Academics

Bio

Mrs. Sunita Kulkarni is a dedicated Assistant Professor at the School of Electronics and Communication Engineering (ECE) with an impressive academic background and extensive teaching experience. She has completed her B.E. in Electronics from Marathwada University in the year 1992 and earned her M.E in Electronics, Digital Systems in 2008 from MIT College, Pune University.

With 27 years of teaching experience under her belt, Mrs. Kulkarni has been imparting her knowledge and expertise to students since July 2005. She is known for her excellent teaching skills and innovative approach to education. Mrs. Kulkarni has mentored and guided numerous undergraduate and postgraduate students in their projects.

Mrs. Kulkarni is an active researcher and has contributed to the field of electronics and communication engineering by publishing more than 40 papers in various conferences and journals. She has also published two research papers in Quartile journals, which are a testament to her research skills and expertise. Her research work is well-regarded and has received recognition from her peers in the academic community.

Apart from her academic pursuits, Mrs. Kulkarni is known for her strong work ethic, dedication to her students, and commitment to the field of electronics and communication engineering. She is an inspiration to her colleagues and students alike and continues to make valuable contributions to the academic community."

Research Areas

Image processing ,Machine Learning ,Deep learning

Publications

1. "Transfer Learning Using Convolutional Neural Network Architectures for Glioma Classification from MRI Images”, International Journal of Computer Science and Network Security (IJCSNS),Vol. 21 No. 2 pp. 198-204, February 2021

2. “Comparative analysis of performance of deep CNN based framework for brain MRI classification using transfer learning” Journal of Engineering Science and Technology, 2021, 16(4), pp. 2901–2917 ISSN: 1823-4690 ,

3. “A framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA) Aug 2020 Volume-11,Issue-8, PP-374-382 https://dx.doi.org/10.14569/IJACSA.2020.0110848

4.” Brain tumor classification using Deep learning algorithm”, International Journal of Engineering and Advanced Technology, ISSN: 2249 – 8958, Volume-9 Issue-3, February, 2020.

5. “Recurrent Neural Networks on EEG based Classification for Brain Computer Interface” International Conference on Recent Innovations in Computer Science and Information Technology, Pune June 2019

6. A Review on image segmentation Techniques for brain tumor detection”, IEEE 2nd International conference on Electronics, Communication and Aerospace Technology, (ICECA 2018),29th April 2018. http://icoeca.org/index.html"

Google Scholar link : https://scholar.google.com/citations?view_op=list_works&hl=en&user=TM0Mcc8AAAAJ

Scopus link :https://www.scopus.com/authid/detail.uri?authorId=57205625150