Department of COMPUTER SCIENCE

Syllabus for
Master of Computer Applications
Academic Year  (2021)

 
1 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA131 DIGITAL LOGIC FUNDAMENTALS - 4 3 100
MCA132 PROBABILITY AND STATISTICS - 4 3 100
MCA133 OPERATING SYSTEMS - 5 4 100
MCA161A INTRODUCTION TO PROGRAMMING AND PROBLEM SOLVING - 3 2 50
MCA161B LINUX ADMINISTRATION - 3 2 50
MCA171 PYTHON PROGRAMMING - 9 5 150
MCA172 PROGRAMMING IN C - 7 5 150
2 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA231 SOFTWARE ENGINEERING - 5 4 100
MCA232 RESEARCH METHODOLOGY - 3 2 50
MCA271 MICROPROCESSOR AND INTERFACING TECHNIQUES - 9 5 150
MCA272 WEB STACK DEVELOPMENT - 9 5 150
MCA273 DATABASE TECHNOLOGIES - 7 5 150
3 Semester - 2020 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA331 MACHINE LEARNING - 4 4 100
MCA341A INTRODUCTION TO DATA ANALYTICS - 5 4 100
MCA341B INTRODUCTION TO ARTIFICIAL INTELLIGENCE - 5 4 100
MCA341C INTRODUCTION TO INTERNET OF THINGS - 5 4 100
MCA371 CLOUD COMPUTING - 5 4 100
MCA372 MOBILE APPLICATIONS - 7 5 150
MCA373A LINUX ADMINISTRATION - 6 5 150
MCA373B DATA ANALYTICS - 6 5 150
MCA373C NEURAL NETWORKS AND DEEP LEARNING - 6 5 150
MCA373D INFORMATION RETRIEVAL AND WEB MINING - 6 5 150
MCA373E .NET TECHNOLOGIES - 6 5 150
MCA374A DATABASE ADMINISTRATION - 6 5 150
MCA374B BUSINESS INTELLIGENCE - 6 5 150
MCA374C COMPUTER VISION - 6 5 150
MCA374D NATURAL LANGUAGE PROCESSING - 6 5 150
MCA374E NOSQL - 6 5 150
MCA381 SPECIALIZATION PROJECT - 4 2 100
4 Semester - 2020 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA481 INDUSTRY PROJECT - 2 12 300
5 Semester - 2019 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA531 SOFTWARE ARCHITECTURE - 4 4 100
MCA541A BLOCKCHAIN ARCHITECTURE AND APPLICATIONS - 4 4 100
MCA541B AGENT BASED COMPUTING - 4 4 100
MCA541C EVOLUTIONARY ALGORITHMS - 4 4 100
MCA541D COMPILER DESIGN - 4 4 100
MCA541E NATURAL LANGUAGE PROCESSING - 4 4 100
MCA541F COMPUTER VISION - 4 4 100
MCA541G OPTIMIZATION TECHNIQUES - 4 4 100
MCA571 CLOUD COMPUTING - 6 5 150
MCA572 .NET TECHNOLOGIES - 6 5 150
MCA573A INFORMATION RETRIEVAL AND WEB MINING - 6 5 150
MCA573B DATABASE ADMINISTRATION - 6 5 150
MCA573C NEURAL NETWORKS AND DEEP LEARNING - 6 5 150
MCA573D ARTIFICIAL INTELLIGENCE - 6 5 150
MCA573E BUSINESS INTELLIGENCE - 6 5 150
MCA573F BIOINFORMATICS - 6 5 150
MCA573G DATA VISUALIZATION - 6 5 150
MCA581 SPECIALIZATION PROJECT - 4 2 100
MCA582 RESEARCH - MODELING / IMPLEMENTATION - 4 2 50
6 Semester - 2019 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MCA681 INDUSTRY PROJECT - 2 10 300
MCA682 RESEARCH PUBLICATION - 4 2 50
      

    

Department Overview:

Department of Computer Science of CHRIST (Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation’s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.

Mission Statement:

Vision

The Department of Computer Science endeavors to imbibe the vision of the University “Excellence and Service”. The department is committed to this philosophy which pervades every aspect and functioning of the department.

Mission

“To develop IT professionals with ethical and human values”. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their car

Introduction to Program:

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

Program Objective:

Programme Objective

  • To strengthen the concept of Computer Science and applications for career growth and employability.
  • To provide multidisciplinary and application oriented programme.
  • To inculcate in students professional and ethical attitude, team work and effective communication skills.
  • Students are encouraged to implement independent projects of their own choice and to use latest tools.

Programme Outcomes

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

Assesment Pattern

CIA: 50%

ESE: 50%

Examination And Assesments

Continuous Internal Assessment: 50% Weightage

End Semester Examination: 50% Weightage

MCA131 - DIGITAL LOGIC FUNDAMENTALS (2021 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.

Learning 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 (2021 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.

Learning Outcome

CO1: Summarize and present the data using exploratory data analysis

CO2: Establish the relationship between the frequency distributions(data) and distribution functions (Model) and important characteristics

CO3: The sampling distributions and their applications in hypothesis testing

CO4: Identify the relationship between the variables and modeling the same

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 (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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

Learning Outcome

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

CO2: Evaluate the process scheduling, Thread scheduling, scheduling criteria, 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:12
FUNDAMENTALS
 

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

Unit-2
Teaching Hours:12
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, Thread scheduling, Multiple-processor scheduling.

Unit-3
Teaching Hours:12
PROCESS COORDINATION
 

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

Unit-4
Teaching Hours:12
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, memory mapped files, Allocating kernel memory.

Memory management concepts can be demonstrated using Linux.

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, Kernel I/O subsystem, Transforming I/O requests to hardware operations.

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

Learning 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 (2021 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.

Learning 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 (2021 Batch)

Total Teaching Hours for Semester:105
No of Lecture Hours/Week:9
Max Marks:150
Credits:5

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.

Learning 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:21
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 comprehensions. 

3.     Demonstrate Dictionary comprehension. 

Unit-2
Teaching Hours:21
OBJECT ORIENTED PROGRAMMING USING PYTHON AND REGULAR EXPRESSIONS
 

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

Lab Exercises:

4.     Demonstrate use of object- oriented programming concepts 

5.     Demonstrate exceptional handling.

6.     Demonstrate use of lambda functions.

Unit-3
Teaching Hours:21
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:

7.     Demonstrate use of custom modules.  

8.     Implement “re” module.

9.     Demonstrate use of “Numpy”.

Unit-4
Teaching Hours:21
MATPLOTLIB and GUI PROGRAMMING
 

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

Lab Exercises:

10.     Implement Pandas to demonstrate data handling and indexing.

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

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

Unit-5
Teaching Hours:21
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:

13.     Create a web application using Django framework.

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

Text Books And Reference Books:

[1] Zhang.Y, An Introduction to Python and Computer Programming, Springer Publications, 2017.

[2] 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 (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:7
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning 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 (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

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.

Learning 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:12
PROCESS MODELS, UNDERSTANDING REQUIREMENTS
 

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

Unit-2
Teaching Hours:12
DESIGN CONCEPTS
 

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

Unit-3
Teaching Hours:12
COMPONENT LEVEL DESIGN, USER INTERFACE DESIGN
 

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

Unit-4
Teaching Hours:12
QUALITY MANAGEMENT, TESTING
 

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

Unit-5
Teaching Hours:12
PROCESS AND PROJECT METRICS
 

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

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 (2021 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.

Learning 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 (2021 Batch)

Total Teaching Hours for Semester:105
No of Lecture Hours/Week:9
Max Marks:150
Credits:5

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. The course further imparts knowledge in differ interfacing techniques using 8255A.

Learning Outcome

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

CO2: Examine programming techniques in developing the assembly language program 

CO3: Critique and write effective ALP with counter, delay and interrupts.

CO4:  Implement various peripherals interfacing techniques for microprocessor-based applications.

Unit-1
Teaching Hours:21
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 N one byte numbers.
  2. Write a program to add two 8-bit BCD numbers.
  3. Write a program to multiply two 8 - bit numbers.
  4. Write a program to check whether a byte belongs to the 2-out-of-5 codes. Display FF if it is a 2-out-of- 5 code otherwise 00. (Number is 2-out-of-5 code if the left most three bits are zero and in the remaining five bits there are exactly two 1’s).
Unit-2
Teaching Hours:21
8085 PROGRAMMING
 

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

Lab Exercises:

  1. Write a program to add two 32 - bit binary numbers. 
  2. Write a program to perform linear search over a set of N numbers. Display FF if found otherwise display 00.
  3. Write a program to find the first 10 terms of a Fibonacci sequence
  4. Write a program to interchange N one bytes of data.
Unit-3
Teaching Hours:21
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:

  1. Write a program to subtract a 16 - bit BCD number from another 16 – bit BCD number.
  2. Write a program to divide a 16 - bit number by an 8 - bit numbers.
  3. Write a program to sort the numbers in ascending and in descending using bubble sort.
  4. Write a program to prepare a look-up table for the squares of one -digit BCD numbers.
Unit-4
Teaching Hours:21
ARCHITECTURE AND INTERRUPTS OF 8085
 

Architecture of 8085 MPU

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

Interrupts            

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

Lab Exercises:

  1. Write a program to simulate a BCD counter to count from 0 to 100.
  2. Write a program to check whether a one-byte number is a palindrome or not.
  3. Write a program to simulate a stopwatch with a provision to stop the watch.
  4. Write a program to determine the HCF of two one-byte numbers.
Unit-5
Teaching Hours:21
PROGRAMMABLE PERIPHERAL INTERFACE 8255A
 

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

Lab Exercises:

  1. Write a program to display a rolling message.
  2. Write a program to interface a keyboard using 8255A interface.
  3. Write a program to interface Seven Segment Display using 8255A interface.         
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 (2021 Batch)

Total Teaching Hours for Semester:105
No of Lecture Hours/Week:9
Max Marks:150
Credits:5

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.

Learning 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:20
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. Develop static pages for a given scenario using HTML

2. Creating Web Animation with audio using HTML5 & CSS3

3. Demonstrate Geolocation and Canvas using HTML5

Unit-2
Teaching Hours:21
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

6. Demonstrate DOM parser

Unit-3
Teaching Hours:22
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 Cookies-Introduction to JSON-JSON vs XML-JSON Objects-Importance of Angular JS in web-Angular Expression and Directives-Single Page Application

Lab Exercises:

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

8. Create a web application using AngularJS with Forms

9. Implement a single page web application using Angular JS

Unit-4
Teaching Hours:22
SERVER SIDE SCRIPTING
 

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

Self-Learning:

Express JS

Lab Exercises:

10. CRUD Operation using AngularJS

11. Implement web application using AJAX with JSON

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

Unit-5
Teaching Hours:20
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:

13. Demonstrate Node.js file system module

14. Demonstrate Node.js events

15. Implement Mysql with Node.JS

16. Implement CRUD Operation using MongoDB

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 (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:7
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning 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:18
INTRODUCTION TO 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 Entity Relationship Diagram

2.     Basic data retrieval queries

Unit-2
Teaching Hours:18
THE 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.     Data retrieval using referential integrity and JOIN

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

Unit-3
Teaching Hours:18
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.     Aggregate functions

6.     Database Design Using Normalization

Unit-4
Teaching Hours:18
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.     Stored Procedure – 1 (PL/SQL-1)

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

Unit-5
Teaching Hours:18
DISTRIBUTED DATABASES AND NOSQL SYSTEMS
 

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

NOSQL Databases

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

Lab Exercises:

9.     NOSQL Exercise - 1

10.   NOSQL Exercise - 2

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 - MACHINE LEARNING (2020 Batch)

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

Learning 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:12
INTRODUCTION
 

Machine Learning-Examples of Machine Applications-Learning Associations-Classification- Regression-Unsupervised Learning-Reinforcement Learning. Supervised Learning: Learning class from examples- Probably Approach Correct(PAC) Learning-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.

Unit-2
Teaching Hours:12
DIMENSIONALITY REDUCTION
 

Introduction- Subset Selection-Principal Component Analysis, Feature Embedding-Factor Analysis-Singular Value Decomposition-Multidimensional Scaling- Linear Discriminant Analysis- Bayesian Decision Theory

Unit-3
Teaching Hours:12
SUPERVISED LEARNING - I
 

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

Kernel Machines

Introduction- optical separating hyperplane- v-SVM, kernel tricks- vertical kernel- vertical kernel- defining kernel- multiclass kernel machines- one-class kernel machines.

Unit-4
Teaching Hours:12
SUPERVISED LEARNING - II
 

Multilayer Pereptron

Introduction, training a perceptron- learning Boolean functions- multilayer perceptron- backpropogation algorithm- training procedures.

Combining multiple Learners

Rationale-Generating diverse learners- Model combination schemes- voting, Bagging- Boosting- fine tuning an Ensemble.

Unit-5
Teaching Hours:12
REINFORCEMENT LEARNING
 

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

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.

Evaluation Pattern

 

CIA

ESE

50%

50%

 

MCA341A - INTRODUCTION TO DATA ANALYTICS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

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.

Learning 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:12
DATA, RELATIONS AND PREPROCESSING
 

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

Additional Reading: Probability Distributions & Inferential Statistics

Unit-2
Teaching Hours:12
CORRELATION AND REGRESSION
 

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

Additional Reading: Least Square Problems and Optimization

Unit-3
Teaching Hours:12
FORECASTING AND CLASSIFICATION
 

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

Additional Reading: Stochastic and Kernel Methods

Unit-4
Teaching Hours:12
CLUSTERING
 

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

Additional Reading: Mining Frequent Patterns

Unit-5
Teaching Hours:12
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 (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

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.

Learning 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:12
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:12
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:12
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:12
GAME PLAYING AND PLANNING
 

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

Unit-5
Teaching Hours:12
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 (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

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.

Learning 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:12
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- Comparing IoT Architectures: The M2M IoT Standardized Architecture- The IoT World Forum (IoTWF) Standardized Architecture- A Simplified IoT Architecture - The Core IoT Functional Stack- IoT Data Management and Compute Stack

Unit-2
Teaching Hours:12
SMART OBJECTS
 

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

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

Unit-3
Teaching Hours:12
CONNECTING SMART OBJECTS
 

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

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

Unit-4
Teaching Hours:12
IP AS THE IOT NETWORK LAYER
 

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

Unit-5
Teaching Hours:12
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 - IoT Application Layer Protocols - CoAP - Message Queuing Telemetry Transport (MQTT)

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 - CLOUD COMPUTING (2020 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
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.

Learning Outcome

CO1: Interpret the deployment and service models of cloud applications.

CO2: Analyse and implement the core technical features of cloud.

CO3: Assess suitable security policies for cloud systems.

 

CO4: Design the appropriate cloud solutions based on the application requirements.

Unit-1
Teaching Hours:16
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 - Healthcare - Transportation Systems - Manufacturing Industry – Government - Education - Mobile Communications.

Lab Exercises:

1.    Creating Virtual Machines using Hypervisors

2.    Compute service: Creating and running Virtual Machines using AWS/GCP/Azure

Unit-2
Teaching Hours:14
CLOUD ENABLING TECHNOLOGIES
 

Virtualization - Load Balancing - Scalability & Elasticity – Deployment –Replication – Monitoring - Software Defined Networking - Network Function Virtualization – MapReduce - Identity and Access Management - Service Level Agreements – Billing.

Lab Exercises:

3. Compute service: Working with OpenStack

4. Storage as a Service: Creating storage

Unit-3
Teaching Hours:16
BASIC CLOUD SERVICES & PLATFORMS
 

Compute Services - Amazon Elastic Compute Cloud - Google Compute Engine    - Windows Azure Virtual Machines - Storage Services - Amazon Simple Storage Service - Google Cloud Storage - Database Services - Amazon Relational Data Store - Amazon DynamoDB - Google Cloud SQL - Google Cloud Datastore.

Lab Exercises:

5. Storage as a Service: Working with Owncloud

6. Database as a Service: Building DB Server

Unit-4
Teaching Hours:15
ADVANCED CLOUD SERVICES & PLATFORMS
 

Application Services -  Content Delivery Services - Amazon CloudFront -  Content Delivery Network - Analytics Services - Google MapReduce Service - Google BigQuery - Amazon Elastic Beanstalk - Amazon CloudFormation.

Lab Exercises:

7. Security as a Service: Working with IAM

8. Cloud features implementation: Autoscaling / Load Balancing 

Unit-5
Teaching Hours:14
CLOUD SECURITY
 

Introduction - CSA Cloud Security Architecture – Authentication - Single Sign-on (SSO) - Authorization - Identity & Access Management - Data Security - Securing Data at Rest - Securing Data in Motion - Key Management.

Lab Exercises

9. Platform as a Service: Working with Google AppEngin

10. Software as a Service: Application development using Salesforce

Text Books And Reference Books:

[1] Kailash Jayaswal, Jagannath Kallakurchi, Donald J. Houde, Dr. Deven Shah, Cloud Computing Black Book, Dreamtech Publishers, 2014.

[2] Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, CreateSpace Independent Publishing Platform, 2014.

Essential Reading / Recommended Reading

[1] Barrie Sosinky, Cloud Computing: Bible, 1st edition, Wiley Publishing, Inc., 2011

[2] Anthony TVelte, Toby JVelteand Robert Elsenpeter, Cloud Computing –A Practical Approach, Tata McGraw Hill Education Pvt Ltd, 2010.

Web Resources:

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

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

[3] https://cloud.google.com › docs

[4] https://docs.microsoft.com › en-us › azure 

Evaluation Pattern

CIA

ESE

50%

50%

MCA372 - MOBILE APPLICATIONS (2020 Batch)

Total Teaching Hours for Semester:105
No of Lecture Hours/Week:7
Max Marks:150
Credits:5

Course Objectives/Course Description

 

Course is concerned with development of Mobile Applications, Course is designed to quickly get speed with writing apps for Android devices. It exposes students to a wide range of concepts and technical skills.

Learning 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:22
INTRODUCTION
 

Brief History of embedded device programming-History of Mobile apps-App development trends of past years-Introduction to Android-get to know the required tools-Web APP-Mobile app-API-IDE concept-Why Android, 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 Controlss.

Unit-2
Teaching Hours:22
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, usernavigation.

Lab Exercises:

 4. Activity and Intents- Implicit and Explicit and camera

 5. Input controls

 6. Menu and pickers

Unit-3
Teaching Hours:20
WORKING WITH BACKGROUND
 

Background Tasks-AsyncTask and AsyncTaskloader, Internet connection-Broadcast receiver- Services-Alarms and Schedulers -Notifications-Alarms- Delightful user experience- Drawables, styles and themes-Material Design- resources for adaptive layouts- UI Testing- Recycler View.

Lab Exercises:

7. User navigation – Recyclerview

8. MediaController

9. Fragments

10. AsyncTask and AsyncTaskloader

Unit-4
Teaching Hours:19
SAVING USER DATA
 

Efficient data transfer and Job Scheduler-Data Storage-shared preferences-App settings- SQLite primer-Room-LiveData and ViewModel-Introduction to Firebase-Firebase data handling-CRUD.

Lab Exercises:

11. Notifications

12. BroadcastReceiver

13. Sharedpreference

Unit-5
Teaching Hours:22
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 ofAPP.

Self study: Kotlin andFlutter

Lab Exercises:

14. SQLite /Firebase

15. APKDeployment

Text Books And Reference Books:

[1] Android programming for beginners, John Horton, Packt-BirminghamMumbai-2nd edition,2018 ISBN – 978-1-78953-850-2

[2] Android Programming: The Big Nerd ranch Guide (4th edition), Bill Philips, Chris Stewart, Kristin Masrsicano, 2019,ISBN :-100136590071

Essential Reading / Recommended Reading

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

[2] Practical Android: 14 Complete Projects on Advanced Techniques and Approaches, Mark Wickham, APRESS, ISBN-13978-1-4842-3332

Evaluation Pattern

CIA

ESE

50%

50%

MCA373A - LINUX ADMINISTRATION (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Acquired concepts of LINUX Operating System, its kernel and different subsystems of kernel

CO2: Demonstrate the systematic approach for configure the Linux environment

CO3: Create customized partitions using LVM

CO4: Managing and Configuring the various Servers

Unit-1
Teaching Hours:18
Installation and System Administration Overview
 

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

Lab Exercises:

1. Breaking the ROOT password and NMCLI configuration

2. Special File Permissions and Access Control Lists

Unit-2
Teaching Hours:18
Linux File System and Security
 

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

Lab Exercises:

3. Process related commands

4. Scheduling process using at and crontab

Unit-3
Teaching Hours:18
Managing Users, Groups & controlling process
 

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

Lab Exercises:

5. LVM Partitions and Extending LVM

6. Swap Partitions

Unit-4
Teaching Hours:18
Linux Kernel and Linux Volume Manager
 

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

Lab Exercises:

7. LVM Snapshot

8. LDAP Server Configuration

Unit-5
Teaching Hours:18
Linux Networking
 

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

Lab Exercises:

9. SAMBA Server Configuration

10. FTP and Mail Configuration

Text Books And Reference Books:

[1]    Mastering Linux Administration, Paul Cobbaut, First Edition, Samurai Media Limited,2016.

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

Essential Reading / Recommended Reading

[1]     Collings Terry and Wall Kurt, Red Hat Linux Networking & System Administration, Wiley Indian, 3rd Edition, reprint 2009.

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA373B - DATA ANALYTICS (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Identify various type of data used for analysis

CO2: Apply various supervised and unsupervised algorithms to real world problems

CO3: Interpret the results of developed models using different visualization techniques

Unit-1
Teaching Hours:18
Data, Relations and preprocessing
 

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

Lab Exercises:

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

2. Implement pre-processing techniques on raw data

Unit-2
Teaching Hours:18
Correlation and Regression
 

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

Lab Exercises:

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

4. Implement neural networks to make predictions

Unit-3
Teaching Hours:18
Association Rule Mining and Classification
 

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

Lab Exercises:

5. Implement and compare Apriori algorithm and FP growth algorithm

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

Unit-4
Teaching Hours:18
Clustering and Time Series Analysis
 

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

Lab Exercises:

7. Implement various clustering techniques and validate them

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

Unit-5
Teaching Hours:18
Visualization and Applications
 

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

Lab Exercises:

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

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

Text Books And Reference Books:
  1. Runkler, Thomas. A, "Data Analytics: Models and Algorithms for Intelligent Data Analysis", Springer, 2012.
  2. Michael Berthhold, David J. Hand, "Intelligent Data Analysis - An Introduction", 2nd Edition, Springer Publications, 2002.
Essential Reading / Recommended Reading
  1. Jiawei Han, Micheline Kamber, Jian Pie, "Data Mining Concept and Techniques", Morgan and Kaufmann Publisher, Third Edition, 2012

Web Resources:

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

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

Evaluation Pattern

CIA

ESE

50%

50%

MCA373C - NEURAL NETWORKS AND DEEP LEARNING (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

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

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

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

Unit-1
Teaching Hours: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. Basic models of ANN-Learning Methods-Activation Functions-Importance Terminologies of ANN.

Lab Exercises:

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

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

Unit-2
Teaching Hours:18
SUPERVISED LEARNING NETWORK
 

Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning Rule-Architecture-Flowchart for training Process-Perceptron Training Algorithm for Single and Multiple Output Classes.

Back Propagation Network- Theory-Architecture-Flowchart for training process-Training Algorithm-Learning Factors for Back-Propagation Network.

Radial Basis Function Network RBFN: Theory, Architecture, Flowchart and Algorithm.

Lab Exercises:

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

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

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:

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

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

Unit-4
Teaching Hours:18
RECURRENT NEURAL NETWORK
 

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

Lab Exercises:

5. Implementation of Convolution Neural Network

6. Implementation of RNN

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

Introduction - Features of Auto encoder Types of Autoencoder.

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

Lab Exercises: 

7. Implementation of autoencoder

8. Implementation of Restricted Boltzmann Machine

 

Text Books And Reference Books:

[1] S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition,2018.

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

 

Essential Reading / Recommended Reading

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

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

[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%

MCA373D - INFORMATION RETRIEVAL AND WEB MINING (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Understand the fundamental concepts of information retrieval systems.

CO2: Apply the theory behind the major components of information retrieval, including crawling, parsing, scoring, indexing, and compression.

CO3: Demonstrate latest techniques of web mining.

CO4: Design and implement parts of an information retrieval system.

Unit-1
Teaching Hours:18
INTRODUCTION TO INFORMATION RETRIEVAL SYSTEMS
 

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

Unit-1
Teaching Hours:18
CATALOGING AND INDEXING
 

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

Lab Exercises:

1. Implement the concept of Search Engine technique

2. Develop the Crawler based on domains

Unit-2
Teaching Hours:18
SEARCHING TECHNIQUES
 

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

Lab Exercises:

3. Extract textual information and Multimedia contents from documents

4. Develop search engine indexing

Unit-3
Teaching Hours:18
WEB CRAWLING
 

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

Lab Exercises:

5. Increase the efficiency of sentiment analysis and opinion mining

6. Develop the effective query refinement mechanism

Unit-4
Teaching Hours:18
WEB STRUCTURE MINING
 

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

Lab Exercises:

7. Implement page ranking algorithm

8. Personalize the search engine

Unit-5
Teaching Hours:18
WEB USAGE MINING
 

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

Lab Exercises: 

9. Personalized web search

10. Case studies and assignments

Text Books And Reference Books:

[1] Kowalski, Gerald, Mark T Maybury: Information Storage and Retrieval Systems: Theory and Implementation, Second Edition, Kluwer Academic Press, 2000.

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

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

Essential Reading / Recommended Reading

[1] G.G. Chowdhury, Introduction to Modern Information Retrieval, Second Edition, NealSchuman Publishers, 2010.

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

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

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

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

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

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

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA373E - .NET TECHNOLOGIES (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Update the students with the latest technologies thereby make them fit for the industry

CO2: Make the students aware of a new development platform

CO3: Efficient application development and deployment

Unit-1
Teaching Hours:18
Introduction to .NET
 

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

Lab Exercises:

1. To design a window based application with common controls

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

Unit-2
Teaching Hours:18
Windows Applications
 

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

Lab Exercises:

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

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

Unit-3
Teaching Hours:18
Windows Presentation Foundation
 

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

Lab Exercises:

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

6. To implement SqlDataReader in ADO.NET

Unit-4
Teaching Hours:18
ASP.NET
 

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

Lab Exercises: 

7. To implement WPF application

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

Unit-5
Teaching Hours:18
Data Access
 

ADO.NET overview. Various data access objects – Connection, Command and DataSet Objects. Binding data to ASP .NET server controls. Accessing data from a database using ADO.NET. Reading from and Writing to an XML document, Using XML DOM objects for data access from XML Documents. Binding data from an XML document to Web form controls. Converting data from Database to XML Data. Xml & Web Services.

Lab Exercises:

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

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

Text Books And Reference Books:

[1]   Jeff Ferguson, Brian Patterson, Jason Beres ,C# Programming Bible , Wiley Publishing Inc., Reprint 2012.

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

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

Essential Reading / Recommended Reading

[1] Publication ASP .NET complete reference, TMH, 2010.

 

Web Resources:

[1] www.w3cschools.com

Evaluation Pattern

CIA

ESE

50%

50%

MCA374A - DATABASE ADMINISTRATION (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Applying SQL to solve real world queries

CO2: Understanding the role of various database users including data base administrator

CO3: Designing various Data models, schemas, architectures and instances

Unit-1
Teaching Hours:18
Creating the Database Environment
 

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

Lab Exercises:

1. Design a program for implementing batch processing mechanisms.

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

Unit-2
Teaching Hours:18
Database Process
 

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

Lab Exercises:

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

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

Unit-3
Teaching Hours:19
Data and Storage Management
 

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

Lab Exercises:

5. Demonstrate a program query processing in distributed databases.

6. Write a program to implement file processing system.

Unit-4
Teaching Hours:17
Backup and Maintenance
 

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

Lab Exercises:

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

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

Unit-5
Teaching Hours:18
User Management and Database Security
 

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

Lab Exercises:

9. Design a program for recovering data using RMAN.

10. Design a program for shared and exclusive lock.

 

Text Books And Reference Books:

[1] Craig S. Mullins, Database Administration, The complete guide to DBA practices and procedures, Addison-Wesley, 2nd Edition,2013.

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

Essential Reading / Recommended Reading

[1] Adam Jorgensen, Jorge Segarra, Patrick Leblanc, Jose Chinchilla and Aaron Nelson, Microsoft sql server bible, Wiley India Pvt.Ltd,2012.

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

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

 Web Resources:

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

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

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

Evaluation Pattern

           CIA          

ESE

50%

50%

MCA374B - BUSINESS INTELLIGENCE (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Understand the fundamentals of business intelligence and link data mining with business intelligence.

CO2: Apply various modeling techniques and business intelligence methods to various situations using data mining principles and techniques

CO3: Implement data analysis techniques to make better business decisions and demonstrate the impact of business reporting, information visualization, and dashboards

Unit-1
Teaching Hours:18
DECISION SUPPORT SYSTEMS AND BUSINESS INTELLIGENCE
 

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

Lab Exercises:

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

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

Unit-2
Teaching Hours:18
BASICS OF DATA INTEGRATION ETL
 

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

Lab Exercises:

3. Configuring and testing the ETL tools.

4. Implement pipeline, sampling.

Unit-3
Teaching Hours:18
INTRODUCTION TO MULTI-DIMENSIONAL DATA MODELING
 

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

Lab Exercises:

5. Implement surrogate keys, change in dimensions.

6. Practice data source views, dimensions, hierarchies.

Unit-4
Teaching Hours:18
KNOWLEDGE MANAGEMENT AND KNOWLEDGE DELIVERY
 

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

Lab Exercises:

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

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

Unit-5
Teaching Hours:18
DATA MINING FUNCTIONALITIES
 

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

Lab Exercises:

9. SQL reporting services.

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

 

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

Text Books And Reference Books:

[1] Efraim Turban, Ramesh Sharda, Dursun Delen, Decision Support and Business Intelligence Systems, 9th Edition, Pearson, 2013.

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

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

Essential Reading / Recommended Reading

[1] Carlo Vercellis, Business Intelligence: Data Mining and Optimization for Decision Making, Wiley Publications, 2009.

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

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

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

 

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

 

MCA374C - COMPUTER VISION (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

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.

Learning 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
 

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

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

Lab Exercises:

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

2. Illustrate Spacial Transformations - Convolution and correlation.

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 – Houghtransform, Statistical based features extraction.

Lab Exercises:

3. Illustrate Histogram equalization.

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

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.", Jun-2012.

 

Web Resources:

[1] Computer Vision Wikipedia -https://en.wikipedia.org/wiki/Computer_vision

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

[3] 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

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

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

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

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

[8] Various MOOC courses – SWAYAM – UDEMY – COURSERAetc.

Evaluation Pattern

           CIA          

ESE

50%

50%

MCA374D - NATURAL LANGUAGE PROCESSING (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

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.

Learning Outcome

CO1: To understand various approaches on syntax and semantics in NLP. 

CO2:Toapplyvariousmethodstodiscourse,generation,dialogueandsummarization 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. Write a program to tokenize text.

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

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 program to tokenize Non-English Languages

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

6. Write a program for stemming Non-English words

Unit-4
Teaching Hours:18
NATURAL LANGUAGE GENERATION AND MACHINE TRANSLATION
 

Natural Language Generation: Architecture of NLG Systems, Applications.

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

Case study: Characteristics of Indian Languages.

7. Write a program for lemmatizing words Using WordNet

8. Write a program to differentiate stemming and lemmatizing words

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 – valuation Lexical Resources: Word Embeddings - Word2vec-Glove.

UNSUPERVISED METHODS IN NLP

Graphical Models for Sequence Labelling in NLP.

Lab Exercises:

9. Write a program for POS Tagging or Word Embeddings

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

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.

Essential Reading / Recommended Reading

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

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

 

Web Resources:

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

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

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

Evaluation Pattern

           CIA          

ESE

50%

50%

MCA374E - NOSQL (2020 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Demonstrate the concepts related to NoSQL databases

CO2: Analyze the working of architecture and internals of NoSQL Databases

CO3: Design NoSQL database applications using the methods of storing, accessing, querying, ordering and indexing

Unit-1
Teaching Hours:18
Introduction to NoSQL
 

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

 Lab Exercises: 

 1. NO SQL CRUD OPERATIONS

 2. NO SQL AGGREGATE FUNCTIONS

Unit-2
Teaching Hours:18
Interacting with NoSQL
 

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

Lab Exercises:

3. LANGUAGE BINDINGS

4. CREATING NOSQL APPLICATIONS

Unit-3
Teaching Hours:18
NoSQL Storage Architecture
 

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

Lab Exercises:

5. ACCESSING DATASTORE

6. IMPLEMENTING STORAGE ARCHITECTURE

Unit-4
Teaching Hours:18
NoSQL Stores
 

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

Lab Exercises:

7. MAP-REDUCE

8. NOSQL DATA IMPORT and EXPORT 

Unit-5
Teaching Hours:18
Indexing and Ordering Data Sets
 

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

Lab Exercises:

9. DATA INDEXING

10. DATA ORDERING

Text Books And Reference Books:

[1] Shashank Tiwari, Professional NoSQL, Wrox Press, Wiley,2011.

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

Essential Reading / Recommended Reading

[1]   Pramod Sadalage and Martin Fowler, NoSQL Distilled, Addison-Wesley Professional,2012.

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

Web Resources:

[1]   https://www.mongodb.com

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

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA381 - SPECIALIZATION PROJECT (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

The course is designed to provide a real-world project development and deployment environment for the students.

Learning 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 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 (Weightage)

ESE (Weightage)

50%

50%

MCA481 - INDUSTRY PROJECT (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:300
Credits:12

Course Objectives/Course Description

 

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

Learning 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%

MCA531 - SOFTWARE ARCHITECTURE (2019 Batch)

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

Course Objectives/Course Description

 

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

Learning Outcome

CO1: An ability to conceptualize and coordinate designs, addressing technological aspects  of architecture.

CO2: An ability to produce "software architects" with sound knowledge and superior competence in building robust, scalable, and reliable software intensive systems in an extremely .

CO3: An ability to recognize and analyze the Architecture.

CO4: An ability to apply and integrate computer technology in design processes and products.

Unit-1
Teaching Hours:12
Introduction
 

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

Unit-2
Teaching Hours:12
Creating An Architecture
 

Understanding the quality Attributes

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

Achieving Qualities

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

Unit-3
Teaching Hours:12
Designing the Architecture
 

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

Unit-4
Teaching Hours:12
Analyzing Architecture
 

ATAM (Architecture Tradeoff Analysis Method)

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

CBAM (Cost Benefit Analysis Method)

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

The World Wide Web

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

Unit-5
Teaching Hours:12
Software Product Lines
 

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

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

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

Text Books And Reference Books:

[1] Len Bass, Paul Clements, Rick Kazman, Software Architecture In Practice, Pearson Education Asia ,2nd Edition, 2003.

Essential Reading / Recommended Reading

[1] Sommerville, Ian, Software Engineering, Addison Wesley, 5th Edition, 2010. 

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

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

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

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA541A - BLOCKCHAIN ARCHITECTURE AND APPLICATIONS (2019 Batch)

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

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Demonstrate the functional aspects of Cryptocurrency ecosystem.

CO2: Understand and assess the emerging abstract models for Blockchain Technology.

CO3: Utilize Cryptocurrency exchanges and wallets safely.

Unit-1
Teaching Hours:12
BLOCKCHAIN BASIC CONCEPTS
 

Blockchain evolution – Blockchain structure – Blockchain characteristics – Blockchain applications example: Escrow – Blockchain Stack – Domain specific Blockchain applications.

Unit-2
Teaching Hours:12
CRYPTOGRAPHY AND CRYPTOCURRENCIES
 

Cryptographic hash functions – Hash pointer and data structures – Digital signatures – Publickeys as identities – A simple Cryptocurrency – Distributed consensus – Consensus without identity using a block chain.

Unit-3
Teaching Hours:12
MECHANICS AND STORAGE OF BITCOIN
 

Bitcoin transactions – Bitcoin scripts – Bitcoin blocks – Bitcoin network – Simple local storage – Online wallets and exchanges – Payment services – Transaction fees – Bitcoin mining – Bitcoin anonymity – Zerocoin – Zerocash.

 

Unit-4
Teaching Hours:12
ALTCOINS AND THE CRYPTOCURRENCY ECOSYSTEM
 

History and motivation – Few altcoins in detail – Relationship between bitcoin and altcoins – Merge mining – Atomic cross-chain swaps – Bitcoin backed altcoins – Ethereum and smart contracts.

Unit-5
Teaching Hours:12
BEYOND CRYPTOCURRENCY
 

Applications of Blockchain in Cyber security – Integrity of information –

E-Governance and other contract enforcement mechanisms – Limitations of Blockchain as a technology – Myths vs. reality of Blockchain technology – Research directions in Blockchain technology.

Text Books And Reference Books:

[1]  Blockchain Applications: A Hands-On Approach. Arshdeep Bahga, Vijay Madisetti. VPT Publisher. First edition,2018.

       [2]Bitcoin and Cryptocurrency technologies: a comprehensive introduction. Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, and Steven Goldfeder. Princeton University Press,

          First edition, 2016.

Essential Reading / Recommended Reading

[1]   The Blockchain and the New Architecture of Trust. Kevin Werbach, Sandra Braman, Paul T. Jaeger., MIT Press. First edition.2018.

[2]  Blockchain: Blueprint for the new economy. Melanie Swan. O’Reilly. First edition.2015.

 

Web Resources:

[1]  https://bitcoinbook.cs.princeton.edu/

[2]  https://github.com/arshdeepbahga/blockchain-applications-book

[3]  https://kba.ai/

 

Evaluation Pattern

CIA-50%

ESE-50%

MCA541B - AGENT BASED COMPUTING (2019 Batch)

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

Course Objectives/Course Description

 

On completion of the course students should have understood Software Agents and its applications, Intelligent learning Methods.

Learning Outcome

CO1: Understand Software Agents, Intelligent Agents and its applications

CO2: Apply Rule learning and Intelligent learning methods

CO3: Build intelligent agents and demonstrate its applications

Unit-1
Teaching Hours:12
Software Agents
 

Introduction to Software Agents: What is a software agent? - Why software agents? - Applications of Intelligent software agents-Practical design of intelligent agent systems.

Unit-2
Teaching Hours:12
Intelligent Agents
 

Intelligent Agent Learning- Approaches to Knowledge base development-Disciple approach for building Intelligent agents- Knowledge representation-Generalization- Problem solving methods- Knowledge elicitation.

Unit-3
Teaching Hours:12
Rule Learning
 

Rule learning problem- Rule learning method- Learned rule characterization. Rule refinement: Rule refinement problem- Rule refinement method- Rule experimentation and verification- Refined rule characterization-Agent interactions.

Unit-4
Teaching Hours:12
Building Intelligent Agents
 

Disciple shell: Architecture of Disciple shell- Methodology for building Intelligent Agents- Expert-Agent interactions during knowledge elicitation process- Expert-Agent interactions during rule learning process- Expert-Agent interactions during rule refinement process.

Unit-5
Teaching Hours:12
Case Studies
 

Case studies in building intelligent agents: Intelligent Agents in portfolio management- Intelligent Agents in financial services- Statistical Analysis assessment and support agent- Design assistant for configuring computer systems.

Text Books And Reference Books:

[1] Nicholas R Jennings, Michael J Wooldridge (Eds.), “Agent Technology – Foundations, Applications and Markets”, Springer, 1997.

Essential Reading / Recommended Reading

[1]   Jeffrey M Bradshaw, “Software Agents”, AAAI Press/ the MIT Press, Standard Edition, 1997.

[2]   Gheorghe Tecuci et al., “Building Intelligent Agents”, Academic Press, 2003.

[3]   Eduardo Alanso, Daniel Kudenko, Dimitar Kazakov (Eds.) “Adaptive Agents and Multi- Agent Systems”, Springer Publications, 2003.

Evaluation Pattern

CIA : 50 %

ESE : 50 %

MCA541C - EVOLUTIONARY ALGORITHMS (2019 Batch)

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

Course Objectives/Course Description

 

Able to understand the core concepts of evolutionary computing techniques and popular evolutionary algorithms that are used in solving optimization problems. Students will be able to implement custom solutions for real-time problems applicable with evolutionary computing.

Learning Outcome

CO1: Understanding of evolutionary computing concepts and techniques

CO2: Classify relevant real-time problems for the applications of evolutionary algorithms

CO3: Design solutions using evolutionary algorithms

Unit-1
Teaching Hours:12
INTRODUCTION TO EVOLUTIONARY COMPTUTING
 

Terminologies – Notations – Problems to be solved – Optimization – Modeling – Simulation – Search problems – Optimization constraints

Unit-1
Teaching Hours:12
GENETIC ALGORITHMS
 

History of genetics – Science of genetics – History of genetic algorithm – Simple binary genetic algorithm – continuous genetic algorithm

Unit-2
Teaching Hours:12
EVOLUTIONARY PROGRAMMING
 

Continuous evolutionary programming – Finite state machine optimization – Discrete evolutionary programming – The Prisoner’s dilemma

Unit-2
Teaching Hours:12
EVOLUTION STRATEGY
 

One plus one evolution strategy – The 1/5 Rule – (μ+1) evolution strategy – Self adaptive evolution strategy

Unit-3
Teaching Hours:12
GENETIC PROGRAMMING
 

Fundamentals of genetic programming – Genetic programming for minimal time control

Unit-3
Teaching Hours:12
EVOLUTIONARY ALGORITHM VARIATION
 

Initialization – Convergence – Population diversity – Selection option – Recombination – Mutation

Unit-4
Teaching Hours:12
ANT COLONY OPTIMIZATION
 

Pheromone models – Ant system – Continuous Optimization – Other Ant System

Unit-4
Teaching Hours:12
PARTICLE SWARM OPTIMIZATION
 

Velocity limiting – Inertia weighting – Global Velocity updates – Fully informed Particle Swarm

Unit-5
Teaching Hours:12
MULTI-OBJECTIVE OPTIMIZATION
 

Pareto Optimality – Hyper volume – Relative coverage – Non-pareto based EAs – Pareto based EAs – Multi-objective Biogeography based optimization.

Text Books And Reference Books:

[1] D. Simon, Evolutionary optimization algorithms: biologically inspired and population-based approaches to computer intelligence. New Jersey: John Wiley, 2013.

 

Essential Reading / Recommended Reading

[1]    Eiben and J. Smith, Introduction to evolutionary computing. 2nd ed. Berlin: Springer,2015.

[2]       D. Goldberg, Genetic algorithms in search, optimization, and machine learning. Boston: Addison-Wesley, 2012.

[3]     K. Deb, Multi-objective optimization using evolutionary algorithms. Chichester: John Wiley & Sons, 2009.

[4]     R. Poli, W. Langdon, N. McPhee and J. Koza, A field guide to genetic programming. [S.l.]: Lulu Press, 2008.

[5]    T. Bäck, Evolutionary algorithms in theory and practice. New York: Oxford Univ. Press, 1996.

Web Resources:

[1]   E. A.E and S. J.E, "Introduction to Evolutionary Computing | The on-line accompaniment to the book Introduction to Evolutionary Computing", Evolutionarycomputation.org, 2015. [Online]. Available: http://www.evolutionarycomputation.org/. [Accessed: 24- Jan-2020].

[2]      F. Lobo, "Evolutionary Computation 2018/2019", Fernandolobo.info, 2018. [Online]. Available: http://www.fernandolobo.info/ec1819/. [Accessed: 24- Jan-2020].

[3]   "EC lab Tools", Cs.gmu.edu, 2008. [Online]. Available: https://cs.gmu.edu/~eclab/tools.html. [Accessed: 24- Jan- 2020].

[4]        "Kanpur Genetic Algorithms Laboratory", Iitk.ac.in, 2008. [Online]. Available: https://www.iitk.ac.in/kangal/codes.shtml. [Accessed: 24- Jan-2020].

[5]   "Course webpage Evolutionary Algorithms", Liacs.leidenuniv.nl, 2017. [Online]. Available:http://liacs.leidenuniv.nl/~csnaco/EA/misc/ga_demo.htm. [Accessed: 24- Jan-2020].

 

 

Evaluation Pattern

CIA-50%

ESE-50%

MCA541D - COMPILER DESIGN (2019 Batch)

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

Course Objectives/Course Description

 

The course is intended to teach  the  students  the  basic  techniques  that  underlie  the  practice of Compiler Construction. The course will introduce the theory and tools that can be employed in order to perform syntax-directed translation of a high-level programming language into an executable code.

Learning Outcome

CO1: To understand the basic concepts and application of Compiler Design and Parsing techniques.

CO2: To understand intermediate code generation and run-time environment.

CO3: To understand various Code optimization Techniques and Error Recovery mechanisms.

Unit-1
Teaching Hours:12
Introduction to Compilers
 

Structure of a compiler – Lexical Analysis – Role of Lexical Analyzer – Input Buffering   Specification of Tokens – Recognition of Tokens – Lex – Finite Automata – Regular Expressions to Automata – Minimizing DFA.

Unit-1
Teaching Hours:12
Lexical Analyzer
 

Introduction to Lexical Analyzer - Input Buffering - Specification of Tokens - Recognition of Tokens - A Language for Specifying Lexical Analyzers - Finite Automata From a Regular Expression - Design of a Lexical Analyzer Generator - Optimization of DFA

Unit-2
Teaching Hours:12
Parsing Theory
 

Top Down and Bottom up Parsing Algorithms - Top Down Parsing - Bottom Up Parsing - Operator Precedence Parsing - LR Parsers - Using Ambiguous Grammars - Parser Generators - Automatic Generation of Parsers. Syntax-Directed Definitions - Construction of Syntax Trees – Bottom Up Evaluation of S-Attributed Definitions - L-Attributed Definitions - syntax directed definitions and translation schemes

Unit-2
Teaching Hours:12
Error Recovery
 

Error Detection & Recovery - Ad-Hoc and Systematic Methods

Unit-3
Teaching Hours:12
Intermediate Code
 

Generation Different Intermediate Forms - Syntax Directed Translation Mechanisms and Attributed Mechanisms and Attributed Definition.

Unit-3
Teaching Hours:12
Run Time Memory Management
 

Source Language Issues - Storage Organization - Storage-Allocation Strategies - and Access to Non local Names - Parameter Passing - Symbol Tables - and Language Facilities for Dynamic Storage Allocation - Dynamic Storage Allocation Techniques.

Unit-4
Teaching Hours:12
Intermediate Code Generation
 

Syntax Directed Definitions - Evaluation Orders for Syntax Directed Definitions - Intermediate Languages: Syntax Tree - Three Address Code - Types and Declarations - Translation of Expressions - Type Checking.

Unit-4
Teaching Hours:12
Run-Time Environment And Code Generation
 

Storage Organization - Stack Allocation Space - Access to Non-local Data on the Stack - Heap Management – Issues in Code Generation – Design of a simple Code Generator - Syntax-Directed TranslationScheme.

Unit-5
Teaching Hours:12
Code Optimization
 

Global Data Flow Analysis - A Few Selected Optimizations like Command Sub Expression Removal - Loop Invariant Code Motion - Strength Reduction.

Unit-5
Teaching Hours:12
Code Generation
 

Issues in the Design of a Code Generator - The Target Machine - Run-Time StorageManagement - Basic Blocks and Flow Graphs - Next-Use Information - A Simple Code Generator - Register Allocation and Assignment - The DAG Representation of Basic Blocks - Peephole Optimization - Generating Code from DAGs - Dynamic Programming Code-Generation Algorithm - Code GeneratorGenerators.

 

Text Books And Reference Books:

[1]        Compilers: Principles, Techniques and Tools By Aho, Lam, Sethi, and Ullman, Second Edition, Pearson, 2018.

[2]     Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman, Compilers: Principles, Techniques andToolsǁ, Second Edition, Pearson Education,2009.

Essential Reading / Recommended Reading

[1]   Steven  S.  Muchnick,  AdvancedCompilerDesign  andImplementationǁ,MorganKaufmann Publishers Elsevier Science, India, Indian Reprint, 2003.

[2]   KeithDCooperandLindaTorczon,Engineering a Compilerǁ,Morgan Kaufmann Publishers Elsevier Science, 2004.

 

Web Resource:

[1] https://nptel.ac.in/courses/106104072

 

 

 

Evaluation Pattern

CIA-50%

ESE-50%

MCA541E - NATURAL LANGUAGE PROCESSING (2019 Batch)

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

 

Learning Outcome

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

CO2:Toapplyvariousmethodstodiscourse,generation,dialogueandsummarization                                                                                                                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:12
INTRODUCTION
 

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

 

Unit-2
Teaching Hours:12
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

Unit-3
Teaching Hours:12
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.

 

Unit-4
Teaching Hours:12
NATURALLANGUAGE GENERATION AND MACHINE TRANSLATION
 

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

 

Unit-5
Teaching Hours:12
INFORMATION RETRIEVAL AND LEXICAL RESOURCES
 

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

UNSUPERVISED METHODS IN NLP

Graphical Models for Sequence Labelling in NLP

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.

 

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; 1 edition, 2009.

 

Web Resource:

[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-50%

ESE-50%

MCA541F - COMPUTER VISION (2019 Batch)

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

Learning 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:12
INTRODUCTION
 

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

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

Unit-2
Teaching Hours:12
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 – Houghtransform, Statistical based features extraction.

 

Unit-3
Teaching Hours:12
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.

Unit-4
Teaching Hours:12
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.

Unit-5
Teaching Hours:12
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.

 

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.", Jun-2012.

 

Web Resources:

[1]     Computer Vision Wikipedia -https://en.wikipedia.org/wiki/Computer_vision

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

[3]     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

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

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


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

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

[8]    Various MOOC courses – SWAYAM – UDEMY – COURSERAetc.

Evaluation Pattern

CIA-50%

ESE-50%

MCA541G - OPTIMIZATION TECHNIQUES (2019 Batch)

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

Course Objectives/Course Description

 

This course will help the students to acquire and demonstrate the implementation of the necessary algorithms for solving advanced level Optimization techniques.

 

Learning Outcome

CO1: Apply the notions of linear programming in solving transportation problems CO2: Understand the theory of games for solving simple games

CO3: Use linear programming in the formulation of shortest route problem.

CO4: Apply algorithmic approach in solving various types of network problems

CO5: Create applications using dynamic programming.

 

Unit-1
Teaching Hours:12
Introduction
 

Operations Research Methods - Solving the OR model - Queuing and Simulation models – Art  of modelling – phases of OR study.

Unit-1
Teaching Hours:12
Modelling with Linear Programming
 

Two variable LP model – Graphical LP solution – Applications. Simplex method and sensitivity analysis – Duality and post-optimal Analysis- Formulation of the dual problem.

Unit-2
Teaching Hours:12
Transportation Model
 

Determination of the Starting Solution – Iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method – Simplex explanation of the Hungarian Method – The trans-shipment Model.

 

Unit-3
Teaching Hours:12
Network Models
 

Minimal Spanning tree Algorithm – Linear Programming formulation of the shortest-route problem. Maximal Flow Model: Enumeration of cuts – Maximal Flow Diagram – Linear Programming Formulation of Maximal Flow Model.

Unit-3
Teaching Hours:12
CPM and PERT
 

Network Representation – Critical Path Computations – Construction of the time Schedule – Linear Programming formulation of CPM – PERT networks.

 

Unit-4
Teaching Hours:12
Game Theory
 

Strategic Games and examples - Nash equilibrium and examples - Optimal Solution of two person zero sum games - Solution of Mixed strategy games - Mixed strategy Nash equilibrium - Dominated action with example.

Unit-4
Teaching Hours:12
Goal Programming
 

Formulation – Tax Planning Problem – Goal Programming algorithms – Weights method – Preemptive method.

Unit-5
Teaching Hours:12
Markov Chains
 

Definition – Absolute and n-step Transition Probability – Classification of states.

Unit-5
Teaching Hours:12
Dynamic Programming
 

Recursive nature of computation in Dynamic Programming – Forward and Backward Recursion

– Knapsack / Fly Away / Cargo-Loading Model – Equipment Replacement Model.

 

Text Books And Reference Books:

[1]  HamdyATaha, OperationsResearch, 9thEdition,Pearson Education, 2012.

[2]  Garrido Jose M. Introduction to Computational Models with Python. CRC Press,2016.

 

Essential Reading / Recommended Reading

[1]  RathindraPSen,OperationsResearch–AlgorithmsandApplications,PHILearningPvt.

Limited, 2011

[2]  R. Ravindran, D. T. Philips and J. J. Solberg, Operations Research: Principles and Practice, 2nd ed., John Wiley & Sons,2007.

[3]  F. S. Hillier and G. J. Lieberman, Introduction to operations research, 8th ed., McGraw-Hill Higher Education, 2004.

[4]  K. C. Rao and S. L. Mishra, Operations research, Alpha Science International, 2005.

[5]  Hart, William E. Pyomo: Optimization Modeling in Python. Springer,2012.

[6]  Martin J. Osborne, An introduction to Game theory, Oxford University Press,2008

Evaluation Pattern

CIA-50%

ESE-50%

MCA571 - CLOUD COMPUTING (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

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.

Learning Outcome

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

CO2: Analyse and reveal 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:12
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 - Healthcare - Transportation Systems - Manufacturing Industry – Government - Education - Mobile Communications

Unit-2
Teaching Hours:12
CLOUD ENABLING TECHNOLOGIES
 

Virtualization - Load Balancing - Scalability & Elasticity – Deployment –Replication – Monitoring - Software Defined Networking - Network Function Virtualization – MapReduce - Identity and Access Management - Service Level Agreements – Billing.

Unit-3
Teaching Hours:12
BASIC CLOUD SERVICES & PLATFORMS
 

Compute Services - Amazon Elastic Compute Cloud - Google Compute Engine - Windows Azure Virtual Machines - Storage Services - Amazon Simple Storage Service - Google Cloud Storage - Windows Azure Storage - Database Services - Amazon Relational Data Store - Amazon DynamoDB - Google Cloud SQL - Google Cloud Datastore - Windows Azure SQL Database - Windows Azure Table Service.

Unit-4
Teaching Hours:12
ADVANCED CLOUD SERVICES & PLATFORMS
 

Application Services - Content Delivery Services - Amazon CloudFront - Windows Azure Content Delivery Network - Analytics Services - Amazon Elastic MapReduce - Google MapReduce Service - Google BigQuery - Windows Azure HDInsight - Deployment & Management Services - Amazon Elastic Beanstalk - Amazon CloudFormation - Identity & Access Management Services - Amazon Identity & Access Management - Windows Azure Active Directory.

Unit-5
Teaching Hours:12
CLOUD SECURITY
 

Introduction - CSA Cloud Security Architecture – Authentication - Single Sign-on (SSO) - Authorization - Identity & Access Management - Data Security - Securing Data at Rest - Securing Data in Motion - Key Management – Auditing.

 

Lab Content (30 Hours)

1. Creating Virtual Machines using Hypervisors

2. Compute service: Creating and running Virtual Machines using AWS/GCP/Azure

3. Compute service: Installing OpenStack

4. Storage as a Service: Creating storage

5. Storage as a Service: Installing Owncloud

6. Database as a Service: Building DB Server

7. Security as a Service: Using IAM

8. Cloud features implementation: Autoscaling / Load Balancing

9. Platform as a Service: Working with Google AppEngine

10. Software as a Service: Application development using Salesforce.

* Exercises 2 to 10 can be implemented using AWS/GCP/Azure

Text Books And Reference Books:

[1] Kailash Jayaswal, Jagannath Kallakurchi, Donald J. Houde, Dr. Deven Shah, Cloud Computing Black Book, Dreamtech Publishers, 2014.

[2] Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, CreateSpace Independent Publishing Platform, 2014.

Essential Reading / Recommended Reading

[1] Barrie Sosinky, Cloud Computing: Bible, 1st edition, Wiley Publishing, Inc., 2011

[2] Anthony TVelte, Toby JVelteand Robert Elsenpeter, Cloud Computing –A Practical Approach, Tata McGraw Hill Education Pvt Ltd, 2010.

Web Resources:

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

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

[3] https://cloud.google.com › docs

[4] https://docs.microsoft.com › en-us › azure 

Evaluation Pattern

50% CIA + 50% ESE 

MCA572 - .NET TECHNOLOGIES (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

 

Learning Outcome

CO1: Update the students with the latest technologies thereby make them fit for the industry

CO2: Make the students aware of a new development platform.

CO3: Efficient application development and deployment

 

Unit-1
Teaching Hours:12
Introduction to .NET
 

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

Unit-2
Teaching Hours:12
Windows Applications
 

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

Unit-3
Teaching Hours:12
Windows Presentation
 

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

 

Unit-4
Teaching Hours:12
ASP.NET
 

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

Unit-5
Teaching Hours:12
Data Access
 

 

ADO.NET overview. Various data access objects – Connection, Command and DataSet Objects. Binding data to ASP .NET server controls. Accessing data from a database using ADO.NET. Reading from and Writing to an XML document, Using XML DOM objects for data access from XML Documents. Binding data from an XML document to Web form controls. Converting data from Database to XML Data. Xml & Web Services

 

Lab Content (30 Hours)

 

1.         To design a window based application with commoncontrols

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

3.         To design a windows based application to store data intodatabase

4.         To design a windows based application to retrieve data fromdatabase

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

6.         To implement SqlDataReader inADO.NET

7.         To implement WPFapplication

8.         To design a web based application to insert data intodatabase

9.         To design a web based application to retrieve data fromdatabase

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

 

Text Books And Reference Books:

[1]   Jeff Ferguson, Brian Patterson, Jason Beres ,C# Programming Bible , Wiley Publishing Inc., Reprint 2012.

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

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

 

Essential Reading / Recommended Reading

[1] Publication ASP .NET complete reference, TMH, 2010

Web Resources:

[1] www.w3cschools.com

Evaluation Pattern

CIA-50%

ESE-50%

MCA573A - INFORMATION RETRIEVAL AND WEB MINING (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Understand the fundamental concepts of information retrieval systems.

CO2: Apply the theory behind the major components of information retrieval, including crawling, parsing, scoring, indexing, and compression.

CO3: Demonstrate latest techniques of web mining.

CO4: Design and implement parts of an information retrieval system.

Unit-1
Teaching Hours:12
INTRODUCTION TO INFORMATION RETRIEVAL SYSTEMS
 

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

 

Unit-1
Teaching Hours:12
CATALOGING AND INDEXING
 

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

 

Unit-2
Teaching Hours:12
SEARCHING TECHNIQUES
 

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

Unit-3
Teaching Hours:12
WEB CRAWLING
 

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

Unit-4
Teaching Hours:12
WEB STRUCTURE MINING
 

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

Unit-5
Teaching Hours:12
WEB USAGE MINING
 

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

Lab Content (30 Hours)

1. Implement the concept of Search Engine technique

2. Develop the Crawler based on domains

3. Extract textual information and Multimedia contents from documents

4. Develop search engine indexing

5. Increase the efficiency of sentiment analysis and opinion mining

6. Develop the effective query refinement mechanism

7. Implement page ranking algorithm

8. Personalize the search engine

9. Personalized web search

10. Case studies and assignments 

Text Books And Reference Books:

[1] Kowalski, Gerald, Mark T Maybury: Information Storage and Retrieval Systems: Theory and Implementation, Second Edition, Kluwer Academic Press, 2000.

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

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

Essential Reading / Recommended Reading

 

 

[1] G.G. Chowdhury, Introduction to Modern Information Retrieval, Second Edition, NealSchuman Publishers, 2010.

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

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

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

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

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

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

Evaluation Pattern

CIA (Weightage)

ESE (Weightage)

50%

50%

MCA573B - DATABASE ADMINISTRATION (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Applying SQL to solve real world queries.

CO2: Understanding the role of various database users including data base administrator

CO3: Designing various Data models, schemas, architectures and instances.

Unit-1
Teaching Hours:12
Creating the Database Environment
 

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

Unit-2
Teaching Hours:12
Database Process
 

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

Unit-3
Teaching Hours:13
Data and Storage Management
 

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

Unit-4
Teaching Hours:11
Backup and Maintenance
 

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

Unit-5
Teaching Hours:12
User Management and Database Security
 

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

 

Lab Content (30 Hours)

 

1.     Design a program for implementing batch processing mechanisms.

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

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

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

5.     Demonstrate a program query processing in distributed databases.

6.     Write a program to implement file processingsystem.

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

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

9.       Design a program for recovering data using RMAN.

         10.  Design a program for shared and exclusive lock

 

Text Books And Reference Books:

[1]                 Craig S. Mullins, Database Administration, The complete guide to DBA practices and procedures, Addison-Wesley, 2nd Edition,2013.

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

Essential Reading / Recommended Reading

[1]  Adam Jorgensen, Jorge Segarra, Patrick Leblanc, Jose Chinchilla and Aaron Nelson, Microsoft sql server bible, Wiley India Pvt.Ltd,2012.

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

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

 

 

Web Resources:

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

[2]https://technology.amis.nl/2015/03/04/compose-end-to-end-tracing-logging-and-monitoring-with

-ecid-set-from-weblogic-in-database-exposed-in-vsession/

     [3]https://www.cdm.depaul.edu/academics/pages/courseinfo.aspx?Subject=CSC&CatalogNbr=4 54

Evaluation Pattern

CIA-50%

ESE-50%

MCA573C - NEURAL NETWORKS AND DEEP LEARNING (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

 

Learning Outcome

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

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

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

Unit-1
Teaching Hours:12
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
 

Neural Networks-Application Scope of Neural Networks- Fundamental Concept of ANN: The Artificial Neural Network-Biological Neural Network-Comparison between Biological Neuron and Artificial Neuron-Evolution of Neural Network. Basic models of ANN-Learning Methods-Activation Functions-Importance Terminologies of ANN

 

Unit-2
Teaching Hours:12
SUPERVISED LEARNING NETWORK
 

Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning Rule-Architecture-Flowchart for training Process-Perceptron Training Algorithm for Single and Multiple Output Classes.

Back Propagation Network- Theory-Architecture-Flowchart for training process-Training Algorithm-Learning Factors for Back-Propagation Network.

Radial Basis Function Network RBFN: Theory, Architecture, Flowchart and Algorithm.

 

Unit-3
Teaching Hours:12
CONVOLUTIONAL NEURAL NETWORK
 

Introduction  -  Components  of  CNN  Architecture  -  Rectified  Linear  Unit  (ReLU)  Layer

-Exponential  Linear  Unit  (ELU,  or SELU) - Unique Properties of CNN -Architectures ofCNN

-Applications of CNN.

Unit-4
Teaching Hours:12
RECURRENT NEURAL NETWORK
 

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

Unit-5
Teaching Hours:12
AUTO ENCODER AND RESTRICTED BOLTZMANN MACHINE
 

Introduction - Features of Auto encoder Types of Autoencoder

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

 

Lab Content(30 hrs)

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

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

2.  a. Classification of AND or OR problem using perceptronnetwork.

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

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

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

5.  Implementation of Convolution Neural Network

6.  Implementation of RNN

7.  Implementation of autoencoder

8.  Implementation of Restricted Boltzmann Machine

Text Books And Reference Books:

[1]    S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, Wiley-India, 3rd Edition,2018.

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

Essential Reading / Recommended Reading

[1]     Charu C. Aggarwal, Neural Networks and Deep Learning, Springer, September2018.

[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-50%

ESE-50%

MCA573D - ARTIFICIAL INTELLIGENCE (2019 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

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.

Learning Outcome

 

CO1: Express the modern view of AI and its foundation

 

CO2: Illustrate Search Strategies with algorithms and Problems

 

CO3: Implement Propositional logic and apply inference rules

 

CO4: Apply suitable techniques for NLP and Game Playing

 

 

 

Unit-1
Teaching Hours:12
Introduction
 

Introduction to AI, The Foundations of AI, AI Technique -Tic-Tac-Toe.Problem characteristics, Production system characteristics, Production systems: 8-puzzle problem. Searching: Uninformed search strategies – Breadth first search, depth first search.

Unit-2
Teaching Hours:12
Local Search Algorithms
 

Generate and Test, Hill climbing, simulated annealing search, Constraint satisfaction problems, Greedy best first search, A* search, AO* search.Toy problems

Unit-3
Teaching Hours:12
Knowledge Representation
 

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

Unit-3
Teaching Hours:12
Self Learning