Sustainable Computing and Intelligent Systems

Dr. Jagdish Chand Bansal is a professor at South Asian University New Delhi and a visiting faculty at Maths and Computer Science, Liverpool Hope University UK. Dr. Bansal has obtained his Ph.D. in Mathematics from IIT Roorkee. Before joining SAU New Delhi, he has worked as an assistant professor at ABV-Indian Institute of Information Technology and Management Gwalior and BITS Pilani. His Primary area of interest is Swarm Intelligence and Nature Inspired Optimization Techniques. Recently, he proposed a fission-fusion social structure-based optimization algorithm, Spider Monkey Optimization (SMO), which is being applied to various problems from engineering domain. He has published more than 70 research papers in various international journals/conferences. He is the editor in chief of the journal MethodsX published by Elsevier. He is the series editor of the book series Algorithms for Intelligent Systems (AIS) and Studies in Autonomic, Data-driven and Industrial Computing (SADIC) published by Springer. Dr. Hussain is working at University of Canberra as an associate professor of Biomedical Robotics. Prior to that he has worked as a lecturer at University of Wollongong, Australia. Dr. Hussain has obtained his Ph.D. in Mechanical Engineering from the University of Auckland, New Zealand, in 2013. His research interests include assistive and rehabilitation robotics, compliant actuation of robots, robot mechanism design and optimization, non-linear dynamics and control of robotic systems, human-robot interaction, biomechanical modelling, engineering education and micro electro-mechanical systems (MEMS). Dr. Hussain has published more than 65 papers in the prestigious journals of the field.Prof. Prashant K. Jamwal earned Ph.D. degree and a post-doctoral fellowship from the University of Auckland, New Zealand. Earlier, he had obtained M.Tech. from I.I.T., India, securing first position in all the disciplines and B. Tech. from MNREC, Allahabad, India. Presently, he is working as a professor at the school of engineering and design sciences, Nazarbayev University (NU), Astana, Kazakhstan and as adjunct professor at University of Canberra, Australia. He is actively pursuing research in robotics and artificial intelligence, multi-objective evolutionary optimization, biomedical engineering, and Renewable energy. Over the past decade, he has applied his research in the development of medical robots for rehabilitation and surgical applications besides developing improved algorithms for cancer data analytics. Prof. Prashant received three prestigious World Bank Grants and has launched two startup companies and established a Center of Excellence in Medical Research and Robotics.

• Evaluation of Mechanical Behavior and Deformation of Brain Tissue Model Simulation using Finite Element Method.- An Integrated Framework for Jailbreak Prevention using Knowledge Graphs and Large Language Models.- Spiral Drawings Test Based Early Diagnosis of Parkinson Disorder Using Tuned Densenet201 Deep Transfer Learning Model.- API Usability Feedback through Complexity Analysis for Networked Services.- Cloud System Logs Analysis Utilizing BERT and AdaBoost Tuned With Modified Sinh Cosh Optimizer.- IoT-Based Smart Shelter and Health Monitoring System for Emergency and Disaster Relief.- Estimating Parameters of the Drift Diffusion Model using Particle Swarm Optimization.- Ellipse Shaped Four-element Microstrip Patch Antenna Array using Corporate Feed Network.- Quantum-Driven Digital Forensics: Evidence Acquisition, Intrusion Detection, Cybercrime Simulation, and DNA Profiling.- Next-Generation Bioelectronic Devices for Sustainable Intelligent Systems: Integrating Neural Interfaces, DNA Nanotechnology, and Soft Robotics.- Footstep Power Generation Simulation using Piezo Sensors in Matlab Simulation.- Explainable Temporal Knowledge Graph Reasoning for Geopolitical Risk Assessment.- AI-Driven Cyber-Physical Framework for Automated Surgical Precision Enhancement.- AI Driven Workday Financial Fraud Detection using Hybrid Optimal Deep Reinforcement Learning.- A Stacking Ensemble Learning Framework for Student Academic Performance Prediction.