1st Edition
Applications of AI for Interdisciplinary Research
Applying artificial intelligence (AI) to new fields has made AI and data science indispensable to researchers in a wide range of fields. The proliferation and successful deployment of AI algorithms are fuelling these changes, which can be seen in fields as disparate as healthcare and emerging Internet of Things (IoT) applications. Machine learning techniques, and AI more broadly, are expected to play an ever-increasing role in the modelling, simulation, and analysis of data from a wide range of fields by the interdisciplinary research community. Ideas and techniques from multidisciplinary research are being utilised to enhance AI; hence, the connection between the two fields is a two-way street at a crossroads. Algorithms for inference, sampling, and optimisation, as well as investigations into the efficacy of deep learning, frequently make use of methods and concepts from other fields of study. Cloud computing platforms may be used to develop and deploy several AI models with high computational power. The intersection between multiple fields, including math, science, and healthcare, is where the most significant theoretical and methodological problems of AI may be found. To gather, integrate, and synthesise the many results and viewpoints in the connected domains, refer to it as interdisciplinary research. In light of this, the theory, techniques, and applications of machine learning and AI, as well as how they are utilised across disciplinary boundaries, are the main areas of this research topic.
• This book apprises the readers about the important and cutting-edge aspects of AI applications for interdisciplinary research and guides them to apply their acquaintance in the best possible manner.
• This book is formulated with the intent of uncovering the stakes and possibilities involved in using AI through efficient interdisciplinary applications.
• The main objective of this book is to provide scientific and engineering research on technologies in the fields of AI and data science and how they can be related through interdisciplinary applications and similar technologies.
• This book covers various important domains, such as healthcare, the stock market, natural language processing (NLP), real estate, data security, cloud computing, edge computing, data visualisation using cloud platforms, event management systems, IoT, the telecom sector, federated learning, and network performance optimisation. Each chapter focuses on the corresponding subject outline to offer readers a thorough grasp of the concepts and technologies connected to AI and data analytics, and their emerging applications.
Chapter 1 ◾ Machine Learning-Based Prediction of Thyroid Disease
Tanjina Rhaman and Sukhpal Singh Gill
Chapter 2 ◾ HeartGuard: A Deep Learning Approach for Cardiovascular Risk Assessment Using Biomedical Indicators Using Cloud Computing
Parinaz Banifatemi and Sukhpal Singh Gill
Chapter 3 ◾ Deep Convolutional Neural Networks-Based Skin Lesion Classification for Cancer Prediction
Neelam Rathore and Sukhpal Singh Gill
Chapter 4 ◾ Explainable AI for Cancer Prediction: A Model Analysis
Aswin Kumar Govindan and Sukhpal Singh Gill
Chapter 5 ◾ Machine Learning-Based Web Application for Breast Cancer Prediction
Shabnam Manjuri and Sukhpal Singh Gill
Part II Natural Language Programming (NLP)
Chapter 6 ◾ Machine Learning-Based Opinion Mining and Visualization of News RSS Feeds for Efficient Information Gain
Jairaj Patil and Sukhpal Singh Gill
Part III Economics and Finance
Chapter 7 ◾ Advanced Machine Learning Models for Real Estate Price Prediction
Satyam Sharma and Sukhpal Singh Gill
Chapter 8 ◾ Stock Market Price Prediction: A Hybrid LSTM and Sequential Self-Attention-Based Approach
Karan Pardeshi, Sukhpal Singh Gill, and Ahmed M. Abdelmoniem
Chapter 9 ◾ Federated Learning for the Predicting Household Financial Expenditure
Ho Kuen Lai, Ahmed M. Abdelmoniem, and Sukhpal Singh Gill
Part IV Computing and Business
Chapter 10 ◾ Deep Neural Network-Based Prediction of Breast Cancer Using Cloud Computing
Sindhu Muthumanickam and Sukhpal Singh Gill
Chapter 11 ◾ Performance Analysis of Machine Learning Models for Data Visualisation in SME: Google Cloud vs. AWS Cloud
Jisma Choudhury and Sukhpal Singh Gill
Part V Security and Edge/Cloud Computing
Chapter 12 ◾ Enhancing Data Security for Cloud Service Providers Using AI
Muhammed Golec, Sai Siddharth Ponugoti, and Sukhpal Singh Gill
Chapter 13 ◾ Centralised and Decentralised Fraud Detection Approaches in Federated Learning: A Performance Analysis
Shai Lynch, Ahmed M. Abdelmoniem, and Sukhpal Singh Gill
Contents ◾ vii
Chapter 14 ◾ AI-Based Edge Node Protection for Optimizing Security in Edge Computing
Muhammed Golec, Waleed Ul Hassan, and Sukhpal Singh Gill
Part VI Telecom Sector and Network
Chapter 15 ◾ Predictive Analytics for Optical Interconnection Network Performance Optimisation in Telecom Sector
Suganya Senguttuvan and Sukhpal Singh Gill
Part VII Emotional Intelligence
Chapter 16 ◾ Machine Learning-Based Emotional State Inference Using Mobile Sensing
Diogo Mota, Usman Naeem, and Sukhpal Singh Gill
Part VIII Internet of Things (IoT) and Mobile Applications
Chapter 17 ◾ Social Event Tracking System with Real-Time Data Using Machine Learning
Muhammad Usman Nazir and Sukhpal Singh Gill
Biography
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.
"In RNA: Computational Methods for Structure, Kinetics, and Rational Design, Professor Peter Clote offers a comprehensive and insightful exploration into the intricate world of RNA, combining the realms of biology, mathematics, and computational science with finesse. This book is the beautiful outcome of Clote's profound expertise in the field, presenting a treasure trove of knowledge that will undoubtedly captivate both novices and seasoned researchers alike.
What sets this book apart is its seamless integration of theoretical principles with practical applications, making complex concepts accessible to readers from diverse backgrounds. Clote's lucid explanations, coupled with illustrative examples and meticulously curated exercises, empower readers to navigate the intricate landscape of RNA research with confidence.
Throughout the book, Clote's passion for his subject matter shines through, infusing each page with enthusiasm and intellectual curiosity. Whether you are a student delving into the fundamentals of RNA or a seasoned researcher seeking to push the boundaries of knowledge, this book is an indispensable companion on your scientific journey.
In summary, RNA: Computational Methods for Structure, Kinetics, and Rational Design is a tour de force that deserves a place on the bookshelf of every scientist, educator, and enthusiast interested in the fascinating world of RNA. With its unparalleled depth, clarity, and visionary insights, this book not only informs but also inspires, leaving an indelible mark on the landscape of computational biology."
--Dr. Henri Orland, Institute of Theoretical Physics (IPhT), Paris-Saclay University, CNRS, CEA, France
