International Conference on Smart Cyber Physical Systems

Dr. Ramesh Chandra Poonia is Professor at the Department of Computer Science, CHRIST (Deemed to be University), India. He holds a double Postdoctoral Fellowship, first from the Cyber- Physical Systems Laboratory Lab, Department of ICT and Natural Sciences, Norwegian University of Science and Technology—NTNU, Norway, and second fellowship on the Collaborative Project of Oakland University USA, and Imam University, Saudi Arabia. He obtained his Ph.D. in computer science from Banasthali University, Banasthali, India, in July 2013. He received his M.Tech. in DSE from Birla Institute of Technology and Science, Pilani, India. He has authored over 126 journal and conference articles and edited 11 books/conference proceedings. He is Main Guest Editor of more than 30 special issues of journals including Springer, Taylor and Francis, Inderscience, IGI Global and Elsevier.Dr. Sugam Sharma is Data Scientist and currently working as System Analyst III for Center for survey Statistics and Methodology, Iowa State University. He obtained his Ph.D. in computer science from Iowa State University, USA, in 2013. His research interests include big data, cloud computing, multidimensional databases, GIS technology, sensor network, and smart home technology. Previously, he also has served as Assistant Professor, Software Consultant for NAVTEQ Inc., and JAVA Developer at Ames Laboratory, USA. Dr. Sharma also holds M.S. in computer science from Jackson State University, Mississippi, USA, and B.E. in Computer Science from Roorkee, India.Ibrahim A. Hameed is a Full Professor and Head of the Robotics Research Group at the Norwegian University of Life Sciences (NMBU), Norway. He is also an Adjunct Professor at VSB – Technical University of Ostrava, Czech Republic, and a Golden Jubilee Honorary Professor at De La Salle University (DLSU), Philippines. Hameed holds a BSc and MSc in Industrial Electronics and Control Engineering from Menoufia University, Egypt, a PhD in Industrial Systems and Information Engineering from Korea University, South Korea, and a PhD in Mechanical Engineering from Aarhus University, Denmark. He is an IEEE Senior Member and a former Chair of the IEEE Computational Intelligence Society (CIS), Norway Section. His research interests include artificial intelligence, machine learning, optimization, control systems, and robotics, with a particular focus on intelligent and autonomous systems. He has extensive teaching and research experience from Aalborg University and the University of Southern Denmark (SDU) in Denmark, as well as the Norwegian University of Science and Technology (NTNU), Norway.Dr. Kamal Upreti is currently working as Associate Professor in Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India. He completed is B.Tech. (Hons) degree from UPTU, M.Tech. (Gold Medalist) from Galgotias University, PGDM (Executive) from IMT Ghaziabad, and Ph.D. from OPJ University in Department of Computer Science and Engineering. Now, he is doing Postdoc from National Taipei University of Business, TAIWAN funded by MHRD. He has published 50+ Patents, 40+ Books, 32+Magazine issues, and 70+ Research papers in various international conferences and reputed journals. His areas of interest are cyber security, machine learning, health care, wireless networking, embedded system, and cloud computing.

• Review and Experimental Evaluation of ML Models for DDoS Detection.- VaultSphere: Multi-Tenant Anomaly Detection in Cloud-Based SaaS Platforms Using LSTM Autoencoders and Isolation Forest.- Multi-Activity APT Detection using Domain-Adaptive Optimized Graph Transformer Framework (DAOGT).- Comparative Analysis of IoT Communication Protocols for Reliability in Low-Connectivity Environments.- IntelliCollar: An AI-Powered Health Scoring System for Livestock.- Smart Cyber-Physical System Implementation Strategy for Real-Time Operational Optimization in Sugar Factories.- Establishing immersive virtual environments for educating physics laboratories.- Advanced Cardiovascular Risk Stratification Using Hybrid Ensemble Learning and Deep Feature Engineering: A Multi-Cohort Validation Study.