Internet of Things and Machine Learning for Type I and Type II Diabetes: Use Cases - Brossura

Brossura

ISBN 10: 0323956866 ISBN 13: 9780323956864

Casa editrice: Elsevier, 2024

Riassunto

Internet of Things and Machine Learning for?Type I and Type II Diabetes: Use Cases provides a medium of exchange of expertise and addresses the concerns, needs, and problems associated with Type I and Type II diabetes. Expert contributions come from researchers across biomedical, data mining, and deep learning. This is an essential resource for both the AI and Biomedical research community, crossing various sectors for broad coverage of the concepts, themes, and instrumentalities of this important and evolving area. Coverage includes IoT, AI, Deep Learning, Machine Learning and Big Data Analytics for diabetes and health informatics.

Su questo libro

Informazioni sugli autori

Sujata Dash is a Senior Member, IEEE who received the Ph.D. degree in computational modeling from Berhampur University, Orissa, India, in 1995. She is currently an Associate Professor with the P.G. Department of Computer Science and Application, North Orissa University, Baripada, India. She has published more than 150 technical articles in international journals, conferences, and book chapters of reputed publications. She has guided many scholars for their Ph.D. degrees in computer science. She is associated with many professional bodies like IEEE, CSI, ISTE, OITS, OMS, IACSIT, IMS, and IAENG. She is a member of the editorial board of several international journals and also reviewer of many international journals. Her current research interests include machine learning, distributed data mining, bioinformatics, intelligent agent, Web data mining, recommender systems, and image processing.

Dr. Subhendu Kumar Pani received his Ph.D. from Utkal University, Odisha, India in the year 2013. He is working as a professor at Krupajal Engineering College under BPUT, Odisha, India. He has more than 20 years of teaching and research experience His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He is the recipient of 5 researcher awards. In addition to research, he has guided two PhD students and 31 M. Tech students. He has published 150 International Journal papers (100 Scopus index). His professional activities include roles as Book Series Editor (CRC Press, Apple Academic Press, Wiley-Scrivener), Associate Editor, Editorial board member and/or reviewer of various International Journals. He is an Associate with no. of the conference societies. He has more than 250 international publications, 5 authored books, 25 edited and upcoming books; 40 book chapters into his account. He is a fellow in SSARSC and a life member in IE, ISTE, ISCA, and OBA.OMS, SMIACSIT, SMUACEE, CSI.

Willy Susilo received his Ph.D. degree in Computer Science from the University of Wollongong, Australia. He is a Distinguished Professor the Head of the School of Computing and Information Technology and the director of the Institute of Cybersecurity and Cryptology (iC2) at the University of Wollongong. Recently, he was awarded an Australian Laureate Fellowship, which is the most prestigious award in Australia, due to his contribution in cloud computing security. He was previously awarded a prestigious ARC Future Fellow by the Australian Research Council (ARC) and the Researcher of the Year award in 2016 by the University of Wollongong. He is a Fellow of IEEE, Australian Computer Society (ACS), IET and AAAI. His main research interests include cybersecurity, cryptography and information security. His work has been cited more than 25,000 times in Google Scholar. He is the Editor-in-Chief of the Elsevier Computer Standards and Interfaces and the MDPI Information journal. He has served as a program committee member in dozens of international conferences. He is currently serving as an Associate Editor in several international journals, including IEEE Transactions in Dependable and Secure Computing. Previously, he has served in many top-tier journals, such as IEEE Transactions in Information Forensics and Security. He has published more than 500 research papers in the area of cybersecurity and cryptology.

Bernard Cheung went to Sevenoaks School and studied Medicine at the University of Cambridge. He was Professor of Clinical Pharmacology and Therapeutics at the University of Birmingham before returning to Hong Kong and being appointed the Sun Chieh Yeh Heart Foundation Professor in Cardiovascular Therapeutics. He was a Consultant Physician of Queen Mary Hospital and the Director of the Phase 1 Clinical Trials Units in Queen Mary Hospital and the University of Hong Kong-Shenzhen Hospital. Currently, he is the Biotechnology Director in the Innovation and Technology Commission. He is also the President of the Federation of Medical Societies of Hong Kong and the Editor-in-Chief of Postgraduate Medical Journal. Prof Cheung’s main research interest is in cardiovascular diseases and risk factors, including hypertension and the metabolic syndrome.

Gary Tse is a medical scientist focusing on personalized care, health informatics and big data analytics. He is a professor in Hong Kong Metropolitan University and holds a joint position as reader at the University of Kent and public health consultant at the Medway Council in local government, UK. He serves as a professor at the Department of Cardiology and principal investigator at the Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, China. He is a visiting professor at the Faculty of Health and Medical Sciences, University of Surrey, and an honorary associate professor at the School of Pharmacy, University College London. He is an author of more than 500 scientific papers and an expert reviewer for >50 international journals in cardiology, cardiac electrophysiology, cardiovascular biology and epidemiology and five grant bodies from New Zealand, Poland, Sweden and the UK.

Dalla quarta di copertina

Internet of Things and Machine Learning for Type I and Type II Diabetes: Use Cases provides a medium of exchange of expertise and addresses the concerns, needs, and problems associated with Type I and Type II diabetes. Expert contributions come from researchers across biomedical, data mining, and deep learning. It is an essential resource for both the AI and Biomedical research community crossing various sectors for broad coverage of the concepts, themes, and instrumentalities of this important and evolving area. Coverage includes IoT, AI, Deep Learning, Machine Learning and Big Data Analytics for diabetes and health informatics.

Dettagli dell'edizione

• Editore: Elsevier
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 0323956866
• ISBN 13: 9780323956864
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 448
• Redattore: Dash, Pani, Susilo, Yung Bernard, Tse
• Contatto del produttore: non disponibile
• Persona responsabile: LSWR Group; b.criscuolo@lswr.it; ; Via Spadolini, 7; Milano; Italia