AI and Machine Learning Paradigms for Health Monitoring System -

AI and Machine Learning Paradigms for Health Monitoring System (eBook)

Intelligent Data Analytics
eBook Download: PDF
2021 | 1. Auflage
XXIV, 513 Seiten
Springer Singapore (Verlag)
978-981-334-412-9 (ISBN)
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181,89 inkl. MwSt
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This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques. The first part covers AI, machine learning and data analytics tools and techniques and their applications to the class of several hospital and health real-life problems. In the later part, the applications of AI, ML and data analytics shall be covered over the wide variety of applications in hospital, health, engineering and/or applied sciences such as the clinical services, medical image analysis, management support, quality analysis, bioinformatics, device analysis and operations. The book presents knowledge of experts in the form of chapters with the objective to introduce the theme of intelligent data analytics and discusses associated theoretical applications. At last, it presents simulation codes for the problems included in the book for better understanding for beginners.


Dr. Hasmat Malik (M'16) received Diploma in Electrical Engineering from Aryabhatt Govt. Polytechnic Delhi, B.Tech. degree in electrical & electronics engineering from the GGSIP University, Delhi, M.Tech degree in electrical engineering from National Institute of Technology (NIT) Hamirpur, Himachal Pradesh, and Ph.D in power system from Electrical Engineering Department, Indian Institute of Technology (IIT) Delhi, India. He is currently a Postdoctoral Scholar at BEARS, University Town, NUS Campus, Singapore. He is a chartered Engineer [IEI]. He is a Life Member of Institute of Engineers (India) (IEI), Indian Society for Technical Education (ISTE), Institution of Electronics and Telecommunication Engineering (IETE), International Association of Engineers (IAENG), Hong Kong, International Society for Research and Development, London (ISRD) and Member of the Institute of Electrical and Electronics Engineers (IEEE), USA and Mir Labs, Asia. He has published more than 100 research articles, including papers in international journals, conferences and book chapters. He is a Guest Editor of Special Issue of Journal of Intelligent & Fuzzy Systems, 2018, 2020 (SCI, Impact Factor 2020:1.851), (IOS Press). He received the POSOCO Power System Award (PPSA-2017) for his Ph.D work for research and innovation in the area of power system. He has received best research papers awards at IEEE INDICON-2015, and full registration fee award at IEEE SSD-2012 (Germany). He has supervised 23 PG students. He is involved in several large R&D projects. His principal area of research interests is artificial intelligence, machine learning and big-data analytics for renewable energy, smart building & automation, condition monitoring and online fault detection & diagnosis (FDD).
 

Dr Nuzhat Fatema has 10 years of experience in intelligent data analytics using AI & Machine learning for hospital and health care management. Dr. Fatema is the Co-founder of the Intelligent Prognostic Private Limited. Dr Fatema speaks nationally and internationally about the importance and power of data in hospital and healthcare systems.
Dr. Fatema a BAMS graduated from Maharashtra University of Health Sciences, India. She has cured many patients with her skills of medicinal knowledge. Later to go beyond the clinical skills, she has achieved post-graduation in hospital management from International Institute of Health Management Research (IIHMR), Delhi. This was the platform where she has utilized her clinical skills with her managerial skills using artificial intelligence (AI), Machine Learning (ML) and Data Analytics. She has worked as a research associate at National Board of Examinations (NBE) India and dealt with the accreditation process for post graduate courses in different multi-specialty hospitals in the country. She has authored one book describing a trouble free tool prepared by using different standardized manuals of medicines in different countries for usage of the most complicated drug like Warfarin. She has published several research papers in renowned international journals and conferences. Presently she is associated with Singapore Polyclinic, Singapore.
Her area of interest is AI, ML and intelligent data analytics application in healthcare, monitoring, prediction, forecasting, detection & diagnosis where she believes that it's a data driven world with stockpile of database in the industry which is to be used to extract value to make better informed, more accurate decisions in diagnosis, management and better outcomes in industry care. Simply throwing the numbers by analyzing any data has zero value; therefore she has produced narratives using data for decision making. She has been doing research study by spotting patterns in data and setting up infrastructure in the real-time industrial monitoring domain. 

Jafar A. Alzubi is Associate Professor at Al-Balqa Applied University, School of Engineering, Jordan. He received Ph.D. degree in Advanced Telecommunications from Swansea University, Swansea, UK (2012), Master of Science degree (Hons.) in Electrical and Computer Engineering from New York Institute of Technology, New York, USA (2005), And Bachelor of Science degree (Hons.) in Electrical Engineering, majoring in Electronics and Communications, from the University of Engineering and Technology, Lahore, Pakistan (2001). Jafar works and researches in multi- and interdisciplinary environment involving machine learning, classifications and detection of Web scams, the Internet of things, wireless sensor networks, cryptography and using Algebraic-Geometric theory in channel coding for wireless networks. As part of his research, he designed the first regular and first irregular block turbo codes using Algebraic-Geometry codes and investigated their performance across various computer and wireless networks. He managed and directed few projects funded by the European Union. He has a cumulative research experience for over ten years, resulted in publishing more than forty papers in highly impacted journals.
Currently, he is serving as Editor for IEEE Access Journal and wireless sensor networks area Editor for Turkish Journal of Electrical Engineering and Computer Sciences. In addition, he is Editorial Board Member and Reviewer in many other prestigious journals in computer engineering and science field. He also managed several special issues in high impacted journals.

This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques. The first part covers AI, machine learning and data analytics tools and techniques and their applications to the class of several hospital and health real-life problems. In the later part, the applications of AI, ML and data analytics shall be covered over the wide variety of applications in hospital, health, engineering and/or applied sciences such as the clinical services, medical image analysis, management support, quality analysis, bioinformatics, device analysis and operations. The book presents knowledge of experts in the form of chapters with the objective to introduce the theme of intelligent data analytics and discusses associated theoretical applications. At last, it presents simulation codes for the problems included in the book for better understanding for beginners.
Erscheint lt. Verlag 14.2.2021
Reihe/Serie Studies in Big Data
Zusatzinfo XXIV, 513 p. 259 illus., 183 illus. in color.
Sprache englisch
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Medizin / Pharmazie
Technik Medizintechnik
Schlagworte Big Data Analytics • Condition Monitoring • Health Management • machine learning • Prices Monitoring • Smart Health Care • smart health services • Smart Hospital • Smart Management System
ISBN-10 981-334-412-1 / 9813344121
ISBN-13 978-981-334-412-9 / 9789813344129
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