Mrs. Shakti Sanjay Kinger

Bio

Prof. Shakti Kinger is a highly experienced professional with more than 20 years in the fields of machine learning, AI, and database management. She is known for guiding many graduate and postgraduate students, encouraging a culture of excellence in their work.

Prof. Kinger has written many articles for both national and international journals, showcasing her dedication to tackling important issues in AI and explainable AI.

Recognized for her innovative thinking, Professor Shakti Kinger remains at the forefront of shaping the future of technology through her expertise and leadership.

Professional Affiliations: CSI

Specialisation: Computer Science and Engineering

Areas of Expertise: AI ,XAi, Data Science

Teaching Experience: 20 Years

Industry/Research Experience: 2 Years

Education:

Ph.D: Ph.D. In Computer Engineering-MITWPU

M.Tech: M.Tech. In Computer Engineering-MIT ,Pune

B.Tech: B.Tech. In Computer Engineering,Amravati University

Publications and Research Work:

1. Kinger, S., Kulkarni, V. Transparent and trustworthy interpretation of COVID-19 features in chest X-rays using explainable AI. Multimed Tools Appl 84, 19853–19881 (2025). https://doi.org/10.1007/s11042-024-19755-y

2. Kinger S. Deep Learning for Automatic Knee Osteoarthritis Severity Grading and Classification. Indian J Orthop. 2024 Sep 11;58(10):1458-1473. doi: 10.1007/s43465-024-01259-4. PMID: 39324090; PMCID: PMC11420401.

3. Kinger, S. Kinger, D., Thakkar, S. et al. Towards smarter hiring: resume parsing and ranking with YOLOv5 and DistilBERT. Multimed Tools Appl 83, 82069–82087 (2024). https://doi.org/10.1007/s11042-024-18778-9,Kinger, S., & Kulkarni, V. (2024). A review of explainable AI in medical imaging: implications and applications. International Journal of Computers and Applications, 46(11), 983–997. https://doi.org/10.1080/1206212X.2024.2404082

4. Kinger, S., & Kulkarni, V. (2024). Demystifying the black box: an overview of explainability methods in machine learning. International Journal of Computers and Applications, 46(2), 90–100. https://doi.org/10.1080/1206212X.2023.2285533

Subjects Taught: DBMS,BDT, DWDM

Scopus Id:
https://www.scopus.com/authid/detail.uri?authorId=55782658100

Google Scholar Id:
https://scholar.google.com/citations?user=sB03APQAAAAJ&hl=en

Orcid Id:
https://orcid.org/0000-0001-6826-9186