Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics: Concepts, Methodologies, Tools, and Applications

ISBN 10: 1119791731 / ISBN 13: 9781119791737

Edité par Wiley-Scrivener, 2022

A propos de cet article

N° de réf. du vendeur 26390151997

Détails bibliographiques

• Titre: Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics: Concepts, Methodologies, Tools, and Applications
• Éditeur: Wiley-Scrivener
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 1119791731
• ISBN à 13 chiffres: 9781119791737
• Reliure: Couverture rigide
• État de l'article: New

A propos de ce titre

Synopsis

BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS

Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics.

The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data.

The 12 chapters in Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things (IoMT).

New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches.

Audience

Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

À propos de l?auteur

Sunil Kumar Dhal, PhD, is a computer scientist and is Head of Department and professor in the Faculty of Management, Sri Sri University, India. He has more than 20 years of teaching experience with more than 60 international publications including eight books and two patents.

Subhendu Kumar Pani, PhD, is a professor in the Department of Computer Science & Engineering, Orissa Engineering College (OEC) Bhubaneswar, India. He has more than 15 years of teaching and research experience and has published more than 50 international journal articles as well as five authored books, 12 edited books, and eight patents.

Srinivas Prasad, PhD, is a professor in the Department of Computer Science and Engineering at GITAM University, Visakhapatnam, India. He has more than 20 years of teaching experience and published more than 60 publications which include journal articles, conference papers, edited volumes, and book chapters.

Sudhir Kumar Mohapatra, PhD, is an associate professor at Addis Ababa Science & Technology University, Addis Ababa, Ethiopia. Besides 10 years of teaching and research, he spent five years in software development in the banking and education domains.