This book presents a collection of high-quality, peer-reviewed research papers from First International Conference on Information Technology and Artificial Intelligence (ITAI 2025), organized by Soft Computing Research Society held in Gurgaon, India, from January 24 25, 2025. It covers topics such as artificial intelligence, data science and applications, cybersecurity, internet of things, cloud computing, information retrieval, and knowledge management.
This book presents a collection of high-quality, peer-reviewed research papers from First International Conference on Information Technology and Artificial Intelligence (ITAI 2025), organized by Soft Computing Research Society held in Gurgaon, India, from January 24 25, 2025. It covers topics such as artificial intelligence, data science and applications, cybersecurity, internet of things, cloud computing, information retrieval, and knowledge management.
Sandeep Kumar is a professor at CHRIST (Deemed to be University), Bangalore, where he has been serving as the Coordinator of Ph.D. 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 Ph.D. 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. Robin T. Bye is a professor at the Department of ICT and Natural Sciences at NTNU in Ålesund. He serves as the program director for the M.Sc. in Mechatronics and Automation (siv.ing.) and the B.Sc. 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. 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.
Inhaltsangabe
1. Quantum-Enhanced Edge Computing for Optimized Resource Allocation in Heterogeneous IoT Networks A Deep Reinforcement Learning Approach.- 2. Comprehensive Framework for Brain Tumor Diagnosis using Integrated Multi-modal Imaging Techniques.- 3. Machine Learning in Domestic Violence Current Trends and Future Prospective.- 4. A Forgery Detection Framework for Facial Images Using Deep Learning.- 5. Empathy and Parasport Engagement Among Young Vietnamese Audiences A Perspective Using Extended TPB.- 6. Enhanced Academic Assessments Using BERT and Top Sampling for Student Performance Optimization.- 7. Harnessing Large Language Models and EHRs for Enhanced Predictive Analytics in Personalized Healthcare.- 8. Optimizing Ride-Hailing Demand Forecasting with Explainable AI and Ensemble Learning Techniques.
1. Quantum-Enhanced Edge Computing for Optimized Resource Allocation in Heterogeneous IoT Networks A Deep Reinforcement Learning Approach.- 2. Comprehensive Framework for Brain Tumor Diagnosis using Integrated Multi-modal Imaging Techniques.- 3. Machine Learning in Domestic Violence Current Trends and Future Prospective.- 4. A Forgery Detection Framework for Facial Images Using Deep Learning.- 5. Empathy and Parasport Engagement Among Young Vietnamese Audiences A Perspective Using Extended TPB.- 6. Enhanced Academic Assessments Using BERT and Top Sampling for Student Performance Optimization.- 7. Harnessing Large Language Models and EHRs for Enhanced Predictive Analytics in Personalized Healthcare.- 8. Optimizing Ride-Hailing Demand Forecasting with Explainable AI and Ensemble Learning Techniques.
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