Mumbai University-Third Year -Semester 7 VII Information Technology Syllabus (Revised) 2010
|
Scheme of Instructions |
Scheme of Examinations |
|||||||||
|
Sr. No |
Subjects |
Lect/ Week |
Pract/ Week |
Tuto/ Week |
Theory |
T/W |
Practical |
Oral |
Total |
|
|
Hours |
Marks |
Marks |
Marks |
Marks |
Marks |
|||||
|
1 |
Data Warehousing, Mining & Business Intelligence |
4 |
2 |
– |
3 |
100 |
25 |
– |
25 |
150 |
|
2 |
Image processing |
4 |
2 |
– |
3 |
100 |
25 |
– |
25 |
150 |
|
3 |
Simulation and Modeling |
4 |
2 |
– |
3 |
100 |
25 |
25 |
– |
150 |
|
4 |
Software testing & Quality Assurance |
4 |
2 |
– |
3 |
100 |
25 |
– |
25 |
150 |
|
5 |
Elective – I |
4 |
2 |
– |
3 |
100 |
25 |
– |
25 |
150 |
|
6 |
Project – I |
– |
4 |
– |
– |
– |
25 |
– |
25 |
50 |
|
|
TOTAL |
20 |
14 |
2 |
3 |
500 |
150 |
25 |
125 |
800 |
Final Year PROJECT Semester 7 Mumbai University
Mumbai University-Third Year -Semester VII Information Technology Syllabus (Revised)Final Year PROJECT Semester 7 Mumbai University
| PROJECT – I | ||||
| CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII | ||||
| HOURS PER WEEK | LECTURES | : | – | |
| TUTORIALS | : | – | ||
| PRACTICALS | : | 04 | ||
| HOURS | MARKS | |||
| EVALUATION SYSTEM: | THEORY | – | – | |
| PRACTICAL | – | – | ||
| ORAL | – | 25 | ||
| TERM WORK | – | 25 | ||
| Objective: The Project work enables students to develop further skills and knowledge gained during the programme by applying them to the analysis of a specific problem or issue, via a substantial piece of work carried out over an extended period. For students to demonstrate proficiency in the design of a research project, application of appropriate research methods, collection and analysis of data and presentation of results. |
Guidelines:
- 1. Project Topic:
- • To proceed with the project work it is very important to select a right topic. Project can be undertaken on any subject addressing IT programme. Research and development projects on problems of practical and theoretical interest should be encouraged.
- • Project work must be carried out by the group of at least two students and maximum three and must be original.
- • Students can certainly take ideas from anywhere, but be sure that they should evolve them in the unique way to suit their project requirements.
- • The project work can be undertaken in a research institute or organization/company/any business establishment.
- • Student must consult internal guide along with external guide (if any) in selection of topic. Out of the total projects 35 percent may be allowed as to be industry projects, 65 percent projects must be in house.
- • Head of department and senior staff in the department will take decision regarding projects.
- • Student has to submit weekly progress report to the internal guide and where as internal guide has to keep track on the progress of the project and also has to maintain attendance report. This progress report can be used for awarding term work marks.
- • In case of industry projects, visit by internal guide will be preferred.
- • Make sure that external project guides are BE graduates.
- 2. Project Report Format:
At the end of semester a project report should preferably contain at least following details:-
- • Abstract
- • Introduction
- • Aims and objectives
- • Literature Surveyed
- • Existing system (if any)
- • Problem Statement
- • Scope
- • Proposed System
- • Methodology (your approach to solve the problem)
- • Analysis
- • Details of Hardware & Software
- • Design details
- • Implementation Plan for next semester
- 3. Term Work:
Distribution of marks for term work shall be as follows:
Weekly Attendance on Project Day 05 Marks Project Report (Spiral Bound) 10 Marks Term End Presentation (Internal) 10 Marks The final certification and acceptance of TW ensures the satisfactory performance on the above three aspects.
- 4. Final Assessment:
Project – I examination should be conducted by two examiners appointed by university. Students have to give demonstration and seminar on the Project – I.
ARTIFICIAL INTELLIGENCE
Mumbai University-Third Year -Semester VII Information Technology Syllabus (Revised) ARTIFICIAL INTELLIGENCE
|
ELECTIVE – I : ARTIFICIAL INTELLIGENCE |
||||
|
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII |
||||
|
HOURS PER WEEK |
LECTURES |
: |
04 |
|
|
TUTORIALS |
: |
– |
||
|
PRACTICALS |
: |
02 |
||
|
|
HOURS |
MARKS |
||
|
EVALUATION SYSTEM: |
THEORY |
|
3 |
100 |
|
PRACTICAL |
|
– |
– |
|
|
ORAL |
|
– |
25 |
|
|
TERM WORK |
|
– |
25 |
|
|
Prerequisite: programming language like JAVA or Python |
||||
|
Objective: This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. Students will develop a basic understanding of the building blocks of AI as presented in terms of intelligent agents. This course will attempt to help students understand the main approaches to artificial intelligence such as heuristic search, game search, logical inference, decision theory, planning, machine learning, neural networks and natural language processing. Students will be able to recognize problems that may be solved using artificial intelligence and implement artificial intelligence algorithms for hands-on experience |
- 1. Artificial Intelligence: Introduction to AI, History of AI, Emergence Of Intelligent Agents
- 2. Intelligent Agents: PEAS Representation for an Agent, Agent Environments, Concept of Rational Agent, Structure of Intelligent agents, Types of Agents.
- 3. Problem Solving: Solving problems by searching, Problem Formulation, Uninformed Search Techniques- DFS, BFS, Iterative Deepening, Comparing Different Techniques, Informed search methods – heuristic Functions, Hill Climbing, Simulated Annealing, A*, Performance Evaluation.
- 4. Constrained Satisfaction Problems: Constraint Satisfaction Problems like, map Coloring, Crypt Arithmetic, Backtracking for CSP, Local Search.
- 5. Adversarial Search: Games, Minimax Algorithm, Alpha Beta pruning.
- 6. Knowledge and Reasoning: A knowledge Based Agent, Introduction To Logic, Propositional Logic, Reasoning in Propositional logic, First Order Logic: Syntax and Semantics, Extensions and Notational Variation, Inference in First Order Logic, Unification, Forward and backward chaining, Resolution.
- 7. Knowledge Engineering: Ontology, Categories and Objects, Mental Events and Objects.
- 8. Planning: Planning problem, Planning with State Space Search, Partial Order Planning, Hierarchical Planning, Conditional Planning.
- 9. Uncertain Knowledge and Reasoning: Uncertainty, Representing knowledge in an Uncertain Domain, Overview of Probability Concepts, Belief Networks, Simple Inference in Belief Networks.
- 10. Learning: Learning from Observations, General Model of Learning Agents, Inductive learning, learning Decision Trees, Introduction to neural networks, Perceptrons, Multilayer feed forward network, Application of ANN, Reinforcement learning: Passive & Active Reinforcement learning.
- 11. Agent Communication: Communication as action, Types of communicating agents, A formal grammar for a subset of English
Text Book:
- 1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd Edition, Pearson Publication.
Reference Books:
- 1. George Lugar, “AI-Structures and Strategies for Complex Problem Solving”, 4/e, 2002, Pearson Educations
- 2. Robert J. Schalkolf, Artificial Inteilligence: an Engineering approach, McGraw Hill, 1990.
- 3. Patrick H. Winston, Artificial Intelligence, 3rd edition, Pearson.
- 4. Nils J. Nilsson, Principles of Artificial Intelligence, Narosa Publication.
- 5. Dan W. Patterson, Introduction to Artificial Intelligence and Expert System, PHI.
- 6. Efraim Turban Jay E.Aronson, “Decision Support Systems and Intelligent Systems” PHI.
- 7. M. Tim Jones, Artificial Intelligence – A System Approach, Infinity Science Press -Firewall Media.
- 8. Christopher Thornton and Benedict du Boulay, “Artificial Intelligence – Strategies, Applications, and Models through Search, 2nd Edition, New Age International Publications.
- 9. Elaine Rich, Kevin Knight, Artificial Intelligence, Tata McGraw Hill, 1999.
- 10. David W. Rolston, Principles of Artificial Intelligence and Expert System Development, McGraw Hill, 1988.
Term Work: Term work shall consist of at least 10 experiments covering all topics and one written test. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment list: (Can be implemented in JAVA)
- 1. Problem Formulation Problems
- 2. Programs for Search
- 3. Constraint Satisfaction Programs
- 4. Game Playing Programs
- 5. Assignments on Resolution
- 6. Building a knowledge Base and Implementing Inference
- 7. Assignment on Planning and reinforcement Learning
- 8. Implementing Decision Tree Learner
- 9. Neural Network Implementation
- 10. Bayes’ Belief Network (can use Microsoft BBN tool)
- 11. Assignment on Agent Communication – Grammar Representation For Simple Domains
ORAL EXAMINATION
Oral examination is to be conducted based on the above syllabus.
GEOGRAPHICAL INFORMATION SYSTEMS
Mumbai University-Third Year -Semester VII Information Technology Syllabus (Revised) GEOGRAPHICAL INFORMATION SYSTEMS
|
Elective – I: GEOGRAPHICAL INFORMATION SYSTEMS |
||||
|
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII |
||||
|
HOURS PER WEEK |
LECTURES |
: |
04 |
|
|
TUTORIALS |
: |
– |
||
|
PRACTICALS |
: |
02 |
||
|
|
HOURS |
MARKS |
||
|
EVALUATION SYSTEM: |
THEORY |
|
3 |
100 |
|
PRACTICAL |
|
– |
– |
|
|
ORAL |
|
– |
25 |
|
|
TERM WORK |
|
– |
25 |
|
|
Prerequisite: Computer Graphics |
||||
|
Objective: To understand fundamental concepts and principles of Geographical Information Systems. |
- 1. Fundamentals of GIS: Introduction, Definition of GIS, Evolution of GIS, Roots of GIS, Four M’s, Definition, GIS Architecture, Models of GIS, Framework for GIS, GIS Categories, Map as a Model, Spatial Referencing System, Map Projections, Commonly Used Map Projections, Grid Systems, Cartographic Symbolization, Types of Maps, Typography, Map Design, Map Productions, Map Applications.
- 2. Data Management, Models and Quality Issues: Conceptual Models, Geographical Data Models, Data Primitives, Data Types – Raster and Vector Approach, Digital Terrain Modeling , Approaches to digital terrain data modeling , Acquisition of digital terrain data, Data Modeling and Spatial Analysis, Sources of Geographical Data, Data Collectors and Providers, Creating Digital Data Sets, Data Presentation, Data Updating, Data Storage, Spatial Data Costs, Quality of GIS Output, Sources of Errors in Spatial Data, Factors affecting Reliability of Spatial Data, Faults from Assumptions, spatial autocorrelation, Quadrat counts and Nearest – Neighbour analysis, Trend surface analysis, Gravity models.
- 3. GIS Data Processing, Analysis and Visualization: Raster based GIS data processing, Vector based GIS data processing, Human computer interaction and GIS, Visualization of geographic information, principles of cartographic design in GIS, Generation of information product, Image Classification and GIS, Visual Image Interpretation, Types of Pictorial Data Products, Image Interpretation Strategy, Image Interpretation Process, Overview of Image Interpretation Equipments.
- 4. Terrain Mapping, Geocoding and Segmentation: Interpolation, Visualization of Continuous Surfaces, Data Sources for Interpolations, Methods for Interpolations, Global Interpolation, Local Deterministic Methods, Comparison of Global and Local Method, Optimal Interpolation Using Geo Statistics – Kriging, Variogram, Geocoding, Applications of Geocoding, Dynamic Segmentation, Applications of Dynamic Segmentation.
- 5. Remote Sensing Fundamentals: Remote Sensing – Basic Principles, Electromagnetic Remote Sensing, Energy Sources, Energy Interactions with Earth’s Surface Materials, Microwave Remote Sensing, The Radar Principle, Factors Affecting Microwave Measurements, Radar Wavebands, SLAR Systems, Sar, Interpreting Sar Images, Geometrical Characteristics, Remote Sensing, Platform and Sensors, Satellite System Parameters, Sensor Parameters, Imaging Sensor Systems, Earth Resources Satellites, Meteorological Satellites.
- 6. GIS Project Design and Management: Software engineering as applied to GIS, GIS project planning, System analysis and study of user requirement, Geographic database design methodology, GIS application software design methodology, system implementation, system maintenance and support.
- 7. Issues and Applications in GIS: Changes in Technology, Data Supply and Users, Role of Satellite Imagery and Data Sets, Trends in GIS, GIS users, Urban and Municipal Applications, Other Applications.
Reference Books:
- 1. Peter A Burrough and McDonell, “Principles of Geographical Information Systems”, Oxford University Press, 1998.
- 2. M. N. DeMers, “Fundamentals of Geographic Information Systems”, 3rd edition, Wiley.
- 3. M. Anji Reddi, “Remote Sensing and Geographical Information Systems”, B. S. Publications, Second Edition, 2001.
- 4. George B Korte, “The GIS Book”, Onword press, Thomson Learning, 5th Edition, 2003.
- 5. Kang-tsung Chang, “Introduction to Geographical Information Systems”, Tata McGraw Hill, Third Edition, 2003.
- 6. Tor Bernhardsen, “Geographic Information Systems – An Introduction”, 3rd edition, Wiley.
- 7. Ian Heywood, Sarah Cornelius & etal., “An Introduction to Geographical Information Systems”, 2nd Edition, Pearson Education.
Term Work: Term work shall consist of at least 10 experiments covering all topics and one written test. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work.
NANOTECHNOLOGY Sem 7 IT Mumbai University
Mumbai University-Third Year -Semester VII Information Technology Syllabus (Revised) NANOTECHNOLOGY
|
Elective I – NANOTECHNOLOGY |
||||
|
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII |
||||
|
HOURS PER WEEK |
LECTURES |
: |
04 |
|
|
TUTORIALS |
: |
– |
||
|
PRACTICALS |
: |
02 |
||
|
|
HOURS |
MARKS |
||
|
EVALUATION SYSTEM: |
THEORY |
|
3 |
100 |
|
PRACTICAL |
|
– |
– |
|
|
ORAL |
|
– |
25 |
|
|
TERM WORK |
|
– |
25 |
|
|
Prerequisite: - |
||||
|
Objective: Students are expected to learn both some basic science and technology and at the same time, some techniques for understanding the social and cultural significance, role, and possible effects of this emerging science. |
- 1. Introduction to Physics of the Solid States: Structure, energy bands, localized particles.
- 2. Methods of Measuring Properties: Introduction, structures, microscopy, spectroscopy.
- 3. Properties of Individual Nanoparticles: Introduction, metal nanoclusters, semiconducting nanoparticles, rare gas and molecular clusters, methods of synthesi.
- 4. Mechanical & Magnetic Properties: Strength of nano crystalline SiC, preparation for strength measurements, mechanical properties, magnetic properties. Super-paramagnetism, material preparation, magnetization of nano particles of magnetite, Mossbauer data of nano particles of magnetite, ESR spectroscopy, small angle neutron scattering.
- 5. Electrical & Optical Properties: Switching glasses with nanoparticles, Electronic conduction with nano particles. Optical properties, special properties and the coloured glasses.
- 6. Investigating and Manipulating materials in the Nanoscale: Electron microscopics, scanning probe microscopics, optical microscopics for nano science and technology, X-ray diffraction.
- 7. Optics and Electronics: Light energy, its capture, and photovoltaics, light production, light transmission, light control and manipulation, electronics, carbon nano tubes, soft molecule electronics, memories, gates & switches, architectures.
- 8. NanoTechnology-Enabled Sensor: Possibilities, relentless integration, advances in processing, diverse nanomaterials, new tools, realities, intensified design problems, the risk of commercialization, diverse applications.
- 9. Microelectronics: Introduction, nano manufacturing product strategy, considering future impacts, identifying potential synergies, existing technologies, future nano electronic device technologies, photonics.
- 10. Smarter Computers, Faster Internet, Cheaper Energy: Building a better Digital brain, routing information at the speed of light, nano flying electronics, getting energy and a cleaner environment with nanotech.
- 11. Nano Medicens: Developing of Nanomedicens, Nanosytems in use, Protocols for nanodrug Administration, Nanotechnology in Diagnostics applications, materials for used in Diagnostics and Therapeutic applications, Molecular Nanomechanics, Molecular devices, Nanotribology, studying tribology at nanoscale, Nanotribology applications.
- 12. Nanobusiness: Boom, Bust, and nanotechnology:- the next industrial revolution?, nanobusiness today, high tech, bio tech, nanotech.
- 13. Nanotechnology and You: Nanotechnology:- here and now, the nature of ethics, ethics of individual behavior, nano ethics, converging technologies, practical responses, promise of nanotechnology.
Reference Books:
- 1. “Introduction to Nanotechnology”, C. P. Poole and F. J. Owens, Wiley.
- 2. “Nano Materials”, A. K. Bandyopadhyay, New Age International Publishers.
- 3. “Nano Essentials”, T. Pradeep, TMH.
- 4. “Nanotechnology: A Gentle Introduction to the Next Big Idea”, M. Ratner and D. Ratner, Pearson Education.
- 5. “Nanotechnology – Science, Innovation, and Opportunity”, L. E. Foster, Pearson Eduction.
- 6. “Nanotechnology – the fun and easy way to explore the science of mater’s smallest particles”, Richard Booker and Earl Boysen, Wiley.
- 7. Nanotechnology: Content and Context, Christopher Kelty and Kristen Kulinowski.
Term Work: Term work shall consist of at least 10 experiments covering all topics and one written test. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment list A group of maximum three students should be formed to carry out the research in various application areas of nano technology as mentioned in the syllabus. As a term work they need to submit a report of maximum five pages on each application they explored on top of the syllabus.
