELS
 

Data Engineering

Introduction to Data Engineering - Data Engineering versus Data Science – Data Engineering tools– Data Engineering Lifecycle.

Data Models

Data Systems – Reliability – Scalability – Maintainability -Data Models and Query Languages. - Relational Model Versus Document Model - Query Languages for Data -Query Languages for Data,Declarative Queries on the Web ,MapReduce Querying ,Graph-Like Data Models Property Graphs ,The Cypher Query Language ,Graph Queries in SQL ,Triple-Stores and SPARQL.

Unit-2
Teaching Hours:9
BUILDING DATA PIPELINES
 

Introduction – Data Engineering ecosystem - Building data pipelines—Extract, Transform, Load -ETL Process – Data Structures related to  Database – Other data integration methods – Benefits and Challenges of ETL – ETL tools.

Data Warehousing - Stars and Snowflakes: Schemas for Analytics- Column-Oriented Storage - Column Compression -Sort Order in Column Storage - Writing to Column-Oriented Storage.

Unit-3
Teaching Hours:9
DATA STORAGE AND RETRIEVAL
 

Data Storage and Retrieval Non Relational data

Non Relational data – NoSQL- Language-Specific Formats JSON, XML, and Binary Variants  - Modes of Dataflow Dataflow Through Databases.

DATA in Distributed systems

Data in distributed systems – Partitioning and Replication - Partitioning of Key-Value Data - Partitioning and Secondary Trouble with Distributed Systems- Faults and Partial Failures - Unreliable Networks - Unreliable Clocks.

Unit-4
Teaching Hours:9
KNOWLEDGE REPRESENTATION
 

Knowledge Representation - Ontological Engineering - Categories and Objects . Events - Mental Events and Mental Objects - Reasoning Systems for Categories -  Reasoning with Default Information Uncertain knowledge and reasoning- Quantifying Uncertainty - Acting under Uncertainty - Basic Probability Notation.

Unit-5
Teaching Hours:9
KNOWLEDGE REPRESENTATION IN AN UNCERTAIN DOMAIN
 

Probabilistic Reasoning-Representing Knowledge in an Uncertain Domain -The Semantics of Bayesian Networks -Efficient Representation of Conditional Distributions -Exact Inference in Bayesian Networks -Relational and First-Order Probability Models.

Text Books And Reference Books:

[1]  Martin Kleppmann, Designing Data-Intensive Applications - The Big Ideas Behind Reliable, Scalable,and Maintainable Systems,  first edition, O’Reilly ,2017

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

Essential Reading / Recommended Reading

[1]  Ted Malaska , Rebuilding Reliable Data Pipelines Through Modern Tools , first edition,  O’Reilly, 2019

[2]  Paul Crickard, Data Engineering with Python,  first edition, Packt Publishing,2020

[3]  Ronald J. Brachman, Hector J. Levesque, KNOWLEDGE REPRESENTATION AND REASONING, Elsevier , 2004

[4]  S.L. Kendal and M. Creen An Introduction to Knowledge Engineering, Springer, 2007

Evaluation Pattern

CIA

ESE

50%

50%

MCA441C - EMBEDDED SYSTEMS AND INTERFACING (2022 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:3

Course Objectives/Course Description

 

It is intended to impart skills essential for the design and implementation of Embedded system and interfacing using appropriate hardware and software tools. To understand fundamentals of IoT and embedded system including essence, basic design strategy and process modeling.    The course eases hands-on experiences on sensor interfacing, blue tooth interfacing, LCD and motor interfacing.

Course Outcome

CO1: Identify and recognize the embedded System & its components design process system

CO2: Demonstrate and distinguish different communication interfaces and interrupt sources

CO3: Critique RTOS, EDLC and Web architectural framework of embedded system

Unit-1
Teaching Hours:9
INTRODUCTION TO EMBEDDED SYSTEMS
 

Introduction: Embedded Systems and general-purpose computer systems, history, classifications, applications and purpose of embedded systems
Core of the embedded system, Memory, Sensors (resistive, optical, position, thermal) and Actuators (solenoid valves, relay/switch, opto-couplers), Communication Interface, Embedded firmware (RTOS, Drivers, Application programs), Power-supply (Battery technology, Solar), PCB and Passive components, Safety and reliability, environmental issues. Ethical practice.

Unit-2
Teaching Hours:9
EMBEDDED NETWORKING AND INTERRUPTS SERVICE MECHANISM
 

Device and Communication Bus for Devices Network IO Types and examples, Serial communication devices, Parallel Device ports, Sophisticated Interfacing Feature in Devices Ports, Wireless Devices, Real time clock, Network Embedded Systems  interrupt sources, Programmed-I/O busy-wait approach without interrupt service mechanism- context and periods for context switching -Introduction to Basic Concept Device Drivers.

Unit-3
Teaching Hours:9
EMBEDDED HARDWARE, SOFTWARE AND PERIPHERAL
 

Custom single purpose processors: Hardware – Combination Sequence – Processor design – RT level design – optimizing software: Basic Architecture – Operation – Programmer’s view – Development Environment – ASIP – Processor Design –Peripherals – Timers and counters – UART – Pulse width modulator – LCD controllers – Key pad controllers– A/D converters – Real time clock.

Unit-4
Teaching Hours:9
RTOS BASED EMBEDDED SYSTEM DESIGN
 

Introduction to basic concepts of RTOS- Task, process & threads, interrupt routines in RTOS, Multiprocessing and Multitasking, Preemptive and non-preemptive scheduling, Task communication shared memory, message passing-, Interprocess Communication – synchronization between processes-semaphores, Mailbox, pipes, priority inversion, priority inheritance-comparison of commercial RTOS features.

Unit-5
Teaching Hours:9
EMBEDDED SYSTEM APPLICATION DEVELOPMENT AND WEB ARCHITECTURAL FRAMEWORK
 

Objectives, different Phases & Modeling of the Embedded product Development Life Cycle (EDLC)-Product specification – Hardware / Software partitioning – Detailed hardware and software design – Integration – Product testing – Selection Processes – Microprocessor Vs Micro Controller – Performance tools – Bench marking – RTOS Micro Controller – Performance tools – Bench marking

WEB ARCHITECTURAL FRAMEWORK FOR EMBEDDED SYSTEM        Embedded as Web Client - Embedded Web servers

Self-study: Case studies on Smart card- Adaptive Cruise control in a Car -Mobile Phone software for key inputs.

Text Books And Reference Books:

[1] Shibu K V, Introduction to Embedded Systems, Mc Graw Hill Education.

[2] Wayne Wolf, Computer as Components: Principles of Embedded Computing System Design, Morgan Kaufmann Publication, 2nd edition, 2008.

[3] Rajkamal, Embedded System: Architecture, Programming and Design, Tata McGraw Hill, 2nd edition, 2010.

[4] Sriram Iyer, Embedded Real time System Programming.

Essential Reading / Recommended Reading

[1] Peckol, Embedded system Design, JohnWiley&Sons, 2010.

[2] Lyla B Das, Embedded Systems-An Integrated Approach, Pearson, 2013.

[3] Elicia White, Making Embedded Systems, O’Reilly Series, SPD, 2011.

[4] Bruce Powel Douglass, Real-Time UML Workshop for Embedded Systems, Elsevier, 2011.

[5] Simon Monk, Make: Action, Movement, Light and Sound with Arduino and Raspberry Pi, O’Reilly Series , SPD, 2016.

[6] Tammy Noergaard,  Embedded System Architecture, A comprehensive Guide for Engineers and Programmers, Elsevier, 2006.

[7] Jonathan W.Valvano, Embedded  Microcomputer Systems: Real Time Interfacing, Cengage Learning, 3rd edition, 2012.

[8] Michael Margolis, Arduino Cookbook, O’Reilly Series, SPD, 2013.

 

Web Resources:

[1] http://www.ti.com/ww/en/launchpad/launchpads-msp430-msp-exp430g2.html#project0

[2] http://coder-tronics.com/msp430-programming-tutorial-pt1/

[3] http://coder-tronics.com/msp430-programming-tutorial-pt2/

Evaluation Pattern

CIA

ESE

50%

50%

MCA471 - MOBILE APPLICATIONS (2022 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:7
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course will enable students to learn to setup Android Application development environment, create user friendly User Interfaces, handle multiple activity, persistent application development, handle data in cloud, test and deploy the App in the market.

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:15
INTRODUCTION TO ANDROID
 

History of Mobile Apps, Trends in Market-Web App Vs Mobile App-Mobile OS-What is Android?-Why Develop apps for Android?-Most popular platform for mobile apps-best experience for app users Android version-the challenges of Android app development-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 EXERCISES:

1. Installation of Android Studio and Hello World

2. Layout Editors

3. Input Controls.

Unit-2
Teaching Hours:15
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- user navigation-RecyclerView-Drawables-styles and themes-material design resources for adoptive layouts and UI Testing.

LAB EXERCISES:

4. Activity and Intents- Implicit and Explicit and camera

5. Input controls

6. Menu and pickers

Unit-3
Teaching Hours:15
WORKING WITH BACKGROUND
 

Background Tasks-AsyncTask and AsyncTaskloader, Internet Connection-Broadcast receiver- Services-Alarms and Schedulers -Notifications-Alarms- Delightful user experience.

LAB EXERCISES:

7. User navigation – Recyclerview

8. MediaController

9. Fragments

Unit-4
Teaching Hours:15
SAVING USER DATA
 

Preference and settings, Storage types, Data Storage, shared preference, App settings, SQLite Primer, Room, LiveData and ViewModel- introduction to Firebase – Firebase data handling CRUD operation.

LAB EXERCISES:

10. AsyncTask and AsyncTaskloader

11. Notifications

12. BroadcastReceiver

Unit-5
Teaching Hours:15
ADVANCED CONCEPTS / UI DESIGN AND DEPLOYMENT
 

Fragments- Fragment lifecycle and communication- sensor basics-Introduction to API usage- using maps in your apps-Animation – Media Playback- video view. Phone calls – SMS Messages-Material design-design concepts-usage-user experience handling- deployment of App in Play store- security aspects of APP -Introduction to Kotlin, concepts of framework and Flutter.

LAB EXERCISES:

13. Sharedpreference

14. SQLite /Firebase

15. APK Deployment

Text Books And Reference Books:

[1] John Horton, Android programming for beginners, Packt-Birmingham, Mumbai, 2nd edition, 2018.

[2] Bill Philips, Chris Stewart, Kristin Masrsicano, Android Programming: The Big Nerd ranch Guide, 4th edition, 2019.

Essential Reading / Recommended Reading

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

[2] Mark Wickham, Practical Android: 14 Complete Projects on Advanced Techniques and Approaches, APRESS.

 Web Resources:

[1] https://developer.android.com/

[2] https://www.tutorialspoint.com/android/index.htm

[3] https://www.youtube.com/channel/UCVHFbqXqoYvEWM1Ddxl0QDg

Evaluation Pattern

CIA

ESE

50%

50%

MCA472 - MACHINE LEARNING (2022 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:7
Max Marks:150
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:15
INTRODUCTION AND 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

Machine Learning - Examples of Machine Learning Applications - Learning Associations - Classification -Regression -Unsupervised Learning - Reinforcement Learning.

 Lab Exercises:

1. Data cleaning and Exploration

2. Dimensionality Reduction using PCA

Unit-2
Teaching Hours:15
SUPERVISED LEARNING - I
 

Supervised Learning: Learning class from examples - 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. Decision Tree – Introduction, Univariate Tree, tree Pruning, Rule Extraction from tree.

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

 Lab Exercises:

1. Classification using Decision tree

2. Linear / Logistic Discrimination

Unit-3
Teaching Hours:15
CLUSTERING
 

Clustering - Introduction - Mixture Densities, K-Means Clustering - Mixtures of Latent Variable Models - Supervised Learning after Clustering - Spectral Clustering - Hierarchical Clustering - Clustering - Choosing the number of Clusters.

 Lab Exercises:

1.    K Means Clustering

2.    Hierarchical Clustering

Unit-4
Teaching Hours:15
SUPERVISED LEARNING - II
 

Kernel Machines - Introduction - optical separating hyperplane kernel tricks - Vectorial Kernels

Multi-Layer Perceptron Introduction, training a perceptron - learning Boolean functions - multilayer perceptron – back propagation algorithm - training procedures.

LAB EXERCISES:

7. Classification using Kernel Machines

8. Classification using MLP

Unit-5
Teaching Hours:15
REINFORCEMENT LEARNING
 

Introduction, Single state case, elements of reinforcement learning, Temporal difference learning, Generalization, partially observed state.

LAB EXERCISES:

9. Temporal reinforcement Learning

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] 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.

 Web Resources:

[1] https://data-flair.training/blogs/data-mining-tutorial/

[2] https://machinelearningmastery.com/

[3] https://towardsdatascience.com/

[4] https://scikit-learn.org/stable/

Evaluation Pattern

CIA

ESE

50%

50%

MCA473A - BIG DATA ANALYTICS (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

The student can understand the Big Data Platform and its Use cases and get an overview of Apache Hadoop. The course will provide HDFS Concepts and Interfacing with HDFS and the student can understand Map Reduce Jobs. It is to give hands on experience on Apache Hadoop architecture, ecosystem, and practices, and use related applications including HDFS, HBase, Spark, and MapReduce with Hive and Pig.

Course Outcome

CO1: Understand the Big Data concepts in a real-time scenario

CO2: Understand the big data systems and identify the main sources of Big Data in the real world with NoSql Databases

CO3: Demonstrate an ability to use the Hadoop / spark framework for processing Big Data for Analytics

CO4: Evaluate the Map Reduce approach for different domain problems.

Unit-1
Teaching Hours:18
INTRODUCTION TO BIG DATA ANALYTICS
 

Big Data Overview: Data Structures - Analyst Perspective on Data Repositories - State of the Practice in Analytics - Current Analytical Architecture - Drivers of Big Data - Emerging Big Data Ecosystem and a New Approach to Analytics - Key Roles for the New Big Data Ecosystem - Examples of Big Data Analytics.

 LAB EXERCISES:

1. Installing and Configuring Hadoop

2. Installing and Configuring HIVE

Unit-2
Teaching Hours:18
NOSQL BIG DATA MANAGEMENT
 

Manipulation - NoSQL Data Architecture Patterns - Key-Value Store - Document Store - Tabular Data - Object Data Store - Graph Database - Variations of NoSQL Architectural Patterns - NoSQL to Manage Big Data - Shared-Nothing Architecture for Big Data Tasks - Choosing the Distribution Models - Ways of Handling Big Data Problems

LAB EXERCISES:

3.Demonstrate the CURD OPERATIONS AND AGGREGATIONS in NOSQL

4. Demonstrate the ARRAY MODIFIERS in NOSQL

Unit-3
Teaching Hours:18
UNDERSTANDING MAPREDUCE
 

Introduction to Hadoop and MapReduce Programming Hadoop Overview, HDFS (Hadoop Distributed File System), Processing– Data with Hadoop, Managing Resources and Applications with Hadoop YARN (Yet another Resource Negotiator). Introduction to MAPREDUCE Programming: Introduction, Mapper, Reducer, Combiner, Partitioner, Searching, Sorting, Compression.Key/value pairs, The Hadoop Java API for MapReduce, Writing MapReduce programs, Hadoop-specific data types, Input/output

 Lab Exercise

5. Finding max and min value in Hadoop.

6. Word count application in Hadoop

Unit-4
Teaching Hours:18
HIVE AND PIG
 

Introduction to Hive - Hive Architecture - Characteristics - Comparison with RDBMS (Traditional Database) – HIVE modes – HIVE Server2(HS2) - Hive Data Types and File Formats - Hive Data Model - Hive Integration and Workflow Steps -Hive Built-in Functions - HiveQL - HiveQL. Data Definition Language (DDL) - HiveQL. Data Manipulation Language (DML) - HiveQL for Querying the Data - Aggregation - Join - Group by Clause –PIG DATA TYPES, Built -in functions used with LOAD and STORE operators – PIG Evaluation functions.

LAB EXERCISES:

7. Demonstrate the HIVE QL for TABLE CREATION & MANIPULATION WITH TABLES

8. Demonstrate the use of HIVE QL for the the given dataset and queries

Unit-5
Teaching Hours:18
SPARK AND BIG DATA ANALYTICS
 

Introduction - Spark - Introduction to Big Data Tool-Spark - Introduction to Data Analysis with Spark - Spark SQL - Using Python Advanced Features with Spark SQL - Data Analysis Operations - Downloading Spark, and Programming using RDDs and MLIB

 LAB EXERCISES:

9.Configure Spark and implement collaborative filtering recommendation using spark.

10.Demonstrate use of Spark Machine learning library.

11. Different Data frame, Dataset and RDD operations

Text Books And Reference Books:

[1] Raj Kamal, Preeti Saxena, Big Data Analytics, Introduction to Hadoop, Spark, and Machine-Learning, McGraw-Hill India, 2019.

[2] Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Professional Hadoop Solutions, Wiley, 2015.

[3] Tom White, Hadoop: The Definitive Guide, O’Reilly Media Inc., 2015.

Essential Reading / Recommended Reading

[1] Pethuru Raj, Anupama Raman, DhivyaNagaraj and Siddhartha Duggirala, High-Performance Big-Data Analytics: Computing Systems and Approaches, Springer, 2015.

[2] Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop Real-World Solutions Cookbook, Packt Publishing, 2013.

[3] Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013.

Web Resources:

[1] https://www.tutorialspoint.com/hadoop/index.htm

[2] https://www.javatpoint.com/hadoop-tutorial

Evaluation Pattern

CIA

ESE

50%

50%

MCA473B - NATURAL LANGUAGE PROCESSING (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course is to make students familiar with the concepts of the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning concepts.

Course Outcome

CO1: understand various approaches on syntax and semantics in NLP

CO2: apply various methods to discourse, generation, dialogue and summarization using NLP.

CO3: analyze various methodologies used in Machine Translation, machine learning techniques used in NLP including unsupervised models and to analyze real time applications

Unit-1
Teaching Hours:18
INTRODUCTION
 

Introduction to NLP- Background and overview- NLP Applications -NLP hard Ambiguity- Algorithms and models, Knowledge Bottlenecks in NLP-Introduction to NLTK, Case study.

LAB EXERCISES:

1. (a) Import NLTK and download the data

   (b) Import and display one of the corpus

   (c) Import and display words from the corpus

   (d) Perform “Searching Text” from a corpus and display the results

   (e) Count and display how often a word occurs in a text and plot the same

2. Write a program to count word frequency and to remove stopwords

Unit-2
Teaching Hours:18
PARSING AND SYNTAX
 

Word Level Analysis: Regular Expressions, Text Normalization, Edit Distance, Parsing and Syntax-Spelling, Error Detection and correction-Words and Word Classes-Part-of speech Tagging, Naive Bayes and Sentiment Classification: Case study.

LAB EXERCISES:

 3. Write a program to tokenize English and Non-English Languages

 4. Write a program to get synonyms and  Antonyms from WordNet

Unit-3
Teaching Hours:18
SMOOTHED ESTIMATION AND LANGUAGE MODELLING
 

N-gram Language Models: N-Grams, Evaluating Language Models-The language modelling problem

SEMANTIC ANALYSIS AND DISCOURSE PROCESSING

Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure.

LAB EXERCISES:

 5. Write a program for stemming Non-English words

 6. Write a program for lemmatizing words using WordNet

Unit-4
Teaching Hours:18
NATURAL LANGUAGE GENERATION
 

Architecture of NLG Systems, Applications

Machine Translation: Problems in Machine Translation- Machine Translation Approaches-Evaluation of Machine Translation systems. Case study: Characteristics of Indian Languages.

LAB EXERCISES:

 7. Write a program to differentiate stemming and lemmatizing words

 8. Write a program for POS Tagging

Unit-5
Teaching Hours:18
INFORMATION RETRIEVAL AND LEXICAL RESOURCES
 

Information Retrieval: Design features of Information Retrieval Systems-Classical, Non- classical, Alternative Models of Information Retrieval. Lexical Resources: Word Embeddings - Word2vec- Glove.

Language models for information retrieval, Language modeling versus other approaches in IR.

UNSUPERVISED METHODS IN NLP: Graphical Models for Sequence Labelling in NLP.

LAB EXERCISES:

 9. Write a program for Word Embeddings.

 10. Case study-based program (IBM) or Sentiment analysis.

Text Books And Reference Books:

[1] Speech and Language Processing, Daniel Jurafsky and James H., 2nd Edition, Martin Prentice Hall, 2013.

[2] Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999.

[3] Introduction to Information Retrieval, Cambridge University Press. 2012.

Essential Reading / Recommended Reading

[1] Foundations of Computational Linguistics: Human-computer Communication in Natural Language, Roland R. Hausser, Springer, 2014.

[2] Steven Bird, Ewan Klein and Edward Loper Natural Language Processing with Python, O’Reilly Media; 1st edition, 2009.

 Web Resources:

[1] https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf

[2] https://nptel.ac.in/courses/106101007/

[3] NLTK – Natural Language Tool Kit-http://www.nltk.org

Evaluation Pattern

CIA

ESE

50%

50%

MCA473C - IOT SYSTEM DESIGN AND DEVELOPMENT (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

To enable students to learn the principles of IoT components, IoT programming, sensors and actuators, and modeling approaches to design and develop real-world applications on the Arduino and Raspberry Pi platforms.

Course Outcome

CO1: understand the principles of IoT components

CO2: apply the modeling approaches using sensors and actuators

CO3: Design and develop real-world applications on Arduino and Raspberry P

Unit-1
Teaching Hours:18
INTRODUCTION
 

Design principles of IoT - IoT architecture & components - OSI model for IoT Protocols - Organizational levels. Sensors: Sensors classification - Working principle of sensors - Criteria to choose a sensor - Generation of sensors. IoT Design Methodology: Design methodology - Challenges in IoT design - IoT system management - IoT servers.

LAB EXERCISES:

1. Interfacing Arduino boards and importing essential libraries with Arduino IDE

2. Realtime data acquisition and plotting with Arduino

Unit-2
Teaching Hours:18
ARDUINO IDE
 

Basic commands for Arduino - LCD commands - Serial communication commands - LED and Arduino - LCD and Arduino. Your First Circuit: Circuit requirement - Basic components - Creating your first circuit - Adding wires - Drawing circuits - Drawing the ground. Constructing and Testing Circuits: The solderless breadboard - Putting a circuit onto a breadboard - Using fewer wires.

LAB EXERCISES:

3. Controlling Arduino remotely over the internet using a mobile application

4. Create a Web Server with Arduino to display the analog input.

Unit-3
Teaching Hours:18
BUILDING PROJECTS WITH ARDUINO
 

Powering your breadboard from an Arduino Uno - Wiring inputs and outputs to an Arduino Uno - A simple Arduino project with LEDs. Interfacing with Arduino: Digital sensor: PIR sensor - DHT sensor - Ultrasonic sensor. Analog sensor: LDR sensor. Actuators: DC Motor - Servo Motor.

LAB EXERCISES:

5. Interfacing Digital Sensor: Building an IoT weather station using digital sensors with Arduino Platform

6. Interfacing Analog Sensor: Developing an intelligent street light design using analog sensors with Arduino Platform

Unit-4
Teaching Hours:18
PROGRAMMING THE RASPBERRY PI
 

Introduction -Raspberry Pi - A tour of the Raspberry Pi, Setting up your Raspberry Pi - Booting up. Basics of Raspberry Pi: Terminal commands - Installation of libraries on Raspberry Pi - Run a program on Raspberry Pi - Interfacing relay with Raspberry Pi. Python Basics for IoT: Mu: Python versions - Python Shell - Editor - Numbers – Variables - For loops - Simulating Dice - If - While - The Python shell from the terminal.

LAB EXERCISES:

7. Building an IoT model using soil moisture sensor with Raspberry Pi 

8. Building a surveillance security system with a Raspberry Pi 

Unit-5
Teaching Hours:18
INTERFACING HARDWARE
 

GPIO pin configurations - Pin functions - Serial interface pins - Power pins - Hats pins. Breadboarding with jumper wires - Digital outputs - Resister on the breadboard - LED on the breadboard - Connect breadboard to the GPIO pins - Analog outputs - Digital inputs - Analog inputs - Hardware, The software. 

LAB EXERCISES:

9. Designing an RFID based attendance management system using Raspberry Pi

10. Building a face tracking and detection using OpenCV and Arduino

Text Books And Reference Books:

[1] Rajesh Singh, Anita Gehlot, Lovi Raj Gupta, Bhupendra Singh, Mahendra Swain, Internet of Things with Raspberry Pi and Arduino, CRC Press (Taylor and Francis Group), 1st edition, 2020.

[2] Jonathan Bartlett, Electronics For Beginners: A Practical Introduction To Schematics, Circuits, And Microcontrollers, Apress, 1st edition, 2020.

[3] Simon Monk, Programming the Raspberry Pi, Getting Started with Python, McGraw-Hill Education, 3rd edition, 2021.

Essential Reading / Recommended Reading

[1] Yogesh Misra, Programming and Interfacing with Arduino, 1st edition, CRC Press, 2022.

[2] Michael Margolis, Brian Jepson & Nicholas Robert Weldin, Arduino Cookbook, O’reilly Media, 3rd edition, 2020.

 Web Resources:

[1] Course: Components And Applications Of Internet Of Things (https://onlinecourses.swayam2.ac.in/arp19_ap52/preview)

[2] Course: IoT Development with Python and Raspberry Pi (https://www.udemy.com/course/iot-development-with-python-and-raspberry-pi/)

[3] Course: A Complete Course on an IOT system - Design and Development (https://www.udemy.com/course/a-complete-course-on-an-iot-system-design-and-development/)

[4] Course: Sensors and Sensor Circuit Design (https://www.coursera.org/learn/sensors-circuit-interface)

[5] Course: Practical NodeMCU-ESP32 IoT Course with Applications (https://www.udemy.com/course/practical-iot-course-using-nodemcu-esp8266-with-applications/)

[6] Tinkercad (https://www.tinkercad.com/)

[7] Proteus (https://www.labcenter.com/)

[8] Course: IoT and Automation with Raspberry Pi, National Institute of Electronics and Information Technology
(
https://www.nielit.gov.in/content/online-course-iot-and-automation-raspberry-pi)

Evaluation Pattern

CIA

ESE

50%

50%

MCA481 - SEMINAR (2022 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:50
Credits:2

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 Information Technology

CO2: Demonstrate the professional presentation abilities

CO3: Apply the acquired knowledge in their research

Unit-1
Teaching Hours:30
Description
 

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

Text Books And Reference Books:

Research Articles

Essential Reading / Recommended Reading

Research Articles

Evaluation Pattern

CIA

ESE

50%

50%

MCA571 - CLOUD COMPUTING (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course gives an overview of the field of Cloud computing and an in-depth study into its enabling technologies and main building blocks. Students will gain hands-on experience solving relevant problems through projects that will utilize existing public cloud tools. The students will develop the skills needed to become a practitioner or carry out projects in this domain.

Course Outcome

CO1: Interpret the types and service models of any given cloud platform.

CO2: Analyse the core issues in line with the security, privacy, and interoperability in cloud platform.

CO3: Assess the comparative advantages and disadvantages of Virtualization technology.

CO4: Create a cloud environment using open source software tools.

Unit-1
Teaching Hours:18
INTRODUCTION & APPLICATIONS
 

Definition of Cloud Computing - Characteristics of Cloud Computing - Cloud Models - Service Models - Deployment Models  - Cloud Services Examples - IaaS: Amazon EC2, Google Compute Engine, Azure VMs - PaaS: Google App Engine - SaaS: Salesforce  - Cloud-based Services & Applications.

LAB EXERCISES:

1. Creating Virtual Machines using Hypervisors (2 Hours)

2. Security as a Service: Working with IAM (4 Hours)

Unit-2
Teaching Hours:18
CLOUD ENABLING TECHNOLOGIES
 

Virtualization - Load Balancing - Scalability & Elasticity  Deployment –Replication – Monitoring - Software Defined Networks - Service Level Agreements –  Security - Billing.

LAB EXERCISES:

3. Compute service: Creating and running compute machines using AWS/GCP/Azure (4 Hours)

4. Storage as a Service: Block storage and Object storage (4 Hours)

Unit-3
Teaching Hours:18
BASIC CLOUD SERVICES
 

Identity and Access Management Services - User, Groups, Roles - Compute Services - Amazon Elastic Compute Cloud - Google Compute Engine - Windows Azure Virtual Machines - Storage Services - Amazon Simple Storage Service - Google Cloud Storage - Windows Azure Storage.

 LAB EXERCISES:

5. Database as a Service: Build DB Server (RDMS and NoSQL) (4 Hours)

6. Network as a Service: Create Virtual Private Network (2 Hours)

7. Cloud features implementation: Autoscaling and Load Balancing  (4 Hours)

Unit-4
Teaching Hours:18
ADVANCED CLOUD SERVICES
 

Amazon Relational Data Store - Amazon DynamoDB -Google Cloud SQL - Google Cloud Datastore - Windows Azure SQL Database -   Amazon Virtual Private Network - Windows Azure Table Service. Application Services - Content Delivery Services - Amazon CloudFront - Windows Azure Content Delivery Network.

LAB EXERCISES:

8. Platform as a Service: Using Google App Engine to Create web apps in Python/Java (6 Hours)

9. Software as a Service: Application development using Force.com (4 Hours)

10. Open source cloud platforms: Working with OpenStack  (2 Hours)

Unit-5
Teaching Hours:18
APPLICATION DEVELOPMENT IN CLOUD
 

PaaS - Google AppEngine - Amazon Elastic Beanstalk - SaaS - Salesfore  - Open source Private Cloud Softwares - Openstack - CloudStack - Eucalyptus – OwnCloud.

LAB EXERCISES:

11. Installation of Owncloud (2 Hours)

12. Mini project (7 hours)

Text Books And Reference Books:

[1] AWS Academy Cloud Foundation Modules, AWS, 2021.

[2] Google Cloud Platform Associated Qwiklabs, 2020.

[3] Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, CreateSpace Independent Publishing Platform, Reprint 2018.

Essential Reading / Recommended Reading

[1] Judith S. Hurwitz and Daniel Kirsch, Cloud Computing For Dummies, 2nd Edition, 2020.

[2] Zaigham Mahmood, Ricardo Puttini and Thomas Erl, Cloud Computing: Concepts, Technology & Architecture, Pearson Publications, 2013.

 Web Resources:

 [1] https://www.w3schools.in/cloud-computing/cloud-computing/

[2] https://docs.aws.amazon.com

[3] https://cloud.google.com

Evaluation Pattern

CIA

ESE

50%

50%

MCA572A - IMAGE ANALYTICS (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course introduces to the students the fundamentals of image processing, covering topicsfrom the following list: image models, image representation, various operations, transforms,techniques for enhancement, restoration, segmentation, morphological operations,compression and image analysis.

1.To provide the students a foundation of image analytics concepts.

2. To build up the capability of implementing various image analytics related algorithms.

Course Outcome

CO1: Apply principles and techniques of digital image processing in applications related to digital imaging system design and analysis

CO2: Analyze and implement image processing algorithms

Unit-1
Teaching Hours:18
INTRODUCTION
 

Image Representation and Image Processing Paradigm - Elements of digital image processing-Imagemodel DIGITAL IMAGE, ITS REPRESENTATIONS Sampling and quantization-Relationships betweenpixelsConnectivity, Distance Measures between pixels - Color image (overview, various colormodels)-Various image formats – bmp, jpeg, tiff, png, gif, etc.

LAB EXERCISES (To be implemented using MATLAB/Python)

1. Image enhancement applications, based on point operations, in grey scale and color images

2. Object/image recognition applications based on digital image transforms

Unit-2
Teaching Hours:18
DIGITAL IMAGE PROPERTIES AND OPERATIONS
 

DIGITAL IMAGE PROPERTIES

Topological Properties of Digital Images-Histograms, Entropy , Eigen Values-Image Quality Metrics-Noise in Images – Sources, types.

OPERATIONS ON DIGITAL IMAGES

Arithmetic operations - Addition, Subtraction, Multiplication, Division-Logical operations – NOT, OR,AND, XOR-Set operators-Spatial operations – Single pixel, neigh bour hood, geometric-ContrastStretching-Intensity slicing-Bit plane slicing Power Law transforms

LAB EXERCISE

3. Digital image restoration applications

4. Quantitative and structural image analysis applications based on binary and grey scale morphology

Unit-3
Teaching Hours:18
IMAGE ENHANCEMENT
 

Spatial and Frequency domain-Histogram processing-Spatial filtering- Smoothening spatial filters-Sharpening spatial filtersDiscrete Fourier Transform-Discrete Cosine Transform-Haar Transform -Hough Transform-Frequency filtering-Smoothening frequency filters- Sharpening frequency filters-Selective filtering

LAB EXERCISE

5. Contour detection, contour extraction, contour-based object models

6. Region based imagesegmentation.

Unit-4
Teaching Hours:18
IMAGE RESTORATION AND IMAGE REGISTRATION
 

IMAGE RESTORATION

Noise models - Degradation models-Methods to estimate the degradation-Image de-blurring-Restoration in the presence of noise only spatial filtering-Periodic noise reduction by frequencydomain filtering-Inverse filtering-Wiener Filtering

IMAGE REGISTRATION

Geometrical transformation-Point based methods-Surface based methods-Intensity based methods

LAB EXERCISES

7. Image analysis systems for visual inspection tasks (object recognition)

8. Image fusion

Unit-5
Teaching Hours:18
IMAGE CODING AND COMPRESSION
 

Lossless compression versus lossy compression-Measures of the compression efficiency- Hufmann coding-Bitplanecoding-Shift codes-Block Truncation coding-Arithmetic coding-Predictive coding techniques-Lossy compression algorithm using the 2-D DCT transform-The JPEG 2000 standard – Baseline lossy JPEG, Recent Trends in Image analytics 

LAB EXERCISES

9. Image compression

10. Image security – Steganography, Watermarking, Encryption, Hashing etc.

Text Books And Reference Books:

[1]. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Third Ed., Prentice-Hall,2008

[2] Sonka, Fitzpatrick, Medical Image Processing and Analysis, 2020

Essential Reading / Recommended Reading

[1] William K. Pratt, Digital Image Processing, John Wiley, 4th Edition, 2007. 2. Anil K. Jain,

[2] Fundamentals of Digital Image Processing, Prentice Hall of India,2020[5] J. F. Roddick and K. Hornsby, Temporal, Spatial, and Spatio-Temporal Data Mining, Springer, 2003.

Evaluation Pattern

CIA                ESE

50%               50%

MCA572B - NEURAL NETWORKS AND DEEP LEARNING (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course provides an introduction to ANN with simple shallow neural network as a foundation for understanding the neuronal network concepts. As in detail it provides Convolution Neural Network algorithm, Recurrent Neural Network algorithm and Auto encoder algorithm along with various real time applications to explore as required by current industry standards.

Course Outcome

CO1: Understand the major technology trends in neural networks and deep learning

CO2: Build, train and apply neural networks and fully connected deep neural networks

CO3: Implement efficient (vectorized) neural networks for real time applications

Unit-1
Teaching Hours:18
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
 

Scope of Neural Networks- Fundamental Concept of ANN: The Artificial Neural Network-Biological Neural Network-Comparison between Biological Neuron and Artificial Neuron-Evolution of Neural Network.

LAB EXERCISES:

1. Implementation of various neural networks and deep learning library functions

2. Calculate the output of a simple neuron with one hidden layer using binary and bipolar sigmoidal activation functions

Unit-2
Teaching Hours:18
BASIC MODELS OF ANN
 

Connections-Learning Methods-Activation Functions-Importance Terminologies of ANN. Shallow neural networks- Difference between neural networks and deep neural networks – Applications of shallow neural network.

LAB EXERCISES:

3. Calculate the output of a simple neuron with multiple hidden layer using binary and bipolar sigmoidal activation functions

4. Classification with one dimensional data pattern using simple shallow neural network.

Unit-3
Teaching Hours:18
CONVOLUTIONAL NEURAL NETWORK
 

Introduction  - components  of  CNN  Architecture  -  Rectified  Linear  Unit  (ReLU)  Layer -Exponential  Linear  Unit  (ELU,  or SELU) - Unique Properties of CNN -Architectures of CNN -Applications of CNN.

LAB EXERCISES:

5. Classification with two dimentional data pattern using simple shallow neural network

6. Classification with multi dimentional data pattern using simple shallow neural network

Unit-4
Teaching Hours:18
RECURRENT NEURAL NETWORK
 

Introduction- The Architecture of Recurrent Neural Network- The Challenges of Training Recurrent Networks- Echo-State Networks- Long Short-Term Memory (LSTM) - Applications of RNN.

LAB EXERCISES:

7. Implementation of Convolution Neural Network using different architectures.

8. Implementation of RNN

Unit-5
Teaching Hours:18
AUTO ENCODER AND RESTRICTED BOLTZMANN MACHINE
 

Introduction - Features of Auto encoder Types of Autoencoder. Introduction to restricted Boltzmann machine.

LAB EXERCISES:

9. Implementation of autoencoder

Text Books And Reference Books:

[1] S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2019.

[2] Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, September 2018.

Essential Reading / Recommended Reading

[1] S Lovelyn Rose, L Ashok Kumar, D Karthika Renuka, Deep Learning Using Python, Wiley India, 1st Edition, 2019.

[2] Francois Chollet, Deep Learning with Python, Manning Publications; 1st edition, 2017.

[3] John D. Kelleher, Deep Learning (MIT Press Essential Knowledge series), The MIT Press, 2019.

Web Resources:

[1] www.coursera.org

[2] http://neuralnetworksanddeeplearning.com

Evaluation Pattern

CIA

ESE

50%

50%

MCA572C - SYSTEM SIMULATION FOR IOT AND SENSOR NETWORKS (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

The course will enable students to understand the simulation tools and their uses. This course will illustrate diverse methods of deploying smart objects and connect them to networks. Design simulation model for different IoT applications and analyze the devices and their performance in virtual environments. The student will be able to compare different Application protocols for IoT. 

Course Outcome

CO1: Understand the basic IoT, sensor networks and supporting protocols

CO2: Design the simulation model for different application domains.

CO3: Compare and analyze different application protocols.

Unit-1
Teaching Hours:18
INTRODUCTION TO SIMULATION
 

Appropriatenes of the Smulation tool, Advantages and disadvantages of Simulation, Areas of application. Systems and System Environment, Components of System, Discrete and continuous systems, Model of a System, Types of Models, Discrete-Event System Simulation, Steps in a Simulation study.

LAB EXERCISES:

1. Installation and configuration of Cooja

2. Hello world simulation in Cooja

Unit-2
Teaching Hours:18
IOT LANDSCAPE AND ARCHITECTURES
 

Introduction: The IoT Landscape, IoT System Architectures, IoT Devices, Event-Driven system analysis, Industrial Internet of things, Security and safety.

Architectures: Single Node architecture, Hardware components, Energy Consumption and sensor nodes, Operating systems and execution environments, Some examples of sensor nodes; Network architecture, Sensor network scenarios, Optimization goals and figures of merit, Design Principles for WSNs, Service interfaces of WSNs, Gateway concepts.

LAB EXERCISES:

3. Creating Motes for Simulation

4. Configuring the Motes

Unit-3
Teaching Hours:18
SIMULATION MODEL AND ANALYSIS
 

Analysis of Simulation Data: Input modeling, Verification Calibration and validation of simulation models, Estimation of absolute performance, Estimation of relative performance

General Principles: Concepts in Discrete-Event Simulation, List Processing; Selection of simulation software, An example simulation.

LAB EXERCISES:

5. Discrete Event driven Simulation

6. Script editor and Sensor Collect

Unit-4
Teaching Hours:18
STATISTICAL MODELING
 

Statistical Models in Simulation: Review of terminology and concepts. Discrete distribution, Continuous distributions, Empirical distribution. Random numbers: Random number generation, random variate generation.

LAB EXERCISES:

7. IPv6 Routing

8. Implementing Quantitative & Qualitative parameters

Unit-5
Teaching Hours:18
SIMULATION OF NETWORKED SYSTEMS
 

Simulation of Networked computer systems: Simulation Tools, Model input, Mobility models in wireless systems, The OSI Stack model, Physical Layer in Wireless Systems, Media Access control, Data link layer, TCP, Model Construction.

LAB EXERCISES:

9. Implementing Proactive protocols & Reactive protocols

10. Generating Graphs

Text Books And Reference Books:

[1] Dimitrios Serpanos & Marilyn Wolf, Internet of Things (IoT) Systems, Architectures, Algorithms, Methodologies, Springer Nature, 2018.

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

[3] Jerry Banks, John S, Barry L Nelson and David M Nicol, Discrete-Event System Simulation, Fifth Edition, Pearson Publications, 2010.

Essential Reading / Recommended Reading

[1] Klaus Wehrle, Mesut Gunes and James Gross, Modeling and Tools for Network Simulation, Springer Publications, 2010.

[2] Teerawat Issariyakul, Ekram Hossain, Introduction to Network Simulator NS2, Springer Publication, 2009.

 Web Resources:

[1] www.tutorialspoint.com/modelling_and_simulation/index.htm

[2] https://github.com/contiki-os/contiki/wiki/An-Introduction-to-Cooja#create-a-hello-world-simulation

Evaluation Pattern

CIA

ESE

50%

50%

MCA573A - QUANTUM MACHINE LEARNING (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

The objective of this course is to provide strong foundation of quantum computing concepts and their application to the field of applied machine learning.

Course Outcome

CO1: Understand basics of quantum computing

CO2: Apply quantum computing for implementation of machine learning algorithms

CO3: Analyze and evaluate benefits of quantum computing for efficient machine learning

Unit-1
Teaching Hours:18
INTRODUCTION
 

An Overview of Quantum Machine Learning, States and Superposition, Density Matrix Representation and Mixed State, Composite Systems and Entanglement, Evolution.

Measurement, Uncertainty Relations, Tunneling, Adiabatic Theorem, No-Cloning Theorem, Qubits and the Bloch Sphere, Quantum Circuits, Adiabatic Quantum Computing, Quantum Parallelism, Grover's Algorithm, Complexity Classes, Quantum Information Theory.

LAB EXERCISES:

[1] Implementation of quantum dot products

[2] Implementation of quantum PCA

Unit-2
Teaching Hours:18
CLUSTERING STRUCTURE AND QUANTUM COMPUTING
 

Quantum Random Access Memory, Calculating Dot Products , Quantum Principal Component Analysis , Towards Quantum Manifold Embedding, Quantum K-Means . Quantum K-Medians, Quantum Hierarchical Clustering, Computational Complexity.

LAB EXERCISES:

[3] Implementation of Quantum K Mean

[4] Implementation of Quantum K medians

Unit-3
Teaching Hours:18
QUANTUM PATTERN RECOGNITION
 

Quantum Associative Memory The Quantum Perceptron, Quantum Neural Networks, Physical Realizations, Computational Complexity.

LAB EXERCISES:

[5] Implementation of Quantum hierarchical clusterin

[6] Implementation of Quantum Perceptron

Unit-4
Teaching Hours:18
QUANTUM CLASSIFICATION
 

Nearest Neighbors, Support Vector Machines with Grover's Search, Support Vector Machines with Exponential Speedup, Computational Complexity.

LAB EXERCISES:

[7] Implementation of Quantum Neural Networks

[8] Implementation of Quantum Nearest Neighbor

Unit-5
Teaching Hours:18
BOOSTING AND ADIABATIC QUANTUM COMPUTING
 

Quantum Annealing, Quadratic Unconstrained Binary Optimization, Ising Model  QBoost, Nonconvexity Sparsity and Generalization Performance, Mapping to Hardware, Computational Complexity.

LAB EXERCISES:

[9] Implementation of Support Vector Machines with Grover's Search

Text Books And Reference Books:

[1] Peter Wittek, Quantum Machine Learning What Quantum Computing Means to Data Mining, , Elsevier, 2014.

[2] Andreas Wichert, Principles of quantum artificial Intelligence, World Scientific, 2014.

Essential Reading / Recommended Reading

[1] David McMohan, Quantum Computing Explained, Wiley Interscience, 2008.

[2] Robert S Sutor, Dancing With qbits, Packt Publishing 2019.

 Web Resources:

[1] https://qiskit.org/learn/

[2] https://pennylane.ai/qml/whatisqml.html

[3] https://www.tensorflow.org/quantum/concepts

Evaluation Pattern

CIA

ESE

50%

50%

MCA573B - COMPUTER VISION (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

To familiarize the student with specific, well known computer vision methods, algorithms and results. Understanding the visual system and basic methods of multi-scale representation. Developing a computer vision system.

Course Outcome

CO1: Identify basic concepts, terminology, theories, models and methods in the field of computer vision

CO2: Describe known principles of human visual system

CO3: Describe basic methods of computer vision related to multi-scale representation, edge detection and detection of other primitives, stereo, motion and object recognition

CO4: Design of a computer vision system for a specific problem

Unit-1
Teaching Hours:18
INTRODUCTION
 

Introduction to computer vision, Brief history, Image formation : Geometric primitives and transformation – 2D transformations and 3D transformations. Photometric Image formation: Lighting, Reflectance and shading, Optics - The Digital Camera – Sampling and Aliasing – Color – Compression.

Image Processing: Point operators, Linear filtering, More neighborhood operators, Fourier transforms, Pyramids and wavelets, Geometric transformations and Global optimization.

LAB EXERCISES:

[1] Illustrate image Manipulation. Read, write, view images and conversion between different formats.

[2]  Illustrate Spacial Transformations - Convolution and correlation.

Unit-2
Teaching Hours:18
FEATURE DETECTION AND MATCHING
 

Points and Patching – Feature Detectors – Feature descriptors – Feature mapping – Feature tracking - Application: Performance-driven animation. Edge Detection and Edge Linking Application: Edge editing and enhancement. Lines: Successive approximation – Hough transform, Statistical based features extraction.

LAB EXERCISES:

[3]  Illustrate Histogram equalization.

[4] Illustrate "Fourier Transform" decompose an image into its sine and cosine components. The output of the transformation represents the image in the Fourier or frequency domain (1D and 2D Fourier transform)

Unit-3
Teaching Hours:18
SEGMENTATION
 

Active contours – snakes – dynamic snakes – level set method. Split and Merge : Watershed, Region Splitting, Region Merging, Graph based segmentation, Probabilistic aggregation. Mean shift and Mode finding : K_Means and mixture of gaussians. Normalized cuts – Graph cuts and energy based methods

Feature based alignment: 2D and 3D feature based alignment – pose estimation(Linear and Iterative) – Geometric intrinsic calibration.

LAB EXERCISES:

[5] Demonstrate various image preprocessing methods.

[6] Illustrate Wavelet transform.

Unit-4
Teaching Hours:18
STRUCTURE FROM MOTION
 

Triangulation: Two-frame structure from motion - Projective (uncalibrated) reconstruction -  Self-calibration.

Application: View morphing – Factorization – constrained structure and motion.

Dense motion estimation: Translational alignment, Parametric motion, Optical flow.

LAB EXERCISES:

[7] Demonstrate various edge detection methods on images.

[8] Illustrate the segmentation method based on Snakes - level set method and Active contour method.

[9] Illustrate the segmentation method based on watershed and Graph based methods.

Unit-5
Teaching Hours:18
IMAGE STITCHING
 

Motion models - Global alignment – Computational Photography – Stereo correspondence  - IMAGE BASED RENDERING : View interpolation, Layered depth images, Light fields – video based rendering.

RECOGNITION: Object detection and recognition – Face recognition (Eigen faces) – Instance recognition – Context and Sense understanding – Recognition databases and test set.

LAB EXERCISES:

[10] Illustrate the Color-Based Segmentation with Live Image Acquisition

[11] Illustrate triangulation / two-frame motion.

[12] Illustrate Object detection and recognition (Optional Program).

Note: Lab programs can be implemented using MATLAB / PYTHON / OCTAVE

Text Books And Reference Books:

[1] Richard Szeliski, Computer Vision: Algorithms and Applications, Springer Science & Business Media, 2010.

[2] Anup Basu, Xiaobo Li, Computer Vision: Systems, Theory and Applications, World Scientific, 1993.

Essential Reading / Recommended Reading

[1] Roberto Cipolla, Sebastiano Battiato, Giovanni Maria Farinella, Machine Learning for Computer Vision, Springer, Jul-2012.

[2] Abhinav Dadhich, Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV, Packt Publishing Ltd, Feb-2018.

[3] Nicu Sebe, Ira Cohen, Ashutosh Garg, Thomas S. Huang, Machine Learning in Computer Vision, Springer Science & Business Media, Mar-2006.

[4] Olivier Faugeras, OLIVIER AUTOR FAUGERAS, Three-dimensional Computer Vision: A Geometric Viewpoint, MIT Press, 1993.

[5] Huimin Lu, Yujie Li, Artificial Intelligence and Computer Vision, Springer, Nov-2016.

[6] John X. Liu, Computer Vision and Robotics, Nova Publishers, 2006.

[7] Rajalingappaa Shanmugamani, Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras, Packt Publishing Ltd, Jan-2018.

[8] Sunila Gollapudi, Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs, Apress, Apr-2019.

[9] J. R. Parker, Algorithms for Image Processing and Computer Vision, John Wiley & Sons, Nov-1996.

[10] Simon J. D. Prince, Computer Vision: Models, Learning, and Inference, Cambridge University Press,Jun-2012.

[11] Jan Erik Solem, Programming Computer Vision with Python: Tools and algorithms for analyzing images, O'Reilly Media, Inc., 2012.

 

Web Resources:

[1] A Gentle Introduction to Computer Vision -  https://machinelearningmastery.com/what-is-computer-vision/ .

[2] Everything You Ever Wanted To Know About Computer Vision -        https://towardsdatascience.com/everything-you-ever-wanted-to-know-about-computer-vision-heres-a-look-why-it-s-so-awesome-e8a58dfb641e

[3] Computer Vision: What it is and why it matters -           https://www.sas.com/en_in/insights/analytics/computer-vision.html.

[4] Computer vision – ScienceDaily - https://www.sciencedaily.com/terms/computer_vision.htm

[5]Introduction to Computer Vision | Algorithmia Blog - https://algorithmia.com/blog/introduction-to-computer-vision

[6] What is Computer Vision? - https://hayo.io/computer-vision/

[7] Various MOOC courses – SWAYAM – UDEMY – COURSERA etc.

Evaluation Pattern

CIA

ESE

50%

50%

MCA573C - IOT DATA ANALYTICS (2022 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:8
Max Marks:150
Credits:4

Course Objectives/Course Description

 

This course provides a way to understand the concepts of big data analytics and its role in the Internet of things. Understanding the architectural components and protocols for application development, identification of data analytics, and data visualization tools according to the problem domain is emphasized. The course facilitates hands-on experience for data collection, storage, and analysis of  IoT data.

Course Outcome

CO1: Demonstrate how to build a data flow to connect an IoT system or device data to the cloud in specific formats

CO2: Explain how to use big data tools to process IoT data in distributed computing

CO3: Employ algorithms to analyze IoT data patterns and extract intelligence

Unit-1
Teaching Hours:18
INTRODUCING IOT ANALYTICS
 

IoT data and Big data- -IoT Analytics Lifecycle and Techniques- IoT Data Collection, IoT Data Analysis, IoT Data Deployment, Operationalization, and Reuse- Defining IoT analytics and challenges-IoT, Cloud and Big Data Integration for IoT Analytics -Cloud-based IoT Platforms- Requirements of IoT Big Data Analytics Platform- Functional Architecture-Data Analytics for the IoT - Characteristics of IoT Generated Data-Data Analytic Techniques and Technologies- Use cases for IoT Data Analytics.

LAB EXERCISES:

1. Domain selection

2. Bluetooth Scan and Wi-Fi Analysis

Unit-2
Teaching Hours:18
IOT DEVICES AND NETWORKING PROTOCOLS
 

The Wild World of IoT Devices-Sensor Types-Networking Basics- IoT Networking Connectivity Protocols- IoT Networking Data Messaging Protocols-MQTT- HTTP and IoT-REST- CoAP- Analyzing Data to Infer Protocol and Device.

LAB EXERCISES:

3. Working with LDR sensors

4. Measuring temperature and humidity using DHT11 sensor

Unit-3
Teaching Hours:18
IOT ANALYTICS FOR THE CLOUD
 

Building Elastic Analytics-Cloud Infrastructure- Elastic Analytics Concepts- Introduction to Building an IoT Analytics Pipeline on Google Cloud, AWS, Azure, ThingSpeak.

LAB EXERCISES:

5. Preparing IoT Cloud setup-AWS EC2

6. Logging sensor data to Cloud

Unit-4
Teaching Hours:18
EXPLORING IOT DATA
 

Exploring and Visualizing Data-Techniques to understand Data Quality- Data Completeness- Data Validity- Assessing Information Lag-Representativeness- Basic Time Series Analysis-The Basics of Geospatial Analysis.

LAB EXERCISES:

7. IoT data exploration

8. Sensor data visualization

Unit-5
Teaching Hours:18
DATA SCIENCE FOR IOT ANALYTICS
 

Machine Learning- Representation-Evaluation-Optimization-Generalization-Feature Engineering-Dealing with missing Values-Time Series Handling, Validation Methods-Understanding Bias-Variance Tradeoff- Machine Learning Models- Use cases for Deep Learning with IoT Data- Data Analytics in Smart Buildings.

LAB EXERCISES:

9. Basic Data Analysis using ML

10. Anomaly detection, Z Score analysis

Text Books And Reference Books:

[1] Andrew Minteer, Analytics for the Internet of Things(IoT), Ingram Short Title, 1st Edition, 2017.

Essential Reading / Recommended Reading

Recommended Reading:

[1]  John Soldatos, Building Blocks for IoT Analytics, River Publishers, 1st Edition, 2017.

 Web Resources:

[1] www.ThingSpeak.com

Evaluation Pattern

CIA

ESE

50%

50%

MCA581 - SPECIALIZATION PROJECT (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:6
Max Marks:100
Credits:2

Course Objectives/Course Description

 

In order to provide experiential learning to the students. This course is based on the specilization courses they studied as elective in the previous courses.

Course Outcome

CO1: Demonstrate the Project Development skills

CO2: Identify and use the suitable technology and tools to provide computerized solution for the real-world problem

Unit-1
Teaching Hours:60
MINI PROJECT
 

Project based on previous semester’s electives

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA

ESE

50%

50%

MCA681 - INDUSTRY PROJECT (2022 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:30
Max Marks:300
Credits:12

Course Objectives/Course Description

 

In order to provide experiential learning to the students. This course is a full time project to be taken up either in the industry or in an R&D organization by the students.

Course Outcome

CO1: Demonstrate the process involved in real-world project development

CO2: Identify and use suitable technology and tools for the application development

Unit-1
Teaching Hours:30
INDUSTRY PROJECT
 

It is a full time project to be taken up either in the industry or in an R&D organization. The students are expected to update the progress of their project work to their respective industry and institutional guide periodically.Industry Project will be evaluated based on three major heads; weekly updates, compliance to the format and content of the report, and performance in viva voce.

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA

ESE

50%

50%