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B01=Mayuri Mehta
B01=Patrick Siarry
B01=Rajanikanth Aluvalu
Category1=Non-Fiction
Category=MBF
Category=MQW
Category=TGPC
Category=UB
Category=UYQ
COP=Singapore
Delivery_Pre-order
Language_English
PA=Not yet available
Price_€100 and above
PS=Active
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Explainable AI in Health Informatics

English

This book provides a comprehensive review of the latest research in the area of explainable artificial intelligence (XAI) in health informatics. It focuses on how explainable AI models can work together with humans to assist them in decision-making, leading to improved diagnosis and prognosis in healthcare. This book includes a collection of techniques and systems of XAI in health informatics and gives a wider perspective about the impact created by them. The book covers the different aspects, such as robotics, informatics, drugs, patients, etc., related to XAI in healthcare.

The book is suitable for both beginners and advanced AI practitioners, including students, academicians, researchers, and industry professionals. It serves as an excellent reference for undergraduate and graduate-level courses on AI for medicine/healthcare or XAI for medicine/healthcare. Medical institutions can also utilize this book as reference material and provide tutorials to medical professionals on how the XAI techniques can contribute to trustworthy diagnosis and prediction of the diseases.

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Current price €145.34
Original price €152.99
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Age Group_Uncategorizedautomatic-updateB01=Mayuri MehtaB01=Patrick SiarryB01=Rajanikanth AluvaluCategory1=Non-FictionCategory=MBFCategory=MQWCategory=TGPCCategory=UBCategory=UYQCOP=SingaporeDelivery_Pre-orderLanguage_EnglishPA=Not yet availablePrice_€100 and abovePS=Activesoftlaunch

Will deliver when available. Publication date 29 Jul 2024

Product Details
  • Dimensions: 155 x 235mm
  • Publication Date: 08 Jul 2024
  • Publisher: Springer Verlag Singapore
  • Publication City/Country: Singapore
  • Language: English
  • ISBN13: 9789819737048

About

Rajanikanth Aluvalu is Senior Member-IEEE Currently working as Director Symbiosis Institute of Technology Hyderabad campus. With over 21 years of teaching experience he has specialization in high-performance computing and holds a PhD. Dr. Aluvalu is an editorial board member for the IJDMMM journal published by Inder science and served as the academic editor for PeerJ Computer Science journal. He is also a reviewer for several Scopus indexed and SCI indexed journals. He has published more than 90 research articles in various peer-reviewed journals and conferences and edited several books  published by Springer DE Gruyters Wiley IGI-Global and Apple Academic Press. Dr. Aluvalu is a life member of ISTE and a member of ACM and MIR Labs. He has organized various international conferences and delivered keynote addresses in his field. Mayuri Mehta is a professor in the Department of Computer Engineering of Sarvajanik College of Engineering and Technology Surat Gujarat India. Her areas of interest include data science artificial intelligence medical image analysis health informatics computer vision and computer algorithms. With 23 years of professional experience she has achieved several academic and research milestones and demonstrated excellent administrative and organizational capabilities. She holds 18 patents including 14 in the healthcare domain to her credit. Additionally she has co-edited 5 books authored  45 research papers and 4 book chapters. She is a Senior Member of IEEE and also a member of professional bodies such as ISTE and CSI. Patrick Siarry was born in France in 1952. He received the PhD degree from the University Paris 6 in 1986 and the Doctorate of Sciences (Habilitation) from the University Paris 11 in 1994. Dr. Siarry was initially involved in the development of analog and digital models of nuclear power plants at Electricité de France (EDF). Since 1995 he has been a professor in automatics and informatics. His main research interests include computer-aided design of electronic circuits and the applications of new stochastic global optimization heuristics to various engineering fields. He is also interested in the fitting of process models to experimental data the learning of fuzzy rule bases and of neural networks.

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