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: 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 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: 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: Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations.

PO6: Understand and commit to professional ethics and cyber regulations, responsibilities, and norms of professional computing practices.

PO7: 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: 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: 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: Function effectively as an individual and as a member or leader in diverse teams and in multidisciplinary environments.

Continuous Internal Assessment: 50% Weightage

End Semester Examination: 50% Weightage

To enable the students to learn the basic functions, principles and fundamental aspects of digital design in terms of digital logic elements and circuits. To provide deep knowledge in designing and analyzing combinational and sequential circuits. The course prepares students to perform the analysis and design of various types of data storage and data transfer circuits.

Number system representation: Decimal number system- Binary number system- octal number system- hexadecimal number system- number system representation- number system conversion- signed number representation- complement system: 1’s complement – 2’s complement- 9’s complement – 10’s complement- Binary arithmetic operations: addition- subtraction- multiplication- division- Coding schemes: BCD, Gray code and ASCII code.

Introduction - Boolean Logics and Logic Gates -Universal Gates and properties- Boolean Algebra Theorems - Boolean Function - Minterms- Maxterms- Karnaugh Map (K-Map)- Sum of Products (SOP) and Product of Sums (POS). Don’t Care Conditions. 

Introduction- Combinational logic- Half Adder – Full adder- Half subtractor-Binary adder -Binary adder subtractor- BCD adder- Binary multiplier- Encoder- Decoder- Multiplexer- Demultiplexer-BCD to seven segment display.

Self-learning: Full subtractor and realization of adder, subtractor and multiplier using NAND gates.

Sequential logic- Introduction-Latches- Clock - Types of Clock – positive, Negative edge triggered -  Flip-Flops (with Timing Diagram) - SR Flip Flop – D Flip Flop – JK Flip Flop -Edge Triggered Flip Flops- Master-Slave JK Flip-Flop-Timing diagram.

Introduction to Register and Counter – Shift registers – Serial Transfer –  Modes of operations-Serial in Serial Out (SISO) -Serial in Parallel out (SIPO) – Parallel in Serial Out (PISO)- Parallel in Parallel out (PIPO)- Bidirectional Shift Register -Synchronous Counter -  Asynchronous Counters -  Binary Counters -  Up/Down counter -BCD counter.

Self -learning: Shift register with Parallel Load

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

[3] William Stallings, Computer Architecture and Organization, PHI, 8th  Edition, 2016.

[4] David A. Patterson and John L.Hennessey, Computer Organization and Design, Morgan Kauffman / Elsevier, 5th edition, 2016.

[5] Ata Elahi ,Computer Systems Digital Design, Fundamentals of Computer Architecture and Assembly Language, Springer International Publishing, 2017.

Web Resources:

[1] NPTEL - Youtube channel : Electronics - Digital Circuits and Systems

[2] https://www.youtube.com/watch?v=CeD2L6KbtVM&list=PL803563859BF7ED8C

[3] NESO Academy - Youtube Channel : Digital Electronics

[4]https://www.youtube.com/watch?v=M0mx8S05v60&list=PLBlnK6fEyqRjMH3mWf6kwqiTbT798eAOm

The main aim of this course is to provide

the grounding knowledge of statistical methods for data analytics.

Data summarization, probability, random variables with properties and distribution functions were included.

Sampling distributions and their applications in hypothesis testing advanced statistical methods like ANOVA and correlation and regression analysis were included.

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

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

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

Statistical hypotheses-Simple and composite, Statistical tests, Critical region, Type I and Type II errors, Testing of hypothesis – null and alternative hypothesis, level of significance, Test of significance using t, F and Chi-Square distributions (large sample case). Concept of interval estimation and confidence interval construction for standard population parameters like, mean, variance, difference of means, proportions (only large sample case).

[2] Douglas C Montgomery, George C Runger, Applied Statistics and Probability for Engineers, Wiley student edition, 2004.

[2] Levine, David M; Berenson, L Mark; Stephen, David, Statistics for Managers Using Microsoft Excel, 2nd Edition, PHI, New Delhi, 2012.

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

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

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

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

Memory Management Strategies: Background, swapping, Memory allocation, Paging, Structure of the page table, Segmentation. Virtual Memory Management: Demand paging, Page replacement, allocation of frames, thrashing, memory mapped files, Allocating kernel memory.

Memory management concepts can be demonstrated using Linux.

File concepts, access methods, directory and disk structure, File system mounting, File sharing, Protection, directory implementation, allocation methods, free-space management. I/O Systems, I/O hardware, Application I/O Interface, Kernel I/O subsystem, Transforming I/O requests to hardware operations.

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

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

The course introduces fundamentals of programming, different types of problem-solving concepts and programming structures to build logic for suitable computational problems.

Types of problems, Problem solving in every day, Difficulties in with problem solving. Constants, variables, data types, Data storage, operators, expressions. Organizing the solution, testing the solution, software development life cycle.

Structuring a solution, modules, cohesion and coupling, local and global variables, Algorithm, flowchart, pseudocode, Sequential logic structure, Solution Development.

The decision logic structure, Straight through logic structure, Positive logic, Negative logic, Logic conversion, Decision Tables. The loop logic structure, nested loops, recursion.

To enable the students to excel in the Linux Platform.

RHEL7.5,breaking root password, Understand and use essential tools for handling files, directories, command-line environments, and documentation - Configure local storage using partitions and logical volumes.

Swapping, Extend LVM Partitions,LVM Snapshot - Manage users and groups, including use of a centralized directory for authentication.

Kernel updations,yum and nmcli configuration, Scheduling jobs,at,crontab -  Configure firewall settings using firewall config, firewall-cmd, or iptables , Configure key-based authentication for SSH ,Set enforcing and permissive modes for SELinux , List and identify SELinux file and process context ,Restore default file contexts.

[2] https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/

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- Problem Solving Using Lists and Functions. Custom and built-in modules.

Lab Exercises:

1.     Demonstrate use of Python data structures.

2.     Demonstrate Lists comprehensions. 

3.     Demonstrate Dictionary comprehension. 

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

Lab Exercises:

4.     Demonstrate use of object- oriented programming concepts 

5.     Demonstrate exceptional handling.

6.     Demonstrate use of lambda functions.

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

Lab Exercises:

7.     Demonstrate use of custom modules.  

8.     Implement “re” module.

9.     Demonstrate use of “Numpy”.

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

Lab Exercises:

10.     Implement Pandas to demonstrate data handling and indexing.

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

12.     Create an GUI application using all the appropriate widgets required.

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

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

Lab Exercises:

13.     Create a web application using Django framework.

14.     Establish database connectivity for an GUI application and demonstrate data manipulation and visualization.

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

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

Web Resources:

[1] https://docs.python.org/3/tutorial/

Major objective of this course is 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.

History of C - Memory concepts - Constants, variables, data types and keywords - Instructions and operators - Decision control structure - if… else construct - Loop control structure - For loop - While loop - Case control structure - Switch case - Break - Continue.

Lab Exercises:

1.     Implement decision control structure - if… else, nested if… else

2.     Implement loop and case 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.

Lab Exercises:

3.     Implement function concept

4.     Implement pointer concept using function

Arrays - Definition - Initialization - 2D arrays - Memory map of 2D arrays - Pointers and 2D arrays - Pointers to arrays - Passing Arrays to functions - Array of pointers - Three dimensional arrays -Strings - Characters - Character handling library - String I/O - String conversion - String comparison - String search - Pointers and strings - 2D array of strings - Array of pointers to strings - Passing strings to functions

Lab Exercises:

5.     Implement array concept - single and 2D

6.     Implement string manipulations - library and user defined

Structure definitions - Initializing structures - Accessing structure members - Array of structures - Pointers to structures - Using structures with functions - Self referential structures -  typedef - Unions - Bitwise operators - Bit fields - Enumeration constants

Lab Exercises:

7.     Implement concept of structures and union to understand difference between them 

8.     Implement bit wise operations

Types of I/O - Formatted and unformatted console I/O functions - Printing integers, floats and strings - Conversion specifiers - Reading formatted input - Command line arguments - File processing - Data hierarchy - File and streams - File operations - Sequential-Access file - Random-Access file - Error handling - Stderr - Exit A case study - Preprocessors - symbolic constants and macros - File inclusion - Conditional compilation

Lab Exercises:

9.     Implement different file related operations

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

[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.

[3] Yashavant Kanetkar, Let us C, BPB, 17th Edition, 2020.

Web Resources:

[1] https://github.com/pdeitel/CHowToProgram9e

[2] https://www.programiz.com/c-programming

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

A generic process model – Defining a framework activity, identifying a Task Set, Process Patterns, Process Assessment and improvement- Prescriptive Process Models-Specialized Process Models. Requirements Engineering-Establishing the groundwork, eliciting requirements, Developing use cases, Building the requirements model – Elements of the requirements Model, Analysis pattern, negotiating requirements, validating requirements-Latest development Methodology-RAD, DevOps, Fish Model-Agile development-Agile Process, Extreme programming,SCRUM Agile Modeling.

The design process – Software quality guidelines and attributes, The evolution of software design, Design concepts – Abstraction, Architecture, Patterns, Separation of  concerns, Modularity, information hiding, Functional Independence, refinement, Aspects, Refactoring, Object Oriented design concepts Design classes,  The design Model – Data Design elements, Architectural Design elements, Interface Design Elements, Component-Level Design elements, Deployments level Design elements. Software architecture – Brief taxonomy of Architectural styles, Architectural Patterns.

Component –Designing class based components – Basic Design Principles, Component-level Design guidelines, Cohesion, Coupling, Functional design at the Component level, Designing traditional components–Component based development-Domain Engineering, Component qualification, Adaptation, and Composition, Analysis and Design for reuse, classifying and retrieving components. User Interface Design- The golden rules, User Interface Analysis and Design models, Interface Analysis and Design steps.

Software Quality- Achieving software quality- Software testing fundamentals- internal and external view of testing, White-box testing, Basic path testing - control structure testing - Black- box testing-Model Based Testing, Testing for specialized environments– Testing GUIs, Testing of Client-Server Architectures, Testing Documentation and Help facilities, testing for Real-Time Systems, Patterns for software testing.

Metrics in the process and project domains-Process metrics and Software Process improvement Project Metrics-software measurement- Metrics for software quality- Observations on estimation, The project planning process, Software scope and Feasibility, Resources, software project estimation, Decomposition techniques- Empirical estimation models – The structure of Estimation model, COCOMO II Model, Software equation.

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

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

Web Resources:

[1] www.nptel.ac.in

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.

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

[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.

To enable the students to incorporate knowledge in the architecture and functional modules of 8085 microprocessor. To dispense an exposure to various 8085 basic and advanced programming techniques. The course further imparts knowledge in differ interfacing techniques using 8255A.

Introduction to 8085 

Introduction to Microprocessor 8085 –Signals -Address Bus, Data Bus. Block Diagram, Registers, Flags- Decoding and executing  an instruction.

Introduction to 8085 programming

8085-programming model, Instruction Classification, Data Format and storage, 8085 instruction Set, Writing simple programs.

Lab Exercises:

1. Write a program to add N one byte numbers.
2. Write a program to add two 8-bit BCD numbers.
3. Write a program to multiply two 8 - bit numbers.
4. Write a program to check whether a byte belongs to the 2-out-of-5 codes. Display FF if it is a 2-out-of- 5 code otherwise 00. (Number is 2-out-of-5 code if the left most three bits are zero and in the remaining five bits there are exactly two 1’s).

8085 Machine cycles and bus Timings -Addressing Modes- Data Transfer Operations -Arithmetic Operations- Logic Operations - Branch Operations 

Lab Exercises:

5. Write a program to add two 32 - bit binary numbers. 
6. Write a program to perform linear search over a set of N numbers. Display FF if found otherwise display 00.
7. Write a program to find the first 10 terms of a Fibonacci sequence
8. Write a program to interchange N one bytes of data.

Additional data transfer and 16-bit Arithmetic Instructions, Arithmetic operations related to memory, Logic operations: Rotate, Compare, Counters and Time delays, Stack and Subroutines.

Lab Exercises:

9. Write a program to subtract a 16 - bit BCD number from another 16 – bit BCD number.
10. Write a program to divide a 16 - bit number by an 8 - bit numbers.
11. Write a program to sort the numbers in ascending and in descending using bubble sort.
12. Write a program to prepare a look-up table for the squares of one -digit BCD numbers.

Architecture of 8085 MPU

Control & status signals, Power supply and Frequency signals, Externally initiated signals, Serial I/O ports - ALU: Timing and Control Unit, Instruction Decoder, Serial I/O Control, Stack, PC, Address/Data Buffers                                                                                                      

Interrupts            

Introduction – INTR, TRAP, RST 7.5, 6.5, 5.5 – RST, SIM and RIM instructions

Lab Exercises:

13. Write a program to simulate a BCD counter to count from 0 to 100.
14. Write a program to check whether a one-byte number is a palindrome or not.
15. Write a program to simulate a stopwatch with a provision to stop the watch.
16. Write a program to determine the HCF of two one-byte numbers.

Block Diagram – Control Logic, Control Word – Modes of operations with examples, Mode 0, Mode 1, BSR Mode, Control word for each mode of operation Programming in 8255A with an example.

Lab Exercises:

17. Write a program to display a rolling message.
18. Write a program to interface a keyboard using 8255A interface.
19. Write a program to interface Seven Segment Display using 8255A interface.         

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

Web Resources:

[1]https://www.youtube.com/watch?v=o6W0opScrKY&list=PLuv3GM6-gsE01L9yDO0e5UhQapkCPGnY3

[2]https://www.youtube.com/watch?v=7pCRYXEgMPQ&list=PLgwJf8NK-2e5vHwmowy_kGtjq9Ih0FzwN

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

Self-Learning:

Introduction to CSS3-CSS2 vs CSS3

Lab Exercises:

1. Develop static pages for a given scenario using HTML

2. Creating Web Animation with audio using HTML5 & CSS3

3. Demonstrate Geolocation and Canvas using HTML5

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

6. Demonstrate DOM parser

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

Lab Exercises:

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

8. Create a web application using AngularJS with Forms

9. Implement a single page web application using Angular JS

Introduction to Node.js-REPL Terminal-Package Manager(NPM)-Node.js Modules and filesystem-Node.js Events-Debugging Node JS Application-File System and streams-Testing Node JS with jasmine

Self-Learning:

Express JS

Lab Exercises:

10. CRUD Operation using AngularJS

11. Implement web application using AJAX with JSON

12. Demonstrate to fetch the information from an XML file with AJAX

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

Self-Learning:

CRUD operation using MongoDB

Lab Exercises:

13. Demonstrate Node.js file system module

14. Demonstrate Node.js events

15. Implement Mysql with Node.JS

16. Implement CRUD Operation using MongoDB

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

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

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

Web Resources:

[1] www.w3cschools.com

[2] http://www.php.net/docs.php

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

Data models, schemas and instances, DBMS architecture and data independence, Database languages and interfaces, database system environment, Classification of DBMS. 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 - Subclasses, Superclasses, and Inheritance – Enhanced Entity Relationship Model - Relational Database Design by ER- and EER-to-Relational Mapping – Role of Information Systems in Organizations - Database Design and Implementation Process

Lab Exercises:

1.     Design Entity Relationship Diagram

2.     Basic data retrieval queries

SQL Data Definition and Data Types, Specifying Constraints in SQL, Basic Retrieval Queries in SQL, INSERT, DELETE, and UPDATE Statements in SQL, Additional features of SQL. Complex Queries, Triggers, Views, and Schema Modification More Complex SQL Retrieval Queries, Specifying Constraints as Assertions and Actions as Triggers, Views (Virtual Tables) in SQL, Schema Change Statements in SQL.

Lab Exercises:

3.     Data retrieval using referential integrity and JOIN

4.     Advanced data retrieval using nested queries, sub queries and Views

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 – Inference Rules, Equivalence and Minimal Cover - Properties of Relational Decompositions - Nulls and Dangling Tuples - File Organization - Organization of Records in Files - Ordered Indices - B+ Tree Index Files - Static Hashing - Bitmap Indices

Lab Exercises:

5.     Aggregate functions

6.     Database Design Using Normalization

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- NO-UNDO/REDO Recovery Based on Deferred Update- Recovery Techniques Based on Immediate Update- Shadow Paging.

Lab Exercises:

7.     Stored Procedure – 1 (PL/SQL-1)

8.     Stored Procedure – 2 (PL/SQL-2)

Introduction to Distributed database concepts- Types of Distributed Database Systems- Data Fragmentation- Replication- and Allocation Techniques for Distributed Database Design. Overview of Transaction Management in Distributed Databases- Overview of Concurrency Control and Recovery in Distributed Databases

NOSQL Databases

Introduction to NOSQL Systems, The CAP Theorem, Document-Based NOSQL Systems and MongoDB, NOSQL Key-Value Stores, Column-Based or Wide Column NOSQL Systems

Lab Exercises:

9.     NOSQL Exercise - 1

10.   NOSQL Exercise - 2

[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.

Web Resources:

1.     www.w3cschools.com

2.     https://archive.ics.uci.edu

This course aims to set the foundation for Data Communication in Networks by introducing the network components, topologies, network models, and important protocols based on the TCP/IP model for the internet.

Introduction: Network Topology, Network Hardware, Network Software: Protocol Hierarchies, Design issues, Connection Oriented Vs Connection less, Service primitives, OSI Reference Model, TCP/IP

Wireless Transmission, Ethernet, Transmission Media, Digital Modulation and Multiplexing, Line codes, Switching.

Error Detection and Correction: Types of Error, Error Detection, Parity Check, The Internet Checksum, Cyclic Redundancy Check, Forward Error Correction. Data Link Control Protocols: Flow Control, Error Control, HDLC. ADSL, xDSL. Medium Access Control Sublayer: Static Channel Allocation, Assumptions for Dynamic Channel Allocation, Multiple Access Protocols – Aloha, CSMA, Collision free Protocols, Limited Contention Protocols. Ethernet, Wireless LANS, Repeaters, Hubs, Bridges, Switches, Routers, and Gateways.

Routing Algorithms: The Optimality Principle, Shortest Path Algorithm, Flooding, Distance Vector Routing, Link State Routing, Hierarchical Routing, Broadcast Routing, Multicast Routing. 

The Network Layer in the Internet: IPv4 Protocol, IP Addresses, IPv6 Protocol, Internet Control Protocols - ARP, RARP, Label Switching and MPLS, OSPF Protocol, BGP Protocol.

Transport Service: Transport Service Primitives, Berkeley Sockets. Elements of Transport Protocols: Addressing, Connection Establishment, Connection Release, Error and Flow Control. 

The Internet Transport Protocols (UDP): Introduction to UDP, Remote Procedure Call, Real-Time Transport Protocols. The Internet Protocols (TCP): Introduction to TCP, TCP Service Model, TCP Segment Header, TCP Connection Establishment, TCP Connection Release, TCP Connection Management Modelling, TCP Sliding Window Protocol.

Electronic Mail, DNS and HTTP: Electronic Mail - SMTP and MIME, Internet Directory Service -DNS, Web Access and HTTP. Internet Multimedia Support: Real-Time Traffic, Voice Over IP, Session Initiation Protocol, Real-Time Transport Protocol (RTP).  

Advanced Networks -Case study: IoT, Mobile Networks, SDN.

[2] Computer Networks, Andrew S. Tanenbaum, David J. Wetherall, Pearson New International, 5th Edition, 2014.

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

Web Resources:

[1] https://www.geeksforgeeks.org/computer-network-tutorials

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

[3] https://www.guru99.com/data-communication-computer-network-tutorial.html

Introduction to Data Analytics course delivers the basics of analytics concepts and various techniques to discover new and hidden knowledge from the data set. The course also covers the concepts of data mining algorithms that play a major part in the CRISP model. This course provides insight into the complete research process in phases as research methodology, data exploration, modeling, evaluation and visualization. R programming, Python programming, MATLAB and Excel are the suggestive tools for implementation.

Introduction; Data and Relations - Scales, relations and measures; Data preprocessing - Errors, Filtering, Data Transformation and Integration, Data Reduction.

Additional Reading: Probability Distributions & Inferential Statistics

Correlation - Linear Correlation, Correlation and Causality, Chi-Square Test; Regression - Linear Regression, Robust Regression, Neural Networks, Radial Basis Function Networks, Cross Validation, Feature Selection.

Additional Reading: Least Square Problems and Optimization

Forecasting - Finite StateMachines, Recurrent Models, Autoregressive Models. Classification - Classificaiton Criteria - Naive Bayes Classifier - Linear DiscriminantAnalysis - Support Vector Machine - Nearest Neighbor Classifier - Learning Vector Quantization -Decision Trees.

Additional Reading: Stochastic and Kernel Methods

Clustering - Clustering Partitions - Sequential Clustering - Prototype-based Clustering - Fuzzy Clustering -Relational Clustering - Cluster Tendency Assessment - Cluster Validity - Self-Organizing Map.

Additional Reading: Mining Frequent Patterns

Visualization - Visualizing Amounts, Distributions, Proportions, x-y relationships, Geospatial Data, Uncertainty.

Example Caselets:  Dr Hans Gosling - Visualizing Global Public Health

Case Study Topics; Text Analytics; Image Analytics, Business Analytics

Additional Reading: Open Source solutions from Kaggle, GitHub resources and Popular Research Labs

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

[3] Wilke, Claus O., Fundamentals of Data Visualization A Primer on Making Informative and Compelling Figures, O’Reilly, 2019.

[2] Leskovec, Jure; Rajaraman, Anand; Ullman, Jeffrey D., Mining of Massive Datasets, Cambridge University Press, 2014.

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

Introduction to AI, The Foundations of AI, AI Technique -Tic-Tac-Toe. Problem characteristics, Production system characteristics, Production systems: 8-puzzle problem. Intelligent Agents: Agents and Environments, Good Behavior: The concept of rationality – The nature of Environments, The Structure of Agents.

Searching: Uninformed search strategies – Breadth first search, depth first search. Generate and Test, Hill climbing, simulated annealing search, Constraint satisfaction problems, Greedy best first search, A* search, AO* search.

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

Overview, Minimax algorithm, Alpha-Beta pruning, Additional Refinements. Classical planning problem, STRIPS- basic process and working of system – Planning and Acting in the Real World.

Introduction, Syntax processing, Semantic Analysis, Pragmatic and DisCourse Description: Analysis - Perception.

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

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

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

Web Resources:

[1] https://ai.google/education/

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

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

In the current era, billions of devices are Internet-connected and Internet of Things (IoT) standards and protocols are stabilizing, and hence technical professionals must increasingly solve real problems with IoT technologies. To latch on to the varied applications in the field of IoT, this course offers an introduction to the underlying concepts of IoT, the basic architecture, challenges, use cases and the ways of connecting smart objects.

Genesis of IoT - IoT and Digitization -IoT Impact -Convergence of IT and OT -IoT Challenges- Security Priorities: Integrity, Availability, and Confidentiality,Introduction to measure the physical quantities, IoT Enabling Technologies - Wireless Sensor Networks, Cloud Computing Big Data Analytics, Communication Protocols- Embedded System- IoT Levels and Deployment Templates.

IoT Network Architecture and Design- Drivers Behind New Network Architectures- Constrained Devices and Networks- Comparing IoT Architectures: The M2M IoT Standardized Architecture- The IoT World Forum (IoTWF) Standardized Architecture- A Simplified IoT Architecture - The Core IoT Functional Stack- IoT Data Management and Compute Stack

The “Things” in IoT: Sensors, Actuators, and Smart Objects- Micro-Electro-Mechanical Systems (MEMS), Sensor Networks, Wireless Sensor Networks (WSNs), Communication Protocols for Wireless Sensor Networks,Introduction to Smart Systems using IoT - IoT Design Methodology- Intoduction to IoT Boards (Rasberry Pi, Arduino) and IDE

Case Study:  Power Utility Industry , Smart and Connected Cities, Transportation, Mining, Public safety, Weather Monitoring

Communications Criteria Range - Frequency Bands - Power Consumption - Topology - Constrained Devices - Constrained-Node Networks- Data Rate and Throughput -Latency and Determinism- Overhead and Payload

IoT Access Technologies - IEEE 802.15.4 -Standardization and Alliances -Physical Layer- MAC Layer -Topology - Security -Competitive Technologies

The Key Advantages of Internet Protocol - Adoption or Adaptation of the Internet Protocol - The Need for Optimization - Constrained Nodes - Constrained Networks - IP Versions - Optimizing IP for IoT - From 6LoWPAN to 6Lo- RPL- Authentication and Encryption on Constrained Nodes- Internet Protocol for Smart Objects (IPSO) Alliance

The Transport Layer - IoT Application Transport Methods -Application Layer Protocol SCADA - A Little Background on SCADA- Adapting SCADA for IP- Tunneling Legacy SCADA over IP Networks -SCADA Protocol Translation-SCADA Transport over LLNs with MAP-T - Generic Web-Based Protocols - IoT Application Layer Protocols - CoAP - Message Queuing Telemetry Transport (MQTT)

[2] Arshdeep Bahga and Vijay Madisetti, Internet of Things: Hands-on approach, Hyderabad University Press, 2015.

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

[4] Waltenegus Dargie and Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, A John Wiley and Sons Ltd., 2010.

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

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

[4] Erdal Çayirci and Chunming Rong, Security in Wireless Ad Hoc and Sensor Networks, John Wiley and Sons, 2009.

[5] Carlos De Morais Cordeiro and Dharma Prakash Agrawal, Ad Hoc and Sensor Networks: Theory and Applications, World Scientific Publishing, 2011.

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

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

To explore elementary data structures in computer science, and learn to implement them in C. The data structures include linked lists, stacks, queues, trees, heaps, hash tables, and graphs. It also introduces different techniques for searching, traversing trees, hashing, manipulating priority queues, sorting, finding shortest paths in graphs.

Introduction to Pseudo code - Overview of Time & Space Complexity – Recursion – Abstract Data Type - Array - Stack - Queue - Linked lists - Traversing - Searching - Insertion - Deletion - Circular Linked list - Two-way Lists (Doubly) - Linked List Implementation of Stack and Queue - Application of stacks and Queues. 

Lab Exercises:

1. Write a program to convert an infix expression to the postfix form.

2. Implement linked list and its operations.

Bubble Sort – Insertion – Selection – Quick – Merge – Linear Search – Binary search – Hashing – Chaining – Collision Resolution – Open Addressing – String Matching Algorithms: Naive, KMP

Lab Exercises:

3. Implement the concept of sorting technique

4. Implement the concept of searching/pattern matching technique

Representation of Graphs - Operations on Graphs - Depth First and Breadth First Search - Topological Sort - Minimum Spanning Tree Algorithms - Binary Tree - Traversing Binary Trees - Binary Heap - Priority Queue - Heap sort.

Lab Exercises:

5. Implementation of Minimum Spanning Tree

6. Implementation of BFS and DFS

Binary Search Trees - Searching, Inserting and deleting in Binary Search Trees - AVL Trees - AVL Balance Factor, Balancing Trees, AVL node structure, AVL Tree Rotate Algorithms 

Lab Exercises:

7. Implementation of BST

8. Implementation of AVL Tree

B Trees – Operations on B Trees - B+ Trees - Red-Black Trees - Properties of Red-black Trees - Rotations - Insertion - Deletion operations

Lab Exercises:

9. Implementation of B Trees

10. Implementation of B+ Trees

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

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

[3] Robert Sedgwick, Kevin Wayne, Algorithms, Addison-Wesley Publishing Company. 4th Edition, 2011.

Web Resources:

[1] https://www.hackerrank.com/domains/data-structures

[2] https://nptel.ac.in/Programming and Data Structure by Dr.P.P. Chakraborty, Department of Computer Science and Engineering, IIT Kharagpur

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

Introduction to Object Oriented Programming(OOP)

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

Class features

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

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.

Exception Handling in Java

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

Lab Exercises:

4. Implement the static keyword – static variable, static block, static function and static class.

5. Implement String and String Buffer classes.

6. Implement this keyword and command line arguments.

Multithreading

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

Generics

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

The Collections Framework

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

Lab Exercises:

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

8. Implement package and interface.

9. Implement Exception Handing in java.

Introducing GUI Programing with Swing

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

Event Handling

Delegation  Event Model -  Event Classes – Key Event Class – Event Listener Interface - Adapter Classes

Database Programming

Connecting to and querying a database – Automatic driver recovery- Connecting to the database - Creating a Statement for executing query - Executing a query - Processing a Query’s ResultSet – PreparedStatements.

Lab Exercises:

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

11. Implement generic class and interface

12. Implement collection Interfaces and classes

Servlets Basics – Life Cycle of a Servlet –A Simple Servlet,The Servlet API – Servlet Interfaces – Generic Servlet Class- HttpServletRequest Interface – HttpServeltResponse Interface – HttpServet Class – The Cookie Class – Handling HTTP GET Request – Handling HTTP POST Request

Lab Exercises:

13. Implement Swing components and containers with event handling.

14. Implement basic CRUD operations in JDBC.

15. Implement Java Servlets.

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

Web Resources:

[1] www.w3cschools.com

[2] https://www.javatpoint.com/

[3] http://stackoverflow.com/

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

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

Lab Exercises:

1. Breaking the ROOT password and NMCLI configuration

2. Special File Permissions and Access Control Lists

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

Lab Exercises:

3. Process related commands

4. Scheduling process using at and crontab

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

Lab Exercises:

5. LVM Partitions and Extending LVM

6. Swap Partitions

Linux Kernel Components-Types of Kernels-Kernel Configuration Options-Recompiling the Kernel-Partitions-Logical Volume Manager – LVM-File System Overview- Extend LVM Partitions-LVM Snapshot- Swap Partition Considerations-Other Partition Considerations.

Lab Exercises:

7. LVM Snapshot

8. LDAP Server Configuration

Network File System: NFS Overview- NFS, Installation- Configuring NFS Server- Configuring NFS Client- Using Auto mount Services- Network Information System: Understanding- Planning and Configuring NIS Server and NIS Client- LDAP Server installation and Configuration-Installing Samba- Creating Samba Users- Starting Samba Server and Connecting to Samba Client. Configuring BIND: DNS- Understanding DNS- Configuring server files- Configuring Sendmail-Configuring FTP Services.

Lab Exercises:

9. SAMBA Server Configuration

10. FTP and Mail Configuration

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

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

To prepare data for analysis. To identify suitable models for respective applications. To understand the visualization models for interpreting results.

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

Lab Exercises:

1. Create new or open existing dataset and print the characteristics, data scales and dimensions of the dataset

2. Implement pre-processing techniques on raw data

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

Lab Exercises:

3. Find correlation and implement linear regression on  the same dataset to understand the importance of correlation

4. Implement neural networks to make predictions

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

Lab Exercises:

5. Implement and compare Apriori algorithm and FP growth algorithm

6. Implement and compare evaluation metrics of different classifiers on a single dataset

Clustering Partitions - Sequential Clustering - Prototype-based Clustering - Fuzzy Clustering - Relational Clustering - Cluster Tendency Assessment - Cluster Validity - Self-Organizing Map - Time Series Analysis: Introduction to Stream Concepts - Stream data model and architecture - Stream Computing, Sampling data in a stream - Filtering streams - Counting distinct elements in a stream.

Lab Exercises:

7. Implement various clustering techniques and validate them

8. Use appropriate model and perform analysis on time series data

Classification of Visual Data Analysis Techniques - Data Type to be visualized - Visualization Techniques - Interaction Techniques - Specific Visual Data Analysis Techniques - Systems and Applications: Diversity of Intelligent Data Analysis (IDA) Applications - Development Issues - Online Visualization Tools: D3 - Fusion Charts - Data Wrapper.

Lab Exercises:

9. Visualise any data set using graphs, charts and histograms

10. Create a dashboard to visualize and interpret results of a prediction

Web Resources:

[1] https://www.springer.com/gp/book/9783540430605

[2] https://www.webfx.com/blog/web-design/free-data-visualization-tools/

Understand the concepts and models of the neural networks and deep learning and its applications.

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 Exercises:

1. a. Calculate the output of a simple neuron using binary and bipolar sigmoidal activation functions.

    b. Classify the given input vectors into 4 categories using perceptron network with two input neurons and two output neurons.

Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning Rule-Architecture-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 Exercises:

2.a. Classification of AND or OR problem using perceptron network.

   b. Classification of an XOR problem using the multilayer perceptron Network.

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

Lab Exercises:

3. Implementation of BPN for training a single-hidden-layer back propagation network.

4. Implementation of BPN for training a multi-hidden-layer back propagation network.

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

Lab Exercises:

5. Implementation of Convolution Neural Network

6. Implementation of RNN

Introduction - Features of Auto encoder Types of Autoencoder.

Restricted Boltzmann Machine- Boltzmann Machine - RBM Architecture -Example - Types of RBM.

Lab Exercises: 

7. Implementation of autoencoder

8. Implementation of Restricted Boltzmann Machine

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

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

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

 Web Resources:

www.coursera.org

http://neuralnetworksanddeeplearning.com

The objective of this course is to provide the fundamentals on information retrieval and web mining techniques. Mainly focus on practical algorithms of indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations and also understand the basics of web search with special emphasis on web crawling.

Introduction: Definition, Objectives, Functional Overview, Relationship to DBMS, Digital libraries and Data Warehouses; Information Retrieval System Capabilities: Search, Browse, Miscellaneous.

Cataloging and Indexing: Objectives, Indexing Process, Automatic Indexing, Information Extraction; AUTOMATIC INDEXING: Automatic Indexing: Classes of automatic indexing, Statistical indexing, Natural language, Concept indexing, Hypertext linkages; Page ranking algorithm

Lab Exercises:

1. Implement the concept of Search Engine technique

2. Develop the Crawler based on domains

User Search Techniques: Search statements and binding, Similarity measures and ranking, Relevance feedback, Selective dissemination of information search, Weighted searches of Boolean systems, Searching the Internet and hypertext; Information Visualization: Introduction, Cognition and perception, Information visualization technologies.

Lab Exercises:

3. Extract textual information and Multimedia contents from documents

4. Develop search engine indexing

Basic Crawler Algorithm: Breadth-First/ depth-First Crawlers, -Universal Crawlers- Preferential Crawlers : Focused Crawlers – Topical Crawlers. INDEXING: Static and Dynamic Inverted Index– Index Construction and Index Compression- Latent Semantic Indexing. Searching using an Inverted Index: Sequential Search - Pattern Matching - Similarity search -Web Scraping. 

Lab Exercises:

5. Increase the efficiency of sentiment analysis and opinion mining

6. Develop the effective query refinement mechanism

Link Analysis - Social Network Analysis- WEB CONTENT MINING: Classification: Decision tree for Text Document- Naive Bayesian Text Classification - Ensemble of Classifiers. Clustering: K-means Clustering - Hierarchical Clustering – Automatic Topic Extraction from Web Documents.

Lab Exercises:

7. Implement page ranking algorithm

8. Personalize the search engine

Web Usage Mining - Data Collection and Pre-Processing - Data Modelling for Web Usage Mining - Affinity Analysis and the A Priori Algorithm – Binning –Web usage mining using Probabilistic Latent Semantic Analysis – Finding User Access Pattern via Latent Dirichlet Allocation Model.

Lab Exercises: 

9. Personalized web search

10. Case studies and assignments

[2] Bing Liu, “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)”, Springer; 2nd Edition 2010

[3] Zdravko Markov, Daniel T. Larose, “Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage”, John Wiley & Sons, Inc., 2012

[2] Yates, Modern Information Retrieval, Pearson Education, Fourth Impression 2009.

[3] Robert Krfhage, Information Storage & Retrieval, John Wiley & sons, 1st Edition 2007.

[4] Guandong Xu ,Yanchun Zhang, Lin Li, “Web Mining and Social Networking: Techniques and Applications”, Springer; 1st Edition.2010.

[5] Soumen Chakrabarti, “Mining the Web: Discovering Knowledge from Hypertext Data”, Morgan Kaufmann, edition 2012.

[6] Adam Schenker, “Graph-Theoretic Techniques for Web Content Mining”, World Scientific Pub Co Inc , 2015

[7] Min Song, Yi Fang and Brook Wu, Handbook of research on Text and Web mining technologies, IGI global, information Science Reference – imprint of IGI publishing, 2011 

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. To design a window based application with common controls

2. To design a window based application with containers, menu strip, status strip, tool strip.

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:

3. To design a windows based application to store data into database

4. To design a windows based application to retrieve data from database

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

Lab Exercises:

5. To design a window based application to update and delete data from database

6. To implement SqlDataReader in ADO.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: 

7. To implement WPF application

8. To design a web based application to insert data into database

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. Reading from and Writing to an XML document, Using XML DOM objects for data access from XML Documents. Binding data from an XML document to Web form controls. Converting data from Database to XML Data. Xml & Web Services.

Lab Exercises:

9. To design a web based application to retrieve data from database

10. To design a web based application to update and delete data from database

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

[3]  ASP.NET 4, Unleashed – Stephen Walther, Kevin Hoffman, Nate Dudek, Pearson,2010.

Web Resources:

[1] www.w3cschools.com

Understand the application and fundamentals of database systems in real- world. Learn the basic issues of transaction processing and concurrency control in distributed database system. Also, impart the knowledge to use of different definition language to write query for a database.

DBMS Architectures-DBMS Clustering-DBMS Proliferation-Hardware Issues-Cloud Database Systems-Installing the DBMS-DBMS Installation Basics-Hardware Requirements-Storage Requirements-Memory Requirements -Configuring the DBMS-Installing and upgrading various database packages (MS SQL Server, Oracle, MySQL)-Connecting the DBMS to Supporting Infrastructure Software-Installation Verification-Database standards and Procedures.

Lab Exercises:

1. Design a program for implementing batch processing mechanisms.

2. Construct a program to demonstrate various types of constraints.

Introduction: Data, Database and System Administration - Types of DBA: System DBA-Database Architect - Database Analyst - Data Modeler - Application DBA - task Oriented DBA - Performance Analyst-Database Design: From Logical Model to Physical Database- Database Performance Design - Denormalization - Views. Application Design: Database Application Development and SQL- Defining Transactions - Locking - BatchProcessing.

Lab Exercises:

3. Design a program to implement the database backup and recovery commands.

4. Create a database / table space program for managing the users: create and delete users and managing roles: Grant and Revoke.

Storage Management Basics: Files and Data Sets - Space Management - Fragmentation and Storage-Storage Options. Data Movement and Distribution: Loading and Unloading Data - EXPORT and IMPORT - Bulk Data Movement - Distributed Databases.

Lab Exercises:

5. Demonstrate a program query processing in distributed databases.

6. Write a program to implement file processing system.

Database Recovery: Typesof Database Failures - Oracle Recovery Process - Performance Recovery with RMAN - Cloning a Database - Techniques for Granular Recovery - Flashback Techniques and Recovery - Using Restore Points-Repairing Data Corruption and Trail Recovery-Troubleshooting Recovery Errors-Flashback Data Archive. Performance Tuning: Approach to Oracle Performance Tuning-Optimizing Oracle Query Processing-SQL Performance Tuning Tools- End-to-End Tracing-SQL Tuning Advisor-Using the Result Cache-Simple Approach to Tuning SQLStatements.

Lab Exercises:

7. Demonstrate the program for different types of database tools for exporting/importing data.

8. Compose a program for end to end trace using trace.

Managing Users - Database Resource Manager - Controlling Database Access - Auditing Database Usage - Authenticating Users - Enterprise User Security - Database Security Do’s and Don’ts.

Lab Exercises:

9. Design a program for recovering data using RMAN.

10. Design a program for shared and exclusive lock.

[2] Sam R. Alapati, Expert Oracle Database 11g Administration, Apress,2009.

[2] RoopeshRamklass, OCA Oracle Database12c, oracle press, McGraw Hill Education,2014.

[3] Tom Best, Maria Billings, Oracle Database 10g: Administration Workshop I, Oracle Press, Edition 3.1, 2008.

 Web Resources:

[1] https://www.tutorialcup.com/dbms/file-processing-system.html 

[2] https://technology.amis.nl/2015/03/04/compose-end-to-end-tracing-logging-and-monitoring-with -ecid-set-from-weblogic-in-database-exposed-in-vsession/

[3] https://www.cdm.depaul.edu/academics/pages/courseinfo.aspx?Subject=CSC&CatalogNbr=454

This course is designed to introduce a concept of Business Intelligence for better business decision. Also gives practical knowledge on implementation of Business Intelligence concepts.

The Concept of Decision Support Systems – A Framework for Business Intelligence - Effective and timely decisions – A Work System View of Decision Support – The Major Tools andTechniques of Managerial Decision Support - Data, information and knowledge – Role of mathematical models – Business intelligence architectures: Cycle of a business intelligence analysis – Enabling factors in business intelligence projects – Development of a business intelligence system.

Lab Exercises:

1. Practice various data access methods. Representation formats: CSV, FLV, ARFF, XML.

2. Implement data conversion. eg. CSV2ARFF file format conversion in Java.

Concepts of data integration - need and advantages of using data integration - introduction to common data integration approaches - introduction to ETL - introduction to data quality, data profiling concepts and applications.

Lab Exercises:

3. Configuring and testing the ETL tools.

4. Implement pipeline, sampling.

Introduction to data and dimension modeling - multidimensional data model - ER Modeling vs. multi-dimensional modeling - concepts of dimensions, facts, cubes, attribute, hierarchies, star and snowflake schema - introduction to business metrics and KPIs - creating cubes using SSAS.

Lab Exercises:

5. Implement surrogate keys, change in dimensions.

6. Practice data source views, dimensions, hierarchies.

Introduction to Knowledge Management – Knowledge Management Activities – Approaches to Knowledge Management - Information Technology(IT) in Knowledge Management - The business intelligence user types - Standard reports - Interactive Analysis and Ad Hoc Querying - Parameterized Reports and Self-Service Reporting - Dimensional analysis -Alerts/Notifications - Visualization: Charts, Graphs, Widgets, Scorecards and Dashboards - Geographic Visualization - Integrated Analytics - Considerations: Optimizing the Presentation for the Right Message.

Lab Exercises:

7. Implement OLAP explorative data analysis with Pivot Tables and metrics.

8. Implement Parent-child hierarchies. ROLAP and MOLAP.

Association rules mining - Mining Association rules from single level, multilevel transaction databases - Classification and prediction - Decision tree induction - Bayesian Classification - k-nearest - neighbour classification - Cluster analysis -Types of data in clustering, categorization of clustering methods.

Lab Exercises:

9. SQL reporting services.

10. Explorer knowledge flow by implementing association rules and classification.

Sample tools: Visual Studio, Tableau, Qlikview, BIRT, Talend, etc.

[2] Cindi Howson, Successful Business Intelligence, Unlock the Value of BI & Big Data Hardcover –Second Edition: Import, 2013.

[3] Gert H.N. Laursen, JesperThorlund, Business Analytics for Managers: Taking Business Intelligence beyond Reporting Paperback, 2013.

[2] David Loshin Morgan, Kaufman, Business Intelligence: The Savvy Manager’s Guide, Second Edition, 2012.

[3] Ralph Kimball , Margy Ross , Warren Thornthwaite, Joy Mundy, Bob Becker, The Data Warehouse Lifecycle Toolkit, Wiley Publication Inc., 2007.

[4] G.K.Gupta, Introduction to Data Mining with case studies, Prentice Hall of India,2011.

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

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

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

Lab Exercises:

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

2. Illustrate Spacial Transformations - Convolution and correlation.

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

Lab Exercises:

3. Illustrate Histogram equalization.

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

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

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

Lab Exercises:

5. Demonstrate various image preprocessing methods.

6. Illustrate Wavelet transform.

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

Lab Exercises: 

7. Demonstrate various edge detection methods on images

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

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

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

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

Lab Exercises:

10. Illustrate the Color-Based Segmentation with Live Image Acquisition

11. Illustrate triangulation / two-frame motion.

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

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

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

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

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

Lab Exercises:

1. Write a program to tokenize text.

2. Write a program to count word frequency and to remove stop words.

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

Lab Exercises:

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

4. Write a program to get synonyms from WordNet

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

SEMANTIC ANALYSIS AND DISCOURSE PROCESSING

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

Lab Exercises:

5. Write a program to get Antonyms from WordNet

6. Write a program for stemming Non-English words

Natural Language Generation: Architecture of NLG Systems, Applications.

Machine Translation: Problems in Machine Translation-Machine Translation Approaches-Evaluation of Machine Translation systems.

Case study: Characteristics of Indian Languages.

7. Write a program for lemmatizing words Using WordNet

8. Write a program to differentiate stemming and lemmatizing words

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

UNSUPERVISED METHODS IN NLP

Graphical Models for Sequence Labelling in NLP.

Lab Exercises:

9. Write a program for POS Tagging or Word Embeddings

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

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

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

Web Resources:

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

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

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

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

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

 Lab Exercises: 

 1. NO SQL CRUD OPERATIONS

 2. NO SQL AGGREGATE FUNCTIONS

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

Lab Exercises:

3. LANGUAGE BINDINGS

4. CREATING NOSQL APPLICATIONS

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

Lab Exercises:

5. ACCESSING DATASTORE

6. IMPLEMENTING STORAGE ARCHITECTURE

Accessing Data from Column-Oriented Databases Like HBase-Querying Redis Data Stores- Querying in Neo4J-Changing Document Databases-Schema Evolution in Column-Oriented Databases-HBase Data Import and Export-Data Evolution in Key/Value Stores-Map-Reduce- Basic Map-Reduce-Map-Reduce Calculations-2 stage example.

Lab Exercises:

7. MAP-REDUCE

8. NOSQL DATA IMPORT and EXPORT 

Essential Concepts Behind A Database Index-Indexing and Ordering in MongoDB-Creating and Using Indexes in MongoDB-Indexing and Ordering in CouchDB-Indexing in Apache Cassandra- Indexing and Ordering in Neo4J.

Lab Exercises:

9. DATA INDEXING

10. DATA ORDERING

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

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

Web Resources:

[1]   https://www.mongodb.com

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

At the end of the semester, the students should develop the working project using Software Engineering, Web technologies, and database concepts.

Students will carry out a group project in any application domain. 

It is a full time project to be taken up either in the industry or in an R&D organization.

-

ESE 50%

To provide a sound technical exposure to the concepts, principles, methods, and best practices in software architecture and software design.

Architecture Business Cycle – Origin of an Architecture , Software Processes and Architectural Business Cycle, A good architecture, Software Architecture, What is & what it is not the software Architecture is, Other points of view, Architectural Pattern, Reference Models and Reference Architectures, The Importance of Software Architecture, Architectural structures & views, Case study in utilizing Architectural Structures.

Understanding the quality Attributes

Functionality and Architecture, Architecture and Quality Attributes, System Quality Attributes, Quality Attributes Scenarios in practice, Other System Quality Attributes, Business Qualities, Architecture Qualities.

Achieving Qualities

Introducing Tactics – Availability, Modifiability, Performance, Security, Testability, Usability, Relationships of Tactics to Architectural Patterns, Architectural Patterns and Style.

Architecture in the life cycle, Designing the Architecture, Forming the Team Structure, Creating the Skeletal System. Documenting Software Architectures, Uses of Architectural Documentation, Views, Choosing the relevant views, Documenting a view, Documentation across views.

ATAM (Architecture Tradeoff Analysis Method)

A comprehensive method for architecture evaluation, participants, outputs, phases of the ATAM, The Nightingale system - A case study in applying the ATAM.

CBAM (Cost Benefit Analysis Method)

A quantitative approach to architecture design decision making: Decision making context, basis for CBAM, Implementing CBAM, A Case Study – The NASA ECS project.   

The World Wide Web

A case study in interoperability  Relationship to the Architecture Business Cycle, Requirements & Quality, Architectural Solution, The evolution of web-based e-commerce architectures, Achieving quality goals, Architecture Business Cycle today.

Reusing Architectural Assets – Overview – Successful working, Scope, Architectures and Difficulties in software product lines.

Celsuis Tech – A Case study in product Line development, Relationship to the Architecture Business Cycle, Requirements & Quality, Architectural Solution.

Building systems from off-the-shelf components – Impact of components on Architecture, Architectural mismatch, Component-based design as search, ASEILM example.

[2] Pressman S Roger, Software Engineering, Mc Graw Hill International Editions, 4th Edition, 2009. 

[3] Jeff Garland,Richard Anthony,  Large-Scale Software Architecture – A Practical Guide Using UML,  Wiley –dreamtech India Pvt.,Ltd., 2000. 

[4] Rumbaugh, James, Object Oriented Modeling and design, Pearson Education, New Delhi, 2005.

This course  introduces basic components of a blockchain. It helps in understanding  blockchain and Cryptocurrency on a technical level and assess their business impact. The course also identifies major research challenges and technical gaps existing between theory and practice in Cryptocurrency domain.