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.

• The Duality of Preference: Reconciling A Priori and A Posteriori Attitudes in Induced OWA Operator.- Tuning Echo State Networks with a Modified Elk Herd Optimizer for Improved Unemployment Rate Prediction.- A Distance metric based Strategy for Detection of Interest Flooding Attack (IFA) in Information-centric Networking (ICN).- A Novel Hybrid Ensemble Architecture for Stroke Risk Prediction using Healthcare Data.- Scientometric Insights into Gesture Recognition Research Using Convolutional Neural Networks and Edge AI.- AI-Based Virtual Interviewer System using NLP and Emotion Detection.- Can LLMs Reliably Predict Personality Traits Through Prompt Engineering? Introducing Expert-Chain-Hypothesis Prompting.- Design and Implementation of a Smart Irrigation System Adapted to Market Gardening in Senegal.- Event Driven Spiking Neural Network using Anglo Mix Signals.- Polarity-Weighted Semantic Expansion with Multinomial Naive Bayes Ensemble for Robust Fake News Detection in Social Media Platforms.- Comparative Analysis of Deep Learning Models using Remote Sensing Image Classification.- Adaptive Augmentation and Synthetic Image Generation (AAg-SiG) Framework for Food Applications.- Prompt Meets Style: A Unified Framework for Text-Guided Artistic Style Transfer.- Pest Infestation Detection and Forecasting using Al.- RL-Based Online Mutation Strategy Selection Techniques in Differential Evolution: A Study.