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

Sommario

Part I Healthcare
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

Info autore

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 has published his PGCAP/PGCert work in highly-ranked Education Conferences and Journals. Before joining CLOUDS Lab, Dr. Gill worked in the Computer Science and Engineering Department of Thapar University, India, as a Lecturer. Dr. Gill received a Doctoral Degree specialization in Autonomic Cloud Computing from Thapar University. He 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 has recieved several awards. He has also served as the PC member for various venues. He has co-authored 150+ peer-reviewed papers and has published in prominent international journals and conferences. He serves as a Guest Editor and is a regular reviewer for multiple journals. He has also edited multiple research books He has also written for magazines such as Ars Technica, Tech Monitor, Cutter Consortium and ICT Academy. For further information, visit www.ssgill.me.

Riassunto

In order 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.

Dettagli sul prodotto

• Autori: Sukhpal Singh (Queen Mary Uni London Gill
• Con la collaborazione di: Sukhpal Singh Gill (Editore)
• Editore: Taylor & Francis Ltd.
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 13.09.2024
• EAN: 9781032733302
• ISBN: 978-1-0-3273330-2
• Pagine: 297
• Categorie: Scienze naturali, medicina, informatica, tecnica > Medicina > Tematiche generali; Artificial Intelligence, COMPUTERS / Computer Science, REFERENCE / Research, Information technology: general issues, COMPUTERS / Artificial Intelligence / General, Applied computing, Research & information: general, Research and information: general