CHRIST (Deemed to University), Bangalore

DEPARTMENT OF COMPUTER SCIENCE

School of Sciences

Syllabus for
Master of Computer Applications
Academic Year  (2022)

 
1 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA131 DIGITAL LOGIC FUNDAMENTALS Core Courses 4 3 100
MCA132 PROBABILITY AND STATISTICS Core Courses 4 3 100
MCA133 OPERATING SYSTEMS Core Courses 4 3 100
MCA161A INTRODUCTION TO PROGRAMMING AND PROBLEM SOLVING Discipline Specific Elective 3 2 50
MCA161B LINUX ADMINISTRATION Discipline Specific Elective 3 2 50
MCA171 PYTHON PROGRAMMING Core Courses 8 4 150
MCA172 PROGRAMMING IN C Core Courses 8 4 150
2 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA231 SOFTWARE ENGINEERING Core Courses 4 3 100
MCA232 RESEARCH METHODOLOGY Core Courses 3 2 50
MCA271 MICROPROCESSOR AND INTERFACING TECHNIQUES Core Courses 8 4 150
MCA272 WEB STACK DEVELOPMENT Core Courses 8 4 150
MCA273 DATABASE TECHNOLOGIES Core Courses 7 4 150
3 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA331 COMPUTER NETWORKS - 4 3 100
MCA341A INTRODUCTION TO DATA ANALYTICS - 4 3 100
MCA341B INTRODUCTION TO ARTIFICIAL INTELLIGENCE - 4 3 100
MCA341C INTRODUCTION TO INTERNET OF THINGS - 4 3 100
MCA371 DATA STRUCTURES IN C - 8 4 150
MCA372 JAVA PROGRAMMING - 8 4 150
MCA381 PROJECT-I - 6 2 100
4 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA441A PREDICTIVE ANALYTICS Discipline Specific Elective 4 3 100
MCA441B DATA ENGINEERING AND KNOWLEDGE REPRESENTATION Discipline Specific Elective 4 3 100
MCA441C EMBEDDED SYSTEMS AND INTERFACING Discipline Specific Elective 4 3 100
MCA471 MOBILE APPLICATIONS Core Courses 7 4 150
MCA472 MACHINE LEARNING Core Courses 7 4 150
MCA473A BIG DATA ANALYTICS Discipline Specific Elective 8 4 150
MCA473B NATURAL LANGUAGE PROCESSING Discipline Specific Elective 8 4 150
MCA473C IOT SYSTEM DESIGN AND DEVELOPMENT Discipline Specific Elective 8 4 150
MCA481 SEMINAR Core Courses 3 2 50
5 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA571 CLOUD COMPUTING Core Courses 8 4 150
MCA572A SPATIO-TEMPORAL DATA ANALYTICS Discipline Specific Elective 8 4 150
MCA572B NEURAL NETWORKS AND DEEP LEARNING Discipline Specific Elective 8 4 150
MCA572C SYSTEM SIMULATION FOR IOT AND SENSOR NETWORKS Discipline Specific Elective 8 4 150
MCA573A QUANTUM MACHINE LEARNING Discipline Specific Elective 8 4 150
MCA573B COMPUTER VISION Discipline Specific Elective 8 4 150
MCA573C IOT DATA ANALYTICS Discipline Specific Elective 8 4 150
MCA581 SPECIALIZATION PROJECT Core Courses 6 2 100
6 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA681 INDUSTRY PROJECT - 3 12 300
    

    

Introduction to Program:

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

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO1: Computational Knowledge: Apply knowledge of computing fundamentals, computing specialisation, mathematics, and domain knowledge appropriate for the computing specialisation to the abstraction and conceptualisation of computing models from defined problems and requirements.

PO2: Problem Analysis: Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines.

PO3: Design/Development of Solutions: Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations.

PO4: Conduct Investigations of Complex Computing Problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.

PO5: Modern Tool Usage: Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations.

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

PO7: Life-long Learning: Recognise the need, and have the ability, to engage in independent learning for continual development as a computing professional.

PO8: Demonstrate knowledge and understanding of the computing and management principles and apply these to one?s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.

PO9: Communication Efficacy: Communicate effectively with the computing community, and with society at large, about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions.

PO10: Societal and Environmental Concern: Understand and assess societal, environmental, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practices.

PO11: Individual and Team Work: Function effectively as an individual and as a member or leader in diverse teams and in multidisciplinary environments.

PO12: Innovation and Entrepreneurship: Identify a timely opportunity and using innovation to pursue that opportunity to create value and wealth for the betterment of the individual and society at large.

Assesment Pattern

CIA: 50%

ESE: 50%

Examination And Assesments

Continuous Internal Assessment: 50% Weightage

End Semester Examination: 50% Weightage

MCA131 - DIGITAL LOGIC FUNDAMENTALS (2022 Batch)

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

Course Objectives/Course Description

 

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.

Course Outcome

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

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

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

Unit-1
Teaching Hours:9
NUMBER SYSTEM AND BINARY CODING
 

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.

Unit-2
Teaching Hours:9
BOOLEAN LOGICS AND LOGIC GATES
 

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. 

Unit-3
Teaching Hours:9
COMBINATIONAL CIRCUITS
 

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.

Unit-4
Teaching Hours:9
SEQUENTIAL CIRCUITS
 

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.

Unit-5
Teaching Hours:9
REGISTERS AND COUNTERS
 

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

Text Books And Reference Books:

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

Essential Reading / Recommended Reading

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

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

[3] William Stallings, Computer Architecture and Organization, PHI, 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

Evaluation Pattern

CIA

ESE

50%

50%

MCA132 - PROBABILITY AND STATISTICS (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Provide the grounding knowledge of statistical methods for data analytics

CO2: Data summarization, probability, random variables with properties and distribution functions were included

CO3: Sampling distributions and their applications in hypothesis testing advanced statistical methods like ANOVA and correlation and regression analysis were included

Unit-1
Teaching Hours:10
EXPLORATORY DATA ANALYSIS
 

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

Unit-2
Teaching Hours:15
PROBABILITY AND RANDOM VARIABLES
 

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

Unit-3
Teaching Hours:10
SAMPLING DISTRIBUTIONS
 

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

Unit-4
Teaching Hours:10
TESTING OF HYPOTHESIS
 

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).

Text Books And Reference Books:

[1] Gupta S.C & Kapoor V.K, Fundamentals of Mathematical statistics, SultanChand & sons, 2020.

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

Essential Reading / Recommended Reading

[1] Freund J.E, Mathematical statistics, Prentice Hall, 2001.

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA133 - OPERATING SYSTEMS (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

CO3: Implement deadlock system and multiple memory management strategies

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

CO5: Analyze the file management concepts using LINUX

Unit-1
Teaching Hours:9
FUNDAMENTALS
 

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

Unit-2
Teaching Hours:9
PROCESS SCHEDULING
 

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

Unit-3
Teaching Hours:9
PROCESS COORDINATION
 

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

 

Unit-4
Teaching Hours:9
MEMORY MANAGEMENT
 

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.

Unit-5
Teaching Hours:12
FILE MANAGEMENT
 

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.

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA161A - INTRODUCTION TO PROGRAMMING AND PROBLEM SOLVING (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Demonstrate the systematic approach for problem solving.

CO2: Apply different programming structure with suitable logic for computational problems.

Unit-1
Teaching Hours:10
INTRODUCTION TO PROBLEM SOLVING AND PROGRAMMING
 

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.

Unit-2
Teaching Hours:10
PROBLEM SOLVING WITH LOGIC STRUCTURES
 

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

Unit-3
Teaching Hours:10
PROBLEM SOLVING WITH DECISION AND LOOP STRUCTURES
 

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

Text Books And Reference Books:

[1]  Maureen Sprankle and Jim Hubbard, Problem solving and programming concepts, PHI, 9th Edition, 2012.

Essential Reading / Recommended Reading

[1]  E Balagurusamy, Fundamentals of Computers, TMH, 2011.

Evaluation Pattern

CIA

ESE

50%

50%

MCA161B - LINUX ADMINISTRATION (2022 Batch)

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

Course Objectives/Course Description

 

To enable the students to excel in the Linux Platform.

Course Outcome

CO1: Demostrate the systematic approach for configure the Liux environment

CO2: Manage the Linux environment to work with open source data science tools

Unit-1
Teaching Hours:10
Topic-1
 

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.

Unit-2
Teaching Hours:10
Topic-2
 

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

Unit-3
Teaching Hours:10
Topic-3
 

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.

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA

ESE

50%

50%

MCA171 - PYTHON PROGRAMMING (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

CO2: Demonstrate significant experience with the Python program development environment

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

Unit-1
Teaching Hours:18
INTRODUCTION TO PYTHON DATA STRUCTURES
 

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

Lab Exercises:

1. Demonstrate use of Python data structures

2. Demonstrate Lists  and Dictionary comprehension

Unit-2
Teaching Hours:18
OBJECT ORIENTED PROGRAMMING USING PYTHON AND REGULAR EXPRESSIONS
 

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

Lab Exercises:

3. Demonstrate use of object- oriented programming concepts

4. Demonstrate exceptional handling.

Unit-3
Teaching Hours:18
INTRODUCTION TO NUMPY, PANDAS
 

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

Lab Exercises:

5. Demonstrate use of lambda functions.

6. Demonstrate use of custom modules.  

Unit-4
Teaching Hours:18
MATPLOTLIB and GUI PROGRAMMING
 

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

Lab Exercises:

7. Implement “re” module.

8. Demonstrate use of “Numpy”.

9. Implement Pandas to demonstrate data handling and indexing.

Unit-5
Teaching Hours:18
INTRODUCTION TO DJANGO FRAMEWORK AND DATABASE PROGRAMMING
 

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

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

Lab Exercises:

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

11. Create a web application using Djangoframework.

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

Text Books And Reference Books:

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

Essential Reading / Recommended Reading

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

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

Web Resources:

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA172 - PROGRAMMING IN C (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Apply control structures appropriately to solve problems

CO2: Ability to understand functional code organization

CO3: Construct code involving arrays, structures and pointer concepts

Unit-1
Teaching Hours:18
C CONTROL STRUCTURES
 

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

Unit-2
Teaching Hours:18
FUNCTIONS AND POINTERS
 

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

Lab Exercises:

3.     Implement function concept

4.     Implement pointer concept using function

Unit-3
Teaching Hours:18
ARRAYS AND STRINGS
 

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

Unit-4
Teaching Hours:18
STRUCTURES, UNIONS, ENUMS AND BIT OPERATIONS
 

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

Unit-5
Teaching Hours:18
CONSOLE I/O, FILE HANDLING AND PREPROCESSORS
 

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

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

[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

Evaluation Pattern

CIA

ESE

50%

50%

MCA231 - SOFTWARE ENGINEERING (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

CO2: : Design software by applying the software engineering principles.

CO3:: Develop the quality software using efficient project management.

Unit-1
Teaching Hours:9
PROCESS MODELS, UNDERSTANDING REQUIREMENTS
 

A generic process model – Defining a framework activity, identifying a Task Set, Process - Prescriptive Process Models-Specialized Process Models. Requirements Engineering- Developing use cases, Elements of the requirements Model, Analysis pattern, negotiating requirements, validating requirements-Latest Methodology-RAD, DevOps, Fish Model,SCRUM Agile Modeling.

Unit-2
Teaching Hours:9
DESIGN CONCEPTS
 

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

Unit-3
Teaching Hours:9
COMPONENT LEVEL DESIGN, USER INTERFACE DESIGN
 

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

Unit-4
Teaching Hours:9
QUALITY MANAGEMENT, TESTING
 

Software Quality- Software testing fundamentals- internal and external view of testing, White-box testing, Basic path testing - control structure testing - Black- box testing-Model Based Testing, Testing GUIs, Testing of Client-Server Architectures, Testing Documentation, testing for Real-Time Systems.

Unit-5
Teaching Hours:9
PROCESS AND PROJECT METRICS
 

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

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

Web Resources:

[1] www.nptel.ac.in

Evaluation Pattern

CIA

ESE

50%

50%

MCA232 - RESEARCH METHODOLOGY (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

CO3: Create scientific reports according to specified standards.

Unit-1
Teaching Hours:6
RESEARCH METHODOLOGY
 

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

Unit-2
Teaching Hours:6
RESEARCH DESIGN
 

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

Unit-3
Teaching Hours:6
RESEARCH DATA
 

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

Unit-4
Teaching Hours:6
SCIENTIFIC WRITING
 

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

Unit-5
Teaching Hours:6
REPORT WRITING
 

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

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA271 - MICROPROCESSOR AND INTERFACING TECHNIQUES (2022 Batch)

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

Course Objectives/Course Description

 

To enable 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.

Course Outcome

CO1: Outline the basic elements, functions architecture of 8085 microprocessor and working of each module.

CO2: Critique and implement effective ALP with counter, delay and interrupts.

CO3: Examine programming techniques and develop applications based on the assembly language program.

Unit-1
Teaching Hours:18
INTRODUCTION TO 8085 AND ALP
 

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 two 8-bit BCD numbers.

2. Write a program to add N-one byte numbers.

3. Write a program to multiply two 8 - bit numbers.

Unit-2
Teaching Hours:18
8085 PROGRAMMING
 

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

Lab Exercises:

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).

5. Write a program to find the first 10 terms of a Fibonacci sequence

6. Write a program to interchange N one bytes of data.

Unit-3
Teaching Hours:18
PROGRAMMING TECHNIQUES WITH ADDITIONAL INSTRUCTIONS
 

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:

7. Write a program to perform linear search over a set of N numbers. Display FF if found otherwise display 00.

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

9. Write a program to sort the numbers in ascending and in descending using bubble sort.

Unit-4
Teaching Hours:18
ARCHITECTURE OF 8085
 

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.                                                                                                      

Lab Exercises:

10. Write a program to simulate a BCD counter to count from 0 to 100.

11. Write a program to check whether a one-byte number is a palindrome or not.

12. Write a program to divide a 16 - bit number by an 8 - bit numbers.

Unit-5
Teaching Hours:18
INTERRUPTS IN 8085
 

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

Lab Exercises:

13. Write a program to subtract a 16 - bit BCD number from another 16 – bit BCD number.

14. Write a program to simulate a stopwatch with a provision to stop the watch.

15. Write a program to display a rolling message.  

Text Books And Reference Books:

[1] Ramesh.S.Goankar, Microprocessor Architecture, Programming & Applications With 8085, 6th Edition, Penram International, 2013. ISBN 81-87972-88-2.

Essential Reading / Recommended Reading

[1] Hall.D.V., Microprocessor and Digital System, McGraw Hill Publishing Company, 3rd Edition, 2017.

[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

Evaluation Pattern

CIA

ESE

50%

50%

MCA272 - WEB STACK DEVELOPMENT (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

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

CO4: Create modern web applications using MEAN

Unit-1
Teaching Hours:18
OVERVIEW OF WEB TECHNOLOGIES AND HTML5
 

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

Self-Learning:

Introduction to CSS3-CSS2 vs CSS3

Lab Exercises:

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

2. Develop static pages for a given scenario using HTML

3. Demonstrate Geolocation and Canvas using HTML5

Unit-2
Teaching Hours:18
XML AND AJAX
 

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

Lab Exercises:

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

5. Demonstrate XSL with XSD

Unit-3
Teaching Hours:18
CLIENT SIDE SCRIPTING
 

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

Lab Exercises:

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

7. Create a web application using AngularJS with Forms.

Unit-4
Teaching Hours:18
SERVER SIDE SCRIPTING
 

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

Self-Learning:

Express JS

Lab Exercises:

8. CRUD Operation using AngularJS

9. Implement web application using AJAX with JSON

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

Unit-5
Teaching Hours:18
JAVA SERVLETS
 

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

Self-Learning:

CRUD operation using MongoDB 

Lab Exercises:

11. Demonstrate Node.js file system module and Demonstrate Node.js file system module

12. Implement Mysql with Node.JS

Text Books And Reference Books:

[1] Internet and World Wide Web:How to Program,  Paul Deitel , Harvey Deitel & Abbey Deitel, Pearson Education, 5th Edition, 2018.

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

Essential Reading / Recommended Reading

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

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

[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

Evaluation Pattern

CIA

ESE

50%

50%

MCA273 - DATABASE TECHNOLOGIES (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

CO3: Apply structured query language to create, retrieve, update and manage a database

Unit-1
Teaching Hours:15
DATABASE SYSTEM CONCEPTS AND CONCEPTUAL MODELING
 

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 ER diagram.

2. Demonstrate use of DDL,DML commands and integrity constraints 

Unit-2
Teaching Hours:15
RELATIONAL DATA MODEL AND SQL
 

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

4. Data Retrieval using simple JOIN and referential integrity

Unit-3
Teaching Hours:15
RELATIONAL DATA MODEL, DATABASE DESIGN AND INTRODUCTION TO FILE ORGANIZATION
 

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. Data Retrieval using OUTER, INNER JOINS

6. Sub Queries and Corelated queries

Unit-4
Teaching Hours:15
TRANSACTION PROCESSING, CONCURRENCY CONTROL AND RECOVERY
 

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. Views and Indexes

8. Stored Procedures and Triggers

Unit-5
Teaching Hours:15
DISTRIBUTED DATABASES AND NOSQL SYSTEMS
 

Distributed databases: Distributed Database concepts- Types - Data Fragmentation- Replication- Allocation Techniques. Overview of Transaction Management - Overview of Concurrency Control and Recovery.

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, NOSQL Graph Databases.

Lab Exercises:

9. Basic operations on NOSQL DB

Text Books And Reference Books:

[1] Elmasri & Navathe, Fundamentals of Database Systems, Addison-Wesley, 7th Edition, 2016.

Essential Reading / Recommended Reading

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

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

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

Web Resources:

1.     www.w3cschools.com

2.     https://archive.ics.uci.edu

Evaluation Pattern

CIA

ESE

50%

50%

MCA331 - COMPUTER NETWORKS (2022 Batch)

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

Course Objectives/Course Description

 

To familiarize the student with specific, well known computer networks theory and methods and algorithms. Understanding the computer network, which is a set of computers sharing resources located on or provided by network nodes. The computers use common communication protocols over digital interconnections to communicate with each other.

Course Outcome

CO1: Demonstrate in depth knowledge of network communications based on TCP/IP models.

CO2: Demonstrate a critical understanding of network models with related key protocols, services and applications.

CO3: Evaluate different techniques / algorithms of standard network models.

CO4: Analyze network protocols for data transmission in various types of networks.

Unit-1
Teaching Hours:9
INTRODUCTION TO NETWORKS, THE PHYSICAL LAYER
 

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.

Unit-2
Teaching Hours:9
THE DATA LINK LAYER
 

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. 

Unit-3
Teaching Hours:9
NETWORK LAYER
 

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. 

Unit-4
Teaching Hours:9
TRANSPORT LAYER
 

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.

Unit-5
Teaching Hours:9
INTERNET APPLICATIONS AND ADVANCED NETWORKS
 

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.

Text Books And Reference Books:

[1] Forouzan, Behrouz A., Mosharraf Firouz., Computer Networks A Top-Down Approach, Tata McGraw Hill publications, 1st Edition, 2012.

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

Essential Reading / Recommended Reading

[1] Data and Computer Communications, William Stalling, Pearson International, 10th 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

Evaluation Pattern

CIA

ESE

50%

50%

MCA341A - INTRODUCTION TO DATA ANALYTICS (2022 Batch)

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

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Understand the fundamental techniques in data analytics

CO2: Perform an exploratory data analysis

CO3: Apply suitable supervised and unsupervised algorithms to real world problems

CO4: Interpret the results of developed models using different visualization techniques

Unit-1
Teaching Hours:9
DATA, RELATIONS AND PREPROCESSING
 

Introduction; Data and Relations –Scales and measures; Data preprocessing - Data Transformation and Integration, Data Reduction.

Additional Reading: Probability Distributions & Inferential Statistics.

Unit-2
Teaching Hours:9
CORRELATION AND REGRESSION
 

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

Additional Reading: Least Square Problems and Optimization.

Unit-3
Teaching Hours:9
FORECASTING AND CLASSIFICATION
 

Introduction to classification - steps involved in classification techniques – Forecasting - Recurrent Models, Autoregressive Models. Classification - Classification Criteria - Naive Bayes Classifier - Linear DiscriminantAnalysis - Support Vector Machine - Nearest Neighbor Classifier - Learning Vector Quantization -Decision Trees. 

 Additional Reading: Stochastic and Kernel Methods 

Unit-4
Teaching Hours:9
CLUSTERING
 

Introduction - Types of Cluster - Centroid based clustering - medoid based - Fuzzy Clustering -Relational Clustering - Cluster Validity - Self-Organizing Map. 

Unit-5
Teaching Hours:9
VISUALISATION AND CASE STUDY
 

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.

Text Books And Reference Books:

[1] Runkler, Thomas. A, Data Analytics: Models and Algorithms for Intelligent Data Analysis, Springer Vieweg, 2012.

[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.

Essential Reading / Recommended Reading

[1] Michael Berthhold, David J. Hand, Intelligent Data Analysis - An Introduction, Springer Publications, 2nd Edition, 2002.

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA341B - INTRODUCTION TO ARTIFICIAL INTELLIGENCE (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Express the modern view of AI and its foundation

CO2: Illustrate Search Strategies with algorithms and Problems.

CO3: Implement Proportional logic and apply inference rules.

CO4: Apply suitable techniques for NLP and Game Playing.

Unit-1
Teaching Hours:9
INTRODUCTION
 

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.

Unit-2
Teaching Hours:9
LOCAL SEARCH ALGORITHM
 

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.

Unit-3
Teaching Hours:9
KNOWLEDGE REPRESENTATION
 

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.

Unit-4
Teaching Hours:9
GAME PLAYING AND PLANNING
 

Overview, Minimax algorithm, Alpha-Beta pruning, Additional Refinements. Classical planning problem, STRIPS- basic process and working of system.

Unit-5
Teaching Hours:9
NATURAL LANGUAGE PROCESSING
 

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

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

[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

Evaluation Pattern

CIA

ESE

50%

50%

MCA341C - INTRODUCTION TO INTERNET OF THINGS (2022 Batch)

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

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Understand the components and characteristics of IoT and enabling technologies

CO2: Gain knowledge on applications and various challenges in IoT

CO3: Develop an understanding of sensor network and its fundamental communication protocols

Unit-1
Teaching Hours:9
INTRODUCTION TO IoT
 

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-A Simplified IoT Architecture - The Core IoT Functional Stack- IoT Data Management and Compute Stack.

Unit-2
Teaching Hours:9
SMART OBJECTS
 

The “Things” in IoT: Sensors, Actuators, and Smart Objects- Micro-Electro-Mechanical Systems (MEMS), Sensor Networks, Introduction to Smart Systems using IoT.

Case Study: Smart and Connected Cities, Transportation and Weather Monitoring.

Unit-3
Teaching Hours:9
CONNECTING SMART OBJECTS
 

Communications Criteria Range - Frequency Bands - Power Consumption -  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.

Unit-4
Teaching Hours:9
IP AS THE IOT NETWORK LAYER
 

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

Unit-5
Teaching Hours:9
APPLICATION PROTOCOLS FOR IOT
 

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.

Text Books And Reference Books:

[1] David Hanes, Gonzalo Salgueiro, IoT Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet of Things,  Cisco Press, 2017.

[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.

Essential Reading / Recommended Reading

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

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

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

[4] 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.

Evaluation Pattern

CIA

ESE

50%

50%

MCA371 - DATA STRUCTURES IN C (2022 Batch)

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

Course Objectives/Course Description

 

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.

Course Outcome

CO1: Describe common applications for arrays, linked structures, stacks, queues, trees, and graphs

CO2: Illustrate various techniques for searching, sorting and hashing

CO3: Design and implement an appropriate data structures to solve real world problems

Unit-1
Teaching Hours:18
ELEMENTARY DATA STRUCTURES
 

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.

Unit-2
Teaching Hours:18
SORTING AND SEARCHING
 

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

Unit-3
Teaching Hours:18
GRAPHS & TREES
 

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

Unit-4
Teaching Hours:18
SEARCH TREES
 

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

Unit-5
Teaching Hours:18
ADVANCED DATA STRUCTURES
 

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

Text Books And Reference Books:

[1] Gilberg, F Richard & Forouzan, A Behrouz, Data Structures A Pseudocode approach with C,

Cengage. 2nd Edition, 2008.

[2] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, Introduction to

Algorithms, MIT Press, 3rd Edition, 2009

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

Essential Reading / Recommended Reading

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

[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

Evaluation Pattern

CIA

ESE

50%

50%

MCA372 - JAVA PROGRAMMING (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

CO2: Demonstrate competence in using Java Programming Language in developing small to medium-sized applications with professionally acceptable coding and performance standards.

CO3: To design and develop solutions to the challenging requirements in enterprise applications.

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

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.

Unit-2
Teaching Hours:18
INHERITANCE, INTERFACES & PACKAGES AND EXCEPTION HANDLING IN JAVA
 

Inheritance in Java

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

Interfaces and Packages

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

Exception Handling in Java

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

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

Multithreading Java

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

Generics

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

The Collections Framework

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

Lab Exercises:

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

8. Implement package and interface.

9. Implement Exception Handing in java.

Unit-4
Teaching Hours:18
INTRODUCING GUI PROGRAMING WITH SWING, EVENT HANDLING AND DATABASE PROGRAMMING
 

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 collection Interfaces and classes

Unit-5
Teaching Hours:18
JAVA SERVLETS
 

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:

12. Implement basic CRUD operations in JDBC with SWING

13. Implement Java Servlets

Text Books And Reference Books:

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

Essential Reading / Recommended Reading

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

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

Web Resources:

[1] http://stackoverflow.com/

[2] https://docs.oracle.com/javase/tutorial/java/index.html

Evaluation Pattern

CIA

ESE

50%

50%

MCA381 - PROJECT-I (2022 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

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

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

Unit-1
Teaching Hours:60
Project-I
 

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

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA

ESE

50%

50%

MCA441A - PREDICTIVE ANALYTICS (2021 Batch)

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

Course Objectives/Course Description

 

The aim of this course is to deliver the concepts of predictive analytics and various techniques involved in preparing the data for model building. The course also covers the concepts of various algorithms for predictive models and also provides various evaluation parameters for predictive models. 

Course Outcome

CO1: : Understand the tools and techniques involved in predictive analytics

CO2:: Perform the process of exploratory and univariate data analysis

CO3:: Perform various methods to understand data.

CO4:: Prediction and evaluation of different models for predictive analytics.

Unit-1
Teaching Hours:9
INTRODUCTION TO DATA MINING AND PREDICTIVE ANALYTICS
 

The Need for Human Direction of Data Mining.  The Cross-Industry Standard Process for Data Mining: CRISP-DM.

Data Preprocessing: Numerical Methods for Identifying Outliers - Flag Variables -Transforming Categorical Variables into Numerical Variables - Binning Numerical Variables - Reclassifying Categorical Variables.

Unit-2
Teaching Hours:9
EXPLORATORY DATA ANALYSIS
 

Hypothesis Testing Versus Exploratory Data Analysis- Getting to Know the Data Set - Exploring Categorical Variables -Exploring Numeric Variables:- Binning Based on Predictive Value. Deriving New Variables: Numerical Variables.

UNIVARIATE STATISTICAL ANALYSIS: -Data Mining Tasks in Discovering Knowledge in Data-Statistical Approaches to Estimation and Prediction - Statistical Inference – Estimates - Hypothesis Testing for the Mean.

Unit-3
Teaching Hours:9
PREPARING TO MODEL THE DATA
 

Supervised Vs Unsupervised Methods -Statistical Methodology and Data Mining Methodology - Cross-Validation-Over fitting - Bias–Variance Trade-Off - Balancing the Training Data Set - Establishing Baseline Performance –  Simple Linear Regression.

MULTIPLE REGRESSION AND MODEL BUILDING: Multiple Regression Equation - Inference in Multiple Regression - Regression with Categorical Predictors.

Unit-4
Teaching Hours:9
CLASSIFICATION MODEL
 

KNN Algorithm -Distance Function - Combination Function -KNN-Algorithm for Estimation and Prediction-Choosing K-Application of KNN Algorithm-DecisionTrees:  Requirements -Classification and Regression Trees.

NEURAL NETWORKS: Input and Output Encoding - NN for Estimation and Prediction-Sigmoid Activation Function - Back-Propagation-Gradient-Descent Method - Back-Propagation Rules - Termination Criteria - Learning Rate - Momentum Term - Sensitivity Analysis -Application of NN.

Unit-5
Teaching Hours:9
LOGISTIC REGRESSION
 

Regression- Multiple Logistic Regressions - Introducing Higher-Order Terms to Handle Nonlinearity - Validating the Logistic Regression Model. Model Evaluation Techniques: Description Task -  Estimation and Prediction Tasks - - Accuracy and Overall Error Rate - Sensitivity and Specificity - False-Positive Rate and False-Negative Rate.

Text Books And Reference Books:

[1] Data Mining and Predictive Analytics, 2nd Edition, Daniel T. Larose, Wiley, 2021.

Essential Reading / Recommended Reading

[1] Hands-On Predictive Analytics with Python: Master the complete predictive analytics process, from problem definition to model deployment, Kindle Edition, Alvaro Fuentes, 2021.

[2] Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Revised and    Updated, Eric Siegel, Wiley, 2016.

Evaluation Pattern

CIA

ESE

50%

50%

MCA441B - DATA ENGINEERING AND KNOWLEDGE REPRESENTATION (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: To store and retrieve data effectively

CO2: To analyze the data from different sources

CO3: To analyze and design knowledge based systems

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

Data Engineering

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

Data Models

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

Unit-2
Teaching Hours:9
BUILDING DATA PIPELINES
 

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

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

Unit-3
Teaching Hours:9
DATA STORAGE AND RETRIEVAL
 

Data Storage and Retrieval Non Relational data

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

DATA in Distributed systems

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

Unit-4
Teaching Hours:9
KNOWLEDGE REPRESENTATION
 

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

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

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

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA441C - EMBEDDED SYSTEMS AND INTERFACING (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

CO2:: Demonstrate and distinguish different communication interfaces and interrupt sources

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

Unit-1
Teaching Hours:9
INTRODUCTION TO EMBEDDED SYSTEMS
 

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

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

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

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

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

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

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

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

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

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

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

Text Books And Reference Books:

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

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

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

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

Essential Reading / Recommended Reading

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

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

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

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

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

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

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

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

 

Web Resources:

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

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA471 - MOBILE APPLICATIONS (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the basic concepts of Mobile application development

CO2: Design and develop user interfaces for the Android platforms

CO3: Apply Java programming concepts to Android application development

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

Unit-1
Teaching Hours:15
INTRODUCTION TO ANDROID
 

History of Mobile Apps, Trends in Market-Web App Vs Mobile App-Mobile OS-What is Android?-Why Develop apps for Android?-Most popular platform for mobile apps-best experience for app users Android version-the challenges of Android app development-Features of Android-Android Software Stack- Android System Architecture-Android Core building blocks- Introduction to Android Studio-Building first Android App-Layouts and resources for UI-Text and Scrolling views.   

LAB EXERCISES:

1. Installation of Android Studio and Hello World

2. Layout Editors

3. Input Controls.

Unit-2
Teaching Hours:15
ACTIVITY AND INTENTS
 

Introduction to Activity and Intent-Activity life cycle and state-Implicit and Explicit Intents-The Android Studio debugger-App testing and Android support library-Understanding the views-components-understanding screen- screen orientation-Button-clickable images-Input controls-Menus and pickers- user navigation-RecyclerView-Drawables-styles and themes-material design resources for adoptive layouts and UI Testing.

LAB EXERCISES:

4. Activity and Intents- Implicit and Explicit and camera

5. Input controls

6. Menu and pickers

Unit-3
Teaching Hours:15
WORKING WITH BACKGROUND
 

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

LAB EXERCISES:

7. User navigation – Recyclerview

8. MediaController

9. Fragments

Unit-4
Teaching Hours:15
SAVING USER DATA
 

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

LAB EXERCISES:

10. AsyncTask and AsyncTaskloader

11. Notifications

12. BroadcastReceiver

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

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

LAB EXERCISES:

13. Sharedpreference

14. SQLite /Firebase

15. APK Deployment

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

 

Web Resources:

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

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA472 - MACHINE LEARNING (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand basic principles of machine learning techniques

CO2: Formulate machine learning problems and their solutions

CO3: Apply machine learning algorithms to solve real world problems

Unit-1
Teaching Hours:15
INTRODUCTION
 

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

Supervised Learning: Learning class from examples - Noise - Learning Multiple classes. Regression-Model Selection and Generalization.

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

LAB EXERCISES:

1. Data Exploration using Parametric Methods and Non-Parametric Methods

2. Dimensionality Reduction using PCA

Unit-2
Teaching Hours:15
CLUSTERING
 

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

LAB EXERCISES:

3. K Means Clustering

4. Hierarchical Clustering

Unit-3
Teaching Hours:15
SUPERVISED LEARNING - I
 

Decision Tree – Introduction, Univariate Tree, tree Pruning, Rule Extraction from tree.

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

LAB EXERCISES:

5. Classification using Decision tree

6. Logistic Discrimination

Unit-4
Teaching Hours:15
SUPERVISED LEARNING - II
 

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

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

LAB EXERCISES:

7. Classification using Kernel Machines

8. Classification using MLP

Unit-5
Teaching Hours:15
REINFORCEMENT LEARNING
 

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

LAB EXERCISES:

9. Temporal reinforcement Learning

Text Books And Reference Books:

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

Essential Reading / Recommended Reading

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

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

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

 

Web Resources:

[1] https://machinelearningmastery.com/

[2] https://towardsdatascience.com/

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA473A - BIG DATA ANALYTICS (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

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

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

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

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

Distributed file system – Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, Big data applications, Algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce.

LAB EXERCISES:

1. Installing and Configuring Hadoop

2. Word count application in Hadoop.

Unit-2
Teaching Hours:18
NOSQL BIG DATA MANAGEMENT
 

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

LAB EXERCISES:

3. Sorting the data using MapReduce.

4. Finding max and min value in Hadoop.

Unit-3
Teaching Hours:18
UNDERSTANDING MAPREDUCE
 

Key/value pairs, The Hadoop Java API for MapReduce, Writing MapReduce programs, Hadoop-specific data types, Input/output.

Developing MapReduce Programs: Using languages other than Java with Hadoop, Analysing a large dataset.

Advanced MapReduce Techniques

Simple, advanced, and in-between Joins, Graph algorithms, using language-independent data structures.

 

LAB EXERCISES:

5. Implementation of decision tree algorithms using MapReduce.

6. Implementation of K-means Clustering using MapReduce.

Unit-4
Teaching Hours:18
HIVE AND PIG
 

Hive - Hive Architecture - Hive Installation - Comparison with RDBMS (Traditional Database) - Hive Data Types and File Formats - Hive Data Model - Hive Integration and Workflow Steps -Hive Built-in Functions - HiveQJ - HiveQJ. Data Definition Language (DDL) - HiveQJ. Data Manipulation Language (DML) - HiveQL for Querying the Data - Aggregation - Join - Group by Clause - Pig - Apache Pig - Grunt Shell - Installing Pig - Pig Latin Data Model - Pig Latin and Developing Pig Latin Scripts.

LAB EXERCISES:

7. Generation of  Frequent Itemset using MapReduce.

8. Count the number of missing and invalid values by joining two large given datasets.

9. Using Hadoop’s map-reduce, Evaluating Number of Products Sold in Each Country in the online shopping portal. Dataset is given.

Unit-5
Teaching Hours:18
APACHE HADOOP
 

Moving Data in and out of Hadoop – Understanding inputs and outputs of MapReduce - Data Serialization, Problems with traditional large-scale systems-Requirements for a new approach-Hadoop – Scaling-Distributed Framework-Hadoop v/s RDBMS-Brief history of Hadoop - Configurations of Hadoop.

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

Downloading, Installing Spark a,nd Getting Started - Programming with RDDs - Machine Learning with MLib - Data En (Extract, Transform and Load) Process - Composing Spark Program Steps for ETL - Introduction to Analytics, Reporting, and Visualizing - Introduction to Analytics - Data/Information Reporting - Data Visualization.

LAB EXERCISES:

10. Analyze the sentiment for product reviews, this work proposes a Map Reduce technique provided by Apache Hadoop.

11. Trend Analysis based on Access Pattern over Web Logs using Hadoop.

12. Service Rating Prediction by Exploring Social Mobile Users Geographical Locations.

Text Books And Reference Books:

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

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

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

Essential Reading / Recommended Reading

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

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

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

Web Resources:

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA473B - NATURAL LANGUAGE PROCESSING (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: To understand various approaches to syntax and semantics in NLP

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

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

Unit-1
Teaching Hours:18
INTRODUCTION
 

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

LAB EXERCISES:

1. (a) Import NLTK and download the data

   (b) Import and display one of the corpus

   (c) Import and display words from the corpus

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

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

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

Unit-2
Teaching Hours:18
PARSING AND SYNTAX
 

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

LAB EXERCISES:

 

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

 

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

 

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

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

SEMANTIC ANALYSIS AND DISCOURSE PROCESSING

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

LAB EXERCISES:

 

5. Write a program for stemming Non-English words

 

6. Write a program for lemmatizing words using WordNet

 

Unit-4
Teaching Hours:18
NATURAL LANGUAGE GENERATION
 

Architecture of NLG Systems, Applications

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

LAB EXERCISES:

 

7. Write a program to differentiate stemming and lemmatizing words

 

8. Write a program for POS Tagging

 

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

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

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

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

LAB EXERCISES:

 

9. Write a program for Word Embeddings.

 

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

 

Text Books And Reference Books:

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

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

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

Essential Reading / Recommended Reading

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

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

 

Web Resources:

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

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA473C - IOT SYSTEM DESIGN AND DEVELOPMENT (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: To understand the principles of IoT components

CO2: To apply the modeling approaches using sensors and actuators

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

Unit-1
Teaching Hours:18
INTRODUCTION
 

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

LAB EXERCISES:

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

2. Realtime data acquisition and plotting with Arduino

Unit-2
Teaching Hours:18
ARDUINO IDE
 

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

LAB EXERCISES:

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

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

Unit-3
Teaching Hours:18
BUILDING PROJECTS WITH ARDUINO
 

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

LAB EXERCISES:

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

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

Unit-4
Teaching Hours:18
PROGRAMMING THE RASPBERRY PI
 

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

LAB EXERCISES:

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

8. Building a surveillance security system with a Raspberry Pi 

Unit-5
Teaching Hours:18
INTERFACING HARDWARE
 

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

LAB EXERCISES:

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

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

Text Books And Reference Books:

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

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

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

Essential Reading / Recommended Reading

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

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

 

Web Resources:

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

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

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

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

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

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

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA481 - SEMINAR (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand new and latest trends in Information Technology

CO2: Demonstrate the professional presentation abilities

CO3: Apply the acquired knowledge in their research

Unit-1
Teaching Hours:30
Description
 

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

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA

ESE

50%

50%

MCA571 - CLOUD COMPUTING (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

CO3: Assess the comparative advantages and disadvantages of Virtualization technology

CO4: Create a cloud environment using open source software tools

Unit-1
Teaching Hours:18
INTRODUCTION & APPLICATIONS
 

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

LAB EXERCISES:

1. Creating Virtual Machines using Hypervisors (2 Hours)

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

Unit-2
Teaching Hours:18
CLOUD ENABLING TECHNOLOGIES
 

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

LAB EXERCISES:

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

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

Unit-3
Teaching Hours:18
BASIC CLOUD SERVICES
 

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

 

LAB EXERCISES:

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

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

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

Unit-4
Teaching Hours:18
ADVANCED CLOUD SERVICES
 

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

LAB EXERCISES:

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

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

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

Unit-5
Teaching Hours:18
APPLICATION DEVELOPMENT IN CLOUD
 

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

LAB EXERCISES:

11. Installation of Owncloud (2 Hours)

12. Mini project (7 hours)

Text Books And Reference Books:

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

[2] Google Cloud Platform Associated Qwiklabs, 2020.

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

Essential Reading / Recommended Reading

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

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

 

 

Web Resources:

 

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

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA572A - SPATIO-TEMPORAL DATA ANALYTICS (2021 Batch)

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

Course Objectives/Course Description

 

This course aims to provide students with a range of techniques for analyzing and modeling spatio-temporal data. Also equip students with the tools and techniques to analyze spatio-temporal datasets.

Course Outcome

CO1: Acquire fundamental knowledge of mathematical and statistical methods for the analysis of space-time data.

CO2: Develop an understanding of the available spatiotemporal models and tools.

CO3: Apply methodologies to analyze the spatiotemporal data, including the practical hands-on procedures.

Unit-1
Teaching Hours:18
INTRODUCTION TO SPATIO-TEMPORAL STATISTICS
 

Space–Time: The Next Frontier - Goals of Spatio-Temporal Statistics - Descriptive, Dynamic and Hierarchical Statistical Models.

FUNDAMENTALS OF TEMPORAL PROCESS

Fundamentals of Temporal Process - Characterization of Temporal Processes -  Introduction to Deterministic Dynamical Systems - Time Series Preliminaries - Basic Time Series Models - Spectral Representation of Temporal Processes -   Hierarchical Modeling of Time Series.

LAB EXERCISES:

[1] Data understanding with temporal process

Unit-2
Teaching Hours:18
FUNDAMENTALS OF SPATIAL RANDOM PROCESS
 

Geostatistical Processes:The Variogram and the Covariance Function,  Kriging (Optimal Spatial Prediction), Spatial Moving Average (SMA) Models - Lattice Processes: Markov-Type Models in

Space, The Markov Random Field (MRF), The CAR Model, Hierarchical Modeling on a Spatial Lattice -  Spatial Point Processes - Random Sets.

LAB EXERCISES:

[2] Data understanding with spatial random process

Unit-3
Teaching Hours:18
EXPLORATORY METHODS FOR SPATIO-TEMPORAL DATA
 

Visualization of Spatio-Temporal Data - Empirical Covariance/Correlation Functions - Spectral Analysis -  Empirical Orthogonal Function (EOF) Analysis -  Spatio-Temporal Canonical Correlation Analysis (CCA).

 

LAB EXERCISES:

 

[3] Exploring Spatio-temporal Data

    1. Data Wrangling
    2. Exploratory Data Analysis
Unit-4
Teaching Hours:18
SPATIO-TEMPORAL STATISTICAL MODELS
 

Spatio-Temporal Covariance Functions - Spatio-Temporal Kriging - Stochastic Differential and Difference Equations -Time Series of Spatial Processes -  Spatio-Temporal Point Processes -  Spatio-Temporal Components-of-Variation Models.

 

LAB EXERCISES:

 

[4] Spatio-temporal Models

    1. Regression Models for Forecasting
    2. Generalized Linear Spatio-Temporal Regression
    3. Spatio-Temporal Kriging
    4. Implementing an IDE Model in One-Dimensional Space
    5. Spatio-Temporal Inference using the IDE Model
Unit-5
Teaching Hours:18
HIERARCHICAL DYNAMICAL SPATIO-TEMPORAL MODELS
 

Data Models for the DSTM - Process Models for the DSTM: Linear Models - Process Models for the DSTM: Nonlinear Models -  Parameter Models-  DSTM Process - Inference for the DSTM Process: Linear/Gaussian Models -  Inference for the DSTM Parameters: Linear/Gaussian Models.

LAB EXERCISES:

[5] Spatio-Temporal Model Validation.

Text Books And Reference Books:

[1] N. Cressie and C. K. Wikle, Statistics for Spatio-Temporal Data, John Wiley & Sons, 2015.

[2] R. P. Haining and G. Li, Modelling, Spatial and Spatial-Temporal Data: A Bayesian Approach, CRC Press, 2020.

[3] C. K. Wikle, A. Zammit-Mangion, and N. Cressie, Spatio-Temporal Statistics with R, CRC Press, 2019.

Essential Reading / Recommended Reading

[1] R. P. Haining and G. Li, Regression Modelling with Spatial and Spatial-Temporal Data, CRC Press, 2020.

[2] R. Bivand et al., Applied Spatial Data Analysis With R, New York: Springer, 2008.

[3] G. W. Peters and T. Matsui, Modern Methodology and Applications in Spatial-Temporal Modeling, Springer, 2015.

[4] N. Andrienko and G. Andrienko, Exploratory Analysis of Spatial and Temporal Data, Springer Science & Business Media, 2006.

[5] J. F. Roddick and K. Hornsby, Temporal, Spatial, and Spatio-Temporal Data Mining, Springer, 2003.

 

Web Resources:

[1] https://gdsl-ul.github.io/san/index.html

[2] Stats 253: Analysis of Spatial and Temporal Data (stanford.edu)

[3] CRAN Task View: Handling and Analyzing Spatio-Temporal Data (r-project.org)

Evaluation Pattern

CIA

ESE

50%

50%

 

MCA572B - NEURAL NETWORKS AND DEEP LEARNING (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

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

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

LAB EXERCISES:

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

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

Unit-2
Teaching Hours:18
BASIC MODELS OF ANN
 

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

LAB EXERCISES:

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

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

Unit-3
Teaching Hours:18
CONVOLUTIONAL NEURAL NETWORK
 

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

LAB EXERCISES:

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

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

Unit-4
Teaching Hours:18
RECURRENT NEURAL NETWORK
 

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

LAB EXERCISES:

7. Implementation of Convolution Neural Network using different architectures.

8. Implementation of RNN

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

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

LAB EXERCISES:

9. Implementation of autoencoder

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

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

Web Resources:

[1] www.coursera.org

[2] http://neuralnetworksanddeeplearning.com

Evaluation Pattern

CIA

ESE

50%

50%

MCA572C - SYSTEM SIMULATION FOR IOT AND SENSOR NETWORKS (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

CO2: Design the simulation model for different application domains.

CO3: Compare and analyze different application protocols.

Unit-1
Teaching Hours:18
INTRODUCTION TO SIMULATION
 

When Simulation is the Appropriate tool, when simulation is not appropriate, Advantages and disadvantages of Simulation, Areas of application. Systems and System Environment, Components of System, Discrete and continuous systems, Model of a System, Types of Models, Discrete-Event System Simulation, Steps in a Simulation study.

LAB EXERCISES:

1. Installation and configuration of Cooja

2. Hello world simulation in Cooja

Unit-2
Teaching Hours:18
IOT LANDSCAPE AND ARCHITECTURES
 

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

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

LAB EXERCISES:

3. Creating Motes for Simulation

4. Configuring the Motes

Unit-3
Teaching Hours:18
SIMULATION MODEL AND ANALYSIS
 

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

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

LAB EXERCISES:

5. Discrete Event driven Simulation

6. Script editor and Sensor Collect

Unit-4
Teaching Hours:18
STATISTICAL MODELING
 

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

LAB EXERCISES:

7. IPv6 Routing

8. Implementing Quantitative & Qualitative parameters

Unit-5
Teaching Hours:18
SIMULATION OF NETWORKED SYSTEMS
 

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

LAB EXERCISES:

9. Implementing Proactive protocols & Reactive protocols

10. Generating Graphs

Text Books And Reference Books:

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

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

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

Essential Reading / Recommended Reading

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

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

 

Web Resources:

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA573A - QUANTUM MACHINE LEARNING (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

CO1: Understand the basics of quantum computing

CO2: Apply quantum computing for the implementation of machine learning algorithms

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

Unit-1
Teaching Hours:18
INTRODUCTION
 

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

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

LAB EXERCISES:

[1] Implementation of quantum dot products

[2] Implementation of quantum PCA

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

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

LAB EXERCISES:

[3] Implementation of Quantum K Mean

[4] Implementation of Quantum K medians

Unit-3
Teaching Hours:18
QUANTUM PATTERN RECOGNITION
 

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

LAB EXERCISES:

[5] Implementation of Quantum hierarchical clusterin

[6] Implementation of Quantum Perceptron

Unit-4
Teaching Hours:18
QUANTUM CLASSIFICATION
 

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

LAB EXERCISES:

[7] Implementation of Quantum Neural Networks

[8] Implementation of Quantum Nearest Neighbor

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

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

LAB EXERCISES:

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

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

 

Web Resources:

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

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA573B - COMPUTER VISION (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

CO2: Describe known principles of human visual system

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

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

Unit-1
Teaching Hours:18
INTRODUCTION
 

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

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

LAB EXERCISES:

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

[2]  Illustrate Spacial Transformations - Convolution and correlation.

Unit-2
Teaching Hours:18
FEATURE DETECTION AND MATCHING
 

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

LAB EXERCISES:

[3]  Illustrate Histogram equalization.

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

Unit-3
Teaching Hours:18
SEGMENTATION
 

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

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

LAB EXERCISES:

[5] Demonstrate various image preprocessing methods.

[6] Illustrate Wavelet transform.

Unit-4
Teaching Hours:18
STRUCTURE FROM MOTION
 

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

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

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

LAB EXERCISES:

[7] Demonstrate various edge detection methods on images.

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

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

Unit-5
Teaching Hours:18
IMAGE STITCHING
 

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

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

LAB EXERCISES:

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

[11] Illustrate triangulation / two-frame motion.

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

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

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

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

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

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

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

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

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

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

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

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

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

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

 

Web Resources:

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

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

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

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

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

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA573C - IOT DATA ANALYTICS (2021 Batch)

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

Course Objectives/Course Description

 

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

Course Outcome

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

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

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

Unit-1
Teaching Hours:18
INTRODUCING IOT ANALYTICS
 

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

LAB EXERCISES:

1. Domain selection

2. Bluetooth Scan and Wi-Fi Analysis

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

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

LAB EXERCISES:

3. Working with LDR sensors

4. Measuring temperature and humidity using DHT11 sensor

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

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

LAB EXERCISES:

5. Preparing IoT Cloud setup-AWS EC2

6. Logging sensor data to Cloud

Unit-4
Teaching Hours:18
EXPLORING IOT DATA
 

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

LAB EXERCISES:

7. IoT data exploration

8. Sensor data visualization

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

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

LAB EXERCISES:

9. Basic Data Analysis using ML

10. Anomaly detection, Z Score analysis

Text Books And Reference Books:

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

Essential Reading / Recommended Reading

Recommended Reading:

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

 

Web Resources:

[1] www.ThingSpeak.com

Evaluation Pattern

CIA

ESE

50%

50%

MCA581 - SPECIALIZATION PROJECT (2021 Batch)

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

Course Objectives/Course Description

 

-

Course Outcome

CO1: Ability to identify and develop socially and environmentally relevant modules in the selected problem

CO2: Ability to apply appropriate design/development methodology and Tools

CO3: Competence to work as a team and effective division of work (work diary)

CO4: Ability to complete the solution as a product

CO5: Professional computing practices and regulations

Unit-1
Teaching Hours:60
MINI PROJECT
 

Project based on previous semester’s electives

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA

ESE

50%

50%

MCA681 - INDUSTRY PROJECT (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:3
Max Marks:300
Credits:12

Course Objectives/Course Description

 

-

Course Outcome

CO1: Understanding the emerging trends of new technologies in the software industry

CO2: Analysis of the project problem in line with the industry standards.

CO3: Developing a software according to the needs and demands of the clients.

Unit-1
Teaching Hours:30
INDUSTRY PROJECT
 

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

Text Books And Reference Books:

-

Essential Reading / Recommended Reading

-

Evaluation Pattern

CIA

ESE

50%

50%