Evolution in Computational Intelligence
Description
The book presents the proceedings of the 13th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2025), held at Intelligent Systems Research Group (ISRG), London Metropolitan University, London, United Kingdom, during June 6-7, 2025. Researchers, scientists, engineers and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. This book is divided into four volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This book is a valuable resource for postgraduate students in various engineering disciplines.
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Persons
Vikrant Bhateja is associate professor in Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India. He is also serving as Head: Computer Sc. Engg. & Information Technology in the same university. He holds a doctorate in ECE (Bio-Medical Imaging) with a total academic teaching experience of 21 years with around 190 publications in reputed international conferences, journals and online book chapter contributions; out of which 39 papers are published in SCIE indexed high impact factored journals. One of his papers published in Review of Scientific Instruments (RSI) Journal (under American International Publishers) has been selected as "Editor Choice Paper of the Issue'' in 2016. Among the international conference publications, four papers have received "Best Paper Award''. He has been instrumental in chairing/co-chairing around 30 international conferences in India and abroad as Publication/TPC chair and edited 55 book volumes from Springer-Nature as a corresponding/co-editor/author on date. He has delivered nearly 25 keynotes, invited talks in international conferences, ATAL, TEQIP and other AICTE sponsored FDPs and STTPs. He has been Editor-in-Chief of IGI Global--International Journal of Natural Computing and Research (IJNCR) an ACM & DBLP indexed journal from 2017-22. He has guest edited Special Issues in reputed SCIE indexed journals under SpringerNature and Elsevier. He is Senior Member of IEEE and Life Member of CSI.
Preeti Patel has been educated in the United Kingdom and holds a BSc in Computer Science (1986), an MSc in Database and Information Systems (1992) and a PhD in Computing (2017) from UK universities. She is currently the Head of Computer Science and Applied Computing at London Metropolitan University. Prior to entering the Higher Education sector, she worked within the IT industry for organisations including British Telecom, The Wellcome Foundation and General Electric Information Services. She has a keen interest in international education and has been previously involved with the NCC global programmes for a number of years and continues to work with international partners. She has been an External Advisor and an External Examiner at various UK universities, and regularly works with the UK QAA. Her current research interests include FAIR and FATE data challenges, synthetic data, big data fusion, the data science curriculum, AI-generative models for Data Science and learning-related issues for database environments and enhancement of student engagement. Professor Patel is a Principal Fellow of Higher Education Academy (Advance HE), a member of the British Computer Society, the Chartered Institute for IT and an associate Member of the Chartered Management Institute.
Vipin Khattri is Professor and HoD-SAS at Faculty of Computer Engineering, Poornima University, Jaipur. He completed his doctorate in Computer Application from Integral University, Lucknow. He was awarded with "Best Faculty" in teaching and "Best Paper" in research. He has also served as a coordinator of academic cell in Shri Ramswaroop Memorial University, Lucknow. He has three years of Industry and 20 years of academic teaching experience. He published several book chapters, researcher papers in international conference proceedings and reputed international journals (Scopus and SCIE). He was awarded with two international patents, two Indian patents, and published two Indian patents. His research area includes Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, and Cyber and Information Security.
Content
Forecasting of Cognitive Neurological Imaging Using Hybrid Algorithms of Statistical Techniques and Machine Learning Models.- MonkeyPix: Optimization of a Pixel-wise Vision Transformer for Monkeypox Detection in Low-resource Environments.- Comparative Analysis of Deep Learning Models for Semantic Segmentation of Indian Remote Sensing (IRS) LISS-III Multispectral Imagery: U-Net, Deeplabv3+, and Tiramisu.- Internet of Things Security Framework Based on Light Gradient-Boosting Machine Optimized by Modified Bat Algorithm.- TANet: A Lightweight Deep Learning Model for Large-Scale Remote Sensing Image Classification.- Comparative Analysis of Random Forest and XGBoost Regression Algorithms for Predicting Mechanical Properties in polymer bio-composites: A Focus on Compressive, Flexural Strength, and Hardness.- F-Clone: A Comparative Analysis of DL-enabled Fingerprint Clone Generators and Anti-cloning Mechanisms.- Tribological Performance of LM25/SiC Composites at High Temperature with Machine Learning Approach.- Peak-Informed Segmentation and WaveNet-CNN: A Novel Approach to Fetal Heart Rate Analysis.- Computational Intelligence Approach for Reliability Enhance-ment in IOT Environment.- Anusandhana AI: A RAG-Based Yoga Chatbot for Enhanced Access to Yoga Research.- An Interpretable Lightweight CNN Framework for FaultDiagnosis in Centrifugal Pumps Using Time-Frequency Scalograms.- Advanced Fault Diagnosis in Milling Machines Using CQ-NSGT and Deep Learning.- Energy Efficient CNN Accelerator with e-FPGAs for XAI based Deep Fake Detection.- Ensemble-Based Machine Learning for Classification Of Enterprise Web Application Based Attacks.