Proceedings of International Conference on Data, Electronics and Computing

Nibaran Das is a full professor in the Department of Computer Science and Engineering at Jadavpur University, Kolkata, where he joined in 2006. He received his M.E. in CSE and Ph.D. in Engineering in 2005 and 2012, respectively, both from Jadavpur University, and completed his postdoctoral research at the University of Évora, Portugal. His research interests include Deep Learning, Pattern Recognition, Image Processing, Artificial Intelligence, Optical Character Recognition, Quantum Computing, and Microscopy Image Analysis. He has authored or co-authored over 220 publications in these fields and published nine books and edited volumes in these domains. Professor Das has been recognized with several accolades, including the Acharya Satyendra Nath Bose Memorial Award 2026 from the Science Association of Bengal and the Best Volunteer Award from the IEEE Kolkata Section in 2015. Dr. Swagata Mandal, at present,  is Assistant Professor at Jalpaiguri Government Engineering College (Autonomous) in West Bengal, India. He received his bachelor of technology (B.Tech.) degree in electronics and communication from West Bengal University of Technology (WBUT) in 2011 and his M.E. degree in electronics and telecommunication communication engineering from Bengal Engineering and Science University (Deemed University), now Indian Institute of Engineering, Science and Technology (IIEST), Shibpur, Howrah, India, in 2013. He has done his doctoral research on the Development of FPGA-based Error Resilient Self-Triggered Readout Chain for CBM Experiment at Variable Energy Cyclotron Centre Kolkata, 20013–2017. Dr. Sraban Kumar Mohanty has been Assistant Professor in computer science and engineering at the PDPM Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur, Madhya Pradesh, India, since July 2011. He got his Ph.D. in computer science and engineering from IIT Guwahati in 2010. His research focused on ways to make matrix computations more efficient in terms of I/O. He got his B.Sc. in mathematics from Utkal University in 1998, his M.Sc. in mathematics in 2001, and his M.Tech. in computer science in 2003. Dr. Mohanty has been working in the field for 14 years. His research interests include unsupervised machine learning, graph-theoretic clustering, huge matrix computations, and cloud computing security. Dr. Ondrej Krejcar is Rector and Full Professor at Skoda Auto University from 2024. In 2008 he received his Ph.D. title in technical cybernetics at Technical University of Ostrava, Czech Republic. At present, he is also affiliated at the Center for Basic and Applied Research at the University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic. In years 2020–2024 he was Vice-Rector for science and creative activities of the University of Hradec Kralove, and in years 2016–2020 he was Vice-Dean for science and research at Faculty of Informatics and Management, UHK. Dr. Debotosh Bhattacharjee is working as Full Professor in the Department of Computer Science and Engineering, Jadavpur University, with twenty years of post-Ph.D. experience. His research interests pertain to the applications of machine learning techniques for face recognition, gait analysis, hand geometry recognition, and diagnostic image analysis. He has authored or coauthored more than 342 journals and conference publications, including several book chapters in Biometrics and Medical Image Processing.

• Sustainable Explainable AI for Diabetes Prediction Using NLP and the PIMA Dataset.- Real Time IoT Healthcare Security Using Adaptive ML and Explainable Hybrid Homomorphic Encryption with Reduced Latency over FHE.- Krishi Dravya  An Artificial Intelligence based application for Sustainability in Agriculture.- Domain independent frequent pattern mining with pattern density.- STACK-DRP A Stacked Ensemble Framework for Depression Risk Prediction using Machine Learning.- A Hybrid Algorithm Combining PSO for Social Network Perception Analysis.- Twitter Sentiment Analysis using Machine Learning A Comparative Study of SVM and CNN.