Applications of AI for Interdisciplinary Research

1. Auflage 2024
ISBN: 978-1-032-73330-2
Verlag: CRC Press

Buch, Englisch, 312 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 788 g

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

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Gill, Sukhpal Singh

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung

Inhaltsverzeichnis

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

Über Autor(innen)

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.