Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering (Hardcover)

Madhusudhan H.S.

ISBN 10: 1041015267 / ISBN 13: 9781041015260

Published by Taylor & Francis Ltd, London, 2025

About this Item

Hardcover. Biomedical engineering is undergoing a transformation because of AI, which is allowing creative solutions that enhance patient outcomes, diagnosis, treatment planning, and healthcare delivery. Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering examines the salient characteristics of AI in biomedical engineering, highlighting its practical applications and new directions. Highlights of the book include:Genome sequence and visualizationThe role of AI and cloud in detection of diseasesNature-inspired algorithms for disease detectionFrameworks for disease classificationWith a focus on designing AI techniques for disease detection, the book explores the role of AI in biomedical engineering. It discusses how machine learning (ML) and deep learning (DL) are at the heart of AI applications in biomedical engineering. ML algorithms, particularly those based on neural networks, enable computers to learn from large datasets, identify patterns, and make predictions or decisions without explicit programming, and implementing ML algorithms is a focus of the book. Another focus is on DL, a subset of ML, and how it uses multi-layered neural networks to achieve high accuracy in such complex tasks as image and speech recognition. Biomedical engineering generates massive amounts of data from medical imaging, genomic sequencing, wearable devices, electronic health records (EHR), and other sources. This book also discusses AI-driven big data analytics, which allows researchers and clinicians to derive from data meaningful insights, aiding in early disease detection, personalized treatment plans, and patient monitoring. Biomedical engineering is undergoing a transformation due to AI and cloud computing, which are allowing creative solutions that enhance patient outcomes, diagnosis, treatment planning, and healthcare delivery. This work examines these two computing paradigms in biomedical engineering, highlighting practical applications and new directions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781041015260

Bibliographic Details

• Title: Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering (Hardcover)
• Publisher: Taylor & Francis Ltd, London
• Publication Date: 2025
• Language: English
• ISBN 10: 1041015267
• ISBN 13: 9781041015260
• Binding: Hardcover
• Condition: new

About this title

Synopsis

Biomedical engineering is undergoing a transformation because of AI, which is allowing creative solutions that enhance patient outcomes, diagnosis, treatment planning, and healthcare delivery. Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering examines the salient characteristics of AI in biomedical engineering, highlighting its practical applications and new directions. Highlights of the book include:

• Genome sequence and visualization
• The role of AI and cloud in detection of diseases
• Nature-inspired algorithms for disease detection
• Frameworks for disease classification

With a focus on designing AI techniques for disease detection, the book explores the role of AI in biomedical engineering. It discusses how machine learning (ML) and deep learning (DL) are at the heart of AI applications in biomedical engineering. ML algorithms, particularly those based on neural networks, enable computers to learn from large datasets, identify patterns, and make predictions or decisions without explicit programming, and implementing ML algorithms is a focus of the book. Another focus is on DL, a subset of ML, and how it uses multi-layered neural networks to achieve high accuracy in such complex tasks as image and speech recognition. Biomedical engineering generates massive amounts of data from medical imaging, genomic sequencing, wearable devices, electronic health records (EHR), and other sources. This book also discusses AI-driven big data analytics, which allows researchers and clinicians to derive from data meaningful insights, aiding in early disease detection, personalized treatment plans, and patient monitoring.

About the Author

Dr. Madhusudhan H S is an associate professor in the Department of Computer Science and Engineering at Vidyavardhaka College of Engineering, Mysuru, India.

Dr. Punit Gupta is an associate professor in the department of Computer and Communication Engineering at Pandit Deendayal Energy University, India.

Dr. Pradeep Singh Rawat is an assistant professor with the Department of Computer Science and Engineering at DIT University, Dehradun, India.

Dr. Dinesh Kumar Saini is a full professor at the School of Computing and Information Technology, Manipal University