Wellness Management Powered by AI Technologies
Machine Learning in Biomedical Science and Healthcare Informatics
1. Auflage Januar 2025
448 Seiten, Hardcover
Wiley & Sons Ltd
This book is an essential resource on the impact of AI in medical systems, helping readers stay ahead in the modern era with cutting-edge solutions, knowledge, and real-world case studies.
Wellness Management Powered by AI Technologies explores the intricate ways machine learning and the Internet of Things (IoT) have been woven into the fabric of healthcare solutions. From smart wearable devices tracking vital signs in real time to ML-driven diagnostic tools providing accurate predictions, readers will gain insights into how these technologies continually reshape healthcare.
The book begins by examining the fundamental principles of machine learning and IoT, providing readers with a solid understanding of the underlying concepts. Through clear and concise explanations, readers will grasp the complexities of the algorithms that power predictive analytics, disease detection, and personalized treatment recommendations. In parallel, they will uncover the role of IoT devices in collecting data that fuels these intelligent systems, bridging the gap between patients and practitioners.
In the following chapters, readers will delve into real-world case studies and success stories that illustrate the tangible benefits of this dynamic duo. This book is not merely a technical exposition; it serves as a roadmap for healthcare professionals and anyone invested in the future of healthcare.
Readers will find the book:
* Explores how AI is transforming diagnostics, treatments, and healthcare delivery, offering cutting-edge solutions for modern healthcare challenges;
* Provides practical knowledge on implementing AI in healthcare settings, enhancing efficiency and patient outcomes;
* Offers authoritative insights into current AI trends and future developments in healthcare;
* Features real-world case studies and examples showcasing successful AI integrations in various medical fields.
Audience
This book is a valuable resource for researchers, industry professionals, and engineers from diverse fields such as computer science, artificial intelligence, electronics and electrical engineering, healthcare management, and policymakers.
1 Exploring Functional Modules Using Co-Clustering of Protein Interaction Networks 1
R. Gowri and R. Rathipriya
2 Natural Language Processing in Healthcare: Enhancing Wellbeing through a COVID-19 Case Study 55
Akib Mohi Ud Din Khanday, Salah Bouktif and Ali Ouni
3 Artificial Intelligence Assisted Internet of Medical Things (AIoMTs) in Sustainable Healthcare Ecosystem 75
Wasswa Shafik
4 An Online Platform for Timely Access to Medical Care with the Help of Real-Time Data Analysis 103
Pancham Singh and Mrignainy Kansal
5 A Comprehensive Review of Cardiac Image Analysis for Precise Heart Disease Diagnosis Using Deep Learning Techniques 133
Anuj Gupta, Vikas Kumar and Aryan Nakhale
6 A Hybrid Machine Learning Model for an Efficient Detection of Liver Inflammation 157
Hema Ramachandran and Syedakbar Syed Yusuff
7 Advancements in Parkinson's Disease Diagnosis through Automated Speech Analysis 173
P. Deepa, Rashmita Khilar and Saumendra Kumar Mohapatra
8 Public Opinion Segmentation on COVID-19 Vaccination and Its Impact on Wellbeing 207
Akib Mohi Ud Din Khanday, Salah Bouktif and K. Nimmi
9 Revolutionizing Healthcare with IoT in Cardiology 231
Aafreen Jan,K. Nimmi and Mohd Anas Wajid
10 Human Biological Analysis Through Fitness Watch Using Deep Learning Algorithm 275
Nilesh Bhaskarrao Bahadure, Ramdas Khomane, Anjali Singh, Anisha Jaiswal, Rashmi Kadu, Rohini Bharne, Bhumika Kosarkar and Sidheswar Routray
11 Decoding Kidney Health: Effectiveness of Machine Learning Techniques in Diagnosis of Chronic Kidney Disease 297
Suhail Rashid Wani, Syed Naseer Ahmad Shah, Roshni Afshan and Asif Adil
12 Integrating Metaheuristics and Machine Learning for Wellbeing Management: Case of COVID-19 313
Safea Matar Al Senani and Salah Bouktif
13 Fusing Sentiment Analysis with Hybrid Collaborative Algorithms for Enhanced Recommender Systems 343
Anindya Nag, Md. Mehedi Hassan, Mohammad Abu Tareq Rony, Biva Das, Riya Sil, Prianka Saha, Pronab Sarker and Anupam Kumar Bairagi
14 The Future of Well-Being: AI-Powered Health Management with Privacy at its Core 363
D. Dhinakaran, S. Edwin Raja, J. Jeno Jasmine, P. Vimal Kumar and R. Ramani
15 Artificial Pancreas: Enhancing Glucose Control and Overall Well-Being 403
Owais Bhat, Syed Tanzeel Rabani, Syed Mohsin Saif, Zubair Jeelani andNawaz Ali Lone
Index 421
Akib Khanday, PhD, is a post-doctoral research fellow in the Department of Computer Science and Software Engineering-CIT, United Arab Emirates University, Abu Dhabi, United Arab Emirates. His research interests include computational social sciences, natural language processing (NLP), and machine/deep learning.
Khursheed Aurangzeb, PhD, is an associate professor in the Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. Over his 15 years of research, he has been involved in several projects related to machine/deep learning and embedded systems. His research interests focus on computer architecture, signal processing, and wireless sensor networks.
Sudhir Kumar Sharma, PhD, is a professor and head of the Department of Computer Science at the Institute of Information Technology & Management, affiliated with GGSIPU, New Delhi, India. His research interests include machine learning, data mining, and security. He has published more than 60 research papers in various international journals and conferences and is the author of seven books in the fields of IoT, wireless sensor networks (WSN), and blockchain.
Parma Nand, PhD, is the dean of the School of Engineering and Technology, Sharda University, Greater Noida, India. His expertise includes wireless and sensor networks, cryptography, algorithms, and computer graphics. He has published more than 85 papers in peer-reviewed journals and filed two patents.
