Shri Mata Vaishno Devi University
Approved Under 12(b) & 2(f) of UGC Act 1956
M. Tech(Computer Science & Engineering) Programme (2 Years, Full-time Program offered by School of Computer Science & Engineering)

Computer Science is playing a vital role in the development of society and it is difficult to find an area where the Computers do not have applications. There is always a requirement for the qualified Computer professionals to provide quality service to the society. With significant increase in the number of Engineering Institutions in the recent past; the availability of qualified faculty has gone down drastically. In order to bridge the gap, there is a need to start an M.Tech program in Computer Science & Engineering (CSE) in the state of J&K. With these motives in mind, it is considered to be the right time and geographical place to start a 2-year full time M.Tech in Computer Science and Engineering, at SMVDU. The thrust areas for M.Tech in CSE shall be the Soft Computing and Intelligent Systems. Communication System Networks, Adhoc Networks, Modelling and Simulation, Multimedia and Virtual Reality and Optimization techniques and the Coding Theory.

Program Structure

Click to Download: Program Structure for M.Tech.( Computer Science & Engineering )

MCA Programme (3 Years, Full-time Program offered by School of Computer Science & Engineering)

A 3 year (6 semesters) MCA Program for the eligible candidates will be offered at School of Computer Science Shri Mata Vaishno Devi University from the academic session 2014-2015. The course has been developed with the commitment to motivate and nurture young minds for production of excellent manpower to meet the ever changing needs of Industry. In view of growing demand of IT professionals in the area of computer application keeping in mind the changing needs of IT industry, a 3 year MCA course is need of the hour. The school of Computer Science & Engineering intends to launch this course from this year (2014) which will be starting from the month of August. This program would prepare a candidate to become a quality software professional with the advanced and updated knowledge base which is necessary to meet the needs of software industries. The primary objective of the course is to initiate young minds into the world of computer applications and provide them with a solid grounding in the domain of software.

Features of the program

The aim of the program is to offer courses with twin objective of producing world class computer professionals and to keep pace with the ever changing demands of Business practices. Efforts of the program is towards developing skilled manpower trained specifically for the various areas of Computer Applications, specialized areas of Business Management and Information Technology along with the specialized courses to develop communication skills of the students. This program offers courses which are highly Lab oriented and a special emphasis is given on experimental learning through project based curriculum and also includes hand-on training of latest packages and technologies of computer science. The students will undergo developing industrial projects for gaining adequate exposure to Computer application in business organizations, culture, concepts, practices & procedures. The curriculum contains the courses like Computer Oriented Optimization, Artificial Intelligence & Expert Systems, Software Engineering, Computer Graphics and Multimedia, Organization behavior & management practices, Management Information System, Distributed Computing System etc. The course structure envelops all aspects required by a successful IT professional and prepares the students to face the challenges of a dynamic and challenging industry. To ensure that the graduating students are ‘industry ready’ special emphasis is on providing good language skills, managerial skills and a semester of industrial internship which will eventually help them in getting good placements.

Highlights of the Program
Program Structure

Click to Download: Program Structure for Master in Computer Application

Being an engineering department, the school is focusing on research work only in those areas where potential application of the research is in-sight and the benefits are tangible. Focus is more on the areas where the research work can result in patents or in products for the industry. This necessarily translates into research areas where experimental work becomes very relevant and supports simulation work.

Currently the school is focusing on the following areas of research:

Research Projects:
S.No.AgencyTitle of the ProjectName of CoordinatorAmount approx.
1.UGCSecure & QoS oriented routing protocol for MANETsDr. Ajay Koul7.68 Lacs.
2.AICTEBipolar NetworksProf. M. L. Garg3 Lacs.
3.MHRDERP MissionMs. Sonika Gupta/ Ajay Koul25 Lacs.
4.MHRDMobile e learning TerminalsDr. Ajay Koul/Dr Sunil Wanchoo40 Lacs.
5.SMVDUEstablished Network Centre of SMVDU Phase-1Dr. Ajay Koul 95 Lakhs
6.SMVDUEstablished Network Centre of SMVDU Phase-II, III,IV,VDr. Naveen Gondhi1 Cr.
7.SMVDUPayroll SystemDr. Sonika Gupta-
8.MHRD & IIT BOMBAYAkash for EducationDr. Naveen Gondhi1 Lakh
9.MHRD & IIT BOMBAYEstablishment of A View ClassroomMr Sudesh3 Lakh
10.
Data Mining

Data mining (or data discovery) is the process of autonomously extracting useful information or knowledge (actionable assets) from large data stores or sets. Data mining can be performed on a variety of data stores, including the World Wide Web, relational databases, transactional databases, internal legacy systems, pdf documents, and data warehouses. Many organizations have compiled a diverse collection of massively large and dynamic datasets over the years. Data mining is a tool that has been actively used to discover interesting and surprising patterns in these datasets. The technology has been successfully utilized by organizations that collect web click streams, financial transactions, observational science data, etc. Our research work would cover major algorithmic advances in data mining with a thrust towards both theoretical underpinnings of problems as well as successful practical deployments. Topics that would be covered in our research would include clustering, association rules, machine learning, web link analysis, data streams, and privacy-preserving algorithms.

Through Data mining techniques, a knowledge model is obtained representing behavior patterns in relevant problem variables or relations between them. Several algorithms are frequently tested generating different models.

The most usual algorithms or techniques are:

Data mining attempts to identify valid novel, potentially useful, and ultimately understandable patterns from huge volume of data. The mined patterns must be ultimately understandable because the purpose of data mining is to aid decision-making. A data mining algorithm is usually inherently associated with some representations for the patterns it mines. Therefore, an important aspect of a data mining algorithm is the comprehensibility of the representations it forms. That is, whether or not the algorithm encodes the patterns it mines in such a way that they can be inspected and understood by human beings.

It is evident that data mining algorithms with good comprehensibility are very desirable. Unfortunately, most data mining algorithms are not very comprehensible and therefore their comprehensibility has to be enhanced by extra mechanisms. Since there are many different data mining tasks and corresponding data mining algorithms, it is difficult for such a short article to cover all of them. So, the following discussions are restricted to the comprehensibility of classification algorithms, but some essence is also applicable to other kinds of data mining algorithms.

With the unprecedented rate at which data is being collected today in almost all fields of human endeavor, there is an emerging economic and scientific need to extract useful information from it. Data mining is the process of automatic discovery of patterns, changes, associations and anomalies in massive databases, and is a highly inter-disciplinary field representing the confluence of several disciplines, including database systems, data warehousing, machine learning, statistics, algorithms, data visualization, and high-performance computing

Data mining refers to the automated or semi-automated search for relationships and global patterning within data. Data mining techniques include data visualization, neural network analysis, and genetic algorithms. Data mining uses complex algorithms to search large amounts of data and find patterns, correlation's, and trends in that data. A data-mining application can create a model that can identify buying habits, shopping trends, credit card purchases as well as perform many non-commercial functions. Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics and pattern recognition.

As data-mining has become recognized as a powerful tool, several different communities have laid claim to the subject:

In recent years, database and data mining communities have focused on a new model of data processing, where data arrives in the form of continuous streams. Because it is not feasible to store all data, it is quite challenging to perform the traditional data mining operations in a streaming environment. Our current and proposed research focuses on many challenges associated with mining streaming data. Our main thrust would be on designing algorithm which would be effective and efficient in frequent item set mining encompassing deterministic bounds on accuracy. The recent trend in algorithm development for this purpose is towards algorithms which are memory efficient and allow mining of datasets with large number of distinct items and/or very low support levels.

State-of-the-art laboratories, containing the computers with latest configuration networked together to ensure that the students get complete facilities to thoroughly understand and explore the concepts of computer science as learnt in the class-room are available in the department. The Department of Computer Science & Engineering focuses on preparing globally competitive professionals who are at ease with both hardware and software aspects of the computer. these professionals are trained to innovate and contribute to the growth of Indian economy in the present era of Information Technology. This Programme has been designed to help students impart basic concepts and skills of Computer Science and their applications in diverse areas of software development, hardware technology and networking.

The Department currently has the following laboratories :
1. PROJECT LAB:

int lab

Project lab has 21 systems of HP, IBM and Dell make. The systems are connected through LAN with one online UPS of 5Kva for constant uninterruptable power backup. In this Lab the students of VIIth and VIIIth semester do their Minor and Major Projects under the guidance of their concerned Project In-charge using different languages and platforms. The students work in the areas like image processing, NLP, Wireless Networks, Artificial Intelligence, Data ware housing, Multimedia etc.

2. DATABASE LAB:

This lab is extensively used by the students for exploring each and every aspect of Database Management System (DBMS). This lab consists the software’s like MY SQL Version 5 , Turbo “C” and MS Office. The students are provided hands on training in the areas of Database Management System (DBMS) Lab, Engineering Computational Methods (ECM) and Data Structure (DS). The Lab is equipped with 30 Dell computers of latest configuration and has its own separate virtual LAN

3. INTERNET LAB:

cse data lab

This lab is established for the purpose of providing the internet surfing facility for the students, it is open 24x7. The students also have been provided the Wi-Fi facility. The Lab consists of 80 terminals connected with the internet bandwidth of 30 Mbps.

4. PROGRAMMING LAB:

cse prog labb

Programming lab is used to inculcate programming skills among the students. This lab is helping them to specialize in their concerned subjects. Programming lab is equipped with 30 DELL Optiplex 330 PC’s . Processor- Pentium Core II dual HDD- 160 GB 1 APC UPS providing power backup in case of power failure. This lab is meant for conducting labs mainly for programming languages like VB.Net, Sql Server Enterprise, Java, C++, Graphics in C & Mini projects.

5. OPERATING SYSTEM LAB:

This lab is well equipped with the latest 30 Dell Optiplex Computer systems for the students. This lab is used to provide students exposure in the areas of Operating Systems & Systems Development. The students are taught Unix, Red Hat Linux, Ms-Windows operating systems besides courses on Data Structures etc.

6. BASIC COMPUTING LABORATORY:

This lab is well equipped with the latest computer systems for the students to discuss each and every aspect of basic computer system. It is used extensively by students for learning of foundation of information technology like MS-WORD, MS-EXCEL, MS-POWER POINT and MS-ACCESS. It is also used for programming using "C". This lab is well equipped with all aspects of the students of the students of B. Tech. The total number of systems in the lab is 30 with online back up.

The Department of Computer Science & Engineering currently  offers a 4 year ( 8 semester) B. Tech. Computer Science & Engineering program. The focus of the course is on preparing students for the industrial environment. Therefore the students are exposed to the latest trends and techniques in the field of hardware and software engineering.

Course Structure of B. Tech Computer Science & Engineering programme Batch 2012-13

Course Structure of B. Tech Computer Science & Engineering programme Batch 2010-11

Program Structure

Semester I
Code Subject L-T-P Credit
SPML 103B Solid State Physics 3-1-0 4
SPML 102B Calculus & Linear Algebra 3-1-0 4
EMEL 101B Engineering Graphics 1-0-3 2.5
PLNL 103B English in Practice 2-0-0 2
ECSL 101B Computer Fundamentals & Information Technology 3-0-0 3
EECL 101B Electrical Science and Engineering 3-1-0 4
SPMP 102B Solid State Physics Lab 0-0-3 1.5
ECSP 101B Basic Computer Lab 0-0-2 1
EECP 103B Basic Electrical Engineering Lab 0-0-2 1
  Total Credits 15-3-7 23
Semester II
Code Subject L-T-P Credit
SPML 101B Electronic Waves & Fields 3-1-0 4
SPML 104B Differential Waves & Vector Calculus 3-1-0 4
ECSL 102B Introduction to Programming with ‘C’ 3-0-0 3
EMEL 103B Elements of Mechanical Engineering 3-0-0 3
EECL 102B Basic Electronics 3-1-0 4
PLNL 101B Communication Skills 2-0-0 2
ECSP 102B Programming Lab 0-0-2 1
SPMP 101B Electromagnetic Waves & Fields Lab. 0-0-3 1.5
EECP 102B Basic Electronics Lab 0-0-2 1
EMEP 101B Workshop Practice 0-0-3 1.5
  Total Credits 17-3-10 25
Semester III
Code Subject L-T-P Credit
SPML 201B Integral Transforms & Complex Analysis 3-1-0 4
EECL 207B Principles of Communication Engineering 3-1-0 4
EECL 208B Analog and Digital Electronics 3-1-0 4
PPCL 201B Introduction to Logic 3-0-0 3
SPML 204B Material Science 3-0-0 3
ECSL 201B Data Structures 3-0-0 3
ECSP 201B Data Structure Lab 0-0-3 1.5
EECP 207B Analog & Digital Electronics Lab 0-0-3 1.5
EECP 206B Communication Engineering Lab 0-0-2 1
  Total Credits 18-3-8 25
Semester IV
Code Subject L-T-P Credit
SPML 205B Engineering Computational Methods 3-0-0 3
SPML 202B Discrete Structures 2-1-0 3
ECSL 202B Design and Analysis of Algorithms 3-1-0 4
ECSL 203B Operating System 3-0-0 3
ECSL 204B Computer Organization & Architecture 3-1-0 4
ECSL 205B Software Engineering 3-0-0 3
ECSL 206B Object Oriented Programming 3-1-0 4
ECSP 207B Engineering Computational Methods Lab 0-0-2 1
ECSP 206B OOPS Lab 0-0-2 1
ECSP 202B Operating System Lab 0-0-2 1
  Total Credits 20-4-6 27
Semester V
Code Subject L-T-P Credit
EECL 303B Microprocessors 3-1-0 4
EECL 304B Signals and Systems 3-1-0 4
ECSL 301B System Programming 3-0-0 3
ECSL 302B Theory of Computation 3-0-0 3
ECSL 303B Java Programming 3-1-0 4
ECSL 304B System Analysis & Design 3-0-0 3
ECSP 303B Java Programming Lab 0-0-3 1.5
EECP 303B Microprocessor Lab 0-0-3 1.5
  Total Credits 18-3-6 24
Semester VI
Code Subject L-T-P Credit
EECL 305B Digital Signal Processing 3-1-0 4
ECSL 305B Compiler Design 3-0-0 3
ECSL 306B Computer Networking & Communication 3-0-0 4
ECSL 307B Visual Programming 3-1-0 4
ECSL 308B Database Management System 3-1-0 4
MENL 351B Engineering Economics & Management 2-0-0 3
ECSP 307B Visual Programming Lab 0-0-4 1.5
ECSP 308B Database Management System Lab 0-0-4 1.5
  Total Credits 18-4-6 25
Semester VII
Code Subject L-T-P Credit
ECSC 401B Colloquium 0-3-0 3
SPML 401B Applied Graph Theory 3-0-0 3
ECSL 401B Parallel & Distributed Computing 3-0-0 3
ECSL 402B Computer Graphics 3-0-0 3
ECSL 403B Web Based Application Design 3-0-0 3
ECSL 44X Elective – I 3-0-0 3
ECSP 41B Computer Graphics Lab 0-0-2 1
ECSD 41B Mini Project 0-0-6 3
  Total Credits 15-3-8 22
Semester VIII
Code Subject L-T-P Credit
ECSL 45X Institutional Elective 3-0-0 3
ECSL 46Y Elective – II 3-0-0 3
ECSD 42B B. Tech. Project 0-0-16 8
  Total Credits 6-0-16 14

LIST OF ELECTIVES

Elective - I Elective - II
Code Subject Code Subject
1 Data Mining & Warehousing 1 image Processing & Pattern Recognition
2 Artificial Intelligence 2 Mobile Cellular Communication
3 Information Theory & Coding 3 Advanced DBMS
4 Rhetoric & Public Speaking 4 Distributed Systems
5 Indian Writing in English 5 Satellite Communication
6 Phonetic & Phonology 6 Industrial Economics
7 Riddles of Existence 7 Marketing Management
8. Project Management for Engineers    
Institutional Elective
Code Subject
1 Neural Network & Fuzzy Set
2 Advanced Computer Architecture
3 E-Commerce & Cyber Laws
 

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