CHRIST (Deemed to University), Bangalore

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

School of Business and Management

Syllabus for
Master of Technology (Computer Science and Engineering)
Academic Year  (2023)

 
1 Semester - 2023 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MTAC121 ENGLISH FOR RESEARCH PAPER WRITING Ability Enhancement Compulsory Courses 2 2 0
MTAC122 DISASTER MANAGEMENT Ability Enhancement Compulsory Courses 2 2 0
MTAC123 VALUE EDUCATION Ability Enhancement Compulsory Courses 1 0 0
MTAC124 CONSTITUTION OF INDIA Ability Enhancement Compulsory Courses 2 0 0
MTCS112 PROFESSIONAL PRACTICE - I Core Courses 2 1 50
MTCS132 ADVANCED ALGORITHMS Core Courses 3 3 100
MTCS133 ADVANCED DATABASE SYSTEMS Core Courses 3 3 100
MTCS134 SOFTWARE PROJECT MANAGEMENT Core Courses 3 3 100
MTCS135 ADVANCED DATA SCIENCE Core Courses 3 3 100
MTCS151 ADVANCED ALGORITHMS LAB Core Courses 2 2 50
MTCS152 ADVANCED DATABASE SYSTEMS LAB Core Courses 4 2 50
MTMC125 RESEARCH METHODOLOGY AND IPR Core Courses 3 3 100
2 Semester - 2023 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MTAC225 PEDAGOGY STUDIES Ability Enhancement Compulsory Courses 2 0 0
MTCS212 PROFESSIONAL PRACTICE-II Core Courses 2 1 50
MTCS231 NETWORK SECURITY Core Courses 3 3 100
MTCS232 DATA AND WEB ANALYTICS Core Courses 3 3 100
MTCS233 ADVANCED DIGITAL IMAGE PROCESSING Core Courses 5 4 100
MTCS241E01 BIG DATA ANALYTICS Discipline Specific Elective Courses 3 3 100
MTCS242E01 IOT ARCHITECTURE & COMPUTING Discipline Specific Elective Courses 3 3 100
MTCS251 NETWORK SECURITY LAB Core Courses 4 2 100
MTCS252 DATA AND WEB ANALYTICS LAB Core Courses 4 2 50
3 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MTCS345E02 ADVANCED ARTIFICIAL INTELLIGENCE Discipline Specific Elective Courses 3 3 100
MTCS381 INTERNSHIP Core Courses 4 2 50
MTCS382 DISSERTATION PHASE - I Core Courses 20 10 200
MTEC361 COMPRESSION AND ENCRYPTION TECHNIQUES Discipline Specific Elective Courses 3 3 100
4 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MTCS483 DISSERTATION PHASE-II Project 32 16 200
    

    

Introduction to Program:

The 2 year Post graduate program M.Tech in Computer Science and Engineering.started in 2011 . The course was started mainly to cater to the increasing demand for higher studies in the country. A growing intake with students from across the nation shows the popularity of the program. The Department strives to give skills essential to practicing engineering professionals; it is also an objective to provide experience in leadership, management, planning, and organization. The department understands its role in developing and evaluating methods that encourage students to continue to learn after leaving the university. We believe that the student opportunities and experiences should lead to an appreciation of the holistic development of individual. We also try to pass to our students our passion for what we do, and to have the students comprehend that we also desire to continue to learn.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO1: PO1: Acquire in-depth knowledge of specific discipline or professional area, including wider and global perspective, with an ability to discriminate, evaluate, analyze and synthesize existing and new knowledge, and integration of the same for enhancement of knowledge.

PO2: Analyze complex engineering problems critically, apply independent judgment for synthesizing information to make intellectual and/or creative advances for conducting research in a wider theoretical, practical and policy context.

PO3: Think laterally and originally, conceptualize and solve engineering problems, evaluate a wide range of potential solutions for those problems and arrive at feasible, optimal solutions after considering public health and safety, cultural, societal and environmental factors in the core areas of expertise.

PO4: Develop and design real time projects more efficiently using math, statistics and analytics tools to deliver quality software solutions.

PO5: Analyze and apply the needs of computing in the society to promote novel and sustainable research ideas.

PO6: Apply ethical and professional skills along with computational intelligence to explore entrepreneurial journey.

Assesment Pattern

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                     : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III: Quizzes/Seminar/Case Studies/Project Work       : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

For subjects having practical as part of the subject

            End semester practical examination                                      : 25 marks

            Records                                                                                   : 05 marks

            Mid semester examination                                                     : 10 marks

            Class work                                                                              : 10 marks

            Total                                                                                       : 50 marks

Examination And Assesments

Assessment of each paper

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

MTAC121 - ENGLISH FOR RESEARCH PAPER WRITING (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:2

Course Objectives/Course Description

 

Course description:

The course is designed to equip the necessary awareness and command on the use of English language in writing a research paper starting from how to compile an appropriate title, language to use at different stages of a paper to make it effective and meaningful. 

Course objectives:

  • Understand that how to improve your writing skills and level of readability
  • Learn about what to write in each section.
  • Understand the skills needed when writing a Title and ensure the good quality of paper at very first-time submission

Course Outcome

C01: Write research paper which will have higher level of readability

C02: Demonstrate what to write in each section

C03: To write appropriate Title for the research paper

CO4: Write concise abstract

C05: Write conclusions clearly explaining the outcome of the research work

Unit-1
Teaching Hours:6
Fundamentals of Research Paper
 

-          Planning and Preparation

-          Word Order & Breaking up long sentences

-          Structuring Paragraphs and Sentences

-          Being Concise and Removing Redundancy

-      Avoiding Ambiguity and Vagueness. 

 

Unit-2
Teaching Hours:6
Essentials of Research Paper & Abstract and Introduction
 

-          Clarifying Who Did What

-          Highlighting Your Findings

-          Hedging and Criticizing

-          Paraphrasing and Plagiarism

-          Sections of a Paper

-      Abstracts. Introduction

 

Unit-3
Teaching Hours:6
Body and Conclusion
 

-          Review of the Literature

-          Methods, Results

-          Discussion

-          Conclusions

-       The Final Check

 

Unit-4
Teaching Hours:6
Key Skill for Writing Research Paper: Part 1
 

-          Key skills for writing a Title, an Abstract, an Introduction.

-      Review of Literature.

 

Unit-5
Teaching Hours:6
Key Skill for Writing Research Paper : Part 2
 

-          Key skills for writing Methods, Results, Discussion, Conclusions

 

 

          -       Useful phrases to ensure the quality of the paper

Text Books And Reference Books:

Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books).

Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht Heidelberg London, 2011

Essential Reading / Recommended Reading

Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press.

Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM. Highman’sbook.

Evaluation Pattern

As it is an audit course thre will be no graded evaluation. 

MTAC122 - DISASTER MANAGEMENT (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:2

Course Objectives/Course Description

 

Course Description

Disaster Management (DM) is an emerging discipline which addresses all facets, namely, Mitigation, Preparedness, Response and Recovery. Global and national policies urge to consider its application in all branches of engineering, science, management and social sciences. The course would help the students to appreciate the importance of disaster science and its applications in reducing risks so as to contribute to national development. It would help the students to enhance critical thinking and to understand interdisciplinary approaches in solving complex problems of societies to reduce the risk of disasters.

Course Objectives

1.    To  demonstrate a critical understanding of key concepts in disaster risk reduction and humanitarian response 

2.    To critically evaluate disaster risk reduction and humanitarian response policy and practice from multiple perspectives. 

3.     To develop an understanding of standards of humanitarian response and practical relevance in specific types of disasters and conflict situations.

4.     To critically understand the strengths and weaknesses of disaster management approaches, planning and programming in different countries, particularly their home country or where they would be working 

Course Outcome

CO1: Explain Hazards and Disasters

CO2: Apply methods and tools for Disaster Impacts

CO3: Explain disaster management developments in India

CO4: Illustrate technology as an enabler of Disaster Preparedness

CO5: Compare disaster risk reduction methods and approaches at the global and local level

Unit-1
Teaching Hours:4
ITRRODUCTION
 

Disaster: Definition, Factors And Significance; Difference Between Hazard And Disaster; Disaster and Hazard characteristics (Physical dimensions)

Unit-2
Teaching Hours:6
DISASTER IMPACTS
 

Repercussions of Disasters and Hazards: Economic Damage, Loss Of Human And Animal Life, Destruction Of Ecosystem. Disaster and Hazard typologies and their applications in Engineering. 

Unit-3
Teaching Hours:4
DISASTER PRONE AREAS IN INDIA
 

Study Of Seismic Zones; Areas Prone To Floods And Droughts, Landslides And Avalanches; Areas Prone To Cyclonic And Coastal Hazards With Special Reference To Tsunami; Post-Disaster Diseases And Epidemics

Unit-4
Teaching Hours:6
DISASTER PREPAREDNESS AND MANAGEMENT
 

Preparedness: Monitoring Of Phenomena Triggering A Disaster Or Hazard; Evaluation Of Risk: Application Of Remote Sensing, Data From Meteorological And Other Agencies, Media Reports: Governmental And Community Preparedness. 

Unit-5
Teaching Hours:10
RISK ASSESSMENT & DISASTER RISK
 

Concept And Elements, Disaster Risk Reduction, Global And National Disaster Risk Situation. Techniques Of Risk Assessment, Global Co-Operation In Risk Assessment And Warning, People’s Participation In Risk Assessment. Strategies for Survival.

Disaster Mitigation Meaning, Concept And Strategies Of Disaster Mitigation, Emerging Trends In Mitigation. Structural Mitigation And Non-Structural Mitigation, Programs Of Disaster Mitigation In India.

Text Books And Reference Books:

T2. Paul, B.K, “ Environmental Hazards and Disasters: Contexts, Perspectives and Management”, Wiley-Blackwell, 2011

T1. Coppola, D, “Introduction to International Disaster Management “. Elsevier, 2015.

Evaluation Pattern

Audit - Non graded

MTAC123 - VALUE EDUCATION (2023 Batch)

Total Teaching Hours for Semester:15
No of Lecture Hours/Week:1
Max Marks:0
Credits:0

Course Objectives/Course Description

 

Course intends to highlight the value of education and self- development which would enable students to imbibe good values and understand the importance of character

Course Outcome

CO1: Understand the importance of self-development

CO2: Understand importance of Human values

CO3: Understand the need for holistic development of personality

Unit-1
Teaching Hours:5
Values and Self-Development
 

Social values and individual attitudes, Work ethics, Indian vision of humanism, Moral and non- moral valuation. Standards and principles. Value judgements

Unit-2
Teaching Hours:2
Importance of Cultivation of Values
 

Sense of duty. Devotion, Self-reliance. Confidence, Concentration, Truthfulness, Cleanliness, Honesty, Humanity. Power of faith, National Unity, Patriotism. Love for nature , Discipline

Unit-3
Teaching Hours:8
Personality and Behaviour Development
 

Soul and Scientific attitude, Positive Thinking. Integrity and discipline, Punctuality, Love and Kindness, Avoid fault Thinking, Free from anger, Dignity of labour, Universal brotherhood and religious tolerance, True friendship, Happiness Vs suffering, love for truth, Aware of self-destructive habits, Association and Cooperation, Doing best for saving nature, Character and Competence –Holy books vs Blind faith, Self-management and Good health, Science of reincarnation, Equality, Nonviolence ,Humility, Role of Women, all religions and same message, Mind your Mind, Self-control, Honesty, Studying effectively

Text Books And Reference Books:

 

Chakroborty, S.K. “Values and Ethics for organizations Theory and practice”, Oxford University Press, New Delhi, 1999

Essential Reading / Recommended Reading

 

Chakraborty S K, "Ethics in Management: Vedantic Perspectives", Oxford University Press, New Delhi, India, 1997

 

Evaluation Pattern

Audit course

MTAC124 - CONSTITUTION OF INDIA (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:0

Course Objectives/Course Description

 

Students will be able to:

 1. Understand the premises informing the twin themes of liberty and freedom from a civil rights perspective.

 2. To address the growth of Indian opinion regarding modern Indian intellectuals’ constitutional role and entitlement to civil and economic rights as well as the emergence of nationhood in the early years of Indian nationalism.

 3. To address the role of socialism in India after the commencement of the Bolshevik Revolution in 1917 and its impact on the initial drafting of the Indian Constitution.

Course Outcome

CO1: Identify with the premises informing the twin themes of liberty and freedom from a civil rights perspective.

CO2: Explain the role of socialism in India after the commencement of the Bolshevik Revolution in 1917 and its impact on the initial drafting of the Indian Constitution.

Unit-1
Teaching Hours:4
Unit-1
 

History of Making of the Indian Constitution: History Drafting Committee, ( Composition & Working)

Unit-2
Teaching Hours:4
Unit-2
 

Philosophy of the Indian Constitution: Preamble Salient Features, ∙Contours of Constitutional Rights & Duties: ∙ Fundamental Rights ∙ Right to Equality ∙ Right to Freedom ∙ Right against Exploitation ∙ Right to Freedom of Religion ∙ Cultural and Educational Rights ∙ Right to Constitutional Remedies ∙ Directive Principles of State Policy ∙ Fundamental Duties.

Unit-3
Teaching Hours:4
Unit-3
 

Organs of Governance: ∙ Parliament ∙ Composition ∙ Qualifications and Disqualifications ∙ Powers and Functions ∙ Executive ∙ President ∙ Governor ∙ Council of Ministers ∙ Judiciary, Appointment and Transfer of Judges, Qualifications ∙ Powers and Functions

Unit-4
Teaching Hours:4
unit 4
 

Local Administration: ∙ District’s Administration head: Role and Importance, ∙ Municipalities: Introduction, Mayor and role of Elected Representative, CEO of Municipal Corporation. ∙Pachayati raj: Introduction, PRI: ZilaPachayat. ∙ Elected officials and their roles, CEO Zila Panchayat: Position and role. ∙ Block level: Organizational Hierarchy (Different departments), ∙ Village level: Role of Elected and Appointed officials, ∙ Importance of grass root democracy

Unit-5
Teaching Hours:4
unit 5
 

Election Commission: ∙ Election Commission: Role and Functioning. ∙ Chief Election Commissioner and Election Commissioners. ∙ State Election Commission: Role and Functioning. ∙ Institute and Bodies for the welfare of SC/ST/OBC and women.

Text Books And Reference Books:
  1. The Constitution of India, 1950 (Bare Act), Government Publication.

  2. Dr. S. N. Busi, Dr. B. R. Ambedkar framing of Indian Constitution, 1st Edition, 2015.

  3. M. P. Jain, Indian Constitution Law, 7th Edn., Lexis Nexis, 2014.

Essential Reading / Recommended Reading

D.D. Basu, Introduction to the Constitution of India, Lexis Nexis, 2015.

Evaluation Pattern

CIA 20 Marks

MTCS112 - PROFESSIONAL PRACTICE - I (2023 Batch)

Total Teaching Hours for Semester:32
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

SUBJECT OBJECTIVE:  

Students are encouraged to use various teaching aids such as over head projectors, power point presentation and demonstrative models. This will enable them to gain confidence in facing the placement interviews and intended to increase the score they earn on the upcoming exam above what they would otherwise earn. 

This course is specially designed for the students of higher degree. It aims to train and equip the students towards acquiring competence in teaching, laboratory skills, research methodologies and other professional activities including ethics in the respective academic disciplines. 

The course will broadly cover the following aspects:     Teaching skills     Laboratory skills and other professional activities     Research methodology For teaching suitable courses where strengthening in the training of the students is required will be identified and the student will be asked to prepare lectures on selected topics pertaining to the courses and present these lectures before a panel of faculty members. The student will also be required to prepare question papers which will test the concepts, analytical abilities and grasp in the subject. Wherever the laboratories are involved, students will also be asked to carry out laboratory experiments and learn about the use and applications of the instruments. The general guiding principle is that the students should be able to teach and participate in the undergraduate degree courses in his/her discipline in an effective manner. The students will also assist the faculty in teaching and research activities. The course will also contain the component of research methodology, in which a broad topic will be assigned to each student and he/ she is supposed to carry out intensive literature survey, data analysis and prepare a research proposal. 

Each group will carry out many professional activities beside teaching and research. Such as, purchase of equipments, hardware, software and planning for new experiments and also laboratories etc. Along with these the students will also be assigned some well defined activities. The student is expected to acquire knowledge of professional ethics in the discipline. 

Course Outcome

CO1: During the seminar session each student is expected to prepare and present a topic on engineering / technology,

CO2: Review and increase their understanding of the specific topics tested.

CO3: Improve their ability to communicate that understanding to the grader.

CO4: Increase the effectiveness with which they use the limited examination time.

Unit-1
Teaching Hours:32
Teaching, Learning and Research Methodologoes
 

Students are encouraged to use various teaching aids such as over head projectors, power point presentation and demonstrative models. This will enable them to gain confidence in facing the placement interviews and intended to increase the score they earn on the upcoming exam above what they would otherwise earn. 

This course is specially designed for the students of higher degree. It aims to train and equip the students towards acquiring competence in teaching, laboratory skills, research methodologies and other professional activities including ethics in the respective academic disciplines. 

The course will broadly cover the following aspects:     Teaching skills     Laboratory skills and other professional activities     Research methodology For teaching suitable courses where strengthening in the training of the students is required will be identified and the student will be asked to prepare lectures on selected topics pertaining to the courses and present these lectures before a panel of faculty members. The student will also be required to prepare question papers which will test the concepts, analytical abilities and grasp in the subject. Wherever the laboratories are involved, students will also be asked to carry out laboratory experiments and learn about the use and applications of the instruments. The general guiding principle is that the students should be able to teach and participate in the undergraduate degree courses in his/her discipline in an effective manner. The students will also assist the faculty in teaching and research activities. The course will also contain the component of research methodology, in which a broad topic will be assigned to each student and he/ she is supposed to carry out intensive literature survey, data analysis and prepare a research proposal. 

Each group will carry out many professional activities beside teaching and research. Such as, purchase of equipments, hardware, software and planning for new experiments and also laboratories etc. Along with these the students will also be assigned some well defined activities. The student is expected to acquire knowledge of professional ethics in the discipline. 

Text Books And Reference Books:

Recent advances in Teaching, Learning and Research Methodologoes

Essential Reading / Recommended Reading

Newer versions of ICT Usage

Evaluation Pattern

Each student will present 3- 4 lectures, which will be attended by all other students and Instructors. These lectures will be evenly distributed over the entire semester. The coordinator will announce the schedule for the entire semester and fix suitable meeting time in the week. 

Each student will also prepare one presentation about his findings on the broad topic of research. The final report has to be submitted in the form of a complete research proposal. The References and the bibliography should be cited in a standard format. The research proposal should contain a) Topic of research b) Background and current status of the research work in the area as evident from the literature review c) Scope of the proposed work d) Methodology e) References and bibliography. 

A report covering laboratory experiments, developmental activities and code of professional conduct and ethics in discipline has to be submitted by individual student. 

The panel will jointly evaluate all the components of the course throughout the semester and the mid semester grade will be announced by the respective instructor to his student. 

A comprehensive viva/test will be conducted at the end of the semester jointly, wherever feasible by all the panels in a particular academic discipline/department, in which integration of knowledge attained through various courses will be tested and evaluated. 

MTCS132 - ADVANCED ALGORITHMS (2023 Batch)

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

Course Objectives/Course Description

 

To learn the systematic way of solving problems.

To understand the different methods of organizing large amounts of data.

To efficiently implement the different data structures.

To efficiently implement solutions for specific problems

Course Outcome

CO1: Summarize the properties of advanced data structures.

CO2: Experiment algorithms and employ appropriate advanced data structures for solving computing problems efficiently.

CO3: Compare the efficiency of algorithms.

CO4: Experiment and implement efficient algorithms for solving computing problems in a high-level object-oriented programming language.

CO5: Compare, contrast, and apply algorithmic trade-offs : time vs. space, deterministic vs. randomized, and exact vs. approximate.

Unit-1
Teaching Hours:9
INTRODUCTION
 

Review of Analysis Techniques: Growth of Functions: Asymptotic notations; Standard notations and common functions; Recurrences and Solution of Recurrence equations- The substitution method, The recurrence – tree method, The master method; Amortized Analysis: Aggregate, Accounting and Potential Methods.

Unit-2
Teaching Hours:9
GRAPH ALGORITHMS AND POLYNOMIALS
 

Graph Algorithms: Bellman - Ford Algorithm; Single source shortest paths in a DAG; Johnson’s Algorithm for sparse graphs; Flow networks and Ford -Fulkerson method; Maximum bipartite matching.

Polynomials and the FFT: Representation of polynomials; The DFT and FFT; Efficient implementation of FFT.

Unit-3
Teaching Hours:9
NUMBER THEORETIC ALGORITHMS
 

Number -Theoretic Algorithms: Elementary notions; GCD; Modular Arithmetic; Solving modular linear equations; The Chinese remainder theorem; Powers of an element; RSA cryptosystem; Primality testing; Integer factorization

Unit-4
Teaching Hours:9
STRING MATCHING ALGORITHMS
 

String-Matching Algorithms: Naïve string Matching; Rabin - Karp algorithm; String matching with finite automata; Knuth-Morris-Pratt algorithm; Boyer – Moore algorithms.

Unit-5
Teaching Hours:9
PROBABILISTIC ALGORITHMS
 

Probabilistic and Randomized Algorithms: Probabilistic algorithms; Randomizing deterministic algorithms, Monte Carlo and Las Vegas algorithms; Probabilistic numeric algorithms.

Case Study: Comparison of Algorithm Design Strategies based on CPU, Memory, Disk and Network usages.

Text Books And Reference Books:
  1. T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd Edition, The MIT Press, 2014.
  2. Kenneth A. Berman, Jerome L. Paul: “Algorithms”, Cengage Learning, 2013.
Essential Reading / Recommended Reading
  1. Horowitz, Sahni, Rajasekaran, “Computer Algorithms”, University press 2008
  2. Tanenbaum A.S., Langram Y, Augestien M.J., ”Data Structures using Java”, Prentice Hall of India, 2009

3.   Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd edition,Pearson Education, 2012.

4. Aho, Hopcroft, Ullman, “Data Structures and Algorithms”, Pearson Education, 2009.

Evaluation Pattern

Assessment of each paper

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

 

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

 

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

 

     Attendance                                                                            : 05 marks

 

     Total                                                                                       : 50 marks

 

MTCS133 - ADVANCED DATABASE SYSTEMS (2023 Batch)

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

Course Objectives/Course Description

 

Data-driven decision making is becoming more common in organizations and businesses. In fact, database systems are at the center of the information systems strategies of most organizations. Users at any level of an organization can expect to work with and use database systems often. So, the ability to use these systems, which includes knowing what they can do and what they can't do, figuring out whether to access data directly or through technical experts, and knowing how to find and use the information well, became essential in every industry. Also, being able to design new systems and applications for them is a clear advantage and a necessity in the modern world. One type of database system that is widely used and the main focus of this course is the Relational Database Management System (RDBMS).

 

Course Objective:

  1. To understand the fundamentals of DBMS along with design concept.
  2. To learn the fundamental SQL commands and its applications in Databases.
  3. To study the Transactional processing concepts.
  4. To understand the Object oriented Database concepts

Course Outcome

CO1: Explain the fundamentals of Database systems.

CO2: Apply the bottom-up method to build the database.

CO3: Examine the basics and advanced concepts of SQL

CO4: Examine the concepts of transactional processing of the database

CO5: Explain the various concepts of Object-Orientation in Query Languages.

Unit-1
Teaching Hours:9
Introduction to DBS
 

Database Management systems Application of DBMS, Advantages of DBMS-ER model, Components of E-R diagram, Cardinality – Relational databases, Converting ER Diagram into Relations/Tables

Unit-2
Teaching Hours:5
Normalization: Database Design Theory
 

Introduction to Normalization using Functional and Multivalued Dependencies: Informal design guidelines for relation schema, Functional Dependencies, Normal Forms based on Primary Keys, Second and Third Normal Forms 

Unit-3
Teaching Hours:9
SQL
 

Simple queries in SQL, queries involving more than one relation, sub queries, full relational operations, Database modifications, defining a relational schema in SQL, view definitions.

Unit-4
Teaching Hours:8
Constraints and Triggers
 

Keys and foreign keys, constraints on attributes and tuples, modification of constraints, schema level constraints and Triggers.

Unit-5
Teaching Hours:8
Transaction Processing
 

Transaction Processing: Introduction to Transaction Processing, Transaction and System concepts, Desirable properties of Transactions, Characterizing schedules based on recoverability, Characterizing schedules based on Serializability, Transaction support in SQL.

Concurrency Control in Databases: Two-phase locking techniques for Concurrency control, Concurrency control based on Timestamp ordering, Multiversion Concurrency control techniques, Validation Concurrency control techniques, Granularity of Data items and Multiple Granularity Locking.

Recovery Concepts, NO-UNDO/REDO recovery based on Deferred update, Recovery techniques based on immediate update, Shadow paging, Database backup and recovery from catastrophic failures.

Unit-6
Teaching Hours:6
Object-Orientation in Query Languages
 

Introduction to OQL, Additional Forms of OQL Expressions, Object Assignment and Creation in OQL, User-Defined Types in SQL, Operations on Object-Relational Data.

Text Books And Reference Books:

Fundamentals of Database Management systems by Ramez Elmasri and Shamkant B. Navathe, 7th Edition, 2017, Pearson.

Database Systems: The Complete Book by  Garcia-Molina, Jeffrey D. Ullman, Jennifer Widom, Pearson Education India; 2nd edition (1 January 2013)

 

 

Essential Reading / Recommended Reading

Database Systems: The Complete Book by  Garcia-MolinaJeffrey D. Ullman, Jennifer Widom, Pearson Education India; 2nd edition (1 January 2013)

 

Evaluation Pattern

50% CIA

50% ESE

MTCS134 - SOFTWARE PROJECT MANAGEMENT (2023 Batch)

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

Course Objectives/Course Description

 

The main goal of software development projects is to create a software system with a predetermined functionality and quality in a given time frame and with given costs. For achieving this goal. models are required for determining target values and for continuously controlling these values. This course focuses on principles, techniques, methods & tools for model-based management of software projects. Assurance of product quality and process adherence (quality assurance), as well as experience-based creation & improvement of models (process management). The goals of the course can be characterized as follows.

• Understanding the specific roles within a software organization as related to project and process management

• Understanding the basic infrastructure competencies (e.g., process modeling and measurement)

• Understanding the basic steps of project planning, and project management. Quality assurance, and process management and their relationships.

 

Course Outcome

CO 1: Understanding the specific roles within a Conventional Software Management organization as related to the project.

CO 2: Describe and determine the purpose and importance of project management from the perspectives of planning, cost, tracking, and completion of the project.

CO 3: Evaluate a project to develop the scope of work, provide accurate cost estimates, and to plan the various activities.

CO 4: Implement a project to manage project schedules, expenses, and resources with the application of suitable project management tools.

CO 5: Identify the resources required for a project and produce a work plan and resource Schedule.

CO 6: Compare and differentiate organization structures and project structures.

Unit-1
Teaching Hours:9
UNIT-1
 

Conventional Software Management: The waterfall model, conventional software Management performance. Evolution of Software Economics: Software Economics. Pragmatic software cost estimation.

Unit-2
Teaching Hours:9
UNIT-2
 

Improving Software Economics: Reducing Software product size, Improving software processes, improving team effectiveness. Improving automation, Achieving required quality, peer inspections. The old way and the new- The principles of conventional software engineering. Principles of modem software management, transitioning to an iterative process.

Unit-3
Teaching Hours:9
UNIT-3
 

Life cycle phases: Engineering and production stages, inception. Elaboration, construction, transition phases. Artifacts of the process: The artifact sets. Management artifacts, Engineering artifacts, programmatic artifacts. Model based software architectures: A Management perspective and technical perspective.

Unit-4
Teaching Hours:9
UNIT-4
 

Work Flows of the process: Software process workflow, Inter trans workflows. Checkpoints of the Process: Major Mile Stones, Minor Milestones, Periodic status assessments. Iterative Process Planning Work breakdown structures, planning guidelines, cost and scheduled estimating, Interaction, planning process, Pragmatic planning.

Unit-5
Teaching Hours:9
UNIT-5
 

Project Control and Process instrumentation: The server care Metrics, Management indicators, and quality indicators. Life cycle expectations pragmatic Software Metrics, Metrics automation. Tailoring the Process: Process discriminates, Example. Future Software Project Management: Modem Project Profiles Next generation Software economics modem Process transitions.

Case Study: The Command Center Processing and Display System. Replacement (CCPDS. R).

 

Text Books And Reference Books:

Text Books

1. Software Project Management. Walker Royce, Pearson Education 2010.

2. Software Project Management, Bob Hughes & Mike Cotterell, fourth edition, Tate McGraw HD 2012.

Essential Reading / Recommended Reading

Reference Books 

1. Applied Software Project Management, Andrew SteIbian 8 Jennifer Greene, O’Reilly. 2006

2. Head First PMP, Jennifer Greene & Andrew Steliman, ORoiHy.2007

3. Software Enneeñng Project Managent. Richard H. Thayer & Edward Yourdon, second edition, Wiley India, 2004.

4. Ale Project Management, Jim Highsniith. Pearson education, 2004

5. The art of Project management. Scott Berkun. O’Reilly, 2005.

6. Software Project Management in Practice. PankajJalote. Pearson Educabon,2002.

7. SEI.CMMI Tutorial, ww.sei.cmu.edu/cmmi/publications/stc.presentations/tutorial.html

Evaluation Pattern

Continuous Internal Assessment 50%.

End Semester Examination 50%.

MTCS135 - ADVANCED DATA SCIENCE (2023 Batch)

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

Course Objectives/Course Description

 

 

 Data science is the study of data to get useful business information from it. It is a method for analyzing a lot of data that uses ideas and methods from math, statistics, artificial intelligence, and computer engineering. This analysis helps data scientists ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results. This course goes over the basics of data science and the algorithms for machine learning.   Using the programming language Python, the algorithm's implementation is discussed about. This course gives an overview of both the distributed environment and the deep learning techniques.

 

Course Outcome

CO 1: Demonstrate the foundations of data processing..

CO 2: Apply the clustering and Classification methods for modeling the data.

CO 3: Analyze the Statistical models and data distributions using Python Programming.

CO 4: Analyze the distributed file system and Data Processing using Spark.

CO 5: Explain the concept of Deep learning techniques for real time datasets.

Unit-1
Teaching Hours:12
INTRODUCTION AND THE DATA SCIENCE
 

 

Data science process – roles, stages in data science project – working with data from files –relational and Non-Relational databases – exploring data – managing data – cleaning and sampling for modeling and validation – Data preprocessing-Statistics for Data Science-Data Distributions.

Unit-2
Teaching Hours:9
MODELING METHODS
 

 

Choosing and evaluating models – mapping problems to machine learning, evaluating clustering models, validating models – cluster analysis – K-means algorithm unsupervised methods. , Naïve Bayes – Memorization Methods – Linear and logistic regression – unsupervised methods. 

Unit-3
Teaching Hours:8
ANALYTICS WITH PYTHON
 

Data Analysis with Numpy  and Pandas – Visualization with Seaborn Matplotlib, Plotly and Cufflinks – Scikit -learn –Regression, KNN, PCA and SVM in Python– Recommender systems – NLP with NLTK – Neural Nets and Deep Learning with Tensor Flow

Unit-4
Teaching Hours:8
SPARK SYSTEMS
 

Introduction –Hadoop vs Spark - Spark Data Frame – Group by and Aggregate –RDD(Resilient Distributed Datasets) – Spark SQL – Spark Running on Cluster–Machine Learning with Mlib–Collaborative Filtering–NLP Applications–Spark Streaming.

Unit-5
Teaching Hours:8
Convolutional Neural Networks
 

 

CNN Architectures – Convolution – Pooling Layers – Transfer Learning – Image Classification using Transfer Learning – Recurrent and Recursive Nets – Recurrent Neural Networks – Deep Recurrent Networks – Recursive Neural Networks – Applications.

Text Books And Reference Books:

T1: Introduction to Data Mining Paperback by Pang-Ning Tan , Michael SteinbachVipin Kumar, Pearson publications 2016.

T2: William McKinney- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Ipython, O'Reilly; Second edition, 2017.

T3 : Sandy Ryza, Uri Laserson.   Advanced Analytics with Spark: Patterns for Learning from Data at Scale  – O'Reilly 2017.

T4: Ian J. Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2017.

 

Essential Reading / Recommended Reading

R1: Jian Pei and Jiawei Han and Micheline Kamber, Data Mining: Concepts And Techniques, 4th Edition, Elsevier Science 2022.

R2: Francois Chollet, “Deep Learning with Python”, Manning Publications, 2018.

 

Evaluation Pattern

CIA - 50 Marks

ESE - 50 Marks

MTCS151 - ADVANCED ALGORITHMS LAB (2023 Batch)

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

Course Objectives/Course Description

 

● To increase the knowledge of advanced paradigms of algorithm design.
● To make the students learn an object oriented way of solving problems.
● To Enhance students’ capability of selecting the best and efficient way for encoding problems.

Course Outcome

CO1: Make use of mathematical techniques to construct robust algorithms.

CO2: Assess and to make critical judgment on the choices of algorithms for modern computer systems.

CO3: To demonstrate the knowledge retrieved through solving problems through a mini project.

Unit-1
Teaching Hours:6
List of Experiments on Algorithms Analysis
 
  1. Implementation of Sorting Techniques like Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort and Heap Sort and Compare their performances.
  2. Implementation of Linear Search and Binary Search Algorithms and Compare their performances.
  3. Mini Project Details: Selection of Topic and Research about the Selected Topic.
Unit-2
Teaching Hours:6
List of Experiments on Graph Algorithms
 
  1. Implementation of Shortest Path Algorithm (Bellman Ford).
  2. Implementation of Single Source Shortest Path in a DAG using Dijkstra's Algorithm.
  3. Mini Project Details: Choosing the right Dataset for the chosen Title/Topic and Exploratory Data Analysis (EDA).
Unit-3
Teaching Hours:6
List of Experiments on Number Theoretic Algorithms
 
  1. Implement Chinese Remainder Theorem Algorithm.
  2. Implement RSA Public-Key Cryptosystem.
  3. Mini Project Details: Implementation (2 Phases) - Phase 1
Unit-4
Teaching Hours:6
List of Experiments on String Matching Algorithms
 
  1. Design, develop, and write a program to solve String Matching Problem using naïve approach, the KMP and Robin Karp algorithm. Compare their performances.
  2. Mini Project Details: Implementation (2 Phases) - Phase 2
Unit-5
Teaching Hours:6
List of Experiments on Randomized Algorithms
 
  1. Design, develop, and write a program to implement a Monte Carlo algorithm to test the primality of a given integer and determine its performance.
  2. Implement any Las Vegas Algorithm used for Randomization.
  3. Mini Project Details: Performance Analysis and Report Submission
Text Books And Reference Books:
  1.  T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd Edition, The MIT Press, 2014.
  2. Kenneth A. Berman, Jerome L. Paul: “Algorithms”, Cengage Learning, 2013.
Essential Reading / Recommended Reading
  1. Horowitz, Sahni, Rajasekaran, “Computer Algorithms”, University press 2008
  2. Tanenbaum A.S., Langram Y, Augestien M.J., ”Data Structures using Java”, Prentice Hall of India, 2009
  3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd edition, Pearson Education, 2012.
  4. Aho, Hopcroft, Ullman, “Data Structures and Algorithms”, Pearson Education, 2009.
Evaluation Pattern

End semester practical examination: 25 marks

Records: 05 marks

Mid semester examination: 10 marks

Class work: 10 marks

Total: 50 marks

MTCS152 - ADVANCED DATABASE SYSTEMS LAB (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course will give students a chance to use what they learn in the lectures, homework, SQL assignments, and a database implementation project.

Course Outcome

Unit-1
Teaching Hours:60
Experiments on DBMS
 

Title1: Study of all SQL commands.  

7hours

Title2: Study of all SQL commands.

7hours

Title3:Study of all SQL commands.

7hours

Title4:Implementation of PL/SQL Programs.

7hours

Title5: Implementation of PL/SQL Programs.

7hours

Title6:Implementation of PL/SQL Programs.

7hours

Title7: Implementation of Cursor, Trigger.  

18hours

Text Books And Reference Books:

NA

Essential Reading / Recommended Reading

NA

Evaluation Pattern

CIA 50 %

ESE 50 %

Total Marks= 50

MTMC125 - RESEARCH METHODOLOGY AND IPR (2023 Batch)

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

Course Objectives/Course Description

 

The aim of the course is to introduce the research methodology, the understanding on the research, methods, designs, data collection methods, report writing styles and various dos and don’ts in research.

Course Outcome

CO1: Explain the principles and concepts of research methodology.

CO2: Understand the different methods of data collection.

CO3: Apply appropriate method of data collection and analyze using statistical/software tools.

CO4: Present research output in a structured report as per the technical and ethical standards.

CO5: Create research design for a given engineering and management problem /situation.

Unit-1
Teaching Hours:9
INTRODUCTION TO RESEARCH METHODOLOGY
 

Meaning, Objectives and Characteristics of research - Research methods Vs Methodology, Different Research Design: Types of research - Descriptive Vs. Analytical, Applied Vs. Fundamental, Quantitative Vs. Qualitative, Conceptual Vs. Empirical, Research process - Criteria of good research - Developing a research plan. 

Unit-2
Teaching Hours:9
LITERATURE REVIEW AND RESEARCH PROBLEM IDENTIFICATION
 

Defining the research problem - Selecting the problem - Necessity of defining the problem - Techniques involved in defining the problem - Importance of literature review in defining a problem - Survey of literature - Primary and secondary sources - Reviews, treatise, monographs, thesis reports, patents - web as a source - searching the web - Identifying gap areas from literature review - Development of working hypothesis. 

Unit-3
Teaching Hours:9
DATA COLLECTION & ANALYSIS
 

Selection of Appropriate Data Collection Method: Collection of Primary Data, Observation Method, Interview Method, Email, Collection of Data through Questionnaires, Collection of Data through Schedules, Collection of Secondary Data – internal & external. Sampling process: Direct & Indirect Methods, Non-probability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools. 

Unit-4
Teaching Hours:9
RESEARCH PROBLEM SOLVING
 

Processing Operations, Types of Analysis, Statistics in Research, Measures of: Central Tendency, Dispersion, Asymmetry and Relationship, correlation and regression, Testing of Hypotheses for single sampling: Parametric (t, z and F), Chi Square, Logistic regression, ANOVA, non-parametric tests. Numerical problems.

Unit-5
Teaching Hours:9
IPR AND RESEARCH WRITING
 

IPR: Invention and Creativity- Intellectual Property-Importance and Protection of Intellectual Property Rights (IPRs)- A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check.

Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation - Layout, structure and language of the report – Illustrations, figures, equations and tables.

Text Books And Reference Books:

T1. Kothari C.R., “Research Methodology Methods and techniques”, New Age International, New Delhi, 2004.

T2. Garg, B.L., Karadia, R., Agarwal, F. and Agarwal, “An introduction to Research Methodology”, RBSA Publishers, 2002.

T3. Day, R.A., “How to Write and Publish a Scientific Paper”, Cambridge University Press, 1992.

Essential Reading / Recommended Reading

R1. Bjorn Gustavii, “How to Write and Illustrate Scientific Papers “ Cambridge University Press, 2/e.

R2. Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell- John wiley & sons, 1/e, 2013.

R3. James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008.

Evaluation Pattern

Continuous Internal Assessment - 50%

End Semester Examination - 50%

MTAC225 - PEDAGOGY STUDIES (2023 Batch)

Total Teaching Hours for Semester:20
No of Lecture Hours/Week:2
Max Marks:0
Credits:0

Course Objectives/Course Description

 

Review existing evidence on the review topic to inform programme design and policy making undertaken by the DfID, other agencies and researchers. Identify critical evidence gaps to guide the development.

Course Outcome

CO1: Explain the policy making undertaken by the DfID, other agencies and researchers.

CO2: Identify critical evidence gaps to guide the development.

Unit-1
Teaching Hours:4
Introduction and Methodology
 

Aims and rationale, Policy background, Conceptual framework and terminology ∙ Theories of learning, Curriculum, Teacher education. ∙ Conceptual framework, Research questions. ∙ Overview of methodology and Searching.

Unit-2
Teaching Hours:4
Thematic overview
 

 Pedagogical practices are being used by teachers in formal and informal classrooms in developing countries.  Curriculum, Teacher education.

Unit-3
Teaching Hours:2
Evidence on the effectiveness of pedagogical practices
 

 Methodology for the in-depth stage: quality assessment of included studies. ∙ How can teacher education (curriculum and practicum) and the school curriculum and guidance materials best support effective pedagogy? ∙ Theory of change. ∙ Strength and nature of the body of evidence for effective pedagogical practices. ∙ Pedagogic theory and pedagogical approaches. ∙ Teachers’ attitudes and beliefsand Pedagogic strategies.

Unit-4
Teaching Hours:4
Professional development
 

Alignment with classroom practices and follow-up support ∙ Peer support ∙Support from the head teacher and the community. ∙ Curriculum and assessment ∙ Barriers to learning: limited resources and large class sizes.

Unit-5
Teaching Hours:4
Research gaps and future directions
 

Research design ∙ Contexts ∙ Pedagogy ∙ Teacher education ∙ Curriculum and assessment ∙ Dissemination and research impact.

Text Books And Reference Books:

i.                 Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools, Compare, 31 (2): 245-261.

Essential Reading / Recommended Reading

i.                Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of Curriculum Studies, 36 (3): 361-379.

ii.              Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher education research project (MUSTER) country report 1. London: DFID.

iii.            Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of basic maths and reading in Africa: Does teacher preparation count? International Journal Educational Development, 33 (3): 272–282.

iv.             Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education. Oxford and Boston: Blackwell.

v.               Chavan M (2003) Read India: A mass scale, rapid, ‘learning to read’ campaign.

 

www.pratham.org/images/resource%20working%20paper%202.pdf.

Evaluation Pattern

NA

MTCS212 - PROFESSIONAL PRACTICE-II (2023 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:1

Course Objectives/Course Description

 

Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:

  • Review and increasetheir understandingof thespecific topics tested.
  • Improvetheir abilityto communicate that understandingto thegrader.
  • Increasetheeffectiveness with which theyusethelimited examinationtime.

 

Course Outcome

 students towards acquiring competence in teaching, laboratoryskills, research methodologies and otherprofessional activities includingethics in the respective academicdisciplines.

The course will broadly cover the following aspects:

  • Teachingskills
  • Laboratoryskills andother professional activities
  • Research methodology

Unit-1
Teaching Hours:30
COURSE NOTICES
 

Notices pertaining to this course will be displayed on the respective departmental notice boards by the panel coordinator/instructor.Students may also check the exam notice board for notices issued by the exam division.

 

MAKEUPPOLICY:  All students are required to attend all the lectures and presentations in the panel. Participation and cooperation will also be taken into account in the final evaluation. Requests for makeup should normally be avoided. However,in genuine cases,panel will decide action on a case by case basis.

 

NOTE:Seminar shall be presented in the department in presence of a committee (Batch of Teachers)constituted by HOD.The seminar marks are to be awarded by the committee. Students shall submit the seminar report in the prescribed Standard format.

Text Books And Reference Books:

Selected domain related text book will be sugessted.

Essential Reading / Recommended Reading

Research papers for the selected domain

Evaluation Pattern

-

MTCS231 - NETWORK SECURITY (2023 Batch)

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

Course Objectives/Course Description

 

This course covers the major aspects of computer and network security. It starts with a general introduction to information security, and then proceeds to cover types of threats and attacks, hacking techniques, network vulnerabilities, security policies and standards, firewalls, cryptography, Authentication & digital signatures, the SSL protocol, Wireless security, intrusion detection and prevention. 

Course Outcome

CO1: Evaluate the factors driving the need for network security.

CO2: Demonstrate the implications of implementing encryption at different levels of the OSI reference model.

CO3: Identify types of firewall implementation suitable for differing security requirements.

CO4: Experiment and explain simple filtering rules based on IP and TCP header information.

CO5: Distinguish between firewalls based on packet-filtering routers, application level gateways and circuit level gateways.

Unit-1
Teaching Hours:9
1
 

 

Security Attacks (Interruption, Interception, Modification and Fabrication), Security Services (Confidentiality,Authentication, Integrity, Non-repudiation, access Control and Availability) and Mechanisms, A model for Internetwork security, Internet Standards and RFCs Conventional Encryption Principles, Conventional encryption algorithms(DES, Triple DES,AES), cipher block modes of operation(CBC,CFB), location of encryption devices, key distribution Approaches of Message Authentication, Secure Hash Functions and HMAC.

Unit-2
Teaching Hours:9
2
 

Public key cryptography principles, public key cryptography algorithms, digital signatures, digital Certificates, Certificate Authority and key management, Kerberos, X.509 Directory Authentication Service.

Unit-3
Teaching Hours:9
3
 

Email privacy: Pretty Good Privacy (PGP) and S/MIME. IP Security Overview, IP Security Architecture, Authentication Header, Encapsulating Security Payload, Combining Security Associations and Key Management.

Unit-4
Teaching Hours:9
4
 

Web Security Requirements, Secure Socket Layer (SSL) and Transport Layer Security (TLS), Secure Electronic Transaction (SET). Intruders,

Unit-5
Teaching Hours:9
5
 

Viruses and related threats. Firewall Design principles, Trusted Systems. Intrusion Detection Systems.

Text Books And Reference Books:

1. Cryptography and network Security, Third edition, Stallings, PHI/ Pearson 2011

2. Principles of Information Security, Whitman, Thomson. 2010

3. Network Security:The complete reference,Robert Bragg,Mark Rhodes, TMH 2010

4. Introduction to Cryptography, Buchmann, Springer. 2012

Essential Reading / Recommended Reading

Network Security Essentials (Applications and Standards) by William Stallings Pearson 

Education, 5th  Edition 2013.

Evaluation Pattern

CIA  Marks:50

ESE Marks:50

MTCS232 - DATA AND WEB ANALYTICS (2023 Batch)

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

Course Objectives/Course Description

 

Course Description:

This course will cover fundamental concepts used in Data and Web Analytics. Models and Algorithms for Intelligent Data Analysis to solve several real-life problems will be covered to have hands-on practice. Data  analytics allows to find relevant information, structures, and patterns, to gain new insights, to identify causes and effects, to predict future developments, or to suggest optimal decisions. We need models and algorithms to collect, preprocess, analyze, and evaluate data. This involves methods from various fields such as statistics, machine learning, pattern recognition, systems theory, operations research, or artificial intelligence. The course aims to learn the most important methods and algorithms for data analytics. This course helps to select appropriate methods for specific tasks and apply these in one’s  own data analytics projects. 

 

 

Course Objective:

·       To understand the need for Data Analytics and Web Analytics

·       To discover various paradigm of Models and Algorithms for Intelligent Data Analysis.

·       To design suitable Data Analytics Algorithms for solving problems

·       To summarize the fundamentals of Web Analytics

·       To demonstrate the various data preprocessing and web analytics data collection techniques

·       To make use of data and web analytics techniques to solve real life problems

                      ·       To experiment with various applications of data analytics and web analytics

Course Outcome

CO1: Illustrate the fundamentals of Data and Web Analytics.

CO2: Summarize the fundamentals of Data Preprocessing and web analytics data collection.

CO3: Experiment with various correlation and regression techniques.

CO4: Make use of Forecasting, Classification and Clustering for solving real life problems.

CO5: Develop Solutions for various applications using the data and web analytics models and techniques.

Unit-1
Teaching Hours:9
Introduction to Data and Web Analytics
 

Introduction, It’s All About Data,  Data Analytics, Data Mining, and Knowledge Discovery, Data and Relations, The Iris Data Set, Data Scales, Set and Matrix Representations, Relations, Dissimilarity Measures, Similarity Measures, Sequence Relations,  Sampling, and Quantization. Differences between Data Analytics and Web Analytics, Case Study – Web Analytics, Current Landscape and Challenges,  Web Analytics Fundamentals,  Capturing Data,  Selecting  Optimal Web Analytics Tool, Understanding Clickstream Data Quality,  Implementing Best Practices, Apply the “Three Layers of So What” Test.

Unit-2
Teaching Hours:9
Data Preprocessing and web analytics data collection
 

Data Preprocessing-Error Types, Error Handling,  Filtering Data Transformation, Data Integration, Problems, Data Visualization  Diagrams, Principal Component Analysis,  Multidimensional Scaling, Sammon Mapping, Auto-encoder, Histograms, Spectral Analysis,  Case Study web analytics, Data Collection—Importance and Options,Understanding the Data Landscape, Clickstream Data, Outcomes Data, Research Data, Competitive Data.

Unit-3
Teaching Hours:9
Correlation and Regression
 

Correlation, Linear Correlation,  Correlation and Causality, Chi-Square Test for Independence, Problems, Regression, Linear Regression, Linear Regression with Nonlinear Substitution, Robust Regression, Neural Networks, Radial Basis Function Networks, Cross-Validation, Feature Selection, Problems .

Unit-4
Teaching Hours:9
Forecasting , Classification and Clustering
 

Forecasting, Finite State Machines,  Recurrent Models, Autoregressive Models, Problems and Use cases, Classification, Classification Criteria, Naive Bayes Classifier, Linear Discriminant Analysis,  Support Vector Machine,  Nearest Neighbor Classifier,  Learning Vector Quantization,  Decision Trees, Problems.

Unit-5
Teaching Hours:9
Clustering
 

Clustering, Cluster Partitions,  Sequential Clustering, Prototype-Based Clustering, Fuzzy Clustering, Relational Clustering, Cluster Tendency Assessment,  Cluster Validity,  Self-organizing Map, Problems and Use cases, Case study related to Web Analytics perspective of Creating a Data-Driven Culture—Practical Steps and Best Practices,  Key Skills to Look for in a Web Analytics Manager/Leader.

Text Books And Reference Books:

1. Runkler, T. A. (2020). Data analytics. Wiesbaden: Springer Fachmedien Wiesbaden.

 

2. Kaushik, A. (2007). Web analytics: An hour a day (W/Cd). John Wiley & Sons.

Essential Reading / Recommended Reading
1. Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques third edition. University of Illinois at Urbana-Champaign Micheline Kamber Jian Pei Simon Fraser University
 
2. Scime, A. (2005). Web Mining: Applications and Techniques, State University of New York College at Brockport.
 
3. Chakrabarti, S. (2002). Mining the Web: Discovering knowledge from hypertext data. Morgan Kaufmann. 
 
4. Liu, B., Liu, B., & Menczer, F. (2011). Web crawling. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 311-362. 

 

Evaluation Pattern

CIA Marks

50

ESE Marks

50

MTCS233 - ADVANCED DIGITAL IMAGE PROCESSING (2023 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

Course Description:

The course will help the students understand the fundamental digital image processing concepts. The students will also gain knowledge of image compression techniques followed by image segmentation. The course will also help the students to use Deep Learning techniques for feature extraction and image pattern classification.

Course Objective:

1. The students will learn the fundamental concepts of Image Processing.

2. The students will learn image compression and segmentation techniques.

3. The students will study feature extraction and pattern classification techniques.

 

 

Course Outcome

CO 1: To understand the image fundamentals and mathematical transforms necessary for image processing and to study the image enhancement techniques.

CO 2: To understand image segmentation and representation techniques.

CO 3: To understand how images are analyzed to extract features of interest.

CO 4: To identify the concepts of image registration and image fusion.

CO 5: To build the constraints in image processing when dealing with 3D data sets.

Unit-1
Teaching Hours:9
REVIEW OF DIGITAL IMAGE PROCESSING
 

Steps in digital image processing-Elements of visual perception- brightness adaptation, Mach band effect. Image enhancement in spatial and frequency domain, Histogram equalization

Unit-2
Teaching Hours:9
SEGMENTATION
 

Edge detection, Thresholding, Region growing, Fuzzy clustering, Watershed algorithm, Active contour models, Texture feature based segmentation, Graph based segmentation, Wavelet based Segmentation - Applications of image segmentation.

Unit-3
Teaching Hours:9
FEATURE EXTRACTION
 

First and second order edge detection operators, Phase congruency, Localized feature extraction -detecting image curvature, shape features, Hough transform, shape skeletonization, Boundary descriptors, Moments, Texture descriptors- Autocorrelation, Co-occurrence features, Runlength features, Fractal model based features, Gabor filter, wavelet features.

Unit-4
Teaching Hours:9
REGISTRATION AND IMAGE FUSION
 

Registration - Preprocessing, Feature selection - points, lines, regions and templates Feature correspondence - Point pattern matching, Line matching, Region matching, Template matching. Transformation functions - Similarity transformation and Affine Transformation. Resampling – Nearest Neighbour and Cubic Splines. 

Unit-5
Teaching Hours:9
3D IMAGE VISUALIZATION
 

Sources of 3D Data sets, Slicing the Data set, Arbitrary section planes, The use of color, Volumetric display, Stereo Viewing, Ray tracing, Reflection, Surfaces, Multiple connected surfaces.

Text Books And Reference Books:

1. Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing', Pearson, Education, Inc., Second Edition, 2004

2. Mark Nixon, Alberto Aguado, “Feature Extraction and Image Processing”, Academic Press, 2008.

Essential Reading / Recommended Reading

1.Madan, “ An Introduction to MATLAB for Behavioural Researchers”, Sage Publications, 2014

2.John C.Russ, “The Image Processing Handbook”, CRC Press,2007.

3.Mark Nixon, Alberto Aguado, “Feature Extraction and Image Processing”, Academic Press, 2008.

4.Ardeshir Goshtasby, “ 2D and 3D Image registration for Medical, Remote Sensing and Industrial Applications”, John Wiley and Sons,2005.

 

Evaluation Pattern

CIA - 70

ESE - 30

MTCS241E01 - BIG DATA ANALYTICS (2023 Batch)

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

Course Objectives/Course Description

 

Course Description:

This course will teach you about the characteristics of Big Data and how to use it in Big Data Analytics. You will learn about the features, benefits, limitations, and applications of various Big Data processing tools. You'll learn how Hadoop, Hive, Apache Spark can help you reap the benefits of Big Data while overcoming some of its challenges. At the end of completing this course students will get job opportunities in the field of data engineering.  

Course Objective:

•To optimize business decisions and create competitive advantage with Big Data analytics.

•To explore the fundamental concepts of big data analytics. 

•To learn to analyze the big data using intelligent techniques. 

•To understand and analyze the applications using Map Reduce Concepts. 

•To introduce programming tools PIG & HIVE in Hadoop echo system.

 

Course Outcome

CO 1: Explain the concept of big data analytics. L2

CO 2: Make use of NoSQL database for storing and analyzing the big data. L3

CO 3: Experiment with various Hadoop commands and programs in Hadoop environment. L3

CO 4: Analyze map-reduce applications in Hadoop platform. L4

CO 5: Discuss various Emerging Hadoop related tools for Big Data Analytics. L6

Unit-1
Teaching Hours:9
UNDERSTANDING BIG DATA
 

What is big data – why big data –Data,  Evolution of Big data; Characteristics of Big Data; Types of data; Sources of data; Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, Types of analytics; Domain Specific Examples of Big Data; Analytics Flow for Big Data;

Experiment 1:  Illustrate a python program to read and analyze the big data using EDA. 

 

Unit-2
Teaching Hours:9
NOSQL DATA MANAGEMENT
 

Getting Started with NoSQL and MongoDB: Introducing NoSQL and MongoDB, Installing and Configuring MongoDB;  Implementing NoSQL in MongoDB: Configuring User Accounts and Access Control Managing Databases and Collections from the MongoDB Shell;  Finding Documents in the MongoDB Collection from the MongoDB Shell; Additional Data-Finding Operations Using the MongoDB Shell; Manipulating MongoDB Documents in a Collection; Utilizing the Power of Grouping, Aggregation, and Map Reduce; Implementing MongoDB in Python Applications;

Experiment 2:  

i.Construct databases, collections and perform CRUD operations in MongoDB shell.

ii.Apply MongoDB in Python Applications;

 

Unit-3
Teaching Hours:9
BASICS OF HADOOP
 

History of Hadoop; HDFC concepts; Design of HDFS; Hadoop Distributed File System and its Features; Components of Hadoop; HDFS Commands;  Analyzing the Data with Hadoop;  Scaling Out ; Hadoop Streaming; Reading and writing data in Hadoop; Directories; Querying the File system; Deleting data; Data flow: Anatomy of a File Read and File Write;

Experiment 3: 

i.Experiment with various Hadoop commands in Hadoop environment.

ii.Develop python/java programs Reading and writing data in Hadoop.

 

Unit-4
Teaching Hours:9
YARN
 

Anatomy of a YARN Application Run – Resource Requests, Application Lifespan, Building YARN Applications; Scheduling in YARN; YARN Distributed-Shell Structure of YARN Applications; 

Experiment 4: Build YARN Applications

 

Unit-5
Teaching Hours:9
DEVELOPING A MAP-REDUCE APPLICATIONS
 

The Configuration API; Setting Up the Development Environment; Writing a Unit Test with MRunit – mapper and reducer; Running Locally on Test Data; Running on a cluster; MapReduce workflows ; Anatomy of MapReduce job run; Failures; shuffle and sort; MapReduce types – input formats – output formats;

Experiment 5: Analyze various map-reduce java/python applications in Hadoop platform.

 

Text Books And Reference Books:

1. Arshdeep Bahga, Vijay Madisetti, “Big Data Science & Analytics: A Hands-on Approach”, hands-on-books-series.com. India, 2020.

2. Brad Dayley  Sams Teach Yourself NoSQL with MongoDB in 24 Hours.

3. Douglas Eadline, “Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem”, 1st Edition, Pearson Education, 2016.

4. Tom White, “Hadoop: The Definitive Guide”, 4th Edition, O’Reilly Media, 2015

Essential Reading / Recommended Reading

1. Boris Lublinsky, Kevin T. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, John Wiley & Sons, 2013.

2. Ankam, Venkat. Big Data Analytics. India: Packt Publishing, 2016.

Evaluation Pattern

Continuous Internal Assessment (CIA)         : 50% (50 marks out of 100 marks)

 

End Semester Examination(ESE)                 : 50% (50 marks out of 100 marks)

MTCS242E01 - IOT ARCHITECTURE & COMPUTING (2023 Batch)

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

Course Objectives/Course Description

 

IoT architecture is the system of multiple heterogeneous elements that range from sensors, protocols, actuators and controllers to AI and cloud services. This course will equip the students to build futuristic IoT solutions utilizing all these elements adhering to global standards. Various computing models for IoT implementations are also introduced with case studies. The students will develop skills in identifying the requirements for the target IoT systems, selecting the appropriate hardware and software platforms and implementing and deploying the solutions.

Course Outcome

Unit-1
Teaching Hours:3
Introduction to IoT landscape and applications
 

Introduction, Architectures, Applications, Devices, Security and privacy

Unit-2
Teaching Hours:3
IoT architecture and standards
 

Introduction to generic IoT architecture, Protocols, Standards, Databases, Time Bases, IoT Devices

Unit-3
Teaching Hours:9
Architecture Reference Model (ARM)
 

The IoT Architectural Reference Model as Enabler, Guidance to the ARM, IoT Reference Model, IoT Reference Architecture, The IoT ARM Reference Manual, Toward a Concrete Architecture

 

Unit-4
Teaching Hours:10
IoT system analysis
 

Standards, Interoperability, Discoverability, Security and Privacy in IoT

 

Unit-5
Teaching Hours:10
Computing for IoT
 

Cloud computing, Big data, Big-stream-oriented Architecture, Performance Evaluation, Fog Computing and the IoT, Virtualization and Replication, The IoT Hub, Operational Scenarios, Synchronization Protocol.

Unit-6
Teaching Hours:10
The IoT in Practice
 

Hardware for the IoT , Classes of Constrained Devices, Hardware Platforms, Software for the IoT, Networking, Programming Model, Integration Challenges, Implementation and Validation.

Text Books And Reference Books:

Serpanos, Dimitrios, and Marilyn Wolf. Internet-of-Things (IoT) Systems Architectures, Algorithms, Methodologies. by Springer Nature, 2018.

Cirani, Simone, et al. Internet of things: architectures, protocols and standards. John Wiley & Sons, 2018.

Essential Reading / Recommended Reading

Bassi, Alessandro, et al. Enabling things to talk. Springer Nature, 2013.

Evaluation Pattern

CIA 50 Marks

ESE 50 Marks

MTCS251 - NETWORK SECURITY LAB (2023 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

This course caters to the hand-on experience of Network security concepts such as encryption techniques  and hashing using security tools.

 

Course Outcome

CO 1: Develop algorithms for the cipher techniques

CO 2: Examine the various security algorithms

CO 3: Analyse different open source tools for network security and analysis

Unit-1
Teaching Hours:40
Experiments
 

Experiment 1Implement the following algorithms a) DES b) RSA Algorithm .

Experiment 2:Implement the following algorithms  Diffiee-Hellman , MD5 , SHA-1

 

Experiment 3: Fire wall implementation using different security requirements.

Experiment 4: Demonstrate intrusion detection system (ids) using any tool (snort or any other s/w)

Experiment 5Implement somesimple filtering rules based on IP and TCP header information

Text Books And Reference Books:

 Cryptography and network Security, Third edition, Stallings, PHI/ Pearson 2011

Essential Reading / Recommended Reading

Network Security Essentials (Applications and Standards) by William Stallings Pearson Education, 5th Edition 2013.

Evaluation Pattern

CIA MARKS:50

ESE MARKS:50

MTCS252 - DATA AND WEB ANALYTICS LAB (2023 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Course Description:

  

This course will cover hands on implementation of the fundamental concepts used in Data and Web Analytics. Models and Algorithms for Intelligent Data Analysis to solve several real-life problems will be covered to have hands-on practice. 

 

 

Course Objective:

  •  To demonstrate the various data preprocessing and web analytics data collection techniques
  • To make use of data and web analytics techniques to solve real life problems
  • To experiment with various applications of data analytics and web analytics

Course Outcome

Unit-1
Teaching Hours:60
DATA AND WEB ANALYTICS LAB
 

Title 1 :  Hands-on experiments about Data and Web Analytics fundamentals using python /matlab / R

Title 2: Hands-on experiments about data Preprocessing and web analytics data collection

Title 3:  Hands-on experiments about Correlation and Regression

Title 4: Hands-on experiments about Forecasting, Classification and Clustering

Title 5:  Hands-on experiments about Clustering 

Text Books And Reference Books:

1. Runkler, T. A. (2020). Data analytics. Wiesbaden: Springer Fachmedien Wiesbaden.

 

2. Kaushik, A. (2007). Web analytics: An hour a day (W/Cd). John Wiley & Sons.

Essential Reading / Recommended Reading
  1. Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques third edition. University of Illinois at Urbana-Champaign Micheline Kamber Jian Pei Simon Fraser University
  2. Scime, A. (2005). Web Mining: Applications and Techniques, State University of New York College at Brockport.
  3. Chakrabarti, S. (2002). Mining the Web: Discovering knowledge from hypertext data. Morgan Kaufmann. 
  4. Liu, B., Liu, B., & Menczer, F. (2011). Web crawling. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 311-362. 
Evaluation Pattern

CIA Marks

25

ESE Marks

25

MTCS345E02 - ADVANCED ARTIFICIAL INTELLIGENCE (2022 Batch)

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

Course Objectives/Course Description

 

This course provides a strong foundation of fundamental concepts in Artificial Intelligence. To provide an empirical evidence and the scientific approachapplyingArtificial Intelligence techniques for problem solving using probabilistic, fuzzy, statistical and Deep Learning Models.

Course Outcome

           To demonstrate the concepts and features of agents, environments and uniformed search strategies.

To understand inference using Bayesian Networks, Hidden Markov Models as an approach to Probabilistic Reasoning

To Apply Fuzzy Logic Systems to Neural Network Architectures

To Compare and contrast performance of different Statistical learning methods used in machine learning

To explore Deep Learning models to image and text processing application

Unit-1
Teaching Hours:9
INTRODUCTION
 

Intelligent Agents – Agents and environments - Good behaviour – The nature of environments – structure of agents - Problem Solving agents – Acting under uncertainty – Inference using full joint distributions; –Independence; Bayes’ rule and its use; –The Wumpus world revisted.

Unit-2
Teaching Hours:9
SEARCHING TECHNIQUES
 

Problem-Solving Agents, Well-defined problems and solutions, Formulating problems, Real-world problems. Uninformed Search Strategies, Breadth-first search, Uniform-cost search, Depth-first search, Depth-limited search, Iterative deepening depth-first search, Bidirectional search, Informed (Heuristic) Search Strategies, Greedy best-first search, A* search: Minimizing the total estimated solution cost, Heuristic Functions. The effect of heuristic accuracy on performance. Beyond Classical Search, Local Search Algorithms and Optimization Problems, Local Search in Continuous Spaces.

Unit-3
Teaching Hours:9
GAME PLAYING
 

Games, Optimal Decisions in Games,The minimax algorithm,Optimal decisions in multiplayer games, Alpha Beta Pruning, Move ordering, Imperfect Real-Time Decisions,Cutting off search, Forward pruning.Stochastic Games,Evaluation functions for games of chance, Partially Observable Games, Krieg spiel: Partially observable chess,Card games, State-of-the-Art Game Programs, Alternative Approaches 

Unit-4
Teaching Hours:9
STATISTICAL AND REINFORCEMENT LEARNING
 

Learning from observations - forms of learning - Inductive learning - Learning decision trees - Ensemble learning - Knowledge in learning – Logical formulation of learning – Explanation based learning – Learning using relevant information – Inductive logic programming - Statistical learning methods - Learning with complete data - Learning with hidden variable - EM algorithm - Instance based learning - Reinforcement learning – Passive reinforcement learning - Active reinforcement learning - Generalization in reinforcement learning.

Unit-5
Teaching Hours:9
DEEP LEARNING
 

Convolutional Neural Networks, Motivation, Convolution operations, Pooling,  Image classification, Modern CNN architectures, Recurrent Neural Network, Motivation, Vanishing/Exploding gradient problem, Applications to sequences, Modern RNN architectures, Tuning/Debugging Neural Networks, Parameter search, Overfitting, Visualizations, Pretrained Models.

Text Books And Reference Books:

1.      Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, 3rd Edition, Pearson Education, 2014.

2.      Elaine Rich and Kevin Knight, “Artificial Intelligence”, 3rd Edition, Tata McGraw-Hill, 2012.

3. Francois Chollet “Deep Learning with Python”, 1st Edition Manning Publication, 2018

 
Essential Reading / Recommended Reading

1.      Nils J. Nilsson, “Artificial Intelligence: A New Synthesis”, 1st Edition, Harcourt Asia Pvt. Ltd., 2012.

2. George F. Luger, “Artificial Intelligence-Structures and Strategies for Complex Problem Solving”, 6th Edition, Pearson Education / PHI, 2009.

 

Evaluation Pattern

Assessment of each paper

 

Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks)

 End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

 

Components of the CIA

 

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

CIA II  :   Mid Semester Examination (Theory)                                   : 25 marks

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

     Attendance                                                                            : 05 marks

           Total                                                                                       : 50 marks

MTCS381 - INTERNSHIP (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Internships are short-term work experiences that will allow  a student to observe and participate in professional work environments and explore how his interests relate to possible careers. They are important learning opportunities trough industry exposure and practices.   More specifically, doing internships is beneficial because they provide the opportunity to:

        Get an inside view of an industry and organization/company

        Gain valuable skills and knowledge

        Make professional connections and enhance student's network

        Get experience in a field to allow the student  to make a career transition

Course Outcome

CO 1: Explain inside view of an industry and organization/company.

CO 2: Make use of professional connections and enhance student's network.

CO 3: Illustrate how to get experience in a field to allow the student to make a career transition.

Unit-1
Teaching Hours:60
Regulations
 

1.The student shall undergo an Internship for 30 days  starting from the end of 2nd semester examination and completing it during the initial period of 3rd semester.

2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of  the students

3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise.

4. The Internship shall be completed by the end of  7th semesters.

5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance.

6. Students can also undergo internships arranged by the department during vacation.

7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.

8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor  and a presentation on the internship given to department constituted panel.

Text Books And Reference Books:

Related to the Internship domain text books are sugessted.

Essential Reading / Recommended Reading

Readings Related to the Internship domain

Evaluation Pattern

Internal 50 Marks

MTCS382 - DISSERTATION PHASE - I (2022 Batch)

Total Teaching Hours for Semester:300
No of Lecture Hours/Week:20
Max Marks:200
Credits:10

Course Objectives/Course Description

 

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

  • Review and increase their understanding of the specific topics identified.
  • Improve their ability to communicate that understanding to the grader.
  • Increase the effectiveness with which they use the limited examination time.

Course Outcome

CO 1: Students will be understanding concepts.

CO 2: Understanding the identified domain.

CO 3: Framing the research problem.

CO 4: Project design analysis.

CO 5: Research literature writing.

Unit-1
Teaching Hours:200
DISSERTATION PHASE -1
 

Assessment of Project Work(Phase I) ▪ Continuous Internal Assessment:100 Marks ♦ Presentation assessed by Panel Members ♦ Guide ♦ Mid-semester Project Report

Text Books And Reference Books:

Journal article, industry white papers text books basedon the domain on which the student will be doing his/her work.

Essential Reading / Recommended Reading

Recommendation will be given Based on the domian in which student will be interested and planning to do the dissertation work

Evaluation Pattern

      Assessment of Project Work(Phase I)

▪          Continuous Internal Assessment:100 Marks

♦        Presentation assessed by Panel Members

♦        Guide

♦        Mid semester Project Report

End semester Examination :100 Marks

Presentation assessed by Panel Members

♦        Guide

♦        End semester Project Report

MTEC361 - COMPRESSION AND ENCRYPTION TECHNIQUES (2022 Batch)

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

Course Objectives/Course Description

 

This course aims at making the students get an understanding of the compression techniques available for multimedia applications and also get an understanding of the encryption that can be implemented along with the compression.

Course Outcome

CO-1: Explain the taxonomy of multimedia compression techniques{L2}{PO1,PO2,PO3}

CO-2: Explain the concept of text compression through the coding techniques {L2}{PO1,PO2}

CO-3: Describe the motion estimation techniques used in video compression {L2}{PO1,PO2,PO3}

CO-4: Explain the concept of encryption with the models employed {L2}{PO1,PO2,PO3}

CO-5: Explain the symmetric ciphers and their techniques & standards {L2}{PO1,PO2,PO3}

Unit-1
Teaching Hours:9
INTRODUCTION TO COMPRESSION
 

Special features of Multimedia – Graphics and Image Data Representations – Fundamental Concepts in Video and Digital Audio – Storage requirements for multimedia applications -Need for Compression - Taxonomy of compression techniques – Overview of source coding

Unit-2
Teaching Hours:9
TEXT COMPRESSION
 

Compaction techniques – Huffmann coding – Adaptive Huffmann Coding – Arithmatic coding – Shannon-Fano coding – Dictionary techniques – LZW family algorithms

Unit-3
Teaching Hours:9
VIDEO COMPRESSION
 

Video compression techniques and standards – MPEG Video Coding I: MPEG – 1 and 2 – MPEG Video Coding II: MPEG – 4 and 7 – Motion estimation and compensation techniques – H.261 Standard

Unit-4
Teaching Hours:9
INTRODUCTION TO ENCRYPTION
 

Introduction: Services, Mechanisms and Attacks, OSI security Architecture, Model for network Security; Classical Encryption Techniques:Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Stegnography;

Unit-5
Teaching Hours:9
CIPHERS
 

Block Ciphers and Data Encryption Standard: Simplified DES, Block Cipher Principles, Data Encryption Standard, Strength of DES, Differential and Linear Crypt Analysis, Block Cipher Design Principles, Block Cipher Modes of Operation

Text Books And Reference Books:

NIL

Essential Reading / Recommended Reading

1. Khalid Sayood : Introduction to Data Compression, Morgan Kauffman Harcourt India, 2nd Edition, 2000

2. David Salomon : Data Compression – The Complete Reference, Springer Verlag New York Inc., 4th Edition, 2006

3. Yun Q.Shi, HuifangSun : Image and Video Compression for Multimedia Engineering -  Fundamentals, Algorithms & Standards, CRC press, 2008

4.Jan Vozer : Video Compression for Multimedia, AP Profes, NewYork, 1995.

5. William Stallings, “Cryptography and Network Security”, 6th. Ed, Prentice Hall of India, New Delhi ,2013

6. William Stallings, “Network Security Essentials”, 5thed. Prentice Hall of India, New Delhi

Evaluation Pattern

CIA-50

ESE-50

MTCS483 - DISSERTATION PHASE-II (2022 Batch)

Total Teaching Hours for Semester:480
No of Lecture Hours/Week:32
Max Marks:200
Credits:16

Course Objectives/Course Description

 

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

  • Review and increase their understanding of the specific topics identified.
  • Improve their ability to communicate that understanding to the grader.
  • Increase the effectiveness with which they use the limited examination time.

Course Outcome

CO 1: Design engineering solutions to complex real world problems using research literature.

CO 2: Use appropriate hardware and software depending on the nature of the project with an understanding of their limitations.

CO 3: Implementation and testing of the project.

CO 4: Understand the impact of the developed projects on environmental factors.

CO 5: Demonstrate project management skills including handling the finances in doing projects for given real world societal problems.

Unit-1
Teaching Hours:480
DISSERTATION PHASE -II
 

Assessment of Project Work(Phase II) and Dissertation ▪ Continuous Internal Assessment:100 Marks ♦ Presentation assessed by Panel Members ♦ Assessed by Guide ♦ Mid Semester Project Report ▪ End Semester Examination:100 Marks ♦ Viva Voce ♦ Demonstration ♦ Project Report

Text Books And Reference Books:

Journal article, industry white papers text books basedon the domain on which the student will be doing his/her work.

Essential Reading / Recommended Reading

Recommendation will be given Based on the domian in which student will be interested and planning to do the dissertation work

Evaluation Pattern

Assessment of Project Work(Phase II) and Dissertation

▪ Continuous Internal Assessment:100 Marks

♦ Presentation assessed by Panel Members

♦ Assessed by Guide

♦ Mid Semester Project Report

▪ End Semester Examination:100 Marks

♦ Viva Voce

♦ Demonstration

♦ Project Report

▪ Dissertation (Exclusive assessment of Project Report): 100 Marks

♦ Internal Review : 50 Marks

♦ External review : 50 Marks