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Computational Intelligence for Medical Internet of Things (MIoT) Applications
Machine Intelligence Applications for IoT in Healthcare
- 1st Edition, Volume 14 - January 25, 2023
- Editors: Yassine Maleh, Ahmed A. Abd El-Latif, Kevin Curran, Patrick Siarry
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 4 2 1 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 0 9 7 - 8
Computational Intelligence for Medical Internet of Things (MIoT) Applications: Machine Intelligence Applications for IoT in Healthcare explores machine intelligence techni… Read more
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Request a sales quoteComputational Intelligence for Medical Internet of Things (MIoT) Applications: Machine Intelligence Applications for IoT in Healthcare explores machine intelligence techniques necessary for effective MIoT research and practice, taking a practical approach for practitioners and students entering the field. This book investigates advanced concepts and applications in the MIoT field, guiding readers through emerging developments and future trends. A wide range of international authors guide readers through advanced concepts, including deep learning, neural network, and big data analytic approaches for the classification, indexing, retrieval, analysis, and inferencing of healthcare data.
- Presents the state-of-the-art in machine intelligence and related technologies and methodologies for IoT in healthcare
- Discusses emerging developments and trends in machine intelligence for business and decision-making strategy in healthcare
- Features new models, practical solutions, prototypes, frameworks and technological advances related to machine intelligence for MIoT applications
Biomedical Engineers, Data Analysts, Researchers, Master and PhD students; Healthcare Professionals
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- About the editors
- Preface
- Section 1. Computational Intelligence for Medical Internet of Things (MIoT): Current States and Challenges
- Chapter 1. AI and IoT working for healthcare: general aspects and application examples
- 1.1. Introduction
- 1.2. Methodology
- 1.3. Activity monitoring during cancer treatment
- 1.4. Connected inhalers
- 1.5. Ingestible sensors
- 1.6. IoMT data security
- 1.7. Artificial intelligence in healthcare: ethical and legal issues
- 1.8. The main points to remember
- 1.9. Discussion
- 1.10. Conclusion
- Chapter 2. AIoMT artificial intelligence (AI) and Internet of Medical Things (IoMT): applications, challenges, and future trends
- 2.1. Introduction
- 2.2. IoMT applications
- 2.3. IoMT security
- 2.4. Current challenges
- 2.5. Discussion and future directions
- 2.6. Conclusion
- Chapter 3. Artificial intelligence in healthcare: current situation and future possibilities
- 3.1. Introduction
- 3.2. Working of each application in smart era
- 3.3. AI in biomedical information processing
- 3.4. Conclusion
- Chapter 4. Exploring the effectiveness of cloud, Internet of Things and fog computing for healthcare monitoring systems
- 4.1. Introduction
- 4.2. A healthcare monitoring system components
- 4.3. A healthcare patient monitoring system using fog and cloud
- 4.4. Artificial intelligence for healthcare services on cloud and Internet of Things
- 4.5. Conclusion
- Chapter 5. Patients using real-time remote health monitoring applications: a review
- 5.1. Introduction
- 5.2. Significance of study
- 5.3. Comprehensive study of remote health monitoring
- 5.4. Communication and location technologies in remote health
- 5.5. Conclusion
- Section 2. Computational Intelligence for Medical Internet of Things (MIoT): Applications
- Chapter 6. A review on the application of the Internet of Things in monitoring autism and assisting parents and caregivers
- 6.1. Introduction
- 6.2. Related work
- 6.3. Research methodology
- 6.4. The use of IoT in autism monitoring
- 6.5. Results
- 6.6. Conclusion
- Chapter 7. Regression analysis of the most frequent medical diagnoses in a Mediterranean country
- 7.1. Introduction
- 7.2. Related work
- 7.3. Exploratory analysis
- 7.4. Analysis of acute diseases
- 7.5. Analysis of chronic diseases
- 7.6. Conclusion
- Chapter 8. A conceptual framework for Artificial Intelligence of Medical Things (AIoMT)
- 8.1. Introduction
- 8.2. IoT in healthcare
- 8.3. Big data in healthcare
- 8.4. Artificial intelligence in healthcare
- 8.5. Artificial Intelligence of Medical Things (AIoMT)
- 8.6. Conclusion
- Chapter 9. Framework for integrating healthcare big data using IoMT technology
- 9.1. Introduction
- 9.2. Hadoop, HBase, and Spark
- 9.3. Related works
- 9.4. The proposed model
- 9.5. IoMT big data integration
- 9.6. IoMT big data storage
- 9.7. Big data analysis in IoMT
- 9.8. Discussions
- 9.9. Conclusion and future works
- Chapter 10. Application of computational intelligence in visual optimization tools to improve the performance of medical MIoT platforms
- 10.1. Introduction
- 10.2. MIoT platform access control
- 10.3. Psychovisual foveal coding and evaluation of the coding quality
- 10.4. Description of the realized recognition platform using AI
- 10.5. Discussion and results
- 10.6. Conclusion and perspectives
- Section 3. Computational Intelligence for Medical Internet of Things (MIoT): Security and Privacy
- Chapter 11. Edge intelligence case study on Medical Internet of Things security
- 11.1. Introduction
- 11.2. Literature review
- 11.3. Edge intelligence miot case study issues
- 11.4. Evaluation
- 11.5. Conclusions
- Glossary
- Chapter 12. Data-driven intelligent Medical Internet of Things (MIoT) based healthcare solutions for secured smart cities
- 12.1. Introduction
- 12.2. Need of intelligent systems in healthcare
- 12.3. IoT, applications, and wearables for healthcare monitoring
- 12.4. Computer vision for medical diagnosis, prognosis, and treatment
- 12.5. Harnessing the power of natural language processing for intelligent healthcare solutions
- 12.6. Data-driven method for bioinformatics
- 12.7. Data-driven drug discovery and vaccine development
- 12.8. Epidemics and pandemics modeling with AI
- 12.9. AI for efficient healthcare resource management and in-hospital care
- 12.10. Remote healthcare
- 12.11. Conclusion
- Chapter 13. A secure and efficient two-factor authentication protocol (SET-AP) for body sensor networks in IoT-enabled healthcare systems
- 13.1. Introduction
- 13.2. Related works
- 13.3. The proposed SET-AP protocol
- 13.4. Formal analysis of SET-AP using BAN logic
- 13.5. Security analysis and performance evaluation
- 13.6. Functionality analysis
- 13.7. Conclusion and future scope
- Chapter 14. Pervasive m-health for chronic diseases
- 14.1. Introduction
- 14.2. Methods
- 14.3. Characteristics of pervasive m-health
- 14.4. Applications
- 14.5. Opportunities and challenges
- 14.6. Conclusion
- Chapter 15. Hybrid intelligence-based cryptosystem: security and privacy enhancement in telemedicine system
- 15.1. Introduction
- 15.2. Background work
- 15.3. Major problems in telehealth security
- 15.4. Objectives and contributions
- 15.5. Proposed methodology
- 15.6. Experimental results analysis
- 15.7. Discussion and conclusion
- Index
- No. of pages: 372
- Language: English
- Edition: 1
- Volume: 14
- Published: January 25, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780323994217
- eBook ISBN: 9780323950978
YM
Yassine Maleh
Prof. Yassine Maleh is a cybersecurity professor and practitioner with industry and academic experience. He is a Ph.D. degree in Computer Sciences. Since 2019, He working as a professor of cybersecurity at Sultan Moulay Slimane University, Morocco. He worked for the National Port agency (ANP) in Morocco as a Senior Security Analyst from 2012 to 2019. He is a senior member of IEEE, a member of the International Association of Engineers and the Machine Intelligence Research Labs. Dr. Maleh has made contributions in information security and privacy, Internet of Things security, and wireless and constrained network security. His research interests include information security and privacy, Internet of Things, networks security, information system and IT governance. He has published over 60 papers (book chapters, international journals, conferences/workshops), 10 edited books, and 3 authored books. He is the editor-in-chief of the International Journal of Smart Security Technologies. He serves as an associate editor for IEEE Access (2019 Impact Factor 4.098), the International Journal of Digital Crime and Forensics, and the International Journal of Information Security and Privacy. He was also a guest editor of a special issue on 'Recent Advances on Cyber Security and Privacy for Cloud-of-Things' of the International Journal of Digital Crime and Forensics, Volume 10, Issue 3, July-September 2019.
Affiliations and expertise
Professor, University Sultan Moulay Slima, Beni-Mellal, MoroccoAE
Ahmed A. Abd El-Latif
Prof. Ahmed Abd El-Latif: (B.Sc. with honor rank in Mathematics and Computer Science in 2005, (M.Sc in Computer Science in 2010), and Ph. D. in Computer Science & Technology at Harbin Institute of Technology, Harbin, P. R. China in 2013. He is an associate professor of Computer Science at Menoufia University, Egypt and School of Information Technology and Computer Science, Nile University, Egypt. He is author and co-author of more than indexed 140 papers. He is a referee of many referred international repute journals and conferences. He received many awards, State Encouragement Award in Engineering Sciences 2016, Arab Republic of Egypt; the best Ph.D. student award from Harbin Institute of Technology, China 2013; Young scientific award, Menoufia University, Egypt 2014. He is a fellow at Academy of Scientific Research and Technology, Egypt. Dr. Abd El-Latif is an associate editor in several Scopus Journals and leading many Special issues in SCI journals.
Affiliations and expertise
Professor of Computer Science at the EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia, and Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, EgyptKC
Kevin Curran
Prof. Kevin Curran is a Professor of Cyber Security, Executive Co-Director of the Legal innovation Centre and group leader of the Ambient Intelligence & Web Technologies Research Group at Ulster University. He is also a senior member of the IEEE. Prof Curran is globally recognized as a security Top Influencer "IFSEC Global influencers in security and fire 2020" in category Security thought leadership. He was ranked #2.
Prof Curran is perhaps most well-known for his work on Internet security & location positioning evidenced by over 800 published works. His expertise has been acknowledged by invitations to present his work at international conferences, overseas universities and research laboratories. He is a regular contributor on TV & radio and in trade and consumer IT magazines with 1000+ interviews in recent years. He is currently the recipient of a Royal Academy of Engineering Senior Research Fellowship and is an IEEE Technical Expert for Internet Security. Professor Curran has performed external panel duties for various Higher Education Institutions and public bodies such as OFCOM and the National Institute for Health Research (NIHR).
Affiliations and expertise
Professor of Cyber Security, Executive Co-Director, Legal innovation Centre and group leader, Ambient Intelligence and Web Technologies Research Group, Ulster University, UKPS
Patrick Siarry
Patrick Siarry was born in France in 1952. He received the Ph.D. degree in computer science and optimization from University Paris VI, Paris, France, in 1986, and the Doctorate of Sciences (Habilitation) degree in computer science and optimization from University Paris XI, Orsay, France, in 1994.,He was first involved in the development of analog and digital models of nuclear power plants with Electricité de France, Paris. Since 1995, he has been a Professor of Automatics and Informatics with Université Paris-Est Créteil, Créteil, France. His main research interests include computer-aided design of electronic circuits, cognitive intelligence, and the applications of new stochastic global optimization heuristics to various engineering fields, also including the fitting of process models to experimental data, the learning of fuzzy rule bases, and of neural networks.
Affiliations and expertise
University Paris XI, Orsay, FranceRead Computational Intelligence for Medical Internet of Things (MIoT) Applications on ScienceDirect