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Assesment Pattern | |
CIA: 50% ESE: 50% | |
Examination And Assesments | |
Continuous Internal Assessment: 50% Weightage End Semester Examination: 50% Weightage | |
Department Overview: | |
Department of Computer Science of CHRIST (Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation’s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field. | |
Mission Statement: | |
Vision The Department of Computer Science endeavors to imbibe the vision of the University “Excellence and Service”. The department is committed to this philosophy which pervades every aspect and functioning of the department. Mission “To develop IT professionals with ethical and human values”. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their career. The department also moulds the students to be socially responsible and ethically sound. | |
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. | |
Program Objective: | |
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. Programme Specific Outcome: NA: NAProgramme Educational Objective: PEO1: Applying innovative thinking and problem-solving skills, individuals will devise novel software solutions to real-world challenges in computing, reflecting a spirit of continuous learning and adaptability.PEO2: Cultivating an entrepreneurial mindset, graduates will demonstrate innovation and the ability to develop and implement creative solutions to address challenges and opportunities in computer applications development. PEO3: Exhibiting ethical leadership qualities and a strong sense of social responsibility, individuals will contribute positively to the well-being of society through their professional endeavors in the field of computer applications. PEO4: Conducting continuous self-learning and rigorous research, individuals actively contribute to advancing knowledge in the field of computer applications throughout their professional careers. | |
MCA131 - MATHEMATICAL FOUNDATION FOR COMPUTER SCIENCE (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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This course aims to provide fundamental knowledge of mathematical foundations for Computer Science. |
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Learning Outcome |
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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
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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
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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
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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
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Basic Concepts of Automata Theory-Deterministic Finite State Automata (DFA) - Non-deterministic Finite State Automata (NDFA) - Conversion of NDFA to DFA
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Unit-5 |
Teaching Hours:6 |
TURING MACHINES
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Introduction-Basic Features of a Turing Machine-Language of a Turing Machine-General Problems of a Turing Machine. | |
Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern CIA-50% ETE-50% | |
MCA132 - PROBLEM SOLVING USING C (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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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 |
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Learning Outcome |
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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
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Tokens in C, data types and keywords - Decision control structures - Loop control structure. | |
Unit-2 |
Teaching Hours:6 |
FUNCTIONS AND POINTERS
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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
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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
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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
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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 (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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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. |
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Learning Outcome |
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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 |
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RESEARCH METHODOLOGY
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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 |
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RESEARCH DESIGN
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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 |
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RESEARCH DATA
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Measurement of Scaling: Quantitative-Qualitative,-Classification of Measure scales- Data Collection- Data Preparation. | |||||
Unit-4 |
Teaching Hours:6 |
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SCIENTIFIC WRITING
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Scientific Writing: Significance- Steps- Layout- Types- Mechanics and Precautions- Paper writing for international journals- Writing scientific report. | |||||
Unit-5 |
Teaching Hours:6 |
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REPORT WRITING
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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
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MCA134 - COMPUTER ORGANIZATION AND DESIGN (2024 Batch) | |||||
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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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. |
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Learning Outcome |
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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
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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
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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
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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
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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
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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 (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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To provide a strong foundation for database design and application development and understand the underlying core database concepts and emerging technologies. |
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Learning Outcome |
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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.
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Unit-1 |
Teaching Hours:9 |
CONCEPTUAL MODELING AND DATABASE DESIGN
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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
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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
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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
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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:
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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 - 50% ETE - 50% | |
MCA171 - PYTHON PROGRAMMING (2024 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
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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. |
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Learning Outcome |
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CO1: Understand and apply Python Data structures CO2: Demonstrate Object Oriented Concepts in Python CO3: Apply NumPy, Pandas and Matplotlib libraries for solving real time problems CO4: Design an application with with database operations
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Unit-1 |
Teaching Hours:12 |
INTRODUCTION TO PYTHON DATA STRUCTURES
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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 sequences, sets, Lists and Dictionary
2. Demonstrate Custom modules with functions | |
Unit-2 |
Teaching Hours:12 |
OBJECT ORIENTED PROGRAMMING USING PYTHON AND REGULAR EXPRESSIONS
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Classes: Classes and Instances-Inheritance—Polymorphism- Abstract Classes-Exceptional Handling- Regular Expressions using “re” module. Lab Exercises: 3. Demonstrate use of object- oriented programming concepts
4. Implement exceptional handling. Apply regular expression for string manipulation | |
Unit-3 |
Teaching Hours:12 |
INTRODUCTION TO NUMPY AND PANDAS
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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: 5. Implement NumPy features
6. Demonstrate Pandas with its operations | |
Unit-4 |
Teaching Hours:12 |
MATPLOTLIB and INTRODUCTION TO DJANGO FRAMEWORK
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Basic functions of Matplotlib-Simple Line Plot, Scatter Plot, Bar Plot, Stem Plot, Histogram, Pie Chart, Violin Plot. Introduction-Web framework-creating model to add database service- Django administration application. Lab Exercises: 7. Demonstrate the use of “Matplotlib” modules.
8.Create a simple web application using Django framework. | |
Unit-5 |
Teaching Hours:12 |
DATABASE PROGRAMMING
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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: 9. Establish database connectivity using DB-API.
10. Demonstrate CURD operations.
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Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern ETE = 50% CIA = 50% | |
MCA172 - WEB STACK DEVELOPMENT (2024 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
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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 |
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Learning Outcome |
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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
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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
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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
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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
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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
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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: [2] https://fullstackopen.com/en/part1/introduction_to_react | |
Evaluation Pattern CIA ESE 50% 50% | |
MCA231 - SOFTWARE ENGINEERING (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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Data visualization techniques allow people to use their perception to better understand the data. The goal of this course is to introduce students to data visualization which includes principles and techniques. Students will learn the value of visualization, specific techniques in information visualization and scientific visualization.
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Learning Outcome |
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CO1: Understand the usage of various visualization structures like tables,tree,network etc.,
CO2: Evaluate information visualization systems and other forms of visual presentation for their effectiveness CO3: Design and build data visualization system |
Unit-1 |
Teaching Hours:6 |
UNIT 1
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Value of Visualization – What is Visualization and Why do it: External representation – Interactivity – Difficulty in Validation. Data Abstraction: Dataset types – Attribute types – Semantics. Task Abstraction – Analyze, Produce, Search, Query. | |
Unit-2 |
Teaching Hours:6 |
UNIT 2
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Four levels of validation – Validation approaches – Validation examples. Marks and Channels. Rules of thumb – Arrange tables: Categorical regions – Spatial axis orientation – Spatial layout density. Arrange spatial data: | |
Unit-3 |
Teaching Hours:6 |
UNIT 3
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Geometry – Scalar fields – Vector fields – Tensor fields. Arrange networks and trees: Connections, Matrix views – Containment. Map color: Color theory, Color maps and other channels. | |
Unit-4 |
Teaching Hours:6 |
UNIT 4
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Manipulate view: Change view over time – Select elements – Changing viewpoint – Reducing attributes. Facet into multiple views: Juxtapose and Coordinate views | |
Unit-5 |
Teaching Hours:6 |
UNIT 5
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Partition into views – Static and Dynamic layers – Reduce items and attributes: Filter – Aggregate. Focus and context: Elide – Superimpose – Distort – Case studies. | |
Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern CIA-50% ETE-50% | |
MCA232 - APPLIED STATISTICS USING R (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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This course covers the concept of applied statistics, probability and R tool in computational perspective. It explores the practical experience of statistics and probability using R programming. |
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Learning Outcome |
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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.
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Unit-1 |
Teaching Hours:9 |
INTRODUCTION TO R
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Basic calculation - Getting Help - Installing Packages - Data and programming: Data Types, Data Structures, programming Basics Lab Exercises:
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Unit-2 |
Teaching Hours:9 |
DESCRIPTIVE STATISTICS
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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. Lab Exercises:
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Unit-3 |
Teaching Hours:9 |
INFERENTIAL STATISTICS
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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 Variance - One-Way ANOVA, Two-Way ANOVA Lab Exercises:
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Unit-4 |
Teaching Hours:9 |
PROBABILITY
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ample Spaces - Events - Model Assignments - Properties of Probability - Counting Methods - Conditional probability - Independent Events - Bayes' Rule - Random Variables Lab Exercises:
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Unit-5 |
Teaching Hours:9 |
CASE STUDY
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Healthcare - Finance - Digital Marketing- Environment-Sports Lab Exercises:
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Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern CIA-50% ETE-50% | |
MCA233 - OPERATING SYSTEM (2024 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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To understand and appreciate the different functions of Operating Systems |
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Learning 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 |
Unit 5
|
|
Process Related commands – Debugging Commands – process synchronization - shell scripting – file related commands – system calls - Socket Programming | |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading
Web Resources:
| |
Evaluation Pattern CIA-50% ETE-50% | |
MCA251 - SOFTWARE PROJECT DEVELOPMENT LAB -PHASE I (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:1 |
Course Objectives/Course Description |
|
|
|
Learning Outcome |
|
CO1: To understand the concepts of Software Engineering CO2: To Identify the problem in the specified area and Analyze the problem, identify the different modules to solve the problems CO3: To Analyze the research gap and propose the novel methodology for given problem
|
Unit-1 |
Teaching Hours:30 |
UNIT
|
|
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
Option – II : Research Project
| |
Text Books And Reference Books: NIL | |
Essential Reading / Recommended Reading NIL | |
Evaluation Pattern CIA only | |
MCA271 - DATA STRUCTURES AND ALGORITHMS (2024 Batch) | |
Total Teaching Hours for Semester:90 |
No of Lecture Hours/Week:8 |
Max Marks:150 |
Credits:5 |
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 |
|
Learning 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 OverLinked 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
| |
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
| |
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
| |
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:
| |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading 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-50% ETE-50% | |
MCA272 - PROGRAMMING USING JAVA (2024 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. |
|
Learning 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 |
NTRODUCTION 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. 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
|
|
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: 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. | |
Essential Reading / Recommended Reading
| |
Evaluation Pattern CIA-50% ETE-50% | |
MCA331 - DATA COMMUNICATION AND CRYPTOGRAPHY (2024 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. |
|
Learning 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:15 |
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:15 |
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:15 |
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:
| |
Essential Reading / Recommended Reading
| |
Evaluation Pattern ETE- 50% CIA-50% | |
MCA332 - DATA MINING (2024 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. |
|
Learning 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
| |
Evaluation Pattern CIA ESE 50% 50% | |
MCA333A - ACCOUNTING AND FINANCE MANAGEMENT (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
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. |
|
Learning 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 TEACHING
|
|
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:
| |
Essential Reading / Recommended Reading - | |
Evaluation Pattern CIA ETE 50% 50% | |
MCA333B - ECONOMETRICS (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
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. |
|
Learning 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:
| |
Essential Reading / Recommended Reading - | |
Evaluation Pattern CIA ETE 50% 50% | |
MCA333C - COMPUTATIONAL SOCIAL SCIENCE (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
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. |
|
Learning 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:
| |
Essential Reading / Recommended Reading - | |
Evaluation Pattern CIA ETE 50% 50% | |
MCA333D - COGNITIVE PSYCHOLOGY (2024 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. |
|
Learning Outcome |
|
CO1: Understanding of Cognitive Psychology Principles CO2: Integration of Cognitive Psychology and AI |
Unit-1 |
Teaching Hours:6 |
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 ETE 50% CIA 50% | |
MCA351 - SOFTWARE PROJECT DEVELOPMENT LAB -PHASE II (2024 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:1 |
Course Objectives/Course Description |
|
|
|
Learning Outcome |
|
CO1: To develop the software project based on requirements. CO2: To solve the research issues using novel methodology. CO3: Able to Develop real time projects / present Paper, publish research articles and Patents |
Unit-1 |
Teaching Hours:30 |
SOFTWARE PROJECT DEVELOPMENT
|
|
Option – I : Software Development
Option – II : Research Project
| |
Text Books And Reference Books: - | |
Essential Reading / Recommended Reading - | |
Evaluation Pattern CIA ETE 50% 50% | |
MCA371 - MOBILE APPLICATION DEVELOPMENT (2024 Batch) | |
Total Teaching Hours for Semester:90 |
No of Lecture Hours/Week:8 |
Max Marks:100 |
Credits:5 |
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 |
|
Learning 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. | |
Evaluation Pattern CIA ESE 50% 50% | |
MCA372A - ADVANCED PYTHON PROGRAMMING (2024 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. |
|
Learning 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 Application
|
|
Introduction to StreamLit - Elements, Markdown, Input Widgets - Data Visualization - Additional Elements - Layouts
Lab Exercises: 3. Demonstrate all StreamLit Elements and Widgets 4. Design a web application to explore the different graphs using layouts | |
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 BigData
|
|
Introducing BigData- Installing PySpark – Programming with RDDs - Big Data Cleaning and Wrangling - Powerful Exploratory Data Analysis with MLlib.
Lab Exercises: 9. Explore the BigData using MLlib 10. Develop a web application for Image Processing/Text Analysis/BigData using StreamLit 11. Create an API
| |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading
| |
Evaluation Pattern CIA 50% ETE 50% | |
MCA372B - VISUAL PROGRAMMING (.NET) (2024 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. |
|
Learning 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:
| |
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:
| |
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
| |
Evaluation Pattern CIA ETE 50% 50% | |
MCA372C - ASSEMBLY LANGUAGE PROGRAMMING USING 8086 (2024 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. |
|
Learning 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: 1. Addition, 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:
| |
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
| |
Evaluation Pattern CIA ETE 50% 50% | |
MCA372D - GO LANG (2024 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. |
|
Learning 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
| |
Evaluation Pattern CIA ETE 50% 50% | |
MCA372E - DRONE PROGRAMMING (2024 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
|
The objective of this course is to provide a comprehensive understanding of drone programming, hardware anatomy, flight dynamics, and real-world applications. |
|
Learning Outcome |
|
CO1: Understand Drone Dynamics for stability control CO2: Design and program drones for controlling Pluto Blocks.
CO3: Evaluate drone's performance through systematic testing and maintenance.
CO4: Apply drones for solving real-world problems.
|
Unit-1 |
Teaching Hours:15 |
Introduction to Drone Programming
|
|
Dynamics of Ariel System - Forces of Flight - Axes of Rotation -Introduction to Drones -Types of Drones – Applications of Drones – Controls of a Drone – Controls of an airplane – Controls of a drone – Building Pluto Drone – Test Flight – Types of Systems- Pluto Blocks. Flight Dynamics of Aerial Vehicles
Definitions of Drone, UAV, RPA, Quad copters -Basic Components and Categories – Principles of Flight - Flight Maneuvers – Airframes - Creating a Frame: Materials, Different Frame Shapes– Building Airframes - Flight dynamics - Applications - Future potential - Comparison with other aerial vehicles
LAB EXERCISES: Programming drone to take-off and landing on Developer Mode and control onboard features, such as controlling LEDs, using input from a remote controller application. | |
Unit-2 |
Teaching Hours:15 |
Sensors and Hardware
|
|
Need for Sensors – Accelerometer - Gyro Sensor - Magnetometer Sensor-Compass Drone Experiment-Barometer Sensor-Distance Sensors-Light-Pulse Distance Sensor-Radio Detection and Ranging-Sonar-Pulse Distance Sensing-Other Sensors-Time of Flight (ToF) Sensors-Thermal Sensors-Chemical Sensors. Hardware Power Train – Propellers, Motors- Total Lift - Electronic Speed Controllers – Flight Battery –Radio transmitter and receiver – Flight Controller – GPS, Compass, Camera Assembling for Quad copter – Connectors, Mounting of Propellers and Powering up.
LAB EXERCISES: Sensor data collection and functioning camera via control applications. | |
Unit-3 |
Teaching Hours:15 |
Propellers, Motors & Batteries
|
|
Parameters of Propellers - Configuration of Propellers on Drones-Reactive Drone Experiment-Types of Motors-Types of Batteries-Parameters of LiPo Batteries. Testing and Maintenance Key Flight Safety Rules - Preflight Checklist and Flight Log Information – Flight Instructions -Repair and Maintenance: Crash analysis, Common issues, Voltage testing. Test and troubleshoot Flight Controller Board (FCB), Electronic Speed Controller (ESC), and its associated peripherals. Perform programming and configure the flight control board (FCB). Identify, explore, and test the interconnectivity of different peripherals with FCB. Establish connection of FCB with motor, GPS, ESC, and sensors.
LAB EXERCISES: Build programs to monitor propeller speed at varying levels and controlling PWM signals for motor through Developer Mode. | |
Unit-4 |
Teaching Hours:15 |
Introduction to Magis
|
|
Pluto Block Elements - Sensors Block - Estimate Block- User Block- Control Block
LAB EXERCISES: Adjusting the drone's spin and regulating the LEDs according to its roll angle. | |
Unit-5 |
Teaching Hours:15 |
Real World Applications
|
|
Beneficial Drones, Aerial Photography, Mapping and Surveying, Precision Agriculture, Search and Rescue, Infrastructure Inspection, and Conservation. Case Studies: Agriculture Weed Classification, Microdrone surveillances.
LAB EXERCISES: Program the FCB for directional movements and control the wheel's colour using the Pluto drone. | |
Text Books And Reference Books: Mastering Drone Design and Programming: A Comprehensive guide to learn drone design and programming, Cybellium Ltd, 2023.
2. Neeraj Kumar Singh, Porselvan Muthukrishnan and Satyanarayana Sanpini, Industrial System Engineering for Drones: A Guide with Best Practices for Designing, Apress, 2019. | |
Essential Reading / Recommended Reading Reg Austin “Unmanned Aircraft Systems UAV design, development and deployment”, Wiley, 2010.
2. Kimon P. Valavanis, “Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy”, Springer, 2007 | |
Evaluation Pattern CIA-50% ETE-50% | |
MCA431 - INTERNET OF THINGS (2023 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
By completing the course, the students will be able to learn the basics of the Internet of Things (IoT) and its execution using multiple robotic sensors, and they will be able to impart knowledge on the infrastructure, sensor technologies, and networking technologies of IoT. It will also help them to analyze, design, and develop IoT solutions. |
|
Learning Outcome |
|
CO1: Evaluate the components of the IoT ecosystem within the context of the robotic paradigm. CO2: Examine fundamental circuits, sensors, data conversion processes, and shield libraries for interfacing with the physical world. CO3: Apply embedded programming constructs and constraints in real-time systems. |
Unit-1 |
Teaching Hours:9 |
INTRODUCTION TO IOT
|
|
IoT Fundamentals- Definition & Characteristics of IoT - Challenges and Issues - Physical Design of IoT, Logical, Design of IoT - IoT Functional Blocks. IoT Reference Architecture- Control Units – Communication modules – Bluetooth – Zigbee – Wi-fi – GPS- IOT Protocols (IPv6, 6LoWPAN, RPL, CoAP etc..), MQTT, Wired Communication, Power Sources, Technologies behind IoT -Four pillars of IOT paradigm, - RFID, Wireless Sensor Networks, SCADA (Supervisory Control and Data Acquisition), M2M - IOT Enabling Technologies – Big-Data Analytics, Cloud Computing, Embedded Systems. | |
Unit-2 |
Teaching Hours:9 |
IOT PHYSICAL DEVICES AND ENDPOINTS
|
|
Introduction to Sensors and Actuator- Sensor and Actuator Characteristics- Primary factors driving the deployment of sensor technology. Generations of IoT sensors. Design Principles of IoT: Design principles of connected devices, data acquiring organizing and analytics in IoT, system architecture of IoT, Prototyping the Embedded Devices for IoT: System hardware and prototyping, sensors, and actuators for IoT, Radio module and wireless sensor network, gateways internet and web, software components | |
Unit-3 |
Teaching Hours:9 |
PRIVACY AND SECURITY IN IOT
|
|
Cyber Physical Systems: IoT and cyber-physical systems, IoT security (vulnerabilities, attacks, and countermeasures), security engineering for IoT development, IoT security lifecycle. IoT as Interconnection of Threats: Network Robustness of Internet of Things, Attack Detection techniques, Malware, Propagation and Control in Internet of Things- Solution-Based Analysis of Attack Vectors for some smart applications. | |
Unit-4 |
Teaching Hours:9 |
ARCHITECTING SMART IOT DEVICES
|
|
Introduction to Arduino and Raspberry Pi- Embedded Programming for IoT (C/Python) Controlling Hardware- Connecting LED, Buzzer, Controlling AC Power devices with Relays, Controlling servo motor, speed control of DC Motor, Sensors- Light sensor, Temperature and Humidity Sensor DHT11, Motion Detection Sensors etc. | |
Unit-5 |
Teaching Hours:9 |
PROGRAMMING FRAMEWORK FOR INTERNET OF THINGS
|
|
IoT Programming Approaches: Node-Centric Programming - Database approach - Model-Driven Development - IoT Programming Frameworks: Android Things – ThingSpeak, Contiki and Cooja, Communication Technologies for Low Power Wireless Interactions e.g. RPL and analysis through simulator | |
Text Books And Reference Books: [1] Tsiatsis, Vlasios, Tsiatsis, Vlasios, Stamatis Karnouskos, Jan Holler, David Boyle, and Catherine Mulligan, Internet of Things: technologies and applications for a new age of intelligence, 2nd edition, Academic Press, 2018. [2] DiMarzio J. F., Beginning Android Programming with Android Studio, 4th edition., Wiley,2016 | |
Essential Reading / Recommended Reading [1] Donald Norris, The Internet of Things: Do-It-Yourself Projects with Arduino, Raspberry Pi, and BeagleBone Black, 1st edition, McGraw Hill Education, 2015 [2] Simone Cirani, Gianluigi Ferrari, Marco Picone, Luca Veltri. Internet of Things: Architectures, Protocols and Standards, 1st edition, Wiley Publications, 2019. [3] Lea, Perry. Internet of Things for Architects: Architecting IoT solutions by implementing sensors, communication infrastructure, edge computing, analytics, and security, 1st edition, Packt Publishing Ltd, 2018. | |
Evaluation Pattern CIA-50% ETE-50% | |
MCA432 - MICROSERVICES (2023 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:2 |
Course Objectives/Course Description |
|
The objective is to import a comprehensive understanding of modern software architecture paradigms, enabling them to design, build, and manage distributed systems effectively. Through theoretical concepts, practical implementation, and case studies, students will develop proficiency in microservices principles. The overarching goal is to equip students with the knowledge and skills necessary to address the complexities of contemporary software development. |
|
Learning Outcome |
|
CO1: Comprehend the thorough of microservices architecture, including its principles, design patterns, and communication protocols. CO2: Analyze the concept of developing and deploying microservices-based applications using industry-standard tools and frameworks, employing effective testing, deployment, and monitoring strategies. CO3: Apply microservices concepts to real-world scenarios, analyzing challenges and implementing scalable, resilient solutions in software development. |
Unit-1 |
Teaching Hours:6 |
BASICS OF MICROSERVICES
|
|
Overview of microservices – need of microservices – key principles, characteristics, and challenges of microservices - microservices architecture – microservices architecture over monolithic systems – Domain driven design (DDD) principles and its relevance to microservices. | |
Unit-2 |
Teaching Hours:6 |
MICROSERVICES DEVELOPMENT AND IMPLEMENTATION
|
|
Technology stack exploration (e.g., Spring Boot, Node.js, Docker) - Designing and implementing microservices-based applications - Communication between services: RESTful APIs, messaging protocols (RabbitMQ, Kafka). | |
Unit-3 |
Teaching Hours:6 |
COMMUNICATION AND INTEGRATION IN MICROSERVICES
|
|
Interservice communication - synchronous vs. asynchronous communication methods – event driven architecture - event sourcing and CQRS - Strategies for service communication and integration patterns. | |
Unit-4 |
Teaching Hours:6 |
TESTING, DEPLOYMENT, AND RESILIENCE STRATEGIES
|
|
Testing methodologies – unit testing – integration testing – continuous development (CI/CD pipelines) – Deployment strategies using centralization (e.g., Docker), orchestration (e.g., Kubernetes) – Building resilience – fault tolerance – circuit breakers – graceful degradation. | |
Unit-5 |
Teaching Hours:6 |
MONITORING, SECURITY, AND SCALABILITY IN MICROSERVICES
|
|
Monitoring tools – metrics – logging – distributed tracing for observability – security measures – authentication – authorization – securing APIs – scalability techniques – horizontal vs vertical scaling – load balancing – cloud native technologies. | |
Text Books And Reference Books: [1] "Building Microservices" by Sam Newman, 2nd Edition, August, 2021. [2] "Production-Ready Microservices: Building Standardized Systems Across an Engineering Organization" by Susan J. Fowler, 1st Edition, December, 2016. | |
Essential Reading / Recommended Reading [1] "Microservices in Action" by Morgan Bruce and Paulo A. Pereira, October 2018. [2] "Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services" by Brendan Burns, February, 2018. | |
Evaluation Pattern ETE-50% CIA-50%
| |
MCA451 - SPECIALIZATION PROJECT (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
NA |
|
Learning Outcome |
|
CO1: Ability to identify and develop socially and environmentally relevant and deployment environment for the students CO2: Ability to apply appropriate design/development methodology and tools
CO3: Develop competence to work as a team and effective division of work (Work Diary)
CO4: Ability to complete the solution as product CO5: Professional computing practices and regulations
|
Unit-1 |
Teaching Hours:6 |
NA
|
|
NA | |
Text Books And Reference Books: NA | |
Essential Reading / Recommended Reading NA | |
Evaluation Pattern CIA-50% ETE-50% | |
MCA471 - CLOUD COMPUTING (2023 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
The primary objective of this course is to provide a comprehensive understanding of Cloud computing, encompassing both a broad overview of the field and a detailed examination of its foundational technologies and key components. By completing this course, students will have acquired the necessary skills and knowledge to effectively work as practitioners in the Cloud computing domain or to competently undertake projects within this domain. |
|
Learning Outcome |
|
CO1: Explore the enabling technologies of cloud computing. CO2: Evaluate the types and service models of a given cloud platform. CO3: Design the appropriate cloud computing solutions and recommendations according to the applications. |
Unit-1 |
Teaching Hours:9 |
INTRODUCTION & APPLICATIONS
|
|
Definition of Cloud Computing - Characteristics of Cloud Computing - Cloud Models - Service Models - Deployment Models - Services Models - Cloud-based Services & Applications - Healthcare - Transportation Systems - Manufacturing Industry – Government - Education - Mobile Communications Lab Exercises 1. Exploring the cloud services and exploring cloud services (AWS/GCP/Azure) 2. Demonstrate IAM service with a usecase
| |
Unit-2 |
Teaching Hours:9 |
CLOUD ENABLING TECHNOLOGIES
|
|
Virtualization - Load Balancing - Scalability & Elasticity – Deployment –Replication – Monitoring – SDN - Network Function Virtualization – Identity and Access Management - Service Level Agreements – Billing. Lab Exercise 3. IaaS: Compute service - Creating and running Virtual Machines | |
Unit-3 |
Teaching Hours:9 |
MEASURING THE CLOUD
|
|
Early adopters and new applications - The laws of cloudonomics - Cloud computing obstacles - Behavioral factors relating to cloud adoption - Measuring cloud computing costs - Avoiding Capital Expenditures - Right-sizing - Computing the Total Cost of Ownership - Specifying Service Level Agreements. Lab Exercises 4. Demonstration of load balancing and autoscaling 5. Demonstration of Storage as a Service: Creating Block storage and Object storage | |
Unit-4 |
Teaching Hours:9 |
BASIC CLOUD SERVICES & PLATFORMS
|
|
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 - Database Services: Amazon Relational Database – Non relational databases Lab Exercise 6. Demonstration of Database as a Service: Build DB Server | |
Unit-5 |
Teaching Hours:9 |
INTRODUCTION TO FOUNDATIONAL CLOUD SERVICES
|
|
Container services - DNS services – Serverless computing – Cloud watch – Content Delivery Network – Edge computing – Queue services – Notification services - Active Directory service– Analytical services – ML services Lab Exercises 7. Demonstration of Serverless computing 8. Demonstration of Containerization | |
Text Books And Reference Books: [1] Kailash Jayaswal, Jagannath Kallakurchi, Donald J. Houde, Dr. Deven Shah, Cloud Computing Black Book, Dreamtech Publishers, 2014. [2] Barrie Sosinky, Cloud Computing: Bible, 1st edition, Wiley Publishing, Inc., 2011 [3] Anthony TVelte, Toby JVelteand Robert Elsenpeter, Cloud Computing –A Practical Approach, Tata McGraw Hill Education Pvt Ltd, 2016. | |
Essential Reading / Recommended Reading [1] Rajkumar Buyya, Christian Vecchiola and S. Thamarai Selvi, “Mastering Cloud Computing” - Foundations and Applications Programming, MK publications, 2022. [2] Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, CreateSpace Independent Publishing Platform, Reprint 2018. [3] AWS Academy Cloud Foundation Courseware, AWS Academy, 2022. (AWS ACF course is part of educational partnership of AWS-Christ University initiative) | |
Evaluation Pattern ETE- 50% CIA-50% | |
MCA472 - ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
|
This course introduces the fundamental concepts and techniques in Artificial Intelligence and Machine Learning. It covers both theoretical aspects and practical applications through hands-on labs.
|
|
Learning Outcome |
|
CO1: Understand the fundamental concepts of artificial intelligence and machine learning, including the difference between the two fields and their applications.
CO2: Apply the ethical and societal implications of artificial intelligence and machine
learning.
CO3: Identify the various machine learning algorithms and techniques, such as supervised and unsupervised learning.
CO4: Analyze and integrate the complexities of bias, transparency, and privacy concerns |
Unit-1 |
Teaching Hours:15 |
INTRODUCTION TO AI
|
|
Introduction to AI, History of AI, The Foundations of AI, AI Technique -Tic-Tac-Toe. Problem characteristics, Production system characteristics, Production systems: 8-puzzle problem | |
Unit-1 |
Teaching Hours:15 |
LOCAL SEARCH ALGORITHM
|
|
Searching: Uninformed search strategies – Breadth first search, depth first search. Generate and Test, Hill climbing, simulated annealing search, Greedy best first search, A* search, AO* search
| |
Unit-2 |
Teaching Hours:15 |
ETHICS AND SOCIAL IMPLICATIONS OF AI
|
|
Ethical Considerations on AI – bias – privacy – philosophical challenge in human judgement – faulty algorithms - Social Implications of AI – Case studies Planning and Acting in the Real World
| |
Unit-3 |
Teaching Hours:15 |
Supervised Learning and Dimensionality Reduction Methods
|
|
Understanding Regression: Simple Linear regression - Ordinary least squares estimation - Gradient Descent - multiple linear regression - Understanding regression trees and model trees - Logistic regression - Bias and Variance Trade-off – Overfitting and underfitting models. Principal Component Analysis – Factor Analysis – Multidimensional Scaling - Linear Discriminant Analysis 1. Open/create a dataset and write all its characteristics.
2. Exploratory data analysis
3. Implementation of Clustering Algorithms
4. Implement various types of linear regression techniques 5. Exploration of dimensionality reduction methods
| |
Unit-4 |
Teaching Hours:15 |
Neural Networks
|
|
Application 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. Basic models of ANN – Learning Methods – Activation Functions – Importance Terminologies of ANN – Single / Multilayer perceptron
Lab Exercises: 1. Implementation of Classifiers 2. Calculate the output of a simple neuron using binary and bipolar sigmoidal activation functions 3. Demonstrate classification using MLP
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Unit-5 |
Teaching Hours:15 |
Reinforcement Learning
|
|
Introduction – Single State Case: K-Armed Bandit – Elements of Reinforcement Learning – Model-Based Learning – Temporal Difference Learning – Generalization – Partially Observable States Lab Exercises:
1. Implementation of Reinforcement Algorithms
| |
Text Books And Reference Books:
[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.
| |
Essential Reading / Recommended Reading
[1] E. Rich and K. Knight, Artificial Intelligence, 2nd Edition. New york: TMH, 2012,ISBN: 9780070087705
[2] S. Russell and P. Norvig, Artificial Intelligence A Modern Approach, 2nd Edition. Pearson Education, 2007.
[3] E. Alpaydin, Introduction to Machine Learning, 3rd Edition, MIT Press, 2014.
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Evaluation Pattern CIA-50% ETE-50%
| |
MCA473A - ADVANCED DATA ANALYTICS (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
|
This course introduces the fundamentals of Text and Image data processing. This course is designed to explore various methods and concepts in social media data and models, representation, various operations, transformation, restoration, and segmentation in Image processing. Also helps to learn how to apply a wide range of classification and clustering algorithms. |
|
Learning Outcome |
|
CO1: Understand the fundamentals of Text and Image Data. CO2: Apply principles and techniques to social media and image data CO3: Analyze and implement pre-processing algorithms CO4: Implement classification and Clustering techniques for real-time text and image data |
Unit-1 |
Teaching Hours:15 |
INTRODUCTION
|
|
Overview of data analytics, Introduction to advanced data analytics techniques,Need for Advanced Data Analytics, Statistical Methods for Data Analysis:Probability distributions, Hypothesis testing, Regression analysis, Correlation and Covariance, Sampling and Estimation, Bayesian Statistics,Time series analysis, Complexities of modern datasets, Recent Technologies and Frameworks for Data Analytics, Role of data analytics in Text,Social Media and Image. Lab Exercises: 1. Implementation of Correlation and Regression 2. Implementation of time series analysis | |
Unit-2 |
Teaching Hours:15 |
TEXT ANALYTICS
|
|
Text Representation- tokenization, stemming, stop words, TF-IDF, NER, N-gram modeling. Mining Textual Data: Text Clustering, Text Classification, Lab Exercises: 3. Implementation of tokenization, stemming, stop words 4. Implementation of TF-IDF, NER and N-gram | |
Unit-3 |
Teaching Hours:15 |
SOCIAL MEDIA ANALYTICS
|
|
Essentials of Social Graphs, Social Networks, Models, Information Diffusion in Social Media. Analyzing social media: Behavioral Analytics, Influence, and Homophily, Recommendation in social media. Lab Exercises: 5. Implementation of user Behavioral Analysis on any social media 6. Implementation of Clustering and Classification in text document/social media data | |
Unit-4 |
Teaching Hours:15 |
IMAGE ANALYTICS
|
|
Digital Image Representation - Elements of digital image processing-Digital Image Properties-Histograms, Entropy -Relationships between pixelsConnectivity, Distance Measures between pixels -Various image formats – bmp, jpeg, tiff, png, gif. Noise in Images – Sources, types, Image Restoration, Image Filtering-Inverse filtering, Wiener Filtering - Segmentation Lab Exercises: 7. Digitization and Implementation of Histogram Equalization | |
Unit-5 |
Teaching Hours:15 |
CASE STUDIES
|
|
Healthcare: Image Analytics/Video Analytics for health image/video, Predictive Analytics for Patient Admissions - Location-based Case study using GIS - Education: Predictive Analytics for Student Success - E-commerce: Personalized Recommendations - Manufacturing: Predictive Maintenance for Equipment Lab Exercises: 9. Implementation of Image-filtering techniques | |
Text Books And Reference Books: [1]John Atkinson-Abutridy, Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis, CRC Press, 2022 [2] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Third Ed., Prentice-Hall,2020
[3] Sonka, Fitzpatrick, Medical Image Processing and Analysis, 2020.
| |
Essential Reading / Recommended Reading [1] William K. Pratt, Digital Image Processing, John Wiley, 4th Edition, 2020. 2.
[2] Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall of India,2020
Web Resources:
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Evaluation Pattern CIA-50% ETE-50% | |
MCA473B - CYBER SECURITY (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
|
Course Objectives This course is designed to understand various types of cyber-attacks and cyber-crimes. It describes the threats and risks within context of the cyber security. An overview of the cyber laws & concepts of cyber forensics and the defensive techniques against these attacks are discussed.
|
|
Learning Outcome |
|
CO1: Demonstrate an understanding of Cyber-Attacks, Types of Cyber Crimes, Cyber Laws and also how to protect them self and ultimately the entire Internet community from such attacks. CO2: Describe the Cyber Security Laws and Computer Forensics CO3: Apply policies and procedures to manage Privacy issues CO4: Analyze and interpret forensically investigated security incidents |
Unit-1 |
Teaching Hours:15 |
IDENTIFYING CUSTOMER?S NEEDS AND GOALS
|
|
Analyzing Business Goals and Constraints: Top-Down Network Design Methodology – Analyzing Business Goals – Analyzing Business Constraints – Analyzing Technical Goals and Tradeoffs: Scalability – Availability – Network Performance – Security – other goals – Characterizing the Existing Internetwork: Characterizing the network infrastructure – Checking Existing Internetwork – Characterizing Network Traffic: - Characterizing Traffic Flow – Characterizing Traffic Load – Characterizing Traffic Behavior – Characterizing Quality of Service Requirements Lab Exercise:
| |
Unit-2 |
Teaching Hours:15 |
LOGICAL NETWROK DESIGN
|
|
Designing a Network Topology – Hierarchical Network Design – Redundant Network design Topologies – Modular Network Design – Designing a Campus Network Design Topology – Designing the Enterprise Edge Topology – Secure Network Design Topologies – Designing Models for Addressing and Numbering – Selecting Switching and Routing Protocols Lab Exercise:
| |
Unit-3 |
Teaching Hours:15 |
NETWORK SECURITY AND MANAGEMENT STRATEGIES
|
|
Network security Design – Security Mechanisms – Modularizing Security Design – Developing Network Management Strategies: Network Management Design – Network Management Architectures – Selecting Network Management Tools and Protocols Lab Exercise:
| |
Unit-4 |
Teaching Hours:15 |
PHYSICAL NETWORK DESIGN
|
|
Selecting Technologies and Devices for Campus Networks:LAN cabling plant Design – LAN Technologies – Selecting Internetworking Devices for a Campus Network Design – Example of a Campus Network Design – Selecting Technologies and Devices for Enterprise Networks: Remote-Access Technologies – Selecting Remote-Access Devices for an Enterprise – WAN Technologies – Example of a WAN Design Lab Exercise:
| |
Unit-5 |
Teaching Hours:15 |
TESTING, OPTIMIZIGN AND DOCUMENTING NETWORK DESIGN
|
|
Industry Tests – Building and Testing a Prototype Network System – Writing and implementing a Test Plan for Network Design – Tools for Testing a Network Design – Optimizing Network Design: Optimizing Bandwidth – Reducing Serialization Delay – Optimizing Network Performance – Cisco IOS Features - Documenting Network Design. Lab Exercise:
| |
Text Books And Reference Books:
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Essential Reading / Recommended Reading
| |
Evaluation Pattern CIA-50% ETE-50% | |
MCA531 - BLOCK CHAIN TECHNOLOGY (2023 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
|
The aim of this course to understand the conceptual elements for Block Chain Technology and Distributed Ledger Technology, to summarize the advancements related to Block Chain and Crypto Currencies, to identify alternate techniques to check the working of Block Chain Protocols and discuss different block chain platforms that can be used in real world applications. |
|
Learning Outcome |
|
CO1: Understand the cryptographic techniques used in block chain to secure data and transactions. CO2: Demonstrate the ability to write, deploy and interact the smart contracts on a block chain platform. CO3: Explore the block chain tools and technologies and utilize the tools and technologies to implement secured real time applications. |
Unit-1 |
Teaching Hours:9 |
Wattenhofer, R. (2019), Blockchain Science: Distributed Ledger Technology, Third Edition, Inverted Forest Publishing. Lipton, A., Treccani, A., (2021) Blockchain and Distributed Ledgers: Mathematics, Technology, and Economics, World Scientific Publi
|
|
Foundation of Blockchain- Introduction to Blockchain-Definition, Evolution of Blockchain, Historical Context and Origin, architecture, elements of blockchain, benefits and limitations, types of blockchain. Cryptography Basics in Blockchain-Introduction, Symmetric cryptography and Asymmetric cryptography. | |
Unit-2 |
Teaching Hours:9 |
CONSENSUS AND DECENTRALIZATION PRINCIPLES
|
|
Consensus – definition, types, consensus in blockchain. Consensus Mechanisms- Proof of Work (PoW), Proof of Stake (PoS), Types of PoS, Peer-to-Peer Networks and Network Models. Decentralization – Decentralization using blockchain, Methods of decentralization, Routes to decentralization, Blockchain and full ecosystem decentralization. Distributed Ledger Technology (DLT) | |
Unit-3 |
Teaching Hours:9 |
BLOCKCHAIN PLATFORMS
|
|
Ethereum- Introduction to Ethereum, Accounts and wallets, consensus in Ethereum. Mastering Ethereum Virtual Machine (EVM), EVM message calls; Smart Contract Design, Smart Contract Life Cycle, Ethereum DApps. Solidity-Basic Introduction and Overview: Introduction to Smart Contracts and Solidity, Blockchain basics of smart contracts and overview, Solidity Layouts, Solidity Types, Operators, Expression and control structure and Functions. Hyperledger- Introduction to Hyperledger, Hyperledger Architecture, Chain code, Applications on Hyperledger. | |
Unit-4 |
Teaching Hours:9 |
BLOCKCHAIN TOOLS
|
|
Introduction to Block Chain Tools, Hyperledger Fabric, Truffle, Ganache, MetaMask, Remix, Geth, Blockchain 2.0, Blockchain 3.0 | |
Unit-5 |
Teaching Hours:9 |
BLOCKCHAIN SECURITY AND PRIVACY, APPLICATIONS AND ALLIED TECHNOLOGIES
|
|
Blockchain Security and Privacy- Security and Privacy, Interoperability issues, Use cases. Blockchain Applications- Government, Health care, Finance, Supply chain management. Case study. Emerging trends in Blockchain and allied technologies – Blockchain and Cloud Computing, Blockchain and Artificial Intelligence. Blockchain in IoT
| |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading
| |
Evaluation Pattern CIA-50% ETE-50% | |
MCA532A - THEORY OF COMPUTATION (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 provides the students with a theoretical framework that enhances their understanding of computation, laying the groundwork for advanced studies and practical application in various domains within the field of computer science. This course includes different models of computation – finite automata, pushdown automata, touring machine. Students will gain knowledge about the limitations of different computing machines.
|
|
Learning Outcome |
|
CO1: Understand the theoretical foundations of the computer science. CO2: Introduce basic types of formal languages and interpret it and identify the machine equivalence. CO3: Explore the capabilities and limits of computation, particular applications and capabilities of deterministic and non-deterministic finite automata, context-free grammars, and Turing machines. |
Unit-1 |
Teaching Hours:6 |
INTRODUCTION TO AUTOMATA THEORY
|
|
Introduction: Structural Representations, Automata and Complexity. General Concepts of Automata Theory: Alphabets Strings, Languages. Finite Automata: The Ground Rules, The Protocol. | |
Unit-2 |
Teaching Hours:6 |
INTRODUCTION TO FINITE AUTOMATA
|
|
Deterministic Finite Automata: Definition, DFA with Strings, Simpler Notations for DFA’s, Extending the Transition Function to Strings, The Language of a DFA
Nondeterministic Finite Automata: The Extended Transition Function, Equivalence of Deterministic and Nondeterministic Finite Automata | |
Unit-3 |
Teaching Hours:6 |
REGULAR EXPRESSIONS AND LANGUAGES
|
|
Regular Expressions: The Operators of regular Expressions, Building Regular Expressions
Finite Automata and Regular Expressions: From DFA’s to Regular Expressions, Converting DFA’s to Regular Expressions, Converting Regular Expressions to Automata. | |
Unit-4 |
Teaching Hours:6 |
CONTEXT-FREE GRAMMARS AND LANGUAGES
|
|
Definition of Context-Free Grammars, Derivations Using a Grammars Leftmost and Rightmost Derivations
Parse Trees-Applications of Context-Free Grammars - Ambiguity in Grammars and Languages: Ambiguous Grammars, Removing Ambiguity From Grammars | |
Unit-5 |
Teaching Hours:6 |
INTRODUCTION TO PUSHDOWN AUTOMATA AND TURING MACHINES
|
|
Pushdown Automata: Formal Definition of Pushdown Automata,A Graphical Notation for PDA’s, Nondeterministic Pushdown Automata: Formal Definitions, transition diagram.
The Turing Machine: The Instantaneous Descriptions for Turing Machines, Transition Diagrams for Turing Machines. | |
Text Books And Reference Books:
N. Chandrashekhar, PHI, 2012. [3] Introduction to the Theory of Computation, Michael Sipser ,ACM Sigact News,2013. | |
Essential Reading / Recommended Reading
| |
Evaluation Pattern CIA-50% ETE-50% | |
MCA532B - SOFT COMPUTING TECHNIQUES (2023 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:3 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
|
The aim is to cultivate skills in applying soft computing techniques for real-world problem-solving, encompassing artificial neural networks, fuzzy logic, genetic algorithms, and associative memory networks. |
|
Learning Outcome |
|
CO1: Apply neural networks, associative memories with supervised and unsupervised networks for solving different computational problems
CO2: Analyze the fuzzy logic and its inference to handle uncertainty and solve various engineering problems CO3: Learn genetic algorithms and optimization algorithms to solve combinatorial optimization problems and evaluate and compare solutions by various soft computing approaches for a given problem. |
Unit-1 |
Teaching Hours:6 |
INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS
|
|
Introduction of soft computing, soft computing vs. hard computing, various types of soft computing techniques- applications of soft computing - Basic concepts of Neural Networks – Activation functions- Perceptron Networks.
| |
Unit-2 |
Teaching Hours:6 |
SUPERVISED LEARNING NETWORKS AND UNSUPERVISED LEARNING NETWORKS
|
|
Adaptive Linear Neuron – Multi Adaptive Linear neuron - Adaptive Resonance Theory Network – Learning vector quantization. | |
Unit-3 |
Teaching Hours:6 |
ASSOCIATIVE MEMORY NETWORKS
|
|
Associative Memory Networks: Training algorithm for pattern Association, Auto associative memory network, bi-directional associative memory, Hopfield networks, iterative auto associative memory networks, temporal associative memory networks. | |
Unit-4 |
Teaching Hours:6 |
FUZZY LOGIC
|
|
Fuzzy Sets: Basic Definition and Terminology, Set-theoretic Operations, Member Function Formulation and Parameterization, Fuzzy Rules and Fuzzy Reasoning, Extension Principle and Fuzzy Relations, Fuzzy If-Then Rules, Fuzzy Reasoning, Fuzzy Inference Systems, Mamdani Fuzzy Models, Defuzzification: Lambda-cuts for fuzzy sets. | |
Unit-5 |
Teaching Hours:6 |
GENETIC ALGORITHMS AND OPTIMIZATION ALGORITHMS
|
|
History of Genetic Algorithm, Basic concepts, Creation of offspring, working principles, encoding, fitness function, reproduction, Genetic modeling: Inheritance operator, crossover, inversion & deletion, mutation operator, Bitwise operator, Generational Cycle, Convergence of GA, Simulated Annealing – Hill Climbing Algorithm | |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading
| |
Evaluation Pattern CIA-50% ETE-50% | |
MCA532C - EMBEDDED SYSTEMS (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 define and explain the characteristics of general computing systems and embedded systems, highlight key differences in hardware, software, and applications, and analyse real-world examples to illustrate the significance of embedded systems across various domains. |
|
Learning Outcome |
|
CO1: Identify the differences between a general computing system and an embedded system through comparison. CO2: Apply embedded programming using assembly and C languages to solve problems. |
Unit-1 |
Teaching Hours:6 |
INTRODUCTION TO EMBEDDED SYSTEMS
|
|
Embedded Systems, Processor Embedded into a System, Embedded hardware units and devices in a system, Embedded software in a System, Examples of Embedded System - Embedded System on a Chip (SoC) and the use of VLSI circuit design technology. | |
Unit-2 |
Teaching Hours:6 |
DEVICE AND COMMUNICATION
|
|
Devices and Communication Buses for Devices Network – IO types and examples – Serial Communication Devices – Parallel Device Ports – Sophisticated Interfacing Features in Devices Ports – Wireless Devices – Timer and Counting Devices - Real time Clock - Network Embedded Systems – Serial Bus Communication Protocols – Parallel Bus Device Protocols – Parallel Communication Network using ISA, PCI, PCI-X and Advanced Buses – Internet Enabled System – Network Protocols – Wireless and Mobile System Protocols. | |
Unit-3 |
Teaching Hours:6 |
PROGRAMMING IN C
|
|
Programming Concepts and Embedded Programming in C, C++ and Java , Software Programming in Assembly Language (ALP) and in High-Level Language C , C Program Elements: Header and Source files and Pre-processor Directives , Program Elements: Macros, Functions, Data Types, Data Structures, Modifiers, Statements, Loops and Pointers – Object- Oriented Programming – Embedded Programming in C++ - Embedded Programming in Java.
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Unit-4 |
Teaching Hours:6 |
REAL TIME OPERATING SYSTEM
|
|
Real Time Operating Systems - Real Time Operating Systems Timing and clocks in embedded system, Task modelling and management: RTOS Task scheduling models -Handling of task scheduling and latency and deadlines as performance metrics – Co-operative Round Robin Scheduling – Cyclic Scheduling with Time Slicing (Rate Monotonics Co-operative Scheduling). | |
Unit-5 |
Teaching Hours:6 |
CASE STUDY ON RTOS
|
|
Pre-emptive Scheduling Model strategy by a Scheduler – Critical Section Service by a Pre-emptive Scheduler – Fixed (Static) Real time scheduling of tasks. Case Study of Embedded System Design and Coding for an Automatic Chocolate Vending Machine using RTOS. | |
Text Books And Reference Books:
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Essential Reading / Recommended Reading
| |
Evaluation Pattern CIA-50% ETE-50% | |
MCA532D - DIGITAL FORENSICS (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 about computer forensic and recognize diverse aspects of forensics science. It is also used to covering the fundamental principles, methodologies, and tools used to collect, preserve, analyze, and present digital evidence in legal proceedings. |
|
Learning Outcome |
|
CO1: Apply various tools and techniques for acquiring and analysing digital evidence and interpret and document digital evidence findings. CO2: Analyse best practices for handling and preserving digital evidence. |
Unit-1 |
Teaching Hours:6 |
FUNDAMENTALS OF DIGITAL FORENSICS
|
|
Definition and scope of digital forensics - History of digital forensics -Digital Evidence-Increasing awareness of Digital Evidence-Types of digital evidence -Challenging aspects of digital Evidence- Legal and ethical considerations – Laws and regulations- Rules of evidence-Chain of custody -Standards and best practices.
| |
Unit-2 |
Teaching Hours:6 |
DIGITAL EVIDENCE ACQUISITION
|
|
Data acquisition methods -Incident response and first responders- Imaging techniques – Bitstream Imaging-Logical Imaging-Live and Memory Imaging-Volatility and live response - Network forensics – Packet capture -Log Analysis-Timeline Analysis-Malware Analysis-Cloud forensics – Data collection and preservation- Legal and Jurisdictional considerations -Cloud Service Models-Meta data Analysis- Mobile forensics
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Unit-3 |
Teaching Hours:6 |
DIGITAL EVIDENCE ANALYSIS
|
|
File system analysis -File System identification and Acquisition- File Carving- File system journal and logs- Registry analysis -Memory forensics -Memory Imaging and Analysis-Artifact extraction- Timeline Analysis-Malware analysis -Data carving and steganography – Definition- Process- Use Cases- Tools and techniques
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Unit-4 |
Teaching Hours:6 |
DIGITAL EVIDENCE INTERPRETATION & REPORTING
|
|
Analysing and interpreting evidence - Documenting findings – Incident tracking- Written reports- Reporting procedures – Evidence collection- Analysis Methodology- Findings and Observations- Interpretation of Evidence- Expert witness testimony – Qualification as an expert- Expert opinion and report- Cross examination- Redirect examination | |
Unit-5 |
Teaching Hours:6 |
EMERGING TRENDS IN DIGITAL FORENSICS
|
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Big data and forensics – Challenges of data volume and variety, data acquisition and collection-real time forensics - Internet of Things (IoT) forensics - Social media forensics – User Profiling-Content and Sentiment Analysis- Geolocation and Network Analysis- Blockchain forensics – Understanding Blockchain Technology-Address and Wallet Analysis-Cryptocurrency Mixers and Tumblers | |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading
| |
Evaluation Pattern CIA-50% ETE-50% | |
MCA571 - COMPUTER VISION (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
|
This course enables the learners to understand various image processing and computer vision algorithms and work in lower, middle and higher level of computer vision tasks. It also enables the learners to impart knowledge on advanced concepts in image representation, analysis, object detection and recognition. This course helps the learners to implement vision algorithms efficiently in research or industry. |
|
Learning Outcome |
|
CO1: Understand the basic concepts, terminologies and methods in computer vision and image processing. CO2: Describe different image enhancement and restoration techniques in spatial and frequency domain. CO3: Design mid-level and higher-level computer vision applications.
CO4: Develop simple object detection and recognition model for a particular application.
|
Unit-1 |
Teaching Hours:15 |
INTRODUCTION TO COMPUTER VISION AND IMAGE PROCESSING
|
|
Introduction to computer vision-Limitations of human vision-Computer vision and Image processing- Different Applications of Computer Vision-Simple image formation model-Types of digital images-Fundamental Steps in Image Processing, Elements of Digital Image Processing System-Correlation and Convolution- Image Sampling and Quantization- resolution of images-Basic relationships: Neighbors, Connectivity, Distance Measures between pixels-Different color models-Geometric transformations. Lab Programs:
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Unit-2 |
Teaching Hours:15 |
IMAGE ENHANCEMENT AND RESTORATION TECHNIQUES
|
|
Spatial Domain: Gray Level Transformations, point operations, Histogram Processing, Histogram equalization, Image Degradation and Restoration Process, Noise Models, Restoration in the presence of Noise- Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters. Frequency Domain: Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening, Frequency Domain Filters, Discrete Cosine Transformation (DCT)-Wavelets. Lab Programs:
4. Illustrate "Fourier Transform" decompose an image into its sine and cosine components and apply the following filters in frequency domain 4.1. Ideal Low Pass Filter 4.2. Ideal High Pass Filter
| |
Unit-3 |
Teaching Hours:15 |
IMAGE FEATURES AND SEGMENTATION
|
|
Image features- Local and global features-Feature detection, description and matching-Point, Line and Edge detection, Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method. Region Based Segmentation – Region Growing and Region Splitting and Merging-Watershed Segmentation-Active Contours-Graph based segmentation.
Lab Programs:
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Unit-4 |
Teaching Hours:15 |
STRUCTURE FROM MOTION
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|
Image Alignment-Triangulation- Structure from motion-Projective (uncalibrated) reconstruction-Self-Calibration-Constrained structure and motion-Motion Segmentation -Translational Alignment-Optical Flow. IMAGE BASED RENDERING: View interpolation, layered depth images, light fields – video-based rendering. Lab Programs:
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Unit-5 |
Teaching Hours:15 |
OBJECT DETECTION AND RECOGNITION
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Object detection and recognition-Detection of classes of objects (faces/ motorbikes/ trees) -Instance Recognition-Classification of images or parts of images for medical or scientific applications. Lab Programs: 11. Develop a simple Object detection and recognition model for any particular application.
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Text Books And Reference Books: [1] Computer Vision: Algorithms and Applications, Richard Szeliski, Springer Science & Business Media, 2nd Edition, ISBN-13: 978-1848829343, 2022. [2] Digital Image Processing, R. C. Gonzalez & R. E. Woods, Pearson Education, 4th Edition, 2018.
| |
Essential Reading / Recommended Reading [1] Abhinav Dadhich, Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV, Packt Publishing Ltd, Feb-2018.
[2] Mastering OpenCV with Practical Computer Vision Projects, by Daniel Lélis Baggio, Shervin Emami, David Millán Escrivá, Khvedchenia Ievgen, Naureen Mahmood, Jasonl Saragih, Roy Shilkrot, Packt Publishing, | |
Evaluation Pattern CIA-50% ETE-50% | |
MCA572 - NEURAL NETWORK AND DEEP LEARNING (2023 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:6 |
Max Marks:100 |
Credits:4 |
Course Objectives/Course Description |
|
The main aim of this course is to provide fundamental knowledge of neural networks and deep learning. On successful completion of the course, students will acquire fundamental knowledge of neural networks and deep learning, such as Basics of neural networks, shallow neural networks, deep neural networks, forward & backward propagation process and build various research projects |
|
Learning 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 application
|
Unit-1 |
Teaching Hours:12 |
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
|
|
Neural Networks-Application 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. Basic models of ANN-Learning Methods-Activation Functions-Importance Terminologies of ANN. Lab Exercise: 1.Create Single Layer Perceptron for Binary Classification 2.Demonstrate Activation Functions in Artificial Neural Networks | |
Unit-2 |
Teaching Hours:12 |
SUPERVISED LEARNING NETWORK
|
|
Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning RuleArchitecture- Flowchart for training Process-Perceptron Training Algorithm for Single and Multiple Output Classes.Back Propagation Network- Theory-Architecture-Flowchart for training process-Training Algorithm-Learning Factors for Back-Propagation Network.Radial Basis Function Network RBFN: Theory, Architecture, Flowchart and Algorithm. Lab Exercise: 3.Develop Backpropagation Network for Handwritten Digit Recognition
4.Demonstrate Radial Basis Function Network for Function Approximation | |
Unit-3 |
Teaching Hours:12 |
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.
5.Create Convolutional Neural Network for Image Classification
6.Demonstrate Convolution Operation in CNN | |
Unit-4 |
Teaching Hours:12 |
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 Exercise:
8.Demonstrate Echo State Networks for Time Series Prediction | |
Unit-5 |
Teaching Hours:12 |
AUTO ENCODER AND RESTRICTED BOLTZMANN MACHINE
|
|
Introduction - Features of Auto encoder Types of Autoencoder Restricted Boltzmann Machine- Boltzmann Machine - RBM Architecture -Example - Types of RBM. Lab Exercise: | |
Text Books And Reference Books: 1. S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition, 2018. 2. Dr. S Lovelyn Rose, Dr. L Ashok Kumar, Dr. D Karthika Renuka, Deep Learning Using Python, Wiley-India, 1st Edition, 2019. | |
Essential Reading / Recommended Reading 1. Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, September 2018. 2. Francois Chollet, Deep Learning with Python, Manning Publications; 1st edition, 2017 | |
Evaluation Pattern CIA-50% ETE-50% | |
MCA573A - DATA VISUALIZATION (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
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The objective of the Data Visualization course is to equip students with the knowledge, skills, and techniques necessary to effectively communicate complex data sets through visually compelling and informative representations. Throughout the course, students will gain a comprehensive understanding of the principles of data visualization, learn to leverage various tools and technologies for creating visualizations, and develop the ability to design and critique visualizations for different audiences and purposes. |
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Learning Outcome |
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CO1: Understand how cultures of practice influence the way data may be collected, described or formatted. CO2: Apply the basics of colors, views, and other popular and important visualization-based issues. CO3: Analyze existing visualizations based on data visualization theory and principles. CO4: Compare and contrast various data visualization techniques and their suitability for different types of data. |
Unit-1 |
Teaching Hours:15 |
INTRODUCTION
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Context of data visualization – Definition, Methodology, Visualization design objectives – Analytics – Data visualization for exploration and explanation - Types of Data. Key Factors – Purpose, visualization function and tone, visualization design options - Seven stages of data visualization - data visualization tools - Data Visualization in Practice. Lab Exercises:
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Unit-2 |
Teaching Hours:15 |
DATA VISUALIZATION AND DESIGN
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Preattentive Attributes: Color – Form - Length and Width - Spatial positioning - movement. Gestalt Principles: Similarity - Proximity - Enclosure – Connection. Data-Ink Ratio - Other Data Visualization Design Issues: Minimizing Eye Travel - Choosing a Font for Text Common Mistakes in Data Visualization Design: Wrong Type of Visualization - Trying to Display Too Much Information - Too Many Attributes - Unnecessary Use of 3D. Lab Exercises:
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Unit-3 |
Teaching Hours:15 |
EXPLORING DATA VISUALLY
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Introduction to Exploratory Data Analysis - Analyzing Variables One at a Time - Relationships between Variables - Analysis of Missing Data - Visualizing Time-Series Data - Visualizing Geospatial Data - Purposeful Use of Color: Color Schemes and Types of Data - Common Mistakes in the Use of Color in Data Visualization. Lab Exercises:
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Unit-4 |
Teaching Hours:15 |
SELECTING A CHART TYPE
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Defining the Goal of Your Data Visualization - Scatter Charts and Bubble Charts - Line Charts, Column Charts, and Bar Charts – Maps - When to Use Tables - Other Specialized Charts. Lab Exercises:
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Unit-5 |
Teaching Hours:15 |
EXPLAINING VISUALLY TO INFLUENCE WITH DATA AND DATA DASHBOARDS
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Know Your Audience - Know Your Message - Storytelling with Charts – Introduction to dashboards - Data Dashboards Taxonomies - Data Dashboard Design - Build a Data Dashboard in Tableau. Lab Exercises:
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Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern CIA-50% ETE-50% | |
MCA573B - NATURAL LANGUAGE PROCESSING (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
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To become Master in NLP concepts with emphasis on word analysis, bridge Probability theory and linguistic principles, and apply cutting-edge Statistical Learning for heightened accuracy in syntactic and semantic text analysis.
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Learning Outcome |
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CO1: Describe NLP stages, comprehend language ambiguity challenges, and grasp statistical foundations for effective language analysis. CO2: Acquire skills in text preprocessing, including character encoding and segmentation, and delve into morphological analysis using Finite State Automata. CO3: Develop expertise in advanced language modeling, statistical inference, and grammar for efficient syntactic and semantic analysis in NLP. CO4: Create and implementing various NLP techniques to generate coherent text, improve large language understanding, and address real-world NLP challenges |
Unit-1 |
Teaching Hours:15 |
INTRODUCTION TO NATURAL LANGUAGE PROCESSING
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Introduction to NLP - Various stages of NLP –The Ambiguity of Language: Why NLP Is Difficult- Parts of Speech: Nouns and Pronouns, Words: Determiners and adjectives, verbs, Phrase Structure. Statistics Essential Information Theory : Entropy, perplexity, The relation to language, Cross Entropy. Hidden Markov models and Speech recognition – Word classes and Part of Speech Tagging. Lab Exercises:
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Unit-2 |
Teaching Hours:15 |
TEXT PREPROCESSING AND MORPHOLOGY
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Character Encoding, Word Segmentation, Sentence Segmentation, Introduction to Corpora, Corpora Analysis. Inflectional and Derivation Morphology, Morphological analysis and generation using Finite State Automata and Finite State transducer. Lab Exercises:
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Unit-3 |
Teaching Hours:15 |
LANGUAGE MODELLING
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Words: Collocations- Frequency-Mean and Variance –Hypothesis testing: The t test, Hypothesis testing of differences, Pearson’s chi-square test, Likelihood ratios. Statistical Inference: n -gram Models over Sparse Data: Bins: Forming Equivalence Classes- N gram model – Statistical Estimators- Combining Estimators Lab Exercises:
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Unit-4 |
Teaching Hours:15 |
GRAMMAR AND PARSING
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Context free Grammars for English – Parsing with Context free Grammar – Features and unification – Lexicalized and Probabilistic Parsing -Language and Complexity. Semantics: Representing meaning – Semantic analysis – Lexical semantics – Word sense disambiguation and Information retrieval. Lab Exercises:
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Unit-5 |
Teaching Hours:15 |
PRAGMATICS AND MACHINE TRANSLATION
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Pragmatics: Discourse – Dialog and Conversational agents – Natural language generation, Statistical alignment and Machine translation: Text alignment – word alignment – statistical machine translation. Lab Exercises:
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Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern CIA-50% ETE-50% | |
MCA573C - QUANTUM COMPUTING (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
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This course covers the basics of quantum computing, including complex numbers, linear algebra, and quantum mechanics postulates, along with practical skills in designing quantum gates, circuits, and error correction. Students will learn about quantum algorithms like Deutsch's and Grover's, explore quantum information theory, and understand pragmatic applications such as cryptography and Quantum Key Distribution for secure communication. |
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Learning Outcome |
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CO1: Gain foundational knowledge of quantum computing, including its fundamental principles, algorithms, and potential applications. CO2: Develop proficiency in modeling quantum circuits using various quantum computation environments and frameworks, enabling the implementation and simulation of quantum algorithms. CO3: Acquire a deep understanding of quantum operations, including noise and error-correction techniques, and their significance in mitigating errors and enhancing the reliability of quantum computations. CO4: Explore various quantum algorithms such as Deutsch's algorithm, the Deutsch'Jozsa algorithm, the Quantum Fourier transform, Grover's Algorithm, and understand their applications in solving computational problems exponentially faster than classical algorithms |
Unit-1 |
Teaching Hours:15 |
MATHEMATICAL FOUNDATION
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Complex Numbers – Observables - The Pauli operators - Outer products – Hermitian - Unitary and Normal operators- Eigen values and Eigen vectors- Spectral decomposition- Trace of an operator- Expectation value of an operator- Unitary transformations- Projection operators - Postulates of Quantum Mechanics - introduction to qubits using Bloch sphere - Dirac Notation. Lab Exercises:
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Unit-2 |
Teaching Hours:15 |
QUBITS, GATES AND CIRCUITS
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Classical gates, Basic single qubit gates - Multiple qubit gates - representing composite states - Computing Inner Products - Tensor Products of Column Vectors - Operators and tensor products - Density operator - Quantum measurement - Superposition circuit - Entanglement circuit - implementing classical circuits using quantum gates (e.g. half adder circuit) Lab Exercises: 3.Development of quantum circuits to simulate classical circuits
4.To create superposition and entanglement in qubits - with a focus on understanding gate composition and circuit optimization (using IBM Qiskit). | |
Unit-3 |
Teaching Hours:15 |
QUANTUM ALGORITHMS
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Quantum complexity theory - Algorithms based on QFT: The Shor’s algorithm - Amplitude amplification-based Algorithms: Grover’s Algorithm - Random walk-based Algorithms - Hybrid algorithms: Quantum Approximate Optimization Algorithm Lab Exercises:
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Unit-4 |
Teaching Hours:15 |
QUANTUM INFORMATION & COMMUNICATION
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Data compression - Shannon’s noiseless channel coding theorem - Schumacher’s quantum noiseless channel coding theorem - Classical information over noisy quantum channels - Classical cryptography basic concepts - Quantum Key Distribution - BB84 - post quantum cryptography - Quantum Random Number Generator Lab Exercises:
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Unit-5 |
Teaching Hours:15 |
QUANTUM ANNEALING
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Traditional Optimization - Introduction to annealing hardware - Ising model and Hamiltonian - mapping optimization problems to icing model - Quadratic Unconstrained Binary Optimization - different annealing models like BQM & DQM - simulated annealing vs quantum annealing - Real world business applications of quantum annealing. Lab Exercises:
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Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern CIA-50% ETE-50% | |
MCA573D - UI/UX DESIGN (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
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The aim of the UI/UX course is to provide students with the knowledge of user interface design and methods. It aims to understand graphic design on screens and windows and demonstrate their skills for product design. |
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Learning Outcome |
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CO1: Understand user-centered design of graphical user interfaces. CO2: Analyze tools in UI/UX CO3: Explore various research methods used in UI&UX.
CO4: To apply the various Research Methods used in Design
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Unit-1 |
Teaching Hours:15 |
INTRODUCTION TO DESIGN
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The User Interface-Introduction, Overview, the importance of user interface – Defining the user interface, The importance of good design, Characteristics of graphical and web user interfaces, Principles of user interface design. Exercises:
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Unit-2 |
Teaching Hours:15 |
BASICS OF UI DESIGN
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Visual and UI principals, Scale, Visual Hierarchy, Balance, Contrast, Gestalt-UI Elements and Patterns-Interaction Behaviors and Principals-Branding-styles Guides, the need for branding in UI Design. Exercises:
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Unit-3 |
Teaching Hours:15 |
Basics of UX Design
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Introduction to User Experience - Why You Should Care about User Experience - Understanding User Experience - Defining the UX Design Process and its Methodology - Research in User Experience Design - Tools and Method used for Research - User Needs and its Goals - Know about Business Goals. Exercises:
Design a mobile application for furniture information system that gives various furniture information to its users. For that select appropriate menu structure and use it as per various guidelines and follow consistency for at least 5 components such as Menu title, Error messages, Menu status report, Menu Instructions, Function key usage of menus. | |
Unit-4 |
Teaching Hours:15 |
WIREFRAMING, PROTOTYPING AND TESTING
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Sketching Principals, Sketching Red Routes, Responsive design, wireframing, creating wire flows, building a prototype, building high fidelity mockups, conducting usability tests, prototype interactions. Exercises:
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Unit-5 |
Teaching Hours:15 |
Research, Designing, Ideating, Information Architecture
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Identifying and writing problem statements, identifying appropriate research methods, creating personas, solution ideation, creating user stories, flow mapping, Information Architecture. Case Study and Hands on Experience Designing UI for Laptops, designing artifacts, Designing interactive screens for Mobile devices with the help of an open tool.
Exercises:
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Text Books And Reference Books: [1] The Essential Guide to User Interface Design: An Introduction o GUI Design Principles and Techniques, Third Edition Wilbert O. Galitz, Wiley Publishing, 2007 [2] The Elements of User Experience: User-Centered Design for the Web and Beyond, Second Edition Jesse James Garrett, Pearson Education. 2011
[3]Joel marsh ux for beginners o'reilly 2022 | |
Essential Reading / Recommended Reading
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Evaluation Pattern CIA-50% ETE-50% | |
MCA573E - AR AND VR (2023 Batch) | |
Total Teaching Hours for Semester:75 |
No of Lecture Hours/Week:7 |
Max Marks:150 |
Credits:5 |
Course Objectives/Course Description |
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Virtual Reality (VR) is changing the interface between people and information technology by offering new ways for the communication of information, the visualization of processes, and the creative expression of ideas. The course objective is to promote the understanding of this technology, underlying principles, its potential and limits and to learn about the criteria for defining useful applications. Furthermore, each student will be exposed to the process of creating virtual environments, by developing a complete VR or Augmented Reality (AR) application as members of a small team. |
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Learning Outcome |
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CO1: The students will learn a ton about Virtual and Augmented Reality, get familiar with the latest technology, techniques and software, and build an application during the course. CO2: To understand fundamental computer vision, computer graphics and human-computer interaction techniques related to VR/AR. CO3: To implement Virtual/Augmented Reality applications. |
Unit-1 |
Teaching Hours:15 |
INTRODUCTION TO AUGMENTED REALITY (AR)
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Introduction: How does AR work; AR examples; Benefits of Augmented Reality. Introduction to Components of a VR system, 3D User Interface Input and Output devices, 3D viewing, Designing & Building VR Systems, Introduction to Augmented Reality (AR). | |
Unit-2 |
Teaching Hours:15 |
AR HARDWARE AND SOFTWARE
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Sensory hardware; Limitations and interactions; AR and VR together; Introduction to AR headset and smart glasses; Various AR software available; Introduction to Spark AR; Create a face detection app; Introduction: What is Unity; Introduction: Why Unity; Introduction: Unity installation; Introduction: What is Software Development Kit (SDK); Introduction to AR foundation; Installing AR foundation SDK; SDK setup. VR Modeling
Geometric modeling, Kinematic, Physical and Behavior modeling; Selection and Manipulation during 3D Interaction, | |
Unit-3 |
Teaching Hours:15 |
3D COMPUTER GRAPHICS
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3D computer graphics basics; Creating 3D objects.
Travel and Wayfinding in Virtual Environments, Strategies for Designing and Developing 3D UIs, Evaluation of 3D User Interfaces, Traditional and Emerging VR/AR applications. Human Factors in Virtual Reality, Case study on Construction of Geographic Virtual World. Implementation of a Virtual/ Augmented Reality Application using open-source toolkits/ libraries such as OpenSceneGraph, Vega, VRML. | |
Unit-4 |
Teaching Hours:15 |
SCRIPTING BASICS & CREATING A VIRTUAL ENVIRONMENT FOR AR
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C-Sharp basics; Unity classes; Vectors in Unity. Introduction to scripting languages (C# for Unity, JavaScript for web-based AR) .Handling user input in AR applications, Implementing gestures and touch controls, User interface (UI) design for AR, Basics of creating a virtual environment for AR; Applying physics. | |
Unit-5 |
Teaching Hours:15 |
Exploring where is AR Helpful & Future of AR
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Introduction; Engaging teaching in classroom; Interactive movies; Healthcare; Measurement in various scales; AR as a marketing tool. AR and VR together; Future of interactions in AR and AI; Future of AR as location-based experiences; Future of AR hardware; Intelligent Virtual Wardrobe trial; Spatial journalism | |
Text Books And Reference Books:
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Essential Reading / Recommended Reading
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Evaluation Pattern CIA-50% ETE-50% | |
MCA681 - INDUSTRY PROJECT (2023 Batch) | |
Total Teaching Hours for Semester:180 |
No of Lecture Hours/Week:12 |
Max Marks:300 |
Credits:12 |
Course Objectives/Course Description |
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NA |
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Learning Outcome |
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CO1: Students will be able to develop and implement a functional system or application by applying learned coding skills and development methodologies. CO2: Students will be able to evaluate the performance of their project, identify potential areas for optimization, and troubleshoot any errors or inefficiencies within the system.
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Unit-1 |
Teaching Hours:12 |
NA
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NA | |
Unit-1 |
Teaching Hours:12 |
NA
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NA | |
Text Books And Reference Books: NA | |
Essential Reading / Recommended Reading NA | |
Evaluation Pattern CIA-50% ETE-50% |