Practical Artificial Intelligence for Internet of Medical Things (eBook, ePUB)

Emerging Trends, Issues, and Challenges
Redaktion: Soufiene, Ben Othman; Almalki, Faris A.; Chakraborty, Chinmay

• Format: ePub

Produktbeschreibung

This book covers the fundamentals, applications, algorithms, protocols, emerging trends, problems, and research findings in the field of AI and IoT in smart healthcare. It includes case studies, implementation and management of smart healthcare systems using AI. Chapters focus on AI applications in Internet of Healthcare Things, provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and AI, with the real-world examples. This book is aimed at Researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics.

Features:

• Focus on the Internet of Healthcare Things and innovative solutions developed for use in the application of healthcare services
• Discusses artificial intelligence applications, experiments, core concepts, and cutting-edge themes
• Demonstrates new approaches to analyzing medical data and identifying ailments using AI to improve overall quality of life
• Introduces fundamental concepts for designing the Internet of Healthcare Things solutions
• Includes pertinent case studies and applications

This book is aimed at researchers and graduate students in Computer Engineering, Artificial Intelligence and Machine Learning, Biomedical Engineering, and Bioinformatics.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 346
• Erscheinungstermin: 28. Februar 2023
• Englisch
• ISBN-13: 9781000831399
• Artikelnr.: 67287581

Autorenporträt

Ben Othman Soufiene is an Assistant Professor of computer science at the University of Gabes, Tunisia from 2016 to 2021. He received his Ph.D. degree in computer science from Manouba University in 2016 for his dissertation on "Secure data aggregation in wireless sensor networks. He also holds M.S. degrees from the Monastir University in 2012. His research interests focus on the Internet of Medical Things, Wireless Body Sensor Networks, Wireless Networks, Artificial Intelligence, Machine Learning and Big Data. Chinmay Chakraborty is an Assistant Professor in the Department of Electronics and Communication Engineering, BIT Mesra, India, and a Post-doctoral fellow of Federal University of Piauí, Brazil. His primary areas of research include Wireless body area network, Internet of Medical Things, point-of-care diagnosis, mHealth/e-health, and medical imaging. Dr. Chakraborty is co-editing many books on Smart IoMT, Healthcare Technology and Sensor Data Analytics with CRC Press, IET, Pan Stanford and Springer. Dr. Chakraborty has published more than 150 papers at reputed international journals, conferences, book chapters, more than 30 books and more than 20 special issues. He received a Young Research Excellence Award, Global Peer Review Award, Young Faculty Award and Outstanding Researcher Award. Faris A. Almalki is an assistant professor in wireless communications and drones at Computer Engineering Dep. at Taif University, a research fellow in the Dep. of Electronic and Computer Engineering at Brunel University London. He holds a BSc in Computer Engineering from Taif University, an MSc in Broadband and Mobile Communication Networks from Kent University and a PhD in Wireless Communication Networks from Brunel University London. He is a Member of the IEEE Communication Society.

Inhaltsangabe

1. IoT-based Telemedicine Network Design: Implementation of a Smart Health
Monitoring System in COVID-19. 2. Detection and Evaluation of Operational
Limitations of Internet Infrastructure of Critical Systems Based on the
Internet of Medical Things in Smart Homes. 3. Fitness Dependent Optimizer
for IoT Healthcare using Adapted Parameters: A Case Study Implementation.
4. Digital Disruption in The Indian Healthcare System. 5. Smart Health Care
Monitoring System Using LoRaWAN IoT and Machine Learning methods. 6. Light
deep CNN approach for multi-label pathology classification using frontal
chest x-ray. 7. Trends in Malware Detection in IoHT using Deep Learning: A
Review. 8. IoT Based Wrist Attitude Sensor Data for Parkinson's disease
Assessment for Healthcare System. 9. Robotics and the Internet of Health
Things to Improve Healthcare: especially during the COVID-19 pandemic. 10.
Artificial intelligence at the service of the detection of covid-19. 11.
Monitoring ECG Signals using E-Health Sensors and Filtering Methods for
Noisy. 12. Artificial Intelligence-Enabled Wearable ECG for Elderly
Patients. 13. Diagnosing of Disease Using Machine Learning in Internet of
Healthcare Things. 14. Heart Attack Risk Predictor using Machine Learning &
Proposed IoT based Smart Watch Drone Healthcare system. 15. Thermal face
image re-identification based on Variational Autoencoder, Cascade Object
Detector using Lightweight architectures. 16. IOT Based Label Distribution
Learning Mechanism for Autism Spectrum Disorder for Healthcare Application.