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1 Semester - 2019 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTCS111E01 | ENGLISH FOR RESEARCH PAPER WRITING | - | 2 | 0 | 0 |
MTCS112 | PROFESSIONAL PRACTICE - I | - | 2 | 1 | 0 |
MTCS131 | RESEARCH METHODOLOGY AND IPR | - | 4 | 3 | 100 |
MTCS133 | ADVANCED ALGORITHMS | - | 4 | 3 | 100 |
MTCS135 | ADVANCED DIGITAL IMAGE PROCESSING | - | 4 | 3 | 100 |
MTCS141E03 | SOFTWARE PROJECT MANAGEMENT | - | 4 | 3 | 100 |
MTCS142E01 | BIG DATA ANALYTICS | - | 4 | 3 | 100 |
MTCS151 | ADVANCED ALGORITHMS LABORATORY | - | 4 | 2 | 50 |
MTCS152 | DIGITAL IMAGE PROCESSING LABORATORY | - | 4 | 2 | 50 |
2 Semester - 2019 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTCS212E03 | STRESS MANAGEMENT BY YOGA | - | 2 | 0 | 0 |
MTCS213 | PROFESSIONAL PRACTICE - II | - | 2 | 1 | 0 |
MTCS231 | COMPUTER COMMUNICATION NETWORKS | - | 4 | 3 | 100 |
MTCS232 | DATA SCIENCE | - | 4 | 3 | 100 |
MTCS243E01 | CLOUD COMPUTING | - | 4 | 3 | 100 |
MTCS244E05 | NETWORK SECURITY | - | 4 | 3 | 100 |
MTCS251 | NETWORKING LABORATORY | - | 4 | 2 | 50 |
MTCS252 | DATASCIENCE LABORATORY | - | 4 | 2 | 50 |
3 Semester - 2018 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
CY01 | CYBER SECURITY | - | 2 | 2 | 50 |
MTCS331E03 | WEB TECHNOLOGY | - | 4 | 3 | 100 |
MTCS332E01 | MACHINE LEARNING | - | 4 | 3 | 100 |
MTCS333E01 | SOFTWARE PROJECT MANAGEMENT | - | 4 | 3 | 100 |
MTCS371 | PROJECT WORK (PHASE I) | - | 12 | 3 | 100 |
MTCS373 | INTERNSHIP | - | 2 | 2 | 50 |
4 Semester - 2018 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTCS471 | PROJECT WORK (PHASE-II) AND DISSERTATION | - | 20 | 9 | 300 |
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Introduction to Program: | |
The 2 year Post graduate program M.Tech in Computer Science and Engineering.started in 2009 . 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. | |
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) |
MTCS111E01 - ENGLISH FOR RESEARCH PAPER WRITING (2019 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
Max Marks:0 |
Credits:0 |
Course Objectives/Course Description |
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Course Outcome |
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Unit-1 |
Teaching Hours:4 |
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Unit-1
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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:4 |
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Unit-2
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Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising, Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction | ||
Unit-3 |
Teaching Hours:4 |
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Unit-3
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Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check. | ||
Unit-4 |
Teaching Hours:4 |
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unit-4
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key skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills are needed when writing an Introduction, skills needed when writing a Review of the Literature | ||
Unit-5 |
Teaching Hours:4 |
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Unit-5
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skills are needed when writing the Methods, skills needed when writing the Results, skills are needed when writing the Discussion, skills are needed when writing the Conclusions, useful phrases, how to ensure paper is as good as it could possibly be the first- time submission | ||
Text Books And Reference Books: Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books) | ||
Essential Reading / Recommended Reading
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Evaluation Pattern - | ||
MTCS112 - PROFESSIONAL PRACTICE - I (2019 Batch) | ||
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
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Max Marks:0 |
Credits:1 |
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Course Objectives/Course Description |
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SUBJECT DESCRIPTION:
During the seminar session each student is expected to prepare and present a topic on engineering / technology, it is designed to: Review and increase their understanding of the specific topics tested. Improve their ability to communicate that understanding to the grader. Increase the effectiveness with which they use the limited examination time.
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. |
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Course Outcome |
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Unit-1 |
Teaching Hours:30 |
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Text Books And Reference Books: - | |
Essential Reading / Recommended Reading - | |
Evaluation Pattern Head of the Department will assign a suitable instructor/faculty member to each student. Students and faculty members covering a broad area will be grouped in a panel consisting of 4-5 students and 4-5 faculty members.
Within one week after registration, the student should plan the details of the topics of lectures, laboratory experiments, developmental activities and broad topic of research etc in consultation with the assigned instructor/faculty. The student has to submit two copies of the written outline of the total work to the instructor within one week.
In a particular discipline, Instructors belonging to the broad areas will form the panel and will nominate one of them as the panel coordinator. The coordinator together with the instructors will draw a complete plan of lectures to be delivered by all students in a semester. 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.
Wherever necessary and feasible, the panel coordinator in consultation with the concerned group may also seek participation of the faculty members from other groups in lectures and comprehensive viva.
Mid semester report and final evaluation report should be submitted in the 9th week and 15th week of the semester respectively. These should contain the following sections:
● Section (A): Lecture notes along with two question papers each of 180 min duration, one quiz paper (CIA-I) of 120 min duration on the topics of lectures. The question paper should test concepts, analytical abilities and grasp of the subject. Solutions of questions also should be provided. All these will constitute lecture material. ● Section (B): Laboratory experiments reports and professional work report.
● Section (C): Research proposal with detailed references and bibliography in a standard format.
Wherever necessary, respective Head of the Departments could be approached by Instructors/panel coordinators for smooth operation of the course. Special lectures dealing with professional ethics in the discipline may also be arranged by the group from time to time. | |
MTCS131 - RESEARCH METHODOLOGY AND IPR (2019 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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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. |
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Course Outcome |
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After going through this course the scholar will be able to 1. Explain the principles and concepts of research methodology. 2. understand the different methods of data collection 3. Apply appropriate method of data collection and analyze using statistical/software tools. 4. Present research output in a structured report as per the technical and ethical standards. 5. Create research design for a given engineering and management problem /situation. |
Unit-1 |
Teaching Hours:9 |
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Introduction to Research Methodology
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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.
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Unit-2 |
Teaching Hours:9 |
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Literature Review and Research Problem Identification
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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.
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Unit-3 |
Teaching Hours:9 |
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Data Collection & Analysis
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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 |
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Research Problem Solving
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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
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Unit-5 |
Teaching Hours:9 |
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IPR and Research Writing
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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:
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Essential Reading / Recommended Reading 1. Bjorn Gustavii, “How to Write and Illustrate Scientific Papers “ Cambridge University Press, 2/e. 2. Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell- John wiley & sons, 1/e, 2013. 3. .James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008. | ||||||
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 : 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 | ||||||
MTCS133 - ADVANCED ALGORITHMS (2019 Batch) | ||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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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 |
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Course Outcome |
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Summarize the properties of advanced data structures. Design algorithms and employ appropriate advanced data structures for solving computing problems efficiently. Analyze and compare the efficiency of algorithms. Design and implement efficient algorithms for solving computing problems in a high-level object-oriented programming language. Compare, contrast, and apply algorithmic trade-offs : time vs. space, deterministic vs. randomized, and exact vs. approximate |
Unit-1 |
Teaching Hours:9 |
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INTRODUCTION
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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 |
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GRAPH ALGORITHMS AND POLYNOMIALS
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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 |
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NUMBER THEORETIC ALGORITHMS
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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 |
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STRING MATCHING ALGORITHMS
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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 |
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PROBABILISTIC ALGORITHMS
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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:
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Essential Reading / Recommended Reading
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 : 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 | |||||||
MTCS135 - ADVANCED DIGITAL IMAGE PROCESSING (2019 Batch) | |||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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Course Outcome |
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Course Outcome 1: Ability to apply the image fundamentals and mathematical transformations necessary for image processing Course Outcome 2: Ability to analyze image enhancement techniques in Spatial &frequency domain Course Outcome 3: Ability to apply restoration models and compression models for image processing Course Outcome 4: Ability to synthesis image using segmentation and representation techniques Course Outcome 5: Ability to analyze and extract potential features of interest from the image Course Outcome 6: Ability to design object recognition systems using pattern recognition techniques |
Unit-1 |
Teaching Hours:9 |
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DIGITAL IMAGE FUNDAMENTALS
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Image formation, Image transforms – Fourier transforms, Walsh, Hadamard, Discrete cosine, Hotelling transforms | ||||||||
Unit-2 |
Teaching Hours:9 |
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IMAGE ENHANCEMENT & RESTORATION
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Histogram modification techniques - Image smoothening - Image Sharpening - Image Restoration - Degradation Model – Noise models - Spatial filtering – Frequency domain filtering | ||||||||
Unit-3 |
Teaching Hours:9 |
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IMAGE COMPRESSION & SEGMENTATION
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Compression Models - Elements of information theory - Error free Compression -Image segmentation –Detection of discontinuities – Region based segmentation – Morphology | ||||||||
Unit-4 |
Teaching Hours:9 |
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REPRESENTATION AND DESCRIPTION
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Representation schemes- Boundary descriptors- Regional descriptors - Relational Descriptors | ||||||||
Unit-5 |
Teaching Hours:9 |
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OBJECT RECOGNITION AND INTERPRETATION
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Patterns and pattern classes - Decision-Theoretic methods - Structural methods-Case studies | ||||||||
Text Books And Reference Books:
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Essential Reading / Recommended Reading Author Name(s), “Book title”, Edition, Publisher Name, Year (if it is old edition, reprint details should be given) | ||||||||
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 : 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 | ||||||||
MTCS141E03 - SOFTWARE PROJECT MANAGEMENT (2019 Batch) | ||||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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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.
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Course Outcome |
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Understanding the specific roles within a Conventional Software Management organization as related to project. Describe and determine the purpose and importance of project management from the perspectives of planning, cost, tracking and completion of project. Evaluate a project to develop the scope of work, provide accurate cost estimates and to plan the various activities. Implement a project to manage project schedule, expenses and resources with the application of suitable protect management tools. Identify the resources required for a project and to produce a work plan and resource Schedule. Compare and differentiate organization structures and project structures. |
Unit-1 |
Teaching Hours:9 |
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Unit-1
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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 |
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unit-2
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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 |
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Unit-3
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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 |
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unit-4
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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 |
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Unit - 5
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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:
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Essential Reading / Recommended Reading
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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 : 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 | |||||||
MTCS142E01 - BIG DATA ANALYTICS (2019 Batch) | |||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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To Understand big data for business intelligence To Learn business case studies for big data analytics To Understand Nosql big data management To manage Big data without SQL To understanding map-reduce analytics using Hadoop and related tools |
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Course Outcome |
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Describe big data and use cases from selected business domains Discuss open source technologies Explain NoSQL big data management Discuss basics of Hadoop and HDFS Discuss map-reduce analytics using Hadoop Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data Analytics |
Unit-1 |
Teaching Hours:9 |
UNDERSTANDING BIG DATA
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What is big data – why big data –.Data!, Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big data– big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics. | |
Unit-2 |
Teaching Hours:9 |
NOSQL DATA MANAGEMENT
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Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships –graph databases – schema less databases – materialized views – distribution models – sharding –– version – Map reduce –partitioning and combining – composing map-reduce calculations | |
Unit-3 |
Teaching Hours:9 |
BASICS OF HADOOP
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Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures | |
Unit-4 |
Teaching Hours:9 |
MAPREDUCE APPLICATIONS
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MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution –MapReduce types – input formats – output formats | |
Unit-5 |
Teaching Hours:9 |
HADOOP RELATED TOOLS
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Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model –cassandra examples – cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queries-case study. | |
Text Books And Reference Books: 1. Tom White, "Hadoop: The Definitive Guide", 4th Edition, O'Reilley, 2012. Eric Sammer, "Hadoop Operations",1st Edition, O'Reilley, 2012. | |
Essential Reading / Recommended Reading 1. VigneshPrajapati, Big data analytics with R and Hadoop, SPD 2013. 2. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012. 3. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011. Alan Gates, "Programming Pig", O'Reilley, 2011. | |
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 : 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 | |
MTCS151 - ADVANCED ALGORITHMS LABORATORY (2019 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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● To increase the knowledge of advanced paradigms of algorithm design. |
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Course Outcome |
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· Effective use of mathematical techniques to construct robust algorithms. · Assess and to make critical judgment on the choices of algorithms for modern computer systems. |
Unit-1 |
Teaching Hours:6 |
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List of Experiments
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Unit-2 |
Teaching Hours:6 |
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Programs on Data Structures and Algorithms
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Unit-3 |
Teaching Hours:6 |
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Programs on Cloud Computing
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Unit-4 |
Teaching Hours:6 |
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Programs on Cloud Computing
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Unit-5 |
Teaching Hours:6 |
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Programs on Advanced Computer Architecture
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Essential Reading / Recommended Reading
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Evaluation Pattern End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks | |||||||
MTCS152 - DIGITAL IMAGE PROCESSING LABORATORY (2019 Batch) | |||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:50 |
Credits:2 |
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Course Objectives/Course Description |
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Students are expected to implement the image processing algorithms and techniques to solve the real life problems. |
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Course Outcome |
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Apply principles and techniques of digital image processing in applications related to digital imaging system design and analysis Analyze and implement image processing algorithms Understand software tools for processing digital images Experiment image processing problems and techniques Examine image processing algorithms on computers Demonstrate algorithms to solve image processing problems |
Unit-1 |
Teaching Hours:12 |
unit 1
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Unit-2 |
Teaching Hours:12 |
unit 2
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Unit-3 |
Teaching Hours:12 |
unit 3
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Unit-4 |
Teaching Hours:12 |
unit 4
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Unit-5 |
Teaching Hours:12 |
unit 5
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Text Books And Reference Books:
1. Rafael C. Gonzalez, Richard E. Woods, Steven Eddins, “Digital Image Processing using MATLAB”, Pearson Education, Inc., 2004. 2. Gonzalez.R.C & Woods. R.E., “Digital Image Processing using MATLAB”, 2nd Edition, McGraw Hill Education (India) Pvt Ltd 2011 (Asia) 3. Madan, “ An Introduction to MATLAB for Behavioural Researchers”, Sage Publications, 2014
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Essential Reading / Recommended Reading David A Forsyth & Jean ponce “Computer Vision: A Modern Approach” 2nd Edition, Pearson Education India 2015. | |
Evaluation Pattern End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks | |
MTCS212E03 - STRESS MANAGEMENT BY YOGA (2019 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
Max Marks:0 |
Credits:0 |
Course Objectives/Course Description |
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To achieve overall health of body and mind To overcome stress |
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Course Outcome |
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- |
Unit-1 |
Teaching Hours:8 |
Unit-1
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Definitions of Eight parts of yog. ( Ashtanga ) | |
Unit-2 |
Teaching Hours:8 |
unit-2
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Yam and Niyam. Do`s and Don’t’s in life. i) Ahinsa, satya, astheya, bramhacharya and aparigraha ii) Shaucha, santosh, tapa, swadhyay, ishwarpranidhan | |
Unit-3 |
Teaching Hours:8 |
Unit-3
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Asan and Pranayam i) Various yog poses and their benefits for mind & body ii)Regularization of breathing techniques and its effects-Types of pranayam | |
Text Books And Reference Books: Yogic Asanas for Group Tarining-Part-I” :Janardan Swami YogabhyasiMandal, Nagpur | |
Essential Reading / Recommended Reading “Rajayoga or conquering the Internal Nature” by Swami Vivekananda, AdvaitaAshrama (Publication Department), Kolkata | |
Evaluation Pattern - | |
MTCS213 - PROFESSIONAL PRACTICE - II (2019 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
Max Marks:0 |
Credits:1 |
Course Objectives/Course Description |
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Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:
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Course Outcome |
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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: |
Unit-1 |
Teaching Hours:30 |
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COURSE NOTICES
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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 - COMPUTER COMMUNICATION NETWORKS (2019 Batch) | ||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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· To understand the concepts of internetwork. · To study the functions of different layers. · To introduce IEEE standards employed in computer networking. · To make the students to get familiarized with different protocols and network components. |
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Course Outcome |
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Recognize the basic requirements of building of network and layering of protocols. Distinguish the concept of internetworking and routing through internet protocol addressing. Discuss the role of different protocols in internetworking. Examine the security issues and congestion control in the networks Determine the features and operations of various application layer protocols. |
Unit-1 |
Teaching Hours:12 |
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INTRODUCTION
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Building a Network, Requirements, Perspectives, Scalable Connectivity, Cost-Effective Resource sharing, Support for Common Services, Manageability, Protocol layering, Performance, Bandwidth and Latency, Delay X Bandwidth Product, Perspectives on Connecting, Classes of Links, Reliable Transmission, Stop-and-Wait , Sliding Window, Concurrent Logical Channels.
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Unit-2 |
Teaching Hours:12 |
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INTERNETWORKING- I
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Switching and Bridging, Datagram’s, Virtual Circuit Switching, Source Routing, Bridges and LAN Switches, Basic Internetworking (IP), Service Model, Global Addresses, Datagram Forwarding in IP, subnetting and classless addressing, Address Translation(ARP), Host Configuration(DHCP), Error Reporting(ICMP), Virtual Networks and Tunnels. | ||||||
Unit-3 |
Teaching Hours:12 |
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INTERNETWORKING- II
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||||||
Network as a Graph, Distance Vector (RIP), Link State (OSPF), Metrics, The Global Internet, Routing Areas, Routing among Autonomous systems (BGP), IP Version 6(IPv6), Mobility and Mobile IP. | ||||||
Unit-4 |
Teaching Hours:12 |
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NETWORK SECURITY
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||||||
Simple Demultiplexer (UDP), Reliable Byte Stream(TCP), End-to-End Issues, Segment Format, Connecting Establishment and Termination, Sliding Window Revisited, Triggering Transmission, Adaptive Retransmission, Record Boundaries, TCP Extensions, Queuing Disciplines, FIFO, Fair Queuing, TCP Congestion Control, Additive Increase/Multiplicative Decrease, Slow Start, Fast Retransmit and Fast Recovery. | ||||||
Unit-5 |
Teaching Hours:12 |
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APPLICATIONS
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Congestion-Avoidance Mechanisms, DEC bit, Random Early Detection (RED), Source-Based Congestion Avoidance. The Domain Name System(DNS),Electronic Mail(SMTP,POP,IMAP,MIME),World Wide Web(HTTP),Network Management(SNMP). | ||||||
Text Books And Reference Books: TEXTBOOKS 1. Larry Peterson and Bruce S Davis “Computer Networks: A System Approach” 5th Edition, Elsevier -2014 2. Douglas E Comer, “Internetworking with TCP/IP, Principles, Protocols and Architecture” 6th Edition, PHI – 2014
REFERENCE BOOKS 1. Uyless Black “Computer Networks, Protocols, Standards and Interfaces” 2nd Edition – PHI 2. Behrouz A Forouzan “TCP /IP Protocol Suite” 4th Edition – Tata McGraw-Hill 3. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education 4th edition, 2012. 4. Larry L.Peterson and Brule S.Davie, “Computer Networks – A System Approach” MarGankangmann – Harcourt Asia, Fifth Edition, 2011. 5. William Stallings, “SNMP, SNMP V2, SNMPV3, RMON 1 and 2”, Pearson 2006 6.J.F Kurose and K.W. Ross, “Computer Networking –A top –down approach featuring the internet”, Pearson, 2012. 7.William Stallings, “Data & Computer Communication”, 6th Edition, Pearson Education, 2007. 8.Mani Subramanian, “Network Management: Principles and Practice”, Addison Wesley, 2000.
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Essential Reading / Recommended Reading | ||||||
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 : 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 | ||||||
MTCS232 - DATA SCIENCE (2019 Batch) | ||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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Course Outcome |
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Understand the foundations of data processing Apply the clustering methods for modelling the data Analysis of Statistical models and data distributions using R Programming Analysis of distributed file system and Map reducing technique using Hadoop Evaluating the results of data science experiment using R Programming. |
Unit-1 |
Teaching Hours:9 |
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INTRODUCTION AND THE DATA SCIENCE
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Data science process – roles, stages in data science project – working with data from files – working with relational databases – exploring data – managing data – cleaning and sampling for modeling and validation – introduction to NoSQL. | ||||||
Unit-2 |
Teaching Hours:9 |
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MODELING METHODS
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Choosing and evaluating models – mapping problems to machine learning, evaluating clustering models, validating models – cluster analysis – K-means algorithm, Naïve Bayes – Memorization Methods – Linear and logistic regression – unsupervised methods. | ||||||
Unit-3 |
Teaching Hours:9 |
|||||
INTRODUCTION TO R
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Reading and getting data into R – ordered and unordered factors – arrays and matrices – lists and data frames – reading data from files – probability distributions – statistical models in R - manipulating objects – data distribution. | ||||||
Unit-4 |
Teaching Hours:9 |
|||||
MAP REDUCE
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||||||
Introduction – distributed file system – algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce – Hadoop - Understanding the Map Reduce architecture - Writing HadoopMapReduce Programs - Loading data into HDFS - Executing the Map phase - Shuffling and sorting - Reducing phase execution. | ||||||
Unit-5 |
Teaching Hours:9 |
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DELIVERING RESULTS
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Documentation and deployment – producing effective presentations – Introduction to graphical analysis – plot() function – displaying multivariate data – matrix plots – multiple plots in one window - exporting graph - using graphics parameters. Case studies. | ||||||
Text Books And Reference Books:
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Essential Reading / Recommended Reading
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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 : 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 | ||||||
MTCS243E01 - CLOUD COMPUTING (2019 Batch) | ||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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Cloud computing is a model for enablingubiquitous, convenient, on-demand access to a shared pool of configurable computing resources. Cloud computing paradigm possesses tremendous momentum but its unique aspects exacerbate security and privacy challenges. Cloud computing enables increasing number of IT services to be delivered over the Internet. The cloud platform enables business to run successfully without dedicated hardware, software and services. |
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Course Outcome |
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Understand the fundamentals of Cloud Storage, Cloud Architecture and Cloud Computing Explain Cloud Computing technologies with respect to platforms, services, network, security and applications Analyze Virtualization techniques, Virtual machines provisioning and Migrating services. Examine Work flow and Map-reduce programming models Assess various Cloud applications, Security and Performance issues |
Unit-1 |
Teaching Hours:9 |
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UNDERSTANDING CLOUD COMPUTING
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Cloud Computing – History of Cloud Computing – Cloud Architecture – Cloud Storage – Why Cloud Computing Matters – Advantages/Disadvantages of Cloud Computing – Types of Cloud – Architecture of Cloud– Cloud Services- Web-Based Application – Pros and Cons of Cloud Service Development. | ||||||
Unit-2 |
Teaching Hours:9 |
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CLOUD COMPUTING ARCHITECTURE
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||||||
Types of Cloud Service Development – Infrastructure / Hardware as a Service-Software as a Service – Platform as a Service – Web Services – On-Demand Computing – Migrating into a Cloud –Types of Clouds-Amazon Ec2 – Google App Engine – Microsoft Azure – IBM Clouds. | ||||||
Unit-3 |
Teaching Hours:9 |
|||||
VIRTUALIZATION TECHNIQUES; VIRTUAL MACHINES PROVISIONING AND MIGRATION SERVICES
|
||||||
Characteristics of Virtualized Environment – Taxonomy of Virtualization Techniques–Virtualization and Cloud Computing – Pros and Cons of Virtualization – Technology Examples: Xen, VMware, Hyper-V- Virtual Machines Provisioning and Manageability–Virtual Machine Migration Services – Provisioning in the Cloud Context. | ||||||
Unit-4 |
Teaching Hours:9 |
|||||
WORKFLOW AND MAP-REDUCE PROGRAMMING MODELS
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||||||
Workflow Management Systems and Clouds- Architecture of Workflow Management Systems – Utilizing Clouds for Workflow Execution – Data-Intensive Computing– Technologies for Data-Intensive Computing – Storage Systems – Programming Platforms- Aneka MapReduce Programming – Major MapReduce Implementations for the Cloud. | ||||||
Unit-5 |
Teaching Hours:9 |
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CLOUD APPLICATIONS: SECURITY AND PERFORMANCE ISSUES
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Case Study: Business and Consumer Applications: CRM and ERP, Social Networking, Multiplayer Online Gaming – Technologies for Data Security in Cloud Computing – Cloud Computing and Data Security Risk- The Cloud, Digital Identity, and Data Security–Content Level Security-Data Privacy and Security Issues – HPC in the Cloud: Performance related Issues. | ||||||
Text Books And Reference Books: 1. RajkumarBuyya, Vecchiola, Selvi, “Mastering Cloud Computing”, McGraw Hill. 2013. 2. Anthony Velte, Toby Velte, and Robert Elsenpeter. “Cloud Computing – A Practical Approach”, McGraw Hill. 2010. 3. RajkumarBuyya, James Broberg, Andrzej M. Goscinski, “Cloud Computing: Principles and Paradigms”, Wiley 2013. | ||||||
Essential Reading / Recommended Reading 1. Massimo Cafaro and Giovanni Aloisio. “Grids, Clouds and Virtualization”. Springer 2012. 2. Michael Miller, “Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online”, Que Publishing, August 2008. | ||||||
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 : 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 | ||||||
MTCS244E05 - NETWORK SECURITY (2019 Batch) | ||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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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. |
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Course Outcome |
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Evaluate the factors driving the need for network security. Demonstrate the implications of implementing encryption at different levels of the OSI reference model. Identify types of firewall implementation suitable for differing security requirements. Experiment and explain simple filtering rules based on IP and TCP header information. Distinguish between firewalls based on packet-filtering routers, application level gateways and circuit level gateways. |
Unit-1 |
Teaching Hours:9 |
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UNit-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 |
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Unit ? 2
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||||
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 |
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Unit ? 3
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||||
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 |
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Unit ? 4
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||||
Web Security Requirements, Secure Socket Layer (SSL) and Transport Layer Security (TLS), Secure Electronic Transaction (SET). Intruders, | ||||
Unit-5 |
Teaching Hours:9 |
|||
Unit ? 5
|
||||
Viruses and related threats. Firewall Design principles, Trusted Systems. Intrusion Detection Systems | ||||
Text Books And Reference Books: Network Security Essentials (Applications and Standards) by William Stallings Pearson Education, 5th Edition 2013. | ||||
Essential Reading / Recommended Reading 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 | ||||
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 : 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 | ||||
MTCS251 - NETWORKING LABORATORY (2019 Batch) | ||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:50 |
Credits:2 |
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Course Objectives/Course Description |
||||
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||||
Course Outcome |
||||
Examine the performances of Routing protocol Experiment with different application layer protocols Experiment with different security techniques over peer to peer medium. |
Unit-1 |
Teaching Hours:60 |
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Design, develop the project to implement following areas in networks
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Text Books And Reference Books:
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Essential Reading / Recommended Reading | |||||||||||
Evaluation Pattern End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks Mid semester practical examination will be conducted during regular practical hour with prior intimation to all candidates. End semester practical examination will have two examiners an internal and external examiner. | |||||||||||
MTCS252 - DATASCIENCE LABORATORY (2019 Batch) | |||||||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:50 |
Credits:2 |
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Course Objectives/Course Description |
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Able to apply fundamental algorithmic ideas to process data. Learn to apply hypotheses and data into actionable predictions. Document and transfer the results and effectively communicate the findings using visualization techniques. |
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Course Outcome |
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- |
Unit-1 |
Teaching Hours:60 |
List of Experiments
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Text Books And Reference Books: - | |
Essential Reading / Recommended Reading - | |
Evaluation Pattern 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 Mid semester practical examination will be conducted during regular practical hour with prior intimation to all candidates. End semester practical examination will have two examiners an internal and external examiner. | |
CY01 - CYBER SECURITY (2018 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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Cyber Security is defined as the body of technologies, processes and practices designed to protect networks, computers, programs and data from attack, damage or unauthorized access. Similar to other forms of security, Cyber Security requires coordinated effort throughout an information system. This course will provide a comprehensive overview of the different facets of Cyber Security. In addition, the course will detail into specifics of Cyber Security for all parties who may be involved keeping view of Global and Indian Legal environment. |
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Course Outcome |
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After learning the course for a semester, the student will be aware of the important cyber laws in the Information Technology Act (ITA) 2000 and ITA 2008 with knowledge in the areas of Cyber-attacks and Cyber-crimes happening in and around the world. The student would also get a clear idea on some of the cases with their analytical studies in Hacking and its related fields. |
Unit-1 |
Teaching Hours:6 |
Unit-I
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Security Fundamentals, Social Media and Cyber Security Security Fundamentals - Social Media –IT Act- CNCI – Legalities | |
Unit-2 |
Teaching Hours:6 |
Unit-II
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Cyber Attack and Cyber Services Vulnerabilities - Phishing - Online Attacks. – Cyber Attacks - Cyber Threats - Denial of Service Vulnerabilities - Server Hardening | |
Unit-3 |
Teaching Hours:6 |
Unit-III
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Risk Management and Assessment - Risk Management Process - Threat Determination Process - Risk Assessment - Risk Management Lifecycle – Vulnerabilities, Security Policy Management - Security Policies - Coverage Matrix, Business Continuity Planning - Disaster Types - Disaster Recovery Plan - Business Continuity Planning - Business Continuity Planning Process. | |
Unit-4 |
Teaching Hours:6 |
Unit-IV
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Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Architectural Integration: Security Zones - Devices viz Routers, Firewalls, DMZ Host, Extenuating Circumstances viz. Business-to-Business, Exceptions to Policy, Special Services and Protocols, Configuration Management - Certification and Accreditation | |
Unit-5 |
Teaching Hours:6 |
Unit-V
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Authentication and Cryptography: Authentication - Cryptosystems - Certificate Services Securing Communications: Securing Services - Transport – Wireless - Steganography and NTFS Data Streams, Intrusion Detection and Prevention Systems: Intrusion - Defense in Depth - IDS/IPS - IDS/IPS Weakness and Forensic Analysis, Cyber Evolution: Cyber Organization - Cyber Future | |
Text Books And Reference Books:
TEXT BOOKS:
REFERENCES:
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Essential Reading / Recommended Reading Research papers from reputed journals. | |
Evaluation Pattern Internal 50 Marks. | |
MTCS331E03 - WEB TECHNOLOGY (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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This course is designed to introduce programming experience to techniques associated with the World Wide Web. The course will introduce web-based media-rich programming tools for creating interactive web pages. Basic animation programming is also introduced with an emphasis on media-rich content creation, distribution and tracking capabilities. |
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Course Outcome |
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· Analyze Build web applications using PHP, JSP and Servlets and client side script technologies like HTML, CSS and JavaScript with Apache web server. · Design and Integrate database environment to web applications being developed. Describe sessions conceptually and implement using cookies and URL. · Analyze the XML applications with DTD and style sheets that span multiple domains and across various platforms. · Analyze the reasons and effects of nonstandard client-side scripting language characteristics, such as limited data types, dynamic variable types and properties, and extensive use of automatic type conversion. |
Unit-1 |
Teaching Hours:9 |
INTRODUCTION
|
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Introduction – Network concepts – Web concepts – Internet addresses - Retrieving Data with URL – HTML – DHTML: Cascading Style Sheets - Scripting Languages: JavaScript.
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Unit-2 |
Teaching Hours:9 |
COMMON GATEWAY INTERFACE
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Common Gateway Interface: Programming CGI Scripts – HTML Forms – Custom Database Query Scripts – Server Side Includes – Server security issues | |
Unit-3 |
Teaching Hours:9 |
XML AND RICH INTERNET APPLICATIONS
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XML- XSL, XSLT, DOM ,RSS, Client Technologies- Adobe Flash, Flex, Microsoft Silverlight. | |
Unit-4 |
Teaching Hours:9 |
SERVER SIDE PROGRAMMING-I
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Server side Programming – PHP- Passing variables between pages, Using tables, Form elements. Active server pages – Java server pages | |
Unit-5 |
Teaching Hours:9 |
SERVER SIDE PROGRAMMING-II & APPLICATIONS
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Java Servlets: Servlet container – Exceptions – Sessions and Session Tracking – Using Servlet context – Dynamic Content Generation – Servlet Chaining and Communications. Simple applications – Internet Commerce – Database connectivity. | |
Text Books And Reference Books: 1. Deitel, Deitel and Neito, “INTERNET and WORLD WIDE WEB – How to program”, Pearson education asia, 4th Edition , 2011 2. Beginning PHP, Apache, MySql Web Development , Timothy, Elizabath, Jason, Wrox ,2012 | |
Essential Reading / Recommended Reading 1. Eric Ladd and Jim O’Donnell, et al, “USING HTML 4, XML, and JAVA1.2”, PHI publications, 2003. 2. Jeffy Dwight, Michael Erwin and Robert Nikes “USING CGI”, PHI Publications, 1999 | |
Evaluation Pattern · 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 : 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 | |
MTCS332E01 - MACHINE LEARNING (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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|
|
Course Outcome |
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Upon Completion of the course, the students will be able to |
Unit-1 |
Teaching Hours:9 |
INTRODUCTION
|
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Machine Learning - Machine Learning Foundations –Overview – applications - Types of machine learning - basic concepts in machine learning Examples of Machine Learning -Applications – Linear Models for Regression - Linear Basis Function Models - The Bias-Variance Decomposition - Bayesian Linear Regression - Bayesian Model Comparison | |
Unit-2 |
Teaching Hours:9 |
SUPERVISED LEARNING
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Linear Models for Classification - Discriminant Functions -Probabilistic Generative Models - Probabilistic Discriminative Models - Bayesian Logistic Regression. Decision Trees – Classification Trees- Regression Trees - Pruning. Neural Networks -Feed-forward Network Functions - Error Backpropagation - Regularization - Mixture Density and Bayesian Neural Networks - Kernel Methods - Dual Representations - Radial Basis Function Networks. Ensemble methods- Bagging- Boosting. | |
Unit-3 |
Teaching Hours:9 |
UNSUPERVISED LEARNING
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Clustering- K-means - EM - Mixtures of Gaussians - The EM Algorithm in General -Model selection for latent variable models - high-dimensional spaces -- The Curse of Dimensionality –Dimensionality Reduction - Factor analysis - Principal Component Analysis - Probabilistic PCA- Independent components analysis | |
Unit-4 |
Teaching Hours:9 |
PROBABILISTIC GRAPHICAL MODELS
|
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Directed Graphical Models - Bayesian Networks - Exploiting Independence Properties – From Distributions to Graphs -Examples -Markov Random Fields - Inference in Graphical Models – Learning –Naive Bayes classifiers-Markov Models – Hidden Markov Models – Inference – Learning- Generalization – Undirected graphical models- Markov random fields- Conditional independence properties - Parameterization of MRFs - Examples - Learning - Conditional random fields (CRFs) - Structural SVMs | |
Unit-5 |
Teaching Hours:9 |
ADVANCED LEARNING
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Sampling – Basic sampling methods – Monte Carlo. Reinforcement Learning - K-Armed Bandit - Elements - Model-Based Learning - Value Iteration - Policy Iteration. Temporal Difference Learning- Exploration Strategies- Deterministic and Non-deterministic Rewards and Actions- Eligibility Traces- Generalization- Partially Observable States- The Setting- Example. Semi - Supervised Learning. Computational Learning Theory - Mistake bound analysis, sample complexity analysis, VC dimension. Occam learning, accuracy and confidence boosting | |
Text Books And Reference Books:
| |
Essential Reading / Recommended Reading 1. Tom Mitchell, "Machine Learning", McGraw-Hill, 1997. | |
Evaluation Pattern · 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 : 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 | |
MTCS333E01 - SOFTWARE PROJECT MANAGEMENT (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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|
Course Outcome |
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Explain and practice the process of project management and its application in delivering successful IT projects. Evaluate a project to develop the scope of work, provide accurate cost estimates and to plan the various activities. Interpret and use risk management analysis techniques that identify the factors that put a project at risk and to quantify the likely effect of risk on project timescales. Identify the resources required for a project and to produce a work plan and resource Schedule. Monitor and evaluate the progress of a project and to assess the risk of slippage, revising targets or counteract drift. Distinguish between the different types of project and follow the stages needed to negotiate an appropriate contract. |
Unit-1 |
Teaching Hours:9 |
Project Evaluation and Project Planning
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Importance of Software Project Management, Activities Methodologies, Categorization of Software Projects , Setting objectives , Management Principles, Management Control, Project portfolio Management, Cost-benefit evaluation technology, Risk evaluation, Strategic program Management, Stepwise Project Planning.
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Unit-2 |
Teaching Hours:9 |
Project Life Cycle and Effort
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Software process and Process Models, Choice of Process models, mental delivery, Rapid Application development, Agile methods, Extreme Programming, SCRUM, Managing interactive processes, Basics of Software estimation, Effort and Cost estimation techniques, COSMIC Full function points, COCOMO II A Parametric Productivity Model, Staffing Pattern. | |
Unit-3 |
Teaching Hours:9 |
Activity Planning and Risk Management
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Objectives of Activity planning, Project schedules, Activities, Sequencing and scheduling, Network Planning models, Forward Pass & Backward Pass techniques, Critical path (CRM) method, Risk identification, Assessment, Monitoring, PERT technique, Monte Carlo simulation, Resource Allocation, Creation of critical patterns, Cost schedules.
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Unit-4 |
Teaching Hours:9 |
Project Management and Control
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Framework for Management and control, Collection of data Project termination, Visualizing progress, Cost monitoring, Earned Value Analysis-Project tracking, Change control-Software Configuration Management, Managing contracts, Contract Management. | |
Unit-5 |
Teaching Hours:9 |
Staffing In Software Projects
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Managing people, Organizational behaviour, Best methods of staff selection, Motivation, The Oldham - Hackman job characteristic model, Ethical and Programmed concerns, Working in teams, Decision making, Team structures, Virtual teams, Communications genres , Communication plans. | |
Text Books And Reference Books:
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Essential Reading / Recommended Reading
3. Software Project Management in Practice by Pankaj Jalote, Pearson Education 2010. 4. Software Project Management Readings and Cases by Chris Kemerer 2010.
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Evaluation Pattern · 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 : 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 | |
MTCS371 - PROJECT WORK (PHASE I) (2018 Batch) | |
Total Teaching Hours for Semester:180 |
No of Lecture Hours/Week:12 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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During this seminar session, each student is expected to prepare and present a topic on engineering/ technology, itis designed to:
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Course Outcome |
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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: |
Unit-1 |
Teaching Hours:45 |
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UNIT-1
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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 § Continuous Internal Assessment:100 Marks ¨ Presentation assessed by Panel Members ¨ Guide ¨ Mid semester Project Report | |||
MTCS373 - INTERNSHIP (2018 Batch) | |||
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
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Max Marks:50 |
Credits:2 |
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Course Objectives/Course Description |
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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:
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Course Outcome |
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Unit-1 |
Teaching Hours:60 |
Regulations
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1.The student shall undergo an Internship for30 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 2nd 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 | |
MTCS471 - PROJECT WORK (PHASE-II) AND DISSERTATION (2018 Batch) | |
Total Teaching Hours for Semester:300 |
No of Lecture Hours/Week:20 |
Max Marks:300 |
Credits:9 |
Course Objectives/Course Description |
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Objective of this course is to encourage students to do research oriented project. |
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Course Outcome |
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The students are expected to comeout with the product implemetation with dissertation details. |
Unit-1 |
Teaching Hours:120 |
Assessment of Project Work(Phase II) and Dissertation
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v § Continuous Internal Assessment:100 Marks ¨ Presentation assessed by Panel Members ¨ Assessment by Guide § Dissertation (Exclusive assessment of Project Report): 100 Marks § End Semester Examination:100 Marks ¨ Viva Voce ¨ Demonstration ¨ Project Report | |
Text Books And Reference Books: Research articles from the identified domain | |
Essential Reading / Recommended Reading Research papers from reputed journals | |
Evaluation Pattern Internal 200 External 100 |