Applications of AI for Interdisciplinary Research

Dr. Sukhpal Singh Gill is a Lecturer (Assistant Professor) in Cloud Computing at School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London (QMUL), UK and he is a member of Network Research Group. Prior to this, Dr. Gill has held positions as a Research Associate @ Evolving Distributed Systems Lab at the School of Computing and Communications, Lancaster University, UK and also as a Postdoctoral Research Fellow at the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia Dr. Sukhpal Singh Gill (FHEA) is a Assistant Professor in Cloud Computing at School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London (QMUL), UK and he is a member of Network Research Group. Prior to this, Dr. Gill has held positions as a Research Associate at Evolving Distributed Systems Lab at the School of Computing and Communications, Lancaster University, UK and also as a Postdoctoral Research Fellow at the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia. He was awarded Fellow of the Higher Education Academy (FHEA) in 2022 after passing PGCAP/PGCert with Distinction. He has published his PGCAP/PGCert work in highly-ranked Education Conferences and Journals such as IEEE EDUCON (top conference for education papers with acceptance rate 26%), Wiley Computer Applications in Engineering Education (Impact Factor = 2.1) and IT NOW – British Computer Society (BCS). Before joining CLOUDS Lab, Dr. Gill also worked in Computer Science and Engineering Department of Thapar University, India, as a Lecturer. Dr. Gill received his Bachelor’s degree in Computer Science and Engineering from Punjab Technical University with Distinction in 2010. Then, he obtained the Degree of Master of Engineering in Software Engineering (Gold Medalist), as well as a Doctoral Degree specialization in Autonomic Cloud Computing from Thapar University. He was a DST (Department of Science & Technology) Inspire Fellow during Doctorate and worked as a Senior Research Fellow (Professional) on DST Project, Government of India. Dr. Gill was a research visitor at Monash University, University of Manitoba, University of Manchester and Imperial College London. He was a recipient of several awards, including the Distinguished Reviewer Award from Software: Practice and Experience (Wiley), 2018, Best Paper Award AusPDC at ACSW 2021, and the EECS Award for the “Widest Academic Staff Contribution” at EECS, QMUL in 2023. He has also served as the PC member for venues such as IEEE PerCom, UCC, CCGRID, CLOUDS, ICFEC, AusPDC. His one review paper has been nominated and selected for the ACM 21st annual Best of Computing Notable Books and Articles as one of the notable items published in computing – 2016. He has co-authored 150+ peer-reviewed papers (with Citations 7500+ and H-index 45+ as per Google Scholar) and has published in prominent international journals and conferences such as IEEE TCC, IEEE TSC, IEEE TSUSC, IEEE TCE, ACM TOIT, IEEE TII, IEEE TNSM, IEEE IoT Journal, Elsevier JSS/FGCS, IEEE/ACM UCC and IEEE CCGRID. Dr. Gill served as a Guest Editor for SPE (Wiley), JCC Springer Journal, Sustainability Journal (MDPI) and Sensors Journal (MDPI). He is a regular reviewer for IEEE TPDS, IEEE TSC, IEEE TNSE, IEEE TSC, ACM CSUR and Wiley SPE. Dr. Gill has reviewed 570+ research articles of high ranked journals and prestigious conferences as per Web of Science. He has edited a research books for Elsevier, Springer and CRC Press. Dr. Gill is serving as an Associate Editor in IEEE IoT Journal, Elsevier IoT Journal, Wiley SPE Journal, Wiley ETT Journal and IET Networks Journal. and Area Editor for Springer Cluster Computing Journal. He is a professional member of ACM. His name appears in the list of the World’s Top 2% of Scientists released by Stanford University and Elsevier BV (2022 and 2023). Dr. Gill has been serving as an editorial board member for IGIGLOBAL JOEUC, IGIGLOBAL IJAEC, and MECS IJEME. One of his articles published by the IEEE IoT Journal is highlighted in IEEE Spectrum (the world’s leading engineering magazine). Dr. Gill wrote articles for international magazines such as Ars Technica, Tech Monitor, Cutter Consortium and ICT Academy. He has been interviewed by Tallinn University, Estonia, to talk about “The capabilities and limitations of ChatGPT for Education“. His research interests include Cloud Computing, Fog Computing, Software Engineering, Internet of Things and Energy Efficiency. For further information, please visit www.ssgill.me.

Chapter 1. Machine Learning based Prediction of Thyroid Disease. Chapter 2.HeartGuard: A Deep Learning Approach to Cardiovascular Risk Assessment Using Biomedical Indicators using Cloud Computing. Chapter 3. Skin Lesion Classification using Deep Learning. Chapter 4. Explainable AI for Cancer Prediction: A Model Analysis. Chapter 5. Machine Learning based Web Application for Breast Cancer Prediction. Chapter 6. Machine Learning based Opinion Mining and Visualization of News RSS Feeds for Efficient Information Gain. Chapter 7. Advanced Machine Learning Models for Real Estate Price Prediction. Chapter 8. Stock Market Price Prediction: A Hybrid LSTM and Sequential Self-Attention based Approach. Chapter 9. Federated Learning for the Predicting Household Financial Expenditure. Chapter 10. Deep Neural Networks based Prediction of Breast Cancer Using Cloud Computing. Chapter 11. Performance Analysis of Machine Learning Models for Data Visualization in SME: Google Cloud vs AWS Cloud. Chapter 12. Enhancing Data Security for Cloud Service Providers using AI. Chapter 13. Centralised and Decentralised Fraud Detection Approaches in Federated Learning: A Performance Analysis. Chapter 14. AI based Edge Node Protection for Optimizing Security in Edge Computing. Chapter 15. Predictive Analytics for Optical Interconnection Network Performance Optimization in Telecom Sector. Chapter 16. Machine Learning based Emotional State Inference Using Mobile Sensing. Chapter 17. Social Event Tracking System with Real Time Data using Machine Learning . Chapter 18. MADDOKE: Real-Time Driver Drowsiness Detection Framework using Low Computational Power IoT Devices for Computer Vision