Proceedings of International Conference on Information Technology and Artificial Intelligence

Sandeep Kumar is a professor at CHRIST (Deemed to be University), Bangalore, where he has been serving as the Coordinator of PhD Research at the Kengeri Campus. He completed his post-doctoral research in sentiment analysis at Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia. An accomplished academic, Dr. Kumar has authored and edited 22 books in the field of computer science and published over 100 research papers in leading international journals and conferences. He has successfully guided 7 PhD scholars, with 3 currently under his supervision. He is an associate editor for the prestigious journal Human-centric Computing and Information Sciences (HCIS) by Springer. Dr. Kumar also serves as the General Chair of multiple international conferences, including the CIS, ICCCT, ICSISCET, and InC4. Dr. Kumar is a life member of the Soft Computing Research Society and an active member of both IEEE and ACM. In recognition of his contributions, he was honored with the Outstanding Volunteer Award by the IEEE Computer Society Bangalore Chapter in 2023 and named Researcher of the Year by CHRIST University in 2024. His research interests span nature-inspired algorithms, swarm intelligence, soft computing, and computational intelligence. Robin T. Bye is a professor at the Department of ICT and Natural Sciences at NTNU in Ålesund. He serves as the programme director for the MSc in Mechatronics and Automation (siv.ing.) and the BSc in Automation and Intelligent Systems. In addition, he leads the Cyber-Physical Systems Laboratory. His research activities include participation in projects like NTNU SFI AutoShip and SAFEMATE, as well as collaborative work with the Ålesund Biomechanics Lab at the Ålesund Hospital. His research interests focus on the interdisciplinary field of cyber-physical systems, with an emphasis on integrating state-of-the-art methods to address challenges related to autonomous systems and biomechatronics. This includes work on autonomous ships, biomechatronic devices, and simulators for computer haptic-assisted orthopaedic surgery (CHAOS). He has also explored virtual prototyping, computational modeling and simulation of human movements, dynamic resource allocation, operations research, and research in teaching and education. These topics remain key areas of interest in his ongoing research endeavors. Dr. Mukesh Prasad is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and IT, at the University of Technology Sydney (UTS). He is recognized for his significant contributions to the fields of machine learning, artificial intelligence, and the internet of things. His research interests extend to big data, computer vision, brain-computer interfaces, and evolutionary computation. Dr. Prasad's work in image processing, data analytics, and edge computing plays a pivotal role in advancing applications in healthcare, biomedical sciences, agriculture, smart cities, education, marketing, and finance. He has authored over 120 research papers in prestigious journals, including IEEE/ACM Transactions, and at international conferences. He began his academic career at UTS in 2017 and is a core member of the Australian Artificial Intelligence Institute (AAII), where he contributes to the development of advanced AI technologies and theoretical foundations. His industry experience includes serving as a principal engineer at Taiwan Semiconductor Manufacturing Company (TSMC) from 2016 to 2017, where he developed machine learning algorithms for image processing and pattern recognition. He also led a Big Data and computer vision team as a postdoctoral researcher at National Chiao Tung University, Taiwan. Dr. Prasad holds an M.S. degree from Jawaharlal Nehru University, India, and a PhD from National Chiao Tung University, Taiwan.

• 1. A Comparative Analysis of Deepfake Detection using Traditional Image Analysis, SVM, RNN, CNN and GAN.- 2. A Hybrid Analysis of Android Malware Detection Method using Ensemble Learning.- 3. Ensemble Based Machine Learning for Chronic Diabetes Disease Prediction.- 4. IoT-Driven Predictive Analytics for Smart Agriculture Using Random Forest Algorithm.- 5. Various OCR techniques for the analysis and recognition of Kannada epigraph documents.- 6. AI-driven Drug Interaction Prediction for Enhanced Medication Safety Using Neural Networks.- 7. Classification of Watermelons based on Ripeness using Multimodal Data.- 8. Real-time landslide monitoring with IoT.