Syllabus for
Master of Computer Applications
Academic Year  (2024)

Master of Computer Applications is a Two year post graduate programme spread over six Trimesters. This programme strives to shape the students into outstanding computer professionals for the challenging opportunities in IT industry. It enables students to evolve from the stereo type thinking to better achievers and prepares them to scale the global standards. Curriculum incorporates the state of the art areas of IT industry to provide opportunity for extended study in an area of specialization.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

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:

Programme Educational Objective:

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.

Continuous Internal Assessment: 50% Weightage

End Semester Examination: 50% Weightage

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

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.

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

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

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

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

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

ETE-50%

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

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

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.

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

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

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

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

50%       50%

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.

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

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.

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

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

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

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

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.

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

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.

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

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

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

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

50%       50%

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

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  

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

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.

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

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

[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.

ETE - 50%

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.

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

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

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 

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.

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

2. Python Tutorial, Guido Rossum, CreateSpace Independent Publishing Platform, 2018

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

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

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

3. Murach's Python Programming (2nd Edition),  Joel Murach, Michael Urban, Mike Murach & Associates, Incorporated, 2021

CIA = 50%

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

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

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

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

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.

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

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

Web Resources:

[1] www.w3cschools.com

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

50%       50%

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.    

 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. 

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:

Geometry – Scalar fields – Vector fields – Tensor fields. Arrange networks and trees: Connections, Matrix views – Containment. Map color: Color theory, Color maps and other channels.

Manipulate view: Change view over time – Select elements – Changing viewpoint – Reducing attributes. Facet into multiple views: Juxtapose and Coordinate views 

1. Tamara Munzner, Visualization Analysis and Design, A K Peters Visualization Series, CRC Press, 2014.

2. Scott Murray, Interactive Data Visualization for the Web, O’Reilly, 2013.

1. Alberto Cairo, The Functional Art: An Introduction to Information Graphics and Visualization, New Riders, 2012

2. Nathan Yau, Visualize This: The FlowingData Guide to Design, Visualization and Statistics, John Wiley & Sons, 2011.

ETE-50%

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.

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

Lab Exercises:

1. Perform basic calculations using R data structures (Vector, Matrices, List, Data Frames)

2. Reshape data structures

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

Lab Exercises:

1. Calculate descriptive statistics

1. Visualize Data using plots (Bar, histogram, pie, scatter, Box)

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:

1. Build simple linear regression model

1. Perform a one-way analysis of variance

1. Perform a Two-way analysis of variance

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

Lab Exercises:

1. Demonstrate conditional probability

1. Demonstrate Bayes' rule

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

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

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

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

3. SimpleR – Using R for Introductory Statistics, John Verzani

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

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

ETE-50%

To understand and appreciate the different functions of Operating Systems

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

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.

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

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

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

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

Web Resources:

1. www.w3cschools.com

2. https://archive.ics.uci.edu

ETE-50%

1. To have hands on experience in developing a software project by using various software engineering principles and methods in each of the phases of software development.

2. Ability to translate end-user requirements into system and software requirements

3. Able to identify and formulate research problem, conduct critical research review based on the domain

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

Option – I : Software Development 

1. Domain Identification, Problem Identification, Requirement Analysis for the specific Problem  - 15 Hours

2. Preparation of SRS Document, DFD, Design the Modules - 15 Hours

Option – II : Research Project 

1. Domain Identification, Conduct the Critical review on the selected research problem, Identify the Research Gap - 15 Hours

2. Formulate research questions, Collect the data based on the research questions, Propose Novel methodology to solve the research issues - 15 Hours

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

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

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

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

Lab Programs

1. Application of Stack (convert an infix expression to the postfix form)

1. Queue Operations using Linked List

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

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

Lab Programs

1. Implementation of Linear and Binary Search 

1. Implementation of Quick / Merge Sort

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

Lab Programs

1. Implementation of Tree Traversal 

1. Construction of BST and operations

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

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

[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.

ETE-50%

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. 

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.

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.

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

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

[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.

1. https://www.javatpoint.com/java-tutorial

2. https://www.geeksforgeeks.org/java/

ETE-50%

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.

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

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.

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.

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

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

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.

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

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

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

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

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

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

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

50%      50%

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

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

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

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

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

50%     50%

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

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

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

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

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

50%          50%

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

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

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

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)

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

50%          50%

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.

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

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

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

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

CIA 50%

1. To inculcate project development skills and various software engineering principles and methods 

2. To use recent technologies for the software development

3. To acquire research skills and able to publish research articles

• In continuation with Semester II Software Development Lab Phase – I, Students are asked to continue their Software Development / Research Project Development
• Students are expected to prepare and submit final report on the project in the IEEE format.

 Option – I : Software Development

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

 Option – II : Research Project

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

50%      50%

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

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

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

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

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

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

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

50%      50%

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.

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

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

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

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

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

2. Streamlit for Data Science, Tyler Richards, packet publication, 2023.

3. Programming Computer Vision with Python, John Erik Solem, OREILLY publication, 2020. 

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

5. Hands-On Big Data Analytics using PySpark,Rudy Lai, Bartlomiej Potaczek, Packt, 2019.

1. Image Processing and Acquisition using Python, Ravishankar Chityala, ‎Sridevi Pudipeddi, CRC Press, Taylor & Francis Group, 2014

2. Learning Apache Spark with Python, Wenqiang Feng, 2021.

ETE   50%

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

.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.

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.

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

Lab Exercises:

1. Program to implement Transformations Scale and Skew.
2. Program to implement Transformations Translate and Rotate

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

Lab Exercises:

1. Program to implement user control and custom control.
2. Design a web-based application to perform CRUD operation into database

[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.

50%         50%

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.

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.

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.

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.

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

1. K.Ray , K M Bhurchandi, “Advanced Microprocessor & Peripherals”, Tata

2. McGraw Hill,3nd Edition,2013

3. Douglas V Hall, “Microprocessor & Interfacing: Programming and Hardware”, Tata

4. McGraw Hill, 2nd Edition,2006.

5. Yn - cheng Liu and Gibson, G.A., “Microcomputer Systems: The 8086 / 8088Family Architecture, Programming and Design”, Prentice Hall of India, 2nd Edition, 2006.

6. Badri Ram , ‘’Advanced Microprocessors and Interfacing”’, McGraw Hill, 2014

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

50%       50%

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.

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.

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

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

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.

[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

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

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

50%         50%

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.

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.

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

 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.

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.

 [2]  DiMarzio J. F., Beginning Android Programming with Android Studio, 4th edition., Wiley,2016

[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.

ETE-50%

 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.

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. 

 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).

 Interservice communication - synchronous vs. asynchronous communication methods – event driven architecture - event sourcing and CQRS - Strategies for service communication and integration patterns.

 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.

 [2]   "Production-Ready Microservices: Building Standardized Systems Across an Engineering Organization" by Susan J. Fowler, 1st Edition, December, 2016.

 [2]   "Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services" by Brendan Burns, February, 2018.

CIA-50%

NA

NA

ETE-50%

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.

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

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 

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 

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

[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.

[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)

CIA-50%

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.

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

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

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

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

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

[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.

[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.

ETE-50%

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.

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

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

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

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
8. Implementation of metrics for Noise measures in Image quality

[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.

 [2] Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall of India,2020

Web Resources:

1. Image Analytics | NIST

ETE-50%

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.

Basic Cyber Security Concepts, layers of security, Vulnerability, threat, Harmful acts, Internet Governance – Challenges and Constraints, Computer Criminals, CIA Triad, Assets and Threat, motive of attackers, active attacks, passive attacks, Software attacks, hardware attacks, Cyber Threats-Cyber Warfare, Cyber Crime, Cyber terrorism, Cyber Espionage - Comprehensive Cyber Security Policy.

Lab Exercises:

1.      Use Nmap to discover devices on a given network, identify open ports, and determine operating systems using scanning techniques like TCP SYN, UDP, and OS detection

2.      Set up OpenVAS (Open Vulnerability Assessment System) in Kali Linux and scan a target machine for vulnerabilities.

active attacks, passive attacks, Software attacks, hardware attacks, Cyber Threats-Cyber Warfare, Cyber Crime, Cyber terrorism

Introduction, Cyber Security Regulations, Roles of International Law. The Indian Cyberspace, National Cyber Security Policy. Introduction, Historical background of Cyber forensics, Digital Forensics Science, The Need for Computer Forensics, Cyber Forensics and Digital evidence, Forensics Analysis of Email, Digital Forensics Lifecycle, Forensics Investigation, Challenges in Computer Forensics.
Lab Exercises:

1.      Capture network traffic using Wireshark and analyze packets exchanged between two machines.

2.      Use OSINT techniques to gather information about a specific target (e.g., a company or individual).

Introduction, Proliferation of Mobile and Wireless Devices, Trends in Mobility, Credit card Frauds in Mobile and Wireless Computing Era, Security Challenges Posed by Mobile Devices, Registry Settings for Mobile Devices, Authentication service Security, Attacks on Mobile/Cell Phones, Organizational security Policies and Measures in Mobile Computing Era, Laptops.
Lab Exercises:

1.      Exploit a known vulnerability on a vulnerable machine using Metasploit modules.

2.      Install and configure OWASP ZAP (Zed Attack Proxy) in Kali Linux to perform a web application vulnerability scan. Detect common web vulnerabilities like XSS (Cross-Site Scripting), SQL injection, and CSRF (Cross-Site Request Forgery).

Introduction, cost of cybercrimes and IPR issues, web threats for organizations, security and privacy implications, social media marketing: security risks and perils for organizations, social computing and the associated challenges for organizations.

Lab Exercises:

1.      Use tools like Aircrack-ng or Wifite to perform a wireless security assessment by cracking WEP/WPA keys or identifying insecure access points.

2.      Simulate a social engineering attack by crafting a phishing email or executing a pretexting scenario.

ESE 50%

This course is aimed to prepare students to design and manage various aspects of organizational network.  This course covers all aspects of Top-Down network design beginning with identifying customer business needs, analyzing technical goals and describing their existing network.  

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:

1. Demonstration of network logs using Wireshark

2. Demonstration of traffic behavior using Wireshark

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:

1. Demonstration of network design topology using Wireshark

2. Exploration of network design topology using CISCO packet tracer

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:

5. Demonstration of intrusion detection using Snort

6. Demonstration of network security analysis using OpenVAS (Open Vulnerability Assessment System)

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:

7. Design a LAN network using Graphic Network Simulator (GNS3)

8. Demonstration of VPN

1. Top-Down Network Design, Priscilla Oppenheimer, Cisco Press, Third Edition, 2011

2. Network Management Concepts and Practice A Hands on Approach, J Richard Burke, Prentice Hall of India, 2004

ETE-50%

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. 

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. 

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)  

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.

Introduction to Block Chain Tools, Hyperledger Fabric, Truffle, Ganache, MetaMask, Remix, Geth, Blockchain 2.0, Blockchain 3.0

1. “Bitcoin and Cryptocurrency Technologies-a Comprehensive Introduction” Aravind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder, Princeton University Press, 2016.

2. “Mastering Blockchain: A deep dive into distributed ledgers, consensus protocols, smart contracts, DApps, cryptocurrencies, Ethereum, and more” Imran Bashir, Packt Publishing, Third edition, 2020.

3. “Hands-On Blockchain with Hyperledger-Building decentralized applications with Hyperledger Fabric and Composer”, Nitin Gaur, Luc Desrosiers, Venkatraman Ramakrishna, Petr Novotny, Dr. Salman A. Baset Anthony O'Dowd, Packt Publishing, 2018.

4. “Mastering Ethereum -Building Smart Contracts and dApps”, Andreas M. Antonopoulos, Dr. Gavin Wood, Ethereum Book LLC, 2019.

ETE-50%

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. 

Introduction: Structural Representations, Automata and Complexity. General Concepts of Automata Theory: Alphabets Strings, Languages. Finite Automata: The Ground Rules, The Protocol. 

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

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. 

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

N. Chandrashekhar, PHI, 2012. 

[3] Introduction to the Theory of Computation, Michael Sipser ,ACM Sigact News,2013. 

1. Sipser, Michael. Introduction to the Theory of Computation. 3rd ed. Cengage Learning, 2012. ISBN: 9781133187790.

2. Srimani PK, Nasir SFB. Finite Automata. In: A Textbook on Automata Theory. Foundation Books; 2007:78-170. 

3. Peter Linz, An Introduction to Formal Languages and Automata, Six Edition, University of California, Davis.

ETE-50%

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. 

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.