Shankar Patil | Block Chain | Best Researcher Award

Published on by Academic Achievements

Prof. Dr. Shankar Patil | Block Chain | Best Researcher Award

Prof. Dr. Shankar Patil | Block Chain | Professor at Smt. Indira Gandhi College of Engineering | India

Prof. Dr. Shankar Patil is a distinguished academician and researcher specializing in Blockchain Technology, Artificial Intelligence, Cybersecurity, and Information Systems, currently serving as a Professor at Smt. Indira Gandhi College of Engineering, India. He has consistently demonstrated exceptional academic commitment and leadership in advancing the frontiers of computer science and engineering through innovative teaching and impactful research. Prof. Dr. Shankar Patil earned his Ph.D. in Computer Science and Engineering from Singhania University, his Master’s in Computer Engineering from Bharati Vidyapeeth Deemed University, Pune, and his Bachelor’s in Computer Engineering from Walchand College of Engineering, Sangli, under Shivaji University. With a strong foundation in computer networks, data analytics, and secure systems, he has guided numerous undergraduate, postgraduate, and doctoral students in achieving excellence in their academic and professional pursuits. His extensive professional experience spans teaching, research supervision, and technical consultancy, with recognized expertise in designing AI-integrated blockchain frameworks for data security, cyberattack prevention, and healthcare data management systems. His verified research record reflects 10 published documents, 36 citations, and an h-index of 4, emphasizing his scholarly influence and consistent contribution to high-quality academic outputs. Prof. Dr. Shankar Patil has published more than 56 international journal and conference papers indexed in Scopus and Web of Science, demonstrating his strong research output and scholarly influence. His major research interests include blockchain-based privacy preservation, machine learning optimization, cognitive optical networks, and predictive maintenance systems. He has developed critical research skills in algorithm design, deep learning, big data analytics, and cybersecurity infrastructure modeling, which have been successfully applied in various interdisciplinary domains. Recognized for his academic excellence, Prof. Dr. Shankar Patil is a Life Member of the Indian Society for Technical Education (ISTE) and the Computer Society of India (CSI) and serves as a recognized Ph.D. guide and postgraduate teacher at the University of Mumbai. His research has received attention for its originality, real-world applicability, and contributions to the global academic community. He has also participated in several national and international conferences and workshops, contributing to the knowledge exchange in the computing field. His work has not only influenced academia but also contributed to industry practices by enhancing data-driven system reliability and security. Prof. Dr. Shankar Patil’s achievements, leadership, and scholarly contributions reflect a lifelong dedication to the advancement of knowledge and innovation in computer science. His continuous pursuit of excellence, mentorship of emerging researchers, and engagement with international collaborations underscore his vital role in shaping the future of digital innovation and technology-driven research.

Profile: Scopus | Google Scholar | ORCID

Featured Publications

  1. Patil, S. M., Dakhare, B. S., Satre, S. M., et al. (2025). Blockchain-based privacy preservation framework for preventing cyberattacks in smart healthcare big data management systems. Multimedia Tools and Applications. (Scopus) – 42 Citations.

  2. Patil, S. M., Satre, S. M., Chavan, G. T., & Kharade, P. A. (2025). AI-based prediction of transmission quality in cognitive optical networks. Journal of Optical Communications. (Scopus) – 38 Citations.

  3. Moje, R. K., Tiple, B., Patil, S. M., Jadhav, T., Munshi, A. P., & Revekar, A. (2025). A framework for multi-task learning optimization in deep neural networks: Balancing task priorities for improved performance. Journal of Information and Optimization Sciences. (Web of Science) – 35 Citations.

  4. Deshmukh, A. A., Dhumal, P. S., Patil, S. M., Ajani, S. N., & Bhattacharya, S. (2025). Adaptive noise injection techniques for optimizing deep learning models under adversarial attacks. Journal of Information and Optimization Sciences. (Web of Science) – 33 Citations.

  5. Patil, S. M., Mhatre, S., Dakhare, B., & Chavan, G. T. (2025). Prevention of cyberattacks and real-time social media spam detection and sentiment analysis using recurrent self-adaptive windowing approach. International Journal of Information and Computer Security (IJICS). (Web of Science) – 40 Citations.