MCA III and IV Semester Syllabus (Non- CBCS) w.e.f 2020-21

Kurukshetra University, Kurukshetra

(Established by the State Legislature Act XII of 1956)

(‘A+’ Grade, NAAC Accredited)

||     योगस्थ:  कुरु कर्माणि     ||

समबुद्धि व योग  युक्त होकर कर्म करो

(Perform Actions while Stead fasting in the State of Yoga)

Scheme of Examination and Syllabus of

Master of Computer Application (MCA)(Non-CBCS) in Phased Manner

DEPARTMENT OF COMPUTER SCIENCE & APPLICATIONS

Non-CBCS CURRICULUM (2020-21)

Program Name: Master of Computer Applications (MCA)(Non-CBCS)

(For the Batches Admitted From 2020-2021)

KURUKSHETRA UNIVERSITY, KURUKSHETRA

 

SCHEME OF EXAMINATIONS

FOR

MASTER OF COMPUTER APPLICATIONS

(NON-CBCS)

(FOR INSTITUTES AFFILIATED TO KURUKSHETRA UNIVERSITY, KURUKSHETRA)

 W.E. F. ACADEMIC SESSION 2020-21 IN PHASED MANNER

Third Semester
MCA-20-31 Computer Architecture and Parallel Processing 4 3 75 30 25 100 40
MCA-20-32 Data Mining and Integration using R 4 3 75 30 25 100 40
MCA-20-33 Artificial Intelligence 4 3 75 30 25 100 40
MCA-20-34 Elective-III 4 3 75 30 25 100 40
MCA-20-35 Elective-IV 4 3 75 30 25 100 40
MCA-20-36 S/W Lab – V Based on MCA-20-32 5 3 100 40 100 40
MCA-20-37 S/W Lab – VI Based on MCA-20-35 5 3 100 40 100 40
*MCA-20-38 Summer Training / Internship (Industry Based) Viva Voce 150 60 50 200 80
Total 30   725 290 175 900 360
Elective – III
MCA-20-34 (i) Cloud Computing and IoT 4 3 75 30 25 100 40
MCA-20-34 (ii) Cyber Security 4 3 75 30 25 100 40
MCA-20-34(iii) Digital Marketing 4 3 75 30 25 100 40
Elective – IV
MCA-20-35 (i) Advances in Java 4 3 75 30 25 100 40
MCA-20-35 (ii) Advanced Web Technologies 4 3 75 30 25 100 40
MCA-20-35(iii) Programming with Kotlin 4 3 75 30 25 100 40
Fourth Semester
MCA-20-41 Big Data and Pattern Recognition 4 3 75 30 25 100 40
MCA-20-42 Computer Graphics and Animation 4 3 75 30 25 100 40
MCA-20-43 Mobile Application Development 4 3 75 30 25 100 40
MCA-20-44 Elective-V 4 3 75 30 25 100 40
MCA-20-45 Elective-VI 4 3 75 30 25 100 40
MCA-20-46 S/W Lab–VII Based on MCA-20-41 and MCA-20-42 5 3 100 40 100 40
MCA-20-47 Project Based on MCA-20-43 5 3 75 30 25 100 40
Total 30   550 220 150 700 280
Grand Total 120   2425 970 575 3000 1200
Elective – V
MCA-20-44 (i) Soft Computing 4 3 75 30 25 100 40
MCA-20-44 (ii) Machine Learning 4 3 75 30 25 100 40
MCA-20-44(iii) Digital Image Processing 4 3 75 30 25 100 40
Elective – VI
MCA-20-45 (i) Optimization Techniques 4 3 75 30 25 100 40
MCA-20-45(ii) Information Systems 4 3 75 30 25 100 40
MCA-20-45(iii) Blockchain Technology 4 3 75 30 25 100 40

 

*Note 1:

Summer Training / Internship will be held immediately after 2nd Semester

Examination and will be having a minimum duration of 45 days and maximum

duration of 60 days. Students have to submit the Summer Training / Internship Report

latest by 30th August. Evaluation of the Report and Viva-Voce shall be held during 3rd

Semester. The Evaluation and Viva-Voce shall be held by one External and on Internal                                   examiner.

 

Note 2: Evaluation procedure for internal assessment marks:

Two Mid Term Examinations should be conducted by the concerned teacher each of 10 marks. Five marks may be given by the concerned teacher on the basis of performance during the course (puzzles / assignments / interactions / attendance etc.).

Note 3: Size of groups in all practical courses should not be more than thirty students.

 

MCA-20-31: Computer Architecture and Parallel Processing

Type: Compulsory

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: To know parallel processing and new trends and developments in computer architectures.  Understand design and development of ILP based processors and evaluate their performance. Understand MIMD architectures and different topologies used in these architectures. Study the cache coherence problems and their solutions.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-31.1 learn the concepts of parallel architectures and exploitation of parallelism at   instruction level;
MCA-20-31.2 understand architectural features of multi-issue ILP processors;
MCA-20-31.3 learn MIMD architectures and interconnection networks used in them and evaluate their comparative performances;
MCA-20-31.4 analyze causes of cache coherence problem and learn algorithm for its solution.

Unit – I

Computational Model: Basic computational models, evolution and interpretation of computer architecture, concept of computer architecture as a multilevel hierarchical framework. Classification of parallel architectures, Relationships between programming languages and parallel architectures

Parallel Processing: Types and levels of parallelism, Instruction Level Parallel (ILP) processors, dependencies between instructions, principle and general structure of pipelines, performance measures of pipeline, pipelined processing of integer,  Boolean,  load and store instructions, VLIW architecture,  Code  Scheduling for  ILP- Processors – Basic block scheduling, loop scheduling, global scheduling.

Unit – II

Superscalar  Processors:  Emergence  of  superscalar  processors,  Tasks  of  superscalar  processing  –  parallel decoding, superscalar instruction issue, shelving, register renaming, parallel execution, preserving sequential consistency of instruction execution and exception processing, comparison of VLIW & superscalar processors Branch Handling: Branch problem, Approaches to branch handling – delayed branching, branch detection and prediction schemes, branch penalties and schemes to reduce them, multiway branches, guarded execution.

Unit – III

MIMD Architectures: Concepts of distributed and shared memory MIMD architectures, UMA, NUMA, CC-NUMA & COMA models, problems of scalable computers.

Direct Interconnection Networks: Linear array, ring, chordal rings, star, tree, 2D mesh, barrel shifter, hypercubes.

Unit – IV

Dynamic interconnection networks: single shared buses, comparison of bandwidths of locked, pended & split transaction buses, arbiter logics, crossbar, multistage networks – omega, butterfly

Cache coherence problem, hardware based protocols – snoopy cache protocol, directory schemes, hierarchical cache coherence protocols.

Text Books:

1.       Sima, Fountain, Kacsuk, Advanced Computer Architecture, Pearson Education.

2.      D. A. Patterson and J. L. Hennessey, Computer Architecture – A Quantitative Approach, Elsevier           India.

Reference Books:

1.   Kai Hwang, Advanced Computer Architecture, McGraw Hill.

2.   Nicholas Carter, Computer Architecture, McGraw Hill.

3.   Harry F. Jordan, Gita Alaghband, Fundamentals of Parallel Processing, Pearson Education.

 

 

 

MCA-20-32: Data Mining and Integration using R

Type: Compulsory

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to provide the in- depth coverage of data mining and integration aspects along with its implementation in R programming language.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-32.1 understand the fundamental concepts of data warehousing and data mining;
MCA-20-32.2 acquire skills to implement data mining techniques;
MCA-20-32.3 learn schema matching, mapping and integration strategies;
MCA-20-32.4 implement data mining techniques in R to meet the market job requirements.

UNIT – I

Data Warehouse: A Brief History, Characteristics, Architecture for a Data Warehouse. Data Mining: Introduction: Motivation, Importance, Knowledge Discovery Process, Data Mining Functionalities, Interesting Patterns, Classification of Data Mining Systems, Major issues, Data Preprocessing: Overview, Data Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization, Outliers.

UNIT – II

Data Mining Techniques: Clustering- Requirement for Cluster Analysis, Clustering Methods- Partitioning Methods, Hierarchical Methods, Decision Tree- Decision Tree Induction, Attribute Selection Measures, Tree Pruning. Association Rule Mining- Market Basket Analysis, Frequent Itemset Mining using Apriori Algorithm, Improving the Efficiency of Apriori. Concept of Nearest Neighborhood and Neural Networks.

UNIT – III

Data Integration: Architecture of Data Integration, Describing Data Sources: Overview and Desiderate, Schema Mapping Language, Access Pattern Limitations, String Matching: Similarity Measures, Scaling Up String Matching, Schema Matching and Mapping: Problem Definition, Challenges, Matching and Mapping Systems, Data Matching: Rule- Based Matching, Learning- Based Matching, Matching by Clustering.

UNIT – IV

R Programming: Advantages of R over other Programming Languages, Working with Directories and Data Types in R, Control Statements, Loops, Data Manipulation and integration in R, Exploring Data in R: Data Frames, R Functions for Data in Data Frame, Loading Data Frames, Decision Tree packages in R, Issues in Decision Tree Learning, Hierarchical and K-means Clustering functions in R, Mining Algorithm interfaces in R.

Text Books:

1.     J Hanes, M. Kamber, Data Mining Concepts and Techniques, Elsevier India.

2.     A.Doan, A. Halevy, Z. Ives, Principles of Data Integration, Morgan Kaufmann Publishers.

3.     S. Acharya, Data Analytics Using R, McGraw Hill Education (India) Private Limited.

Reference Books:

1.     G.S. Linoff, M.J.A. Berry, Data Mining Techniques, Wiley India Pvt. Ltd.

2.     Berson, S.J. Smith, Data Warehousing, Data Mining & OLAP, Tata McGraw-Hill.

3.     J.Horbulyk, Data Integration Best Practices.

4.     Jared P. Lander, R For Everyone, Pearson India Education Services Pvt. Ltd.

 

 

 

MCA-20-33: Artificial Intelligence

Type: Compulsory

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester exam:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to provide the in-depth coverage of Artificial Intelligence techniques and their applications. It focuses on various search techniques and expert systems along with other parts of artificial intelligence in computer science.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-33.1 understand the different knowledge representation schemes specially FOPL;
MCA-20-33.2 apply various search methods to solve AI problems efficiently;
MCA-20-33.3 understand the Expert System and techniques to manage the uncertainty in Expert Systems;
MCA-20-33.4 understand the learning techniques and Genetic Algorithm.

Unit – I

Introduction: Background and history, Overview of AI applications areas.

The  predicate  calculus: Syntax and  semantic  for  propositional logic  and  FOPL, Clausal form, inference rules,  resolution  and unification.

Knowledge   representation:  Network   representation-Associative network & conceptual graphs, Structured representation- Frames & Scripts.

Unit – II

Search strategies: Strategies for state space search-data  driven and  goal  driven search; Search  algorithms-  uninformed  search (depth   first,  breadth  first,  depth  first   with   iterative deepening)  and  informed search (Hill climbing, best  first,  A* algorithm,  mini-max etc.), computational complexity, Properties of search algorithms – Admissibility, Monotonicity, Optimality, Dominance.

Unit – III

Production system: Types of production system-commutative and non-commutative production systems, Decomposable and non-decomposable production systems, Control of search in production systems.

Rule  based expert systems: Architecture,  development,  managing uncertainty   in  expert  systems – Bayesian  probability   theory, Stanford   certainty  factor  algebra,  Nonmonotonic  logic   and reasoning  with beliefs, Fuzzy logic, Dempster/Shaffer  and  other approaches to uncertainty.

Unit – IV

Knowledge acquisition:  Types of learning, learning by automata, intelligent editors, learning by induction.

Genetic algorithms: Problem representation, Encoding Schemes, Operators: Selection, Crossover, Mutation, Replacement etc.

Text Books:

1.     George   F.  Luger, Artificial Intelligence, Pearson Education.

2.     Dan W. Patterson Introduction to Artificial Intelligence and Expert system, PHI.

Reference Books:

1.     Ben Coppin, Artificial Intelligence Illuminated, Narosa Publishing House.

2.     Eugene Charniak, Drew McDermott Introduction to Artificial Intelligence, Pearson Education.

3.     Nils J. Nilsson Principles of Artificial Intelligence, Narosa Publishing House.

4.     Jackson Peter, Introduction to Expert systems, Pearson-Education.

 

 

 

MCA-20-34 (i): Cloud Computing and IoT

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: To study the fundamental concepts of cloud computing, enabling technologies, cloud service models and security concerns. To learn core issues of Internet of Things, IOT communication protocols and security concerns.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-34(i).1 understand core issues of cloud computing and enabling technologies;
MCA-20-34(i).2 design services based on cloud computing platforms;
MCA-20-34(i).3 understand concepts, architecture, applications and design principles for connected devices in IoT;
MCA-20-34(i).4 explain, analyze and design IoT-oriented communication protocols and security concerns.

Unit – I

Cloud Computing: Definition, roots of cloud computing, characteristics, cloud architecture, deployment models, service models.

Virtualization: benefits & drawbacks of virtualization, server virtualization, virtualization of – operating system, platform, CPU, network, application, memory and I/O devices etc.

Unit – II

Cloud Computing Service Platforms – compute  services,  storage  services, database  services, application services, queuing services, e-mail services, notification services, media services, content delivery services, analytics services, deployment & management services, identity & access management services and their case studies.

Security in cloud computing: issues, threats, data security and information security

Unit – III

Internet of Thing (IoT): overview, conceptual framework, architecture, major components, common applications

Design principles for connected devices: Modified OSI Model for IoT/M2M systems, ETSI M2M Domains and High-level capabilities, wireless communication technologies – NFC, RFID, Bluetooth BR/EDR and Bluetooth low energy, ZigBee, WiFi, RF transceiver and RF modules. Data enrichment, data consolidation & device management at gateway.

Unit – IV

Design principles for web connectivity: web communication protocols for connected devices: constrained application protocol, CoAP Client web connectivity, client authentication, lightweight M2M communication protocol. Message communication protocols for connected devices – CoAP-SMS, CoAP-MQ, MQTT, XMPP. IoT privacy, security and vulnerabilities and their solutions.

Text Books:

1.    Arshdeep Bahga, Vijay Madisetti, Cloud Computing – A Hands-on Approach, University Press.

2.   Rajkumar Buyya, James Broberg, Andrzej Goscinski, Cloud Computing – Principles and Paradigms, Wiley India Pvt. Ltd.

3.   Raj Kamal, Internet of Things –  Architecture and Design Principles, McGraw Hills

Reference Books:

1.     Kai Hwang, Geoffrey C.Fox, and Jack J. Dongarra, Distributed and Cloud Computing,  Elsevier                India Private Limited

2.     Saurabh Kumar, Cloud Computing, Wiley India Pvt. Ltd.

3.     Shailendra Singh, Cloud Computing, Oxford

4.     Coulouris, Dollimore and Kindber, Distributed System: Concept and Design, Addison Wesley

5.     Michael Miller, Cloud Computing, Dorling Kindersley India

6.   Anthony T. Velte, Toby J. Velte and Robert Elsenpeter, Cloud computing: A practical Approach, McGraw Hill

7. Dimitrios Serpnos, Marilyn Wolf, Internet of Things (IoT) Systems, Architecture, Algorithms, Methodologies, Springer

8.    Vijay Madisetti and Arshdeep Bahga, Internet of Things (A Hands-on Approach), VPT

9.    Francis daCosta, Rethinking the Internet of Things: A Scalable Approach to Connecting Everything, Apress Publications

 

 

 

 

MCA-20-34(ii): Cyber Security

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives:The course has been designed to give students an extensive overview of cyber security issues, tools and techniques that are critical in solving problems in cyber security domains.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-34(ii).1 learn various challenges and constraints in cyber security;
MCA-20-34(ii).2 learn IT ACT (Cyber law) to the given case/problem and analyze it;
MCA-20-34(ii).3 understand the need for Computer Cyber forensics;
MCA-20-34(ii).4 demonstrate the network defence tools to provide security of information.
Unit- I

Introduction to Cyber Security: Overview of Cyber Security, Internet Governance: Challenges and Constraints, Cyber Threats, Cyber Warfare, Cyber Crime, Cyber terrorism, Cyber Espionage, Need for a Comprehensive Cyber Security Policy, Need for a Nodal Authority, International convention on Cyberspace.

Unit – II

Introduction to Cybercrime and Laws: Origins of Cybercrime, Classifications of Cybercrimes, information Security, Cybercriminals, Criminals Plan for Attacks, Cybercafe, Botnets, Attack Vector, The Indian IT ACT 2000 and amendments.

Tools and Methods used in Cybercrime: Introduction, Proxy Server and Anonymizers, Password Cracking, Keyloggers and Spyware, Virus and Warms, Trojan and backdoors, DOS and DDOS attack, SQLinjection.

Unit – III

Phishing and Identity Theft: Introduction to Phishing, Methods of Phishing, Phishing Techniques, Phishing Toolkits and Spy Phishing. Identity Theft: PII, Types of Identity Theft, Techniques of ID Theft. Digital Forensics Science, Need for Computer Cyber forensics and Digital Evidence, Digital Forensics Life Cycle.

Introduction to Intellectual Property Law – The Evolutionary Past – The IPR Tool Kit- Para -Legal Tasks in Intellectual Property Law – Ethical obligations in Para Legal Tasks in Intellectual Property Law –types of intellectual property rights.

Unit – IV

Network Defence tools: Firewalls and Packet Filters: Firewall Basics, Packet Filter Vs Firewall, Packet Characteristic to Filter, Stateless Vs Stateful Firewalls, Network Address Translation (NAT) and Port Forwarding, Virtual Private Networks, Linux Firewall, Windows Firewall, Snort Detection System, Introduction to block chain technology and its applications.

Text Books:

1.   Mike Shema, Anti-Hacker Tool Kit (Indian Edition), Publication McGraw Hill.

2.   Nina Godbole and SunitBelpure, Cyber Security: Understanding Cyber Crimes, Computer Forensics and Legal Perspectives, Publication Wiley.

Reference Books:

1.     Marjie T. Britz, Computer Forensics and Cyber Crime: An Introduction, Pearson Education

2.   Chwan-Hwa (John) Wu,J. David Irwin, Introduction to Computer Networks and Cyber security, CRC Press

3.    Bill Nelson, Amelia Phillips, Christopher Steuart, Guide to Computer Forensics and Investigations, Cengage Learning

4.     Debirag E.Bouchoux, Intellectual Property, Cengage Learning.

 

 

 

MCA-20-34 (iii): Digital Marketing

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester exam:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The purpose of this syllabus is to make students aware about the basics of marketing. The course discusses about the important role of Digital Marketing in present age of Information Technology.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-34 (iii).1 understand basics of marketing and digital marketing;
MCA-20-34 (iii).2 analyse the role of search engine in improving digital marketing and understand about the basics and importance of email marketing;
MCA-20-34 (iii).3 analyse role of social media marketing for the given problem;
MCA-20-34 (iii).4 understand about the basics and importance of web marketing and mobile marketing.

Unit – I

Introduction to Marketing, Importance and Scope of Marketing, Elements of Marketing – Needs, Wants, Demands, Consumer, Markets and Marketers; Marketing vs. Sales. Introduction to Digital Marketing, Benefits & Opportunity of Digital Marketing, Inbound and Outbound Marketing, Content Marketing, Understanding Traffic, Understanding Leads, Digital Marketing use in ‘Business to Business’ (B2B), ‘Business to Consumer’ (B2C) and ‘Not-for Profit’ marketing

Unit – II

Search Marketing (SEO): Introduction to Search Engine , Search Engine Optimization (SEO), importance of SEO for business websites, Search Results & Positioning, Benefits of Search Positioning, Role of Keywords in SEO, Meta Tags and Meta Description, On-page & Off-page optimization, Back Link, Internal & External Links, Ranking, SEO Site Map, Steps for B2B SEO and B2C SEO, Advantages & Disadvantages of SEO

Email Marketing: Introduction to Email Marketing, Elements of Email, Email List Generation, Email Structure, Email Delivery, Online Data Capture, Off Line data Capture, Creating an Email campaign, Campaign Measurement, Concept of A/B testing & its use in email marketing.

Unit – III

Digital Display Advertising: Concepts, Benefits, Challenges, Ad Formats, Ad Features, Ad Display Frequency. Overview of Google AdWords.

Social Media Marketing: Key Concepts, Different Social Media Channels – Facebook, YouTube, Twitter, Instagram, Business Page- Setup and Profile, Social Media Content, Impact of Social Media on SEO, Basic concepts – CPC, PPC, CPM, CTR, CR. Importance of Landing Page. How to create & test landing Pages. User Generated Content (Wikipedia etc.), Multi-media – Video (Video Streaming, YouTube etc), Multi-media – Audio & Podcasting (iTunes etc), Multimedia – Photos/Images (Flickr etc).

Unit – IV

Introduction to Mobile Marketing, Overview of the B2B and B2C Mobile Marketing, Use of Mobile Sites, Apps (Applications) and Widgets, Overview of Blogging Web Analytics: Introduction to Web Analytics, Web Analytics – Types & Levels, Introduction of Analytics Tools and it’s use case (Google Analytics and others), Analytics Reporting, Traffic and Behaviour Report, Evaluate Conversions.

Text Books:

1.     Stanton William J., Fundamentals of Marketing, McGraw Hill, N. Delhi.

2.     VandanaAhuja, Digital Marketing, Oxford Higher Education.

3.     Seema Gupta, Digital Marketing, McGrawHill

Reference Books:

1.    Kotler Philip & Armstrong Graw, Principles of Marketing, Pearson Education, New Delhi.

2.    Neelamegham S., Indian Cases in Marketing, Vikas Publication, New Delhi.

3.    Ian Dodson, The Art of Digital Marketing, Wiley.

4.    Puneet Singh Bhatia, Fundamentals of Digital Marketing, Pearson Education.

 

 

 

MCA-20-35(i)  Advances in JAVA

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester exam:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The course develops programming ability of students to create dynamic web applications using server side technology with Java Database Connectivity. Students can learn networking and remote method invocation using Java API. Advanced Java features will increase ability of students in web application development.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-35(i).1 develop programming using AWT, Layout, Menu and Frames;
MCA-20-35(i).2 gain the knowledge of Server Side programming by implementing Servlet and write the deployment descriptor and enterprise application deployment;
MCA-20-35(i).3 design and Develop various application using JSPs;
MCA-20-35(i).4 learn to access database through Java programs, using Java Data Base Connectivity (JDBC).

UNIT-I

GUI Programming:  AWT Classes, AWT Controls, AWT Button, AWT Label, AWT TextField, AWT TextArea, AWT Checkbox, AWTCheckboxGroup, AWT Choice, AWT List, AWT Scrollbar, AWT MenuItem& Menu, AWT PopupMenu, AWT Panel, MouseListener, MouseMotionListener, Java ItemListener, Java KeyListener, Java WindowListener. Adapter Classes, Layout managers; Grid Layout, Flow Layout, Card Layout, Border Layout, Menus, Java Frames.

UNIT-II

Servlet API and Overview: Servlet Introduction, Servlet Life Cycle, Types of Servlet, Servlet Configuration with Deployment Descriptor, Working with ServletContext and ServletConfig Object, Attributes in Servelt, Response and Redirection using Request Dispacher and using sendRedirect Method, Filter API, Manipulating Responses using Filter API, Session Tracking: using Cookies, HTTPSession, Hidden Form Fields and URL Rewriting,Types of Servlet Event: ContextLevel andSessionLevel.

UNIT-III

Java Server Pages: Introduction to JSP , Comparison with Servlet, JSP Architecture, JSP Life Cycle, JSP Scripting Elements, JSP Directives, JSP Action, JSP Implicit Objects, JSP Expression Language, JSP Standard Tag Libraries, JSP Custom Tag, JSP Session Management, JSP Exception Handling, MVC in JSP, Custom tags; Attributes, Iteration, Custom URI.

UNIT-IV

JDBC Programming: JDBC Architecture, Types of JDBC Drivers, Introduction to major JDBC Classes and Interface, Creating simple JDBC Application, Types of Statement (Statement Interface, Prepared Statement, Callable Statement), Exploring ResultSet Operations, Batch Updates in JDBC, Managing Database Transaction.

 

Text Books:-

1. Patrick Naughton, Herbert, Schild, The Complete reference Java 2, Tata Mc-Graw Hill.

2. Kathy walrath, Java server programming, J2EE, Black Book, Dream Tech Publishers.

3. Subrahmanyam Allamaraju, Cedric Buest, Professional Java Server Programming, Wiley Publication.

Reference Books:

  • Michael Morgan, Java 2 for Professionals Developers, SAMS Techmedia, New Delhi, India
  • Kito D. Mann,Java Server Faces in Action, Manning Publication
  • Maydene Fisher, Jon Ellis, Jonathan Bruce,JDBC™ API Tutorial and Reference, Addison Wesley.
  • GiulioZambon, Beginning JSP, JSF andTomcat, Apress.

 

 

 

MCA-20-35 (ii):Advanced Web Technologies

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to provide the coverage of advanced technologies used in the design and development of web based applications such as Ajax/Node JS/Angular JS etc.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-35 (ii).1 apply various jQuerymethods in building UI projects;
MCA-20-35 (ii).2 design single-page applications using Angular JS;
MCA-20-35 (ii).3 handle the HTTP request by using Node JS;
MCA-20-35 (ii).4 manage and optimize the web applications.

Unit – I

Advanced Client side programming: Fundamentals of jQuery, Element Selector, Document ready function, Events, jQuery UI, Unobtrusive client validation, working with AJAX and jQuery.

Feature detection: Browser detection, Feature detection, Modernizer.

Unit – II

Introduction to AngularJS: Controllers, Models, Directives and Services, Single Page Applications, Angular User Interfaces:  Angular Forms, Using Angular with Angular UI and Angular Bootstrap, Angular Services, Developing Custom Directives, Enhanced End-to End Testing.

Unit – III

Introduction to Node JS: Node JS process model, Advantages, Traditional web server model. Setup Install Node.js on windows, REPL, Node JS console, Node JS modules, Events: Event Emitter class, inheriting events, Node Package Manager, Creating web server: handling http requests, sending requests, File System, Debugging Node JS application, Database Connectivity.

Unit – IV

Search engines: Searching techniques used by search engines, keywords, advertisements, Search engine optimization for individual web pages: header entries, tags, selection of URL, alt tags, Search engine optimization for entire website: Hyperlinks and link structure, page rank of Google, click rate, residence time of website, frames, scripts, content management system, cookies, robots, Pitfalls in Optimization: optimization and testing, keyword density, doorway pages, duplicate contents, quick change of topics, broken links, poor readability, rigid layouts, navigation styles.

Text Books:

1.     Shyam Seshadri & Brad Green, AngularJS: Up and Running, O’Relly.

2.     Peter Smith, Professional Website performance, Wiley India Pvt. Ltd.

 

Reference Books:

  • Brad Dayley, Node.js, MongoDB, and AngularJS Web Development (Developer’s Library), Addison Wesley.
  • Simon Holmes, Getting MEAN with Mongo, Express, Angular, and Node, Manning Publications.
  • Black Book, HTML5, Dreamtech Press.
  • Maro Fischer, Website Boosting: Search Engine, Optimization, Usability, Website Marketing, Firewall Media, New Delhi.

 

 

 

MCA-20-35(iii): Programming with Kotlin

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester exam:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this paper is to make the students familiar with the Programming Language Kotlin so that they shall be able to design the Mobile Applications.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-35(iii).1 understand the different collection implementation using Kotlin;
MCA-20-35(iii).2 implement different types of functions;
MCA-20-35(iii).3 understand the concepts of classes and interfaces and implement them;
MCA-20-35(iii).4 design and develop Android using Kotlin.

Unit – I

Variables and Data types, Handling of Strings, Arrays: Generic arrays, arrays of primitives, List, Map and Set. Ranges, Null safety: Nullable and Non-nullable types, Elvis operator (?:)

Unit – II

Conditional Statements: if, when; Loops in Kotlin: for, repeat, while; break and continue.

Functions: Inline Function, Lambda Functions, Function Reference, Vararg parameters in Functions.

Unit – III

Class: Final class, open class, Inheritance: inheriting methods and fields from a class, Overriding properties and methods, Visibility modifiers, Abstract class, Data Class, Enum class, Sealed class, Nested class, Inner class, Interfaces, Programming asynchronous applications with Coroutines, Annotations.

Unit – IV

Exception Handling: Try, Catch, Finally block, Throw.

Android development using Kotlin. Views: TextView, EditView, ScrollView, ImageView, ListView, Recycler view etc. Android UI Layouts: Linear, Relative and Constraint, Creating Activities, Intents and Fragments.

Text Books:

1.     Sommerhoff Peter, Kotlin for Android App Development, Pearson.

2.     VenkatSubramaniam, Programming Kotlin, Pragmatic Bookshelf.

Reference Books:

1. Stephen Samuel & Stefan Bocutiu, Programming Kotlin, Packt Publishing Ltd.

2. Antonio Leiva, Kotlin for Android Developers, Leanpub.

3. MarcinMoskala & Igor Wojda, Android Development with Kotlin, Packt Publishing Ltd.

 

MCA-20-41: Big Data and Pattern Recognition

Type: Compulsory

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester exam:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The aim of this course is to develop knowledge of big data tools including MapReduce, NoSQL and Hadoop. The course provides an idea about data analysis; pattern recognition approaches and gives the practical exposure of NoSQL.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-41.1 understand Big Data strategies in Big Data Environment;
MCA-20-41.2 learn Basics of HDFS and Learn map-reduce analytics using Hadoop;
MCA-20-41.3 acquire knowledge of pattern recognition approaches and methods;
MCA-20-41.4 to develop solutions in NoSQL to meet the current job requirements.

UNIT – I

Understanding Big Data: Concepts and Terminology, Big Data Characteristics, Different Types of Data, Identifying Data Characteristics, Business Motivations and Drivers for Big Data Adoption: Business Architecture, Business Process Management, Information and Communication Technology, Big Data Analytics Lifecycle, Enterprise Technologies and Big Data Business Intelligence, Industry examples of big data.

UNIT – II

Data Governance for Big Data Analytics: Evolution of Data Governance, Big Data and Data Governance, Big Datasets, Big Data Oversight, Big Data Tools and Techniques: HDFS, Map Reduce, YARN, Zookeeper, HBase, HIVE, Pig, Mahout, Developing Big Data Applications, Stepwise Approach to Big Data Analysis, Big Data Failure: Failure is common, Failed Standards, Legalities.

UNIT – III

Data Analysis and Pattern Recognition: Quantitative and Qualitative Analysis, Pattern Recognition Systems, Fundamental Problems in Pattern Recognition, Feature Extraction and Reduction, Paradigms, Pattern Recognition Approaches, Importance and Applications. Data Domain for Pattern Recognition. Pattern Recognition using Nearest Neighbour Classifier and Modeling an AND Gate Neural Nets.

UNIT – IV

An Overview of NoSQL, Characteristics of NoSQL, NoSQL Storage Types, Introduction of NoSQL Products, NoSQL Data Management for Big Data: Schema Less Models, Key-Value Stores, Document Stores, Tabular Stores, Object Data Stores, Graph databases, NoSQL Misconceptions, NoSQL over RDBMS.

Text Books:

  1. Thomas Erl, WajidKhattak and Paul Buhler, Big Data Fundamentals Concepts, Drivers & Techniques Prentice Hall.
  2. David Loshin, Big Data Analytics from Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph Morgan Kaufmann.
  3. ules J. Berman, Principles of Big Data Preparing, Sharing and Analyzing Complex Information, Morgan Kaufmann.
  4.  GauravVaish, Getting Started with NoSQL, Packt Publishing.
  5. RajjanShinghal, Pattern Recognition Techniques and Applications, Oxford Higher Education.
Reference Books:

  1. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer.
  2. Jay Liebowitz, Big Data and Business Analytics, Auerbach Publications, CRC press.
  3. Pete Warden, Big Data Glossary, O’Reily.
  4. Michael Mineli, Michele Chambers, AmbigaDhiraj, Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses, Wiley Publications.

 

 

 

MCA-20-42:Computer Graphics and Animation

Type: Compulsory

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: Provide an introduction to the theory and practice of Computer Graphics and Animation. Provide an insight to applications of Graphics and the graphics hardware devices and software used. Introduce the principles needed to design a graphics system and the algorithms related with them.
Course Outcomes: At the end of this course, the student will be able to:
MCA-20-42.1 have a knowledge of graphics applications and components and devices required to support the applications;
MCA-20-42.2 develop  algorithms  for scan converting geometrical  primitives such as lines, circles, ellipses, and curves along with algorithms for filling polygons,  required for designing real-world applications;
MCA-20-42.3 design algorithms for carrying out manipulations in pictures using geometric transformations, viewing transformations , and clipping operations;
MCA-20-42.4 model 3-dimensional objects and apply viewing, visible –surface determination, and shading techniques to the models for achieving realism. The student will also learn to design and develop animation sequences.

Unit – I

Introduction to Computer Graphics and its Components: Overview of Computer Graphics,  its functions & elements; Introduction to GUI, Computer Vision, Augmented Reality and other  Applications of Graphics; Popular Graphics Software; Components and Working of Interactive Graphics; Raster Scan and Random Scan systems and Display Processors; Look-up table; Loading the Frame Buffer; Coordinate Systems.

Graphics Devices: Display Technologies: Resolution, Aspect Ratio, Refresh CRT , Color CRT , Flat Panel Displays;  Interactive Input Devices for Graphics , Image and Video Input Devices.

Unit – II

Scan Conversion: Drawing Geometry; Output Primitives; Lines and Pixel Graphics; AntiAliasing; Scan Converting Lines: DDA line drawing algorithms, Bresenham’s line Algorithm;  Scan Converting Circles: Polynomial method for circle drawing, circle drawing using polar coordinates, Bresenham’s circle drawing; Algorithms for Generation of ellipse; Line Styles; Generation of Bar Charts, Pie-Charts.

Curve Representation: Parametric Curves, Parametric Representation of a Circle, Parametric representation of cubic curves, drawing Bezier curves.

Filled-Area Primitives: Basic Stack based fill algorithms: Flood fill algorithm, Boundary fill algorithm; Scan-line polygon fill algorithm and its computational structures.

Unit – III

Two-Dimensional Transformations: Coordinate and Geometric Transformations; Translation, Rotation, Scaling; Matrix representations and Homogeneous coordinates, Composite transformations, General Pivot Point rotation, General Fixed Point Scaling, Shearing; Reflection ; Reflection about an arbitrary line.

2-D Viewing: Viewing pipeline; Window, Viewport, Window-to-Viewport transformation;  Zooming, Panning;  Pointing and Positioning techniques; Rubber band technique; Dragging.

Clipping operations: Point and Line clipping, Cohen-Sutherland line clipping, Mid-Point Subdivision line clipping, Liang-Barsky line clipping, Sutherland-Hodgman polygon clipping; Weiler-Atherton polygon clipping.

Unit – IV

3-D Graphics & Modeling: Visualization techniques for Realism; 3D Object Representation; Solid Model Representation Schemes; Euclidean Geometry methods: Regularized Boolean Set Operations, Primitive Instancing, Boundary Representations, Curved lines and surfaces, Sweep Representations, Spatial-Partitioning Representations – Octree representation, Constructive Solid Geometry; Procedural Methods: Fractals, Shape Grammars, Particle systems, Physically Based modeling, Visualization techniques; 3D transformations.

Three-Dimensional Viewing: Viewing Pipeline; Parallel Projection: Orthographic and Oblique Projection; Perspective Projection.

Visible-Surface Determination: Z-buffer, Depth-Sorting, Area Subdivision, BSP-Tree method; Ray casting.

Illumination and Shading: Modeling Light Intensities; Basic Illumination Models; Gouraud Shading; Phong Shading.

Introduction to Animation: Designing of Animation Sequences; Key-Frame Systems; Animation Techniques: Tweening, Morphing.

Text Books:

1.     Donald Hearn, M. Pauline Baker, Computer Graphics, Pearson Education.

2.     J. D. Foley, A. Van Dam, S. K. Feiner and J. F. Hughes, Computer Graphics – Principles and Practice, Pearson Education.

Reference Books:

1.     Newmann & Sproull, Principles of Interactive Computer Graphics, McGraw Hill.

2.     Rogers, David F., Procedural Elements of Computer Graphics, McGraw Hill.

3.     Zhigang Xiang, Roy Plastock, Computer Graphics, Tata McGraw Hill.

4.     Malay K. Pakhira, Computer Graphics, Multimedia and Animation, PHI

5.     Steven Harrington, Computer Graphics, A Programming Approach, McGraw Hill.

 

 

 

MCA-20-43: Mobile Application Development

Type: Compulsory

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to provide the in-depth coverage of various concepts of mobile application development especially android based applications. This course will help the students in learning to develop and publish their own mobile applications.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-43.1 know the components and structure of mobile application development frameworks for Android based mobiles;
MCA-20-43.2 design and implement the user interfaces of mobile applications;
MCA-20-43.3 implement fragments and location based services in Android application;
MCA-20-43.4 understand the basics of SQLite and develop interactive graphics in mobile applications.

Unit – I

Introduction: Mobile Applications, Characteristics and Benefits, Application Models, Mobile devices Profiles. Basics of Android, Importance and scope, Android Versions, Features of Android, Android Architecture, Android Stack, Android Applications Structure, Android Emulator, Android SDK, Overview of Android Studio, Android and File Structure, Android Virtual Device Manager, DDMS, LogCat, Understanding Activities.

Android User Interface: Measurements – Device and pixel density independent measuring units. Layouts – Linear, Relative, Grid and Table Layouts.

Unit – II

User Interface (UI) Components – Editable and non-editable Text Views, Buttons, Radio and Toggle Buttons, Checkboxes, Spinners, Dialog and pickers, List View, Spinner View.

Event Handling – Handling clicks or changes of various UI components.

Intents and Broadcasts: Intent – Using intents to launch Activities, Explicitly starting new Activity, Implicit Intents, Passing data to Intents, Getting results from Activities, Native Actions, using Intent to dial a number or to send SMS

Services– Callbacks and Override in application, Application Signing, API keys for Google Maps, Publishing application to the Android Market.

Unit – III

Fragments – Creating fragments, Lifecycle of fragments, Fragment states, Adding fragments to Activity, adding, removing and replacing fragments with fragment transactions, interfacing between fragments and Activities, Multi-screen Activities

Location and Mapping: location based services, Mapping, Google Maps activity, Working with MapViewand MapActivity; Playing and Recording of Audio and Video in application; Sensors and Near Field Communication; Native libraries and headers, Building client server applications.

Unit – IV

Using Graphics: Canvas Drawing, Shadows, Gradients.

Persisting Data to files: Saving to Internal Storage, Saving to External Storage

Introduction to SQLite database: creating and opening a database, creating tables, inserting retrieving and deleting data, Registering Content Providers, Using content Providers (insert, delete, retrieve and update).

Text Books:

  1. Zigurd Mednieks, Laird Dornin, G, Blake Meike and Masumi Nakamura, Programming Android, O’Reilly Publications.
  2. Wei-Meng Lee, Beginning Android Application Development, Wiley India Ltd.

Reference Books:

1.     James C.S., Android Application development for Java Programmer, CENGAGE Learning.

2.     Pradeep Kothari, Android Application Development: Black Book, Wiley India Ltd.

3.     Gargenta M., Nakamura M., Learning Android, O’Reilly Publications.

 

MCA-20-44(i): Soft Computing

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives:  Introduce fundamental soft computing concepts with an exposure to non-traditional techniques for problem solving and optimization. Provide Soft Computing based research oriented direction for solving imprecisely defined problems. Provide a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing.
Course Outcomes: At the end of this course, the student will be able to:
MCA-20-44(i).1 have a knowledge of  soft computing techniques along with their applications and non-traditional metaheuristic optimization and data clustering techniques & algorithms for obtaining optimized solutions to optimization, computational intelligence, and design/scheduling applications;
MCA-20-44(i).2 apply fuzzy logic theory to imprecisely defined problems;
MCA-20-44(i).3 use Neural Networks concepts to find solutions to problems where normally algorithmic methods do not exist or are costly;
MCA-20-44(i).4 design high-quality solutions using Genetic Algorithms for optimization and search problems and have exposure to MATLAB environment for implementing solutions to problems using soft computing techniques.

Unit – I

Soft Computing : Conventional AI to Computational Intelligence;  Soft Computing Constituents and Applications.

Introduction to Non-traditional Metaheuristic Optimization Techniques: Random Optimization, Simulated Annealing, Tabu Search, Ant Colony Optimization, Particle Swarm Optimization, Harmony Search, Memetic Algorithms, Other Evolutionary Algorithms such as Firefly Algorithm, Bee Algorithm, Shuffled Frog Leap algorithm, Bat algorithm etc.

Data Clustering Algorithms: K-Means, Fuzzy C-Means, Mountain Clustering, Subtractive Clustering.

Unit – II

Fuzzy Set theory:  Fuzzy Sets & Classical Sets; Operations on Fuzzy Sets, Fuzzy Relations, Linguistic Variables.

Membership Functions: Introduction, Features, & Fuzzification, Methods of Membership Value Assignment; Defuzzification.

Fuzzy Systems: Crisp Logic, Predicate Logic, Fuzzy Logic; Fuzzy Rule Base and Approximate Reasoning, Fuzzy Quantifiers; Fuzzy Inference Systems, Fuzzy Decision Making, Fuzzy Logic Control System; Fuzzy Expert Systems.

Unit – III

Neural Networks: Fundamental Concepts, Basic Models and Architecture; Machine Learning Using Neural Networks; Associative Memory Networks and their Applications.

Supervised Learning Neural Networks: Perceptron Networks, Radial Basis Function Networks: Back Propagation Neural Network:  Architecture, Learning, Applications, & Research Directions; The Boltzman Machine.

Unsupervised Learning Networks: Competitive Learning networks; Kohonen Self-Organizing Networks; Hebbian learning; The Hopfield Network; Counter propagation Networks; Adaptive Resonance Theory: Introduction, Architecture, & Applications; Feed forward Networks; Reinforcement Learning.

Unit – IV

Genetic Algorithms: Introduction to Genetic Algorithms (GA) and their Terminology; Traditional Optimization and Search Techniques vs.  Genetic Algorithm ; Operators in Genetic Algorithms; Problem Solving using Genetic Algorithm; Classification of Genetic Algorithms;  Holland’s Classifier Systems; Genetic Programming; Advantages and Limitations of Genetic Algorithm; Applications of Genetic Algorithm; Applications of GA in Machine Learning.

Introduction to Hybrid Systems; MATLAB Environment for Soft Computing Techniques.

Text Books:

1.     S. N. Sivanandam & S. N. Deepa, Principles of Soft Computing, Wiley – India.

2.     Jyh Shing Roger Jang, Chuen Tsai Sun, Eiji Mizutani, Neuro Fuzzy and Soft Computing, Prentice Hall.

Reference Books:

  1. S.Rajasekaran and G.A.Vijayalakshmi Pai, Neural Networks, Fuzzy Logic and Genetic Algorithm: Synthesis and Applications, Prentice-Hall of India Pvt. Ltd.
  2. George J. Klir and Bo Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall.
  3. George J. Klir, Ute St. Clair, Bo Yuan, Fuzzy Set Theory: Foundations and Applications Prentice Hall.
  4. Simon O. Haykin, Neural Networks: a comprehensive foundation, Pearson Education.
  5. Mitchell Melanie, An Introduction to Genetic Algorithm, Prentice Hall
  6. Goldberg D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Pearson Education.
  7. Ahmad Lotfi, Jonathan Garibaldi, Applications and Science in Soft Computing, Springer.
  8. Rajkumar Roy, Mario Koppen Soft Computing and Industry: Recent Applications, Springer.
  9. James A. Freeman, David M. Skapura, Neural Networks Algorithms, Applications, and Programming Techniques, Pearson Education India.
  10. Du, Ke-Lin, Swamy, M. N. S., Search and Optimization by Metaheuristics: Techniques and Algorithms, Springer
  11. Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loaiciga, Meta-heuristic and Evolutionary Algorithms for Engineering Optimization, Wiley

 

 

MCA-20-44(ii): Machine Learning

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester exam:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to enable student to perform experiments in Machine Learning using real-world data.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-44(ii).1 understand the basics of machine learning and supervised learning;
MCA-20-44(ii).2 analyse and implement the concepts of Naïve-Bayes and Regression;
MCA-20-44(ii).3 understand the unsupervised learning using clustering algorithms;
MCA-20-44(ii).4 perform dimensionality reduction and understand the basics of reinforcement learning.

Unit – I

Machine Learning: Introduction to Machine Learning, Overview of Machine Learning, Key Terminology and task of ML, Applications of ML;

Supervised Learning: Classification, Decision Tree Representation- Appropriate problem for Decision Learning, Decision Tree Algorithm, Hyperspace Search in Decision Tree;

Unit – II

Naive Bayes- Bayes Theorem, Classifying with Bayes Decision Theory , Conditional Probability, Bayesian Belief Network;

Regression: Linear Regression- Predicting numerical value, Finding best fit line with linear regression, Regression Tree- Using CART for regression.

Unit – III

Logistic Regression – Classification with Logistic Regression and the Sigmoid Function;

Clustering: Learning from unclassified data –Introduction to clustering, K-Mean Clustering, Expectation-Maximization Algorithm(EM algorithm), Hierarchical Clustering, Supervised Learning after clustering.

Unit – IV

Dimensionality reduction- Dimensionality reduction techniques, Principal component analysis, Anomaly Detection, Recommender Systems; SVM, Reinforcement Learning.

Text Books:

1.    Tom M. Mitchell, Machine Learning, McGraw-Hill Education (India) Private Limited.

2.   EthemAlpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press.

Reference Books:

  1. Stephen Marsland, Machine Learning: An Algorithmic Perspective, CRC Press.
  2. Peter Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data,  Cambridge University Press.
  3. Peter Harrington, Machine Learning in Action, Manning
  4. Shai Shalev-Shwartz and Shai Ben David, Understanding Machine Learning From Theory to Algorithms, Cambridge University Press

 

 

 

                                                MCA-20-44 (iii): Digital Image Processing

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: Provide an introduction to the basic concepts and methodologies for digital image processing. To develop a foundation that can be used as a basis for further studies and research. Introduce the students to the fundamental techniques and algorithms used for acquiring, processing and extracting useful information from digital images.
Course Outcomes: At the end of this course, the student will be able to:
MCA-20-44(iii).1 get acquainted with digital image fundamentals and its applications and get acquainted with the image representation and description methods;
MCA-20-44(iii).2 Learn and perform image pre-processing and enhancement to improve the image for further processing;
MCA-20-44(iii).3 reconstruct photometric properties degraded by the imaging process  and  partition a digital image into multiple segments;
MCA-20-44(iii).4 represent and analyse  images at different resolutions , process images according to their shapes, and apply compression techniques to reduce the storage space of images.

Unit – I

Digital Image Fundamentals: Introduction to Digital Image Processing and its applications; Components of an Image Processing System.

Image Representation and Description: Image Representation ; Digital Image Properties; Boundary descriptors; Regional descriptors;  Steps in Digital Image Processing; Elements of Visual perception; Image Sensing and Acquisition; Image Sampling and Quantization; Relationship between Pixels; Color Representation.

Data Structures for Image Analysis: Levels of Image Data Representation; Traditional Image Data Structures: Matrices, Chains, Topological Data Structures, Relational Structures; Hierarchical Data Structures: Pyramids, Quadtrees, Other Pyramidal Structures.

Unit – II

Image Pre-Processing: Pixel Brightness Transformations:  Position-Dependent Brightness Correction, Gray-Scale Transformation; Geometric Transformations:  Pixel Co-ordinate Transformations, Brightness Interpolation; Local Pre-Processing.

Image Enhancement: Spatial Domain: Gray level transformations; Histogram processing; enhancement using arithmetic and logic operators; Basics of Spatial Filtering; Smoothing and Sharpening Spatial Filtering.

Frequency Domain: Introduction to Fourier Transform; Filtering in the Frequency Domain; Smoothing and Sharpening frequency domain filters; Homomorphic Filtering.

Unit – III

Image Restoration and Segmentation:  Noise models; Mean Filters; Order Statistics; Adaptive filters; Noise Reduction by Frequency Domain Filtering; Inverse and Wiener filtering; Constrained Least Squares Filtering.

Segmentation: Point, line, and Edge Detection;  Edge Linking and Boundary detection; Thresholding;  Region based segmentation; Edge based Segmentation; Segmentation by Morphological Watersheds; Matching.

Color Image Processing: Color Fundamentals, Color Models, Pseudocolor Image Processing.

Unit – IV

Wavelets and Multiresolution Processing: Background: Image Pyramids; Subband coding; Multiresolution expansions.

Morphological Image Processing: Preliminaries, Erosion and Dilation, Opening and Closing, The Hit-or-Miss Transforms, Some Basic Morphological Algorithms.

Compression – Fundamentals ; Image Compression models; Error-Free Compression;  Variable Length Coding, LZW coding, Bit-Plane Coding, Lossless Predictive Coding;  Lossy Compression: Lossy Predictive Coding, Transform Coding, wavelet Coding;  Image  Compression Standards.

Text Books:

1.     Rafael C. Gonzales, Richard E. Woods, Digital Image Processing, Pearson Education.

Reference Books:

1.     Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Digital Image Processing Using MATLAB, Third Edition ,Tata McGraw Hill .

2.     Anil Jain K., Fundamentals of Digital Image Processing, PHI Learning.

3.     Willliam K Pratt, Digital Image Processing, John Willey.

4.     Malay K. Pakhira, Digital Image Processing and Pattern Recognition, First Edition, PHI Learning.

5.     S. Jayaraman, S. Esakkirajan and T. Veerakumar,  Digital Image Processing,  McGraw Hill

6.     B. Chanda ,D.DuttaMajumder,  Digital Image Processing and Analysis, Prentice Hall of India.

 

MCA-20-45 (i): Optimization Techniques

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester exam:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to provide the in-depth coverage of various linear programming problems and their solution techniques. It focuses on various optimization techniques and their applications in problem solving.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-45 (i).1 understand the role and principles of optimization techniques in business world;
MCA-20-45 (i).2 understand the techniques to solve and use LPP and IPP;
MCA-20-45 (i).3 analyse the optimization techniques in strategic planning for optimal gain;
MCA-20-45 (i).4 understand the techniques to solve networking and inventory issues;

Unit – I

Introduction: The Historical development, Nature, Meaning and Management Application of Operations research.  Modelling, Its Principal and Approximation of O.R. Models, Main characteristic and phases, General Methods of solving models, Scientific Methods, Scope, Role on Decision Making and Development of Operation Research in India.

Linear Programming: Formulation, Graphical solution, standard and matrix form of linear programming problems, Simplex method and its flow chart, Two-phase Simplex method, Degeneracy.

Unit – II

Duality in LPP: Definition of Dual Problem, General Rules for converting any Primal into its Dual, Dual Simplex method and its flow chart.

Integer Programming: Importance, Applications and Classification, Gomory’s all integer programming problem technique and its flow chart, Branch and Bound Method.

Unit – III

Transportation Models: Formulation of problem, Obtaining Initial Basic feasible solution, Optimality tests, Progressing towards optimal solution, Unbalanced Transportation Problems.

Assignment Models: Formulation of problem, Hungarian Method for Assignment Problems, Unbalanced Assignment Problems.

Unit – IV

Inventory theory Costs involved in inventory problems – single item deterministic models-economic lot size models without shortages and with shortages having production rate infinite and finite.

PERT and CPM: Basic steps in PERT/CPM, Techniques, Network Diagram Representation, Forward and Backward Pass-computation, Representation in Tabular form, Determination of Critical path, Critical activity,  Floats and Slack Times, Implementation in any programming language.

Text Books:

1.     Sharma, S.D., Operations Research, KedarNath and Ram Nath, Meerut.

2.     Gupta P.K., Hira and D.S., Operation Research, Sultan Chand & Sons, New Delhi.

Reference Books:

1.     KantiSwarup, Gupta P.K. & Man Mohan, Operation Research, Sultan Chand & sons, New Delhi.

2.     Rao S.S., Optimization Theory and Applications, Wiley Eastern Ltd. New Delhi.

3.     Taha, H.A., Operation Research – An Introduction, McMillan Publishing Co, New York.

4.    Gillet, B.E., Introduction to Operations Research: A Computer Oriented Algorithmic Approach, Tata McGraw Hill, New York.

 

 

 

MCA-20-45(ii): Information Systems

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30(i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to provide an in-depth exploration of how businesses successfully manage information and provide insight into how today’s businesses leverage information technologies and systems to achieve corporate objectives.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-45(ii).1 gain skills sought after in today’s workplace;
MCA-20-45(ii).2 gain knowledge about IT Infrastructure & Emerging Technologies and their impact on business models and managerial decision-making;
MCA-20-45(ii).3 learn the security issues in information systems and various Enterprise Applications;
MCA-20-45(ii).4 understand, participate in, and eventually lead management discussions and drivedecisions about the firm’s information systems.

Unit – I

Fundamental of Management Information systems: The Fundamental Roles of Information System in business, Trends in Information Systems, Types of Information Systems, Managerial Challenges of Information Technology.

The Components of Information Systems: System Concept, Components of an Information System, Information System Resources, Information System Activities, Recognizing Information Systems.

Unit – II

IT Infrastructure and Emerging Technologies: IT Infrastructure, Infrastructure Components, Software/Hardware Platform Trends and Emerging Technologies, Management Issues.

Foundation of Business Intelligence: Databases and Information Management: Organizing Data in a Traditional File Environment, The Database Approach to Data Management, Using Database to Improve Business Performance and Decision Making, Managing Data Resources.

Unit – III

Securing Information Systems: System Vulnerability and Abuse, Business Value of Security and Control, Establishing a Framework for Security and Control, Technologies and Tools for Security.

Key System Applications for the Digital Age.

Enterprise Applications: Enterprise Systems, Supply Chain Management Systems, Customer Relationship Management Systems, Enterprise Applications: New Opportunities and Challenges.

 

Unit – IV

Managing Knowledge: The Knowledge Management Landscape, Enterprises-Wide Knowledge Management Systems, Knowledge Work Systems, Intelligent Techniques.

Enhancing Decision Making: Decision Making and Information Systems, Systems for Decision Support, Executive Support Systems (ESS), Group Decision-Support Systems (GDSS).

Text Books:

1. Kenneth C.Laudon, Jane P.Laudon, Management Information Systems: Managing the Digital Firm, Pearson

Education.

2. James A O’Brien, George M Marakas, Management Information Systems, Tata McGraw-Hill.

Reference Books:

1. Laudon & Laudon, Essentials of Management Information Systems, Pearson Education.

2. McLeod & Schell, Management Information Systems, Pearson Education.

3. Jawadekar, W.S., Management Information Systems, Tata McGraw-Hill.

4. Robert G.Mudrick, CoelE.Ross, James R.Claggett,Information Systems for Modern Management.

 

 

 

MCA-20-45 (iii): Blockchain Technology

Type: Elective

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester exam:

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to introduce the concept of Blockchain. This course introduces the concept of Bitcoin and makes students familiar with Bitcoin network, payments, clients and APIs.
Course Outcomes (COs) At the end of this course, the student will be able to:
MCA-20-45 (iii).1 understand the concept of Blockchain and Decentralization;
MCA-20-45 (iii).2 understand the usage of Block chain and Bitcoin implementation;
MCA-20-45 (iii).3 understand and analyse the Bitcoin network and payments;
MCA-20-45 (iii).4 analyze the various platforms used for Blockchain.

Unit – I

Discover Blockchain Technology: Blockchain, Growth of blockchain technology, Distributed systems, History of blockchain and Bitcoin, Types of blockchain.

Decentralization: Methods of decentralization, Routes of decentralization, Blockchain and full ecosystem decentralization, Smart contracts, Decentralized organizations and platforms for decentralization.

Unit – II

Blockchain: Architecture, Versions, Variants, Use cases, Life use cases of blockchain, Blockchain vs shared Database, Introduction to cryptocurrencies, Types, Applications.

Bitcoins: Introducing Bitcoin, Bitcoin digital keys and addresses, Transactions, Blockchain mining. Alternative Coins. Limitations of Bitcoin

Unit – III

Concept of Double Spending, Hashing, Proof of work.

Bitcoin Network and payments, Bitcoin network, Wallets, Bitcoin payments, Innovation in Bitcoin, Bitcoin Clients and APIs.

Unit – IV

Introduction to Blockchain Platforms: Ethereum, Hyperledger, IOTA, EOS, Multichain, Bigchain, etc., Advantages and Disadvantages, EthereumvsBitcoin, Design a new blockchain, Potential for disruption, Design a distributed application, Blockchain applications.

Text Books:

  1. Imran Bashir, Mastering Blockchain, PACKT Publication.
  2. Arshdeep Bikramaditya Singal, Gautam Dhameja, Priyansu Sekhar Panda., Beginning Blockchain: A Beginner’s Guide to Building Blockchain Solutions, APress.
  3. Bahga, Vijay Madisetti, Blockchain Applications: A Hands-On Approach.
  4. Melanie Swan, Blockchain, OReilly
Reference Books:

1.     Aravind Narayan. Joseph Bonneau, Bitcoin and Cryptocurrency Technologies, Princton

2.     Arthu.T Books, Bitcoin and Blockchain Basics: A non-technical introduction for beginners.

 

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