• 23 June 2024
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Technical Staff

STAFF

Name of the Staff Name of the Staff Name of the Staff
Mr. Dilip V. Bhingare
Mr. Vikas V Rode
Mr. Ravindra B. Hatkar
The department of Information Technology was established in the year 2001 with B. Tech. program having intake of 60 students under the umbrella of department of Computer Engineering. The B.Tech. programme in Information Technology was shifted to the newly created department in 2004. The first research scholar for Ph.D. programme enrolled in the department in 2015. Department has seven regular faculty, three laboratory supporting staff- Laboratory Assistant Technical, Laboratory Assistant and Laboratory Attendant. All the faculty members have completed their M.E. /M.Tech. Four faculty has completed PhD and one faculty members are pursuing their PhDs. The Department has set tradition of achievements in sports, cultural activities, extracurricular activities, competitive examinations like GATE, MBA-CET etc. and campus placements.
Welcome to the Department of Information Technology. It is my pleasure to introduce the Department of Information Technology at the main campus of Dr. Babasaheb Ambedkar Technological University, Lonere. The Department of Information Technology has recorded consistent improvement in its academic, research and placement performance. It offers a range of innovatively designed programs whose curricula are constantly updated to meet the changing requirement of the industry and also to meet the needs of major stakeholders. To fulfill industry demand we introduced five Add-on courses which is supposed to be teach by adjunct faculty from reputed industry. Department offers opportunities for pursuing advanced research in Information technology to motivated and talented students with a keen sense of scientific inquiry. Eight Research students are enrolled for Phd programme which contributes to the large research group on the campus which is our major achivement. We believe that our students have been well accepted in their job profiles and have consistently exceeded expectations of the corporate world. During study at the department, the students are encouraged to get hands-on experience in the corporate world through internship projects with reputed organizations. They also undertake projects benefiting local industries or dealing with local problems. These projects enable them to understand the relevance of working in a group and also help them to realize the finer aspects and importance of teamwork. Last year we created fun learning environment to have smart class room and modern technology that eases the learning process for all students. We also encourage students to participate, organise Technical events , and also get involved in activities of social relevance. With all these inputs one finds our students hardworking, practical-oriented and effective in any work environment. We have designed our syllabus to strike a balance between professional knowledge and personal skills. We are confident that our current curriculum has enabled overall development of budding managers to come up to the expectations of the corporate world. The curriculum is taught by a distinguished faculty combining academic excellence and real world experience with dedication and commitment. We are encouraged to see many industries coming back to our department, which reinforces our belief in the effectiveness of our curriculum and its suitability to the dynamic corporate world. With this brief , I welcome you to be a part of our journey towards being a world class centre of excellence in education, training and research. Last but not least, we must not forget that while living professional life we should hold high moral and ethical values. I welcome you all and wish bright and successful career ahead.
Dr. S. M. jadhav, Professor & Head  B.E. (Computer Engineering), M. TECH. (Computer Engineering), PH. D.  Email : [email protected]
Advisory BoardBoard of Studies
DR. G. SIVAKUMAR
IITB, MUMBAI
DR. S. M. JADHAV,
CHAIRMAN, DR.BATU, LONERE
DR. B. B. MESHRAM
VJTI, MUMBAI
Dr. S. R. SUTAR
DR.BATU, LONERE
MR. SHAM DHAGE
IBM, PUNE
DR. SHARVARI TAHMANE
JNEC, AUGANGABAD
 DR. V. P. SONAWANE
DR.KALAM SKILL DEVELOPMENT CENTER JALGAON.
 PROF. D.V. BIRADAR
M.S.BIDVE COE, LATUR

The process of defining PEOs

The Program Educational Objectives (PEO) are the broad statements of the objectives for which the Program is run. These objectives should help in achieving the mission of the department. The graduates are expected to lead a useful and healthy life in the society. These objectives should be in line, to the extent possible, with the current state in the industries and research organizations. The industry needs are measured through the feedbacks, interactions, expert talks by industry people and recruiting industries’ communications with Training and Placement Office (TPO). Feedback of Alumni working in top industries and reputed institutes was also useful. The steps followed are as below:

  1. Based on the needs of the country, society, and industries, PEOs were formulated by the faculty members of the department through discussions.
  2. The PEOs were communicated to the stakeholders for their suggestions, if any.
  3. The PEOs were finalized.

Specific Educational Objectives

The following specific educational objectives aim to achieve these global and regional expectations. The following table shows the PEO identifiers and objectives.

PEO IDENTIFIER OBJECTIVES
PEO 01 To enable graduates gain strong skills for employment in multidisciplinary domains driven by IT
PEO 02 To enable graduates to pursue higher education and research
PEO 03 To enable graduates to develop entrepreneurship and leadership skills
PEO 04 To enable graduates to contribute to the society in accordance with the highest standards of ethics
PEO 05 To develop breakthrough solutions enabling transformations in a rapidly changing IT world

Program Outcomes (PO’s)

OUTCOME IDENTIFIER GRADUATE ATTRIBUTE OUTCOME
PO 01 Engineering knowledge Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
PO 02 Problem analysis Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
PO 03 Design/development of solutions Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
PO 04 Conduct investigations of complex problems Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
PO 05 Modern tool usage Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
PO 06 The engineer and society Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
PO 07 Environment and sustainability Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
PO 08 Ethics Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
PO 09 Individual and team work Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
PO 10 Communication Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
PO 11 Project management and finance Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
PO 12 Life-long learning Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

Program Specific Outcomes (PSO’s)

OUTCOME IDENTIFIER OUTCOME
PSO 01 Learn and apply modern skills, techniques, and engineering tools to automate things and simplify real world problems and human efforts.
PSO 02 Understand the state-of-the-art development in IT to identify research gaps and hence provide solutions by new ideas and innovations.

Major Strengths:

  • Young and experienced faculty members
  • Dedicated staff and faculty
  • Campus-wide networking
  • Latest Teaching/Learning aids like smart classroom
  • Adequate infrastructure

Weaknesses:

  • Lack of Ph.D. qualified faculty
  • Shortage of faculty
  • Higher faculty positions are vacant

Opportunities:

  • Starting PG courses
  • Starting finishing schools
  • Establishment of Departmental library
  • Training for New Technology awareness
  • Consultancy like cyber crime detection
  • Enrollment of faculty for Higher studies

Challenges:

  • Curriculum development as per industrial needs
  • Dynamic nature of IT field
  • Training of faculty
  • Research facilities
Short term
  • Training of students in industry, Interaction with industry, Industry Defined Problems for final year projects, Conduction of Project competition, technical fair, Best idea generation competition, Training programme on soft skill development
  • Skill Improvement and qualification improvement for faculty (Supporting faculty members for registering and completing their PhDs, Supporting faculty members to make proposals for financial assistance from Government agencies)
  • Feedback from teachers, Program Evaluation from Employers and Alumni
  • Creation of virtual classrooms, Creation of e-storage for providing NPTEL video lectures, video lectures of faculties, notes, assignments, e-book and similar e-contents.
  • Networking with alumni
  • Adopting SWAYAM platform in credit courses
  • To create, maintain and develop a curricular data repository for the students

3 Years
  • Entrepreneurship and Innovation Cell, Grievance Redressal Cell
  • Deployment of Moodle as a Learning Management System, Modernization of all classrooms for e-content delivery mode
  • New Courses (M.Tech. in Information Technology)
  • Faculty Training in Industry, Time Management/Stress management programs for faculty and students
  • Finishing School Programmes (MoU with Training academics like Cisco, Microsoft and Redhat)
  • Deployment and testing various question paper generation algorithms
  • Accreditation- NAAC/NBA and NIRF ranking
PhD Awarded
Roll No. Name Thesis Topic Guide
RS20161101 Dr. Vinod Jagannath Kadam Medical Decision Support Systems for Breast Cancer Diagnosis using Ensemble and Deep Learning Dr. Shivajirao M. Jadhav
RS20151101 Dr. Samir Shrihari Yadav Machine Learning Based Disease Diagnosis using Clinical Findings Dr. Shivajirao M. Jadhav
Research Scholars
Sr.No. Roll No. Name Research Topic Guide
1 RS20161102 Mr. Pankaj Eknath Kasar MRI Modality Based Medical Disease Diagnosis using Machine Learning Techniques. Dr. Shivajirao M. Jadhav
2 RS20161103 Mrs. Mahi Khemchandani Machine Learning based Segmentation and Brain Tumor Identification Using Multi Modal MR Images Dr. Shivajirao M. Jadhav
3 RS20181103 Mr. Jayanand Ambadas Kamble Reinforcement Learning in Healthcare Dr. Shivajirao M. Jadhav
4 RS20181101 Mr. R. R. Kotkondawar Applications of ML in Healthcare Dr. Sanjay R Sutar
5 RS20181102 Mr. Subhash Vitthal Pingale Design and Analysis of contemporary Neural Architectures for network intrusion detection system Dr. Sanjay R. Sutar (Presynopsis date 28/04/2023)
6 RS20181104 Mrs. Nanda R Wagh Application of Machine Learning for Women and Children Safety Dr. Sanjay R Sutar
7 RS20191101 Mrs. Pranita Pradip Jadhav Machine Learning in Education Dr. Shivajirao M. Jadhav
8 RS20191102 Mrs. Sapna Shivaji Barphe Software Education Dr. Sanjay R Sutar
9 RS20191103 Mr. Dinesh Achyut Zende Artificial intelligence in agriculture Dr. Sanjay R Sutar
S. S. Yadav, V. J. Kadam, S. M. Jadhav, S. Jagtap and P. R. Pathak, Machine Learning based Malaria Prediction using Clinical Findings, 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), 2021, pp. 216-222, doi: 10.1109/ESCI50559.2021.9396850.(Publisher:IEEE)
Williamson, S., Vijayakumar, K. & Kadam, V.J. Predicting breast cancer biopsy outcomes from BI-RADS findings using random forests with chi-square and MI features.Multimed Tools Appl(2021).https://doi.org/10.1007/s11042-021-11114-5Publisher: (Springer).Indexed in [SCI & Scopus]
Vijayakumar K, Kadam VJ, Sharma SK. Breast cancer diagnosis using multiple activation deep neural network.Concurrent Engineering. June 2021. doi:10.1177/1063293X211025105Publisher: (Sage).Indexed in [SCI & Scopus]
S. S. Yadav, S. More, S. M. Jadahv , S. R. Sutar, Convolutional Neural Networks Based Diagnosis of Myocardial Infarction in Electrocardiograms Date of Conference:19-20 Feb. 2021, DOI:10.1109/ICCCIS51004.2021.9397193, Publisher:IEEE
S. S. Yadav, S. R. Sutar, Alzheimer’s Disease Diagnosis using Structural MRI and Machine Learning Techniques”, International Conference on Machine Vision and Augmented Intelligence , (MAI- 2021, Springer) , IIITDM, Jabalpur, February, 2021
S. S. Yadav, S. B. More, S. M. Jadhav, S. R. Sutar, Convolutional Neural Networks Based Diagnosis of Myocardial Infarction in Electrocardiograms”, IEEE co-sponsored International Conference on Computing, Communication, and Intelligent Systems, (IEEE Xplore), 19-21 February, 2021, Sharda University, Noida
V. T. Lokare , A. W. Kiwelekar, S. S. Barphe, L. D. Netak, “Increasing Students Engagement during Virtual Classroom Teaching through Effective use of Online Tools”, 8th International Conference on Transformations in Engineering Education, Hydrabad on 7th January 2021.
Yadav S., Kadam V., Jadhav S. (2021) Machine Learning Algorithms for the Diagnosis of Cardiac Arrhythmia in IoT Environment. In: Santosh K.C., Gawali B. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2020. Communications in Computer and Information Science, vol 1381. Publisher: (Springer).
S. S. Barphe , V. T. Lokare, A. W. Kiwelekar,S. R. Sutar,Effective Online Tools for Teaching Java Programming Course on an Online Platform”, 5th World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4 2021), London, UK on 29th – 30th July 2021.
S. S. Yadav, S. M. Jadahv, S. Nagrale, N. Patil, Application of Machine Learning for the Detection of Heart Disease”DOI:10.1109/ICIMIA48430.2020.9074954, Conference Location:Bangalore, India, Date Added to IEEEXplore:23 April 2020
S. S. Yadav, S. M. Jadahv , R. Bonde, S. Chaudhari, Automated Cardiac Disease Diagnosis Using Support Vector Machine Date of Conference:3-4 April 2020 , Publisher, CSCITA, DOI:10.1109/CSCITA47329.2020.9137817
S.V.Pingale, S. R. Sutar, Analysis of Web Application Firewalls, Challenges, and Research Opportunities, International Conference on Data Sciences, Machine Learning and Applications, ICDSMLA (Springer), Pune, December, 2020
Kadam V. J., Jadhav S. M. (2020), Performance analysis of hyperparameter optimization methods for ensemble learning with small and medium-sized medical datasets,Journal of Discrete Mathematical Sciences and Cryptography, Volume 23, Issue 1, pages 115-123, ISSN: Print 0972-0529 Online 2169-0065, DOI:10. 1080/09720529. 2020. 1721871, Publisher: (Taylor & Francis), Indexed in: [ESCI & Scopus]
Kadam V. J., Yadav S. S., Jadhav S. M. (2020), Soft-Margin SVM Incorporating Feature Selection Using Improved Elitist GA for arrhythmia Classification,In Abraham A., Cherukuri A., Melin P., Gandhi N. (eds) Intelligent Systems Design and Applications. ISDA 2018, Advances in Intelligent Systems and Computing, Volume 941, pages 965-976, ISBN: Print 978-3-030-16659-5 Online 978-3-030-16660-1, Series ISSN: Print 2194-5357 Online 2194-5365, DOI: 10. 1007/978-3-030-16660-1_94, Publisher: (Springer), Cham, Indexed in: [ Scopus]
Vinod Kadam, Shivajirao Jadhav, Mahesh Shirsath, Atharv Kurdukar (2020),Parkinson’s disease prediction using a stacked generalization ensemble of kNN and its variants with mRMR feature selection,S. D. Purohit et al. (eds),Proceedings of International Conference on Communication and Computational Technologies, Algorithms for Intelligent Systems, Chapter no. 39, DOI: 10. 1007/978-981-15-5077-5_39, Publisher: (Springer).
Yadav S. S., Kadam V. J., Jadhav S. M. (2020), Comparative Analysis of Ensemble Classifier and Single Base Classifier in Medical Disease Diagnosis,In: Bansal J., Gupta M., Sharma H., Agarwal B. (eds) Communication and Intelligent Systems. ICCIS 2019, Lecture Notes in Networks and Systems, Volume 120, ISBN: Print 978-981-15-3324-2 Online 978-981-15-3325-9, DOI: 10.1007/978-981-15-3325- 9_37, Publisher: (Springer), Singapore.
Kadam, Vinod, Shivajirao Jadhav, Samir Yadav (2020), Bagging based ensemble of support vector machines with improved elitist GA-SVM features selection for cardiac arrhythmia classification,International Journal of Hybrid Intelligent Systems, Volume 16, Issue 1, pages 25-33, ISSN: Print 1448-5869 Online 1875-8819, DOI: 10. 3233/HIS-190276, Publisher: (IOS Press)
V. J. Kadam, S. M. Jadhav, A. A. Kurdukar and M. R. Shirsath, Arrhythmia Classification using Feature Ensemble Learning based on Stacked Sparse Autoencoders with GA-SVM Guided Features, 2020 International Conference on Industry 4.0 Technology (I4Tech), Pune, India, 2020, pp. 94-99, doi: 10.1109/I4Tech48345.2020.9102675. (Publisher:IEEE)
Kadam Vinod, Thematic Issue Intelligent Data Mining for Data Analysis and Knowledge Discovery”, Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), Volume 13, Number 3, 2020, pp. 433-434(2) DOI: https://doi.org/10.2174/266625581303200609114251 Publisher:Bentham Science Publishers
A. R. Babhulgaonkar ,S. Sonavane , International Conference on Machine Intelligence & Smart Computing (ICMISC-2020) organized by Government College of Engineering, Karad 21-22 May, 2020 Published in Solid State Technology Journal Vol. 63 No. 2 (2020) ISSN 0038-111X
Kadam V. J., Jadhav Shivajirao M., K. Vijayakumar (2019), Breast cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression,Journal of Medical Systems Volume 43, Issue 8, Article number 263, ISSN: Print 0148-5598 Online 1573-689X, DOI: 10. 1007/s10916-019-1397-z, Publisher: (Springer), Indexed in [SCI & Scopus]
Kadam V.J., Jadhav S.M. (2019) Feature Ensemble Learning Based on Sparse Autoencoders for Diagnosis of Parkinson’s Disease. Computing, Communication and Signal Processing. Advances in Intelligent Systems and Computing, vol 810.(Springer, Singapore)
Kadam V. J., Jadhav Shivajirao M. (2019), Optimal weighted feature vector and deep belief network for medical data classification,International Journal of Wavelets, Multiresolution and Information Processing, Volume 18, Issue 2, pages 2050006, ISSN: Print 0219-6913 online 1793-690X, DOI: 1142/S021969132050006X, Publisher: (World Scientific), Indexed in [SCI & Scopus]
Yadav, S.S., Jadhav, S.M. Deep convolutional neural network based medical image classification for disease diagnosis.J Big Data6,113 (2019). https://doi.org/10.1186/s40537-019-0276-2
Kadam V. J., Jadhav S. M. (2019), Feature Ensemble Learning Based on Sparse Autoencoders for Diagnosis of Parkinson’s Disease,In Iyer B., Nalbalwar S., Pathak N. (eds) Computing, Communication, and Signal Processing, Advances in Intelligent Systems and Computing, Volume 810, pages 567-581, ISBN: Print 978-981-13-1512-1 Online 978-981-13-1513-8, Series ISSN: Print 2194-5357 Online 2194-5365, DOI: 10. 1007/978-981-13-1513-58, Publisher: (Springer), Singapore, Indexed in: [ Scopus]
A. R. Babhulgaonkar ,S. Sonavane , International Journal of Computer Sciences and Engineering (IJCSE), UGC Listed, Special Issue-1, pp. 48-54, Feb 2018 E-ISSN: 2347-2693 https://doi.org/10.26438/ijcse/ v6si1.1925
S. M. Jadhav, S. L. Nalbalwar, Ashok Ghatol, Artificial Neural Network based cardiac arrhythmia classification using ECG signal dataINSPEC Accession Number:11515363 DOI:10.1109/ICEIE.2010.5559887 Publisher IEEE
S. M. Jadhav, S. L. Nalbalwar, Ashok Ghatol Generalized Feed forward Neural Network based cardiac arrhythmia classification from ECG signal data ,date Added to IEEEXplore:14 February 2011, ISBN Information:, INSPEC Accession Number:11824149, Publisher:IEEE
Kadam V.J., Yadav S.S., Jadhav S.M., Soft-Margin SVM Incorporating Feature Selection Using Improved Elitist GA for Arrhythmia Classification. Intelligent Systems Design and Applications. Advances in Intelligent Systems and Computing, vol 941. (Springer, Cham)
A. R. Babhulgaonkar ,S. Sonavane, National Level Research Symposium on Computing – RSC 2016 organized by dept. of CSE & IT under ACM chapter at WCE Sangli
A. R. Babhulgaonkar ,S. Sonavane , Published in International Advances in Intelligent Systems and Computing (AISC) ISBN 978-981-32-9515-5 https://doi.org/10.1007/978- 981-32-9515-5_50
A. R. Babhulgaonkar ,S. Sonavane , International Journal of Future Generation Communication and Networking (IJFGCN) ISSN: 2233-7857 IJFGCN https://sersc.org/journals/ index.php/IJFGCN/article/view/ 27005/14749
A. R. Babhulgaonkar ,S. Sonavane , Published in International Advances in Intelligent Systems and Computing (AISC) ISBN: 978-981-15-4851-2 https://doi.org/10.1007/978- 981-15-4851-2_31
A. R. Babhulgaonkar ,S. Sonavane , International Journal of Information Technology (IJIT) ISSN 2511-2104 DOI:10.1007/s41870-020- 00503-y
Bharad S. V., Statistical Machine Translation: Foundation, Challenges and Recent advances”, International coference on Intelligent systems & Information Management, October 2017.
Bharad S. V., Prediction of carrier status of students using ID3 algorithm”, International conference on Reliability, Infocom Technologies and Optimization, January 2013.
Kasar, Pankaj Eknath, Shivajirao M. Jadhav, and Vineet Kansal. “Brain Tumor Segmentation using UNET and SEGNET: a Comparative Study.” (2022).
Sharma, S.K., Vijayakumar, K., Kadam, V.J. et al. Breast cancer prediction from microRNA profiling using random subspace ensemble of LDA classifiers via Bayesian optimization. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-021-11653-x
Empirical Analysis of Phrase-Based Statistical Machine Translation System for English to Hindi Language
Arun Babhulgaonkar (1Department of Information Technology, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, Maharashtra, India) and Shefali Sonavane (2Department of Information Technology, Walchand College of Engineering, Sangli, Maharashtra, India)
Vietnam Journal of Computer Science 2022 09:02, 135-162
  • Mr. S.M. Barhe – Network Engineer
  • Mr. Dilip V. Bhingare – Instructor
  • Mr. Vikas V. Rode – Lab Assistant
  • Mr. Ravindra B. Hatkar – Lab Attendant
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  • Department of Information Technology (माहिती तंत्रज्ञान विभाग ), Dr. Babasaheb Ambedkar Technological University, Lonere, Tal- Mangaon, Dist – Raigad. Maharashtra (India). 402103 Telephone and Fax. +91-2140 - 275142 Email: hod-it(at)dbatu(dot)ac(dot)in