Academics

Bio

With over 19 years of experience in Academics and Teaching, Dr. Sushila Palwe specializes in teaching Databases, Machine Learning, Data Science, Data Mining, and Business Intelligence. She has developed innovative teaching methodologies to enhance the learning process, benefiting numerous students. Dr. Palwe has received grants for research projects and international workshops from SPPU BUCD. She has mentored and guided undergraduate and post-graduate students as well as PhD candidates, with over 55 research publications and book chapters to her credit. She has authored three books and organized several seminars, workshops, and FDPs. Dr. Palwe has also played a leading role in academic responsibilities, such as NAAC Criteria-1, NBA Criteria-II, ISO, and Infosys Campus Connect Program InCharge, among others. Dr. Palwe currently serves as the B Tech CSE(AI-DS) Programmer Coordinator and is involved in overall curriculum development for B Tech CSE(AI-DS). Dr. Palwe has also contributed to various consultancy projects sponsored by companies such as Blackberry, Whirlpool, and Idiada.

Research Area

Computer Vision, Machine Learning, Databases

Publication

1. Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach, Business Systems Research, Vol. 13 No. 1, pp.35-45, DOI: 10.2478/bsrj-2022-0003

2. Check Solve Pass: A New Technique for Student Centric Learning, Journal of Engineering Education Transformations 35, 299-302

3. Early detection of Parkinson's disease using machine learning, Procedia Computer Science, Volume 218,2023, Pages 249-261, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.01.007.

4. Statistical tree-based feature vector for content-based image retrieval, International Journal of Computational Science and Engineering, Vol. 21, No. 4, pp 556-563 https://doi.org/10.1504/IJCSE.2020.106868

5. On Discrimination Power of Image Feature Vector, Data, Engineering and Applications. vol 907. Springer, Singapore. https://doi.org/10.1007/978-981-19-4687-5_34

6. Feature Vector Creation Using Hierarchical Data Structure for Spatial Domain Image Retrieval, Procedia Computer Science, Volume 167, 2020, Pages 2458-2464, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.03.298

Google Scholar Link

https://scholar.google.com/citations?user=1MEhK_gAAAAJ&hl=en

Scopus Link

https://www.scopus.com/authid/detail.uri?authorId=36560915000

Orcid Link

https://orcid.org/0000-0002-0870-2467