Proceedings of International Conference on Data Science and Artificial Intelligence
Description
This book includes selected papers presented at International Conference on Data Science and Artificial Intelligence (DSAI 2025), organized by Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India, during April 1-2, 2025. The book covers a variety of topics, such as expert applications and artificial intelligence/machine learning; advance web technologies such as IoT, big data, cloud computing in expert applications; information and cyber-security threats and solutions; and multimedia applications in forensics, security, and intelligence.
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Persons
Dr. Udai Shanker is presently Professor & Head of the Department of Computer Sc. & Engineering of MMMUT Gorakhpur-273010, India. He is credited with PhD from IIT-Roorkee and M. E, from Jadavpur University Calcutta. He is recipient of awards from Institution of Engineers (IE) India, Calcutta, India twice for his technical papers. He is authors of 133 research papers, two edited books and nine book chapters published in various National & International Journals/Conferences. He is Editorial Board Member of various International Journals. His research interest focuses on Real Time Systems, Distributed Real Time Database Systems, Mobile Distributed Real Time Database Systems and Grid Databases. He is life member of IE, ISTE, CSI India and fellow of The Institution of Electronics and Telecommunication Engineers.
Dr. Rohit Kumar Tiwari is working as Assistant Professor in the Department of Computer Science and Engineering, MMMUT-Gorakhpur, India. He has completed M.Tech in Computer Engineering with a gold medal from the NIT-Kurukshetra and PhD from MMMUT-Gorakhpur. His research interest focuses on task scheduling, object detection and recognition in computer vision.
Dr. Anshul Verma received M. Tech. and Ph.D. degrees in Computer Science & Engineering from ABV-IIITM-Gwalior. He has done Post-doctorate from IIT-Kharagpur. Currently, he is serving as Assistant Professor in the Department of Computer Science, Institute of Science, BHU-Varanasi. He has also served as faculty member in the Department of Computer Science & Engineering at MNNIT-Allahabad and NIT-Jamshedpur. His research interests include IoT, Mobile ad-hoc networks, Distributed systems, Formal verification, Mobile computing etc. He is serving as Editor of the Journal of Scientific Research of the Banaras Hindu University.
Mohammad Mehedi Hassan is a Full Professor in the Department of Information Systems, CCIS, at KSU-Riyadh, Saudi Arabia. He earned his Ph.D. in Computer Engineering from KHU-South Korea, in 2011. His research interests include AI, Cloud and Edge Computing, IoT, Machine Learning, Big Data Analytics and Cybersecurity. He has authored and co-authored over 422 publications including 386 SCI/ISI-indexed journal papers. He has been recognized among the top 2% of scientists globally as per Research.com. He has received the Distinguished Researcher Award from CCIS, KSU (2020), the Excellence in Research Award (2015 & 2016), the Outstanding Editors Award from Future Generation Computer Systems (2021) and the Best Journal Paper Award from IEEE Systems Journal (2018). He is also an editorial board member and guest editor of SCI/ISI-Indexed journals. He is currently a Senior Member of IEEE.
Content
1. Real-Time Monitoring and Data Management for Smart Meters Using IoT and Cloud Technologies.- 2. Automated Disease Segmentation in X-Ray Images Using Multi-Model Based on Vision Transformer Architecture.- 3. A Study of Performance and Interpretability of Ensemble Learning in Binary Classification.- 4. Non-Invasive Method To Detect Wildlife Animals Using High Resolution Satellite Images.- 5. Comprehensive Financial Fraud Detection Using Ensemble Machine Learning Models: A Case Study on Transactional Data.- 6. Communication-Aware Fog Device Deployment in Edge-Fog-Cloud Systems using Jaya Algorithm.- 7. Skin Lesions Classification Using an Ensemble Deep Learning Model.- 8. Combinatorial Hashing with Left-Right Random Probing.