Edge-of-Things in Personalized Healthcare Support Systems
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Edge-of-Things in Personalized Healthcare Support Systems discusses and explores state-of-the-art technology developments in storage and sharing of personal healthcare records in a secure manner that is globally distributed to incorporate best healthcare practices. The book presents research into the identification of specialization and expertise among healthcare professionals, the sharing of records over the cloud, access controls and rights of shared documents, document privacy, as well as edge computing techniques which help to identify causes and develop treatments for human disease. The book aims to advance personal healthcare, medical diagnosis, and treatment by applying IoT, cloud, and edge computing technologies in association with effective data analytics.
Key Features
- Provides an in-depth analysis of how to model and design applications for state-of-the-art healthcare systems
- Discusses and explores the social impact of the intertwined use of emerging IT technologies for healthcare
- Covers system design and software building principles for healthcare using IoT, cloud, and edge computing technologies with the support of effective and efficient data analytics strategies
- Explores the latest algorithms using machine and deep learning in the areas of cloud, edge computing, IoT, and healthcare analytics
Readership
Computer Science researchers and professionals working on IoT, Cloud, and Edge computing
Table of Contents
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Chapter 1. Exploring the dichotomy on opportunities and challenges of smart technologies in healthcare systems
- Abstract
- 1.1 Introduction
- 1.2 Internet of things in healthcare system
- 1.3 Cloud computing in healthcare system
- 1.4 Edge computing in healthcare system
- 1.5 Artificial intelligence in healthcare system
- 1.6 Future trends and research challenges in healthcare system
- 1.7 Conclusion
- References
- Chapter 2. The architecture of smartness in healthcare
- Abstract
- 2.1 Introduction
- 2.2 Healthcare
- 2.3 Technology based smartness
- 2.4 Security
- 2.5 Conclusion
- References
- Chapter 3. Personalized decision support for cardiology based on deep learning: an overview
- Abstract
- 3.1 Introduction
- 3.2 Big Data in cardiology
- 3.3 Deep learning
- 3.4 Deep learning applications in cardiology
- 3.5 Challenges and limitations
- 3.6 Summary
- References
- Chapter 4. Data-driven models for cuffless blood pressure estimation using ECG and PPG signals
- Abstract
- 4.1 Introduction
- 4.2 Review of data-driven models in the literature
- 4.3 Methodology
- 4.4 Results
- 4.5 Discussion
- 4.6 Limitations
- 4.7 Conclusions
- References
- Chapter 5. A recommendation system for the prediction of drug–target associations
- Abstract
- 5.1 Introduction
- 5.2 Approach
- 5.3 Implementation
- 5.4 Conclusions
- References
- Chapter 6. Towards building an efficient deep neural network based on YOLO detector for fetal head localization from ultrasound images
- Abstract
- 6.1 Introduction
- 6.2 Related works
- 6.3 The dataset
- 6.4 Object detection
- 6.5 YOLO family of algorithms
- 6.6 Proposed methodology
- 6.7 Experimental outcomes—training
- 6.8 Results and discussion
- 6.9 Conclusion
- References
- Further reading
- Chapter 7. FunNet: a deep learning network for the detection of age-related macular degeneration
- Abstract
- 7.1 Introduction
- 7.2 Background
- 7.3 Proposed method
- 7.4 Discussion
- 7.5 Conclusion
- References
- Chapter 8. An improved method for automated detection of microaneurysm in retinal fundus images
- Abstract
- 8.1 Introduction
- 8.2 Results and discussion
- 8.3 Conclusion
- 8.4 Future scope
- References
- Chapter 9. Integration and study of map matching algorithms in healthcare services for cognitive impaired person
- Abstract
- 9.1 Introduction
- 9.2 Literature review
- 9.3 Speculative computation cognitive assistant module with trajectory mining methodologies
- 9.4 Experimental evaluations
- 9.5 Conclusion
- References
- Further reading
- Chapter 10. Emotion-recognition-based music therapy system using electroencephalography signals
- Abstract
- 10.1 Introduction
- 10.2 Background
- 10.3 Proposed method
- 10.4 Results and discussion
- 10.5 Conclusion
- 10.6 Future scope
- References
- Chapter 11. Feedback context-aware pervasive systems in healthcare management: a Boolean Network approach
- Abstract
- 11.1 Introduction and related works
- 11.2 A Boolean Control Network-based methodology
- 11.3 The case study
- 11.4 The Boolean Control Network system model
- 11.5 Real-life properties and their mathematical formalization
- 11.6 Evaluation and conclusions
- Acknowledgment
- References
- Chapter 12. Mental stress detection using a wearable device and heart rate variability monitoring
- Abstract
- 12.1 Introduction
- 12.2 Related work
- 12.3 Methodology
- 12.4 Discussion
- 12.5 Conclusion
- References
- Chapter 13. Knowledge discovery and presentation using social media analysis in health domain
- Abstract
- 13.1 Introduction
- 13.2 Related work
- 13.3 Coronology: proposed ontology for representing COVID-19 information
- 13.4 Conclusion
- References
- Chapter 14. Computationally efficient integrity verification for shared data in cloud storage
- Abstract
- 14.1 Introduction
- 14.2 Related works
- 14.3 Proposed work CL-IVS
- 14.4 Security analysis of CL-IVS
- 14.5 Performance analysis
- 14.6 Conclusion and future works
- References
- Further reading
- Chapter 15. Intelligent analysis of multimedia healthcare data using natural language processing and deep-learning techniques
- Abstract
- 15.1 Introduction
- 15.2 Deep-learning-based methods
- 15.3 Conclusions and future scope
- References
- Chapter 16. Measurement of the effects of parks on air pollution in megacities: do parks support health betterment?
- Abstract
- 16.1 Introduction
- 16.2 Related works
- 16.3 The proposed method
- 16.4 Discussion
- 16.5 Conclusion
- References
- Chapter 17. Internet of Things use case applications for COVID-19
- Abstract
- 17.1 Introduction
- 17.2 IoT key role in COVID-19
- 17.3 Monitoring
- 17.4 Diagnosing
- 17.5 Tracing
- 17.6 Disinfecting
- 17.7 Vaccinating
- 17.8 Conclusion
- References
- Index
Product details
- No. of pages: 436
- Language: English
- Copyright: © Academic Press 2022
- Published: June 19, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323905855
- eBook ISBN: 9780323907088
About the Editors
Rajeswari Sridhar
Rajeswari Sridhar is currently working as an Associate Professor in the Department of Computer Science and Engineering at the National Institute of Technology, Tiruchirappalli, India. Her research interests include Cloud computing, Natural language processing, Information Retrieval, etc. She has guided 2 PhD students in the areas of Resource provisioning in Cloud computing and Access policies for data sharing through the cloud. She is currently guiding 5 research scholars in similar areas. She has published 70 articles in reputed journals and conferences. She is a member of the IEEE, ACM and CSI.
Affiliations and Expertise
Associate Professor, Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India
G. R. Gangadharan
G R Gangadharan is working as an Associate Professor in the National Institute of Technology, Tiruchirappalli, India. His research interests are mainly located on the interface between technological and business perspectives. He has published around 95 publications in the reputed international journals, conferences, and book chapters. He has also edited two books. He has received Ph.D. degree in Information and Communication Technology (2008) from the University of Trento, Trento, Italy and European University Association and an M.S. in Information Technology (2004) from Scuola Superiore Sant’Anna, Pisa, Italy. He is a Senior Member of IEEE and ACM.
Affiliations and Expertise
Associate Professor, National Institute of Technology, Tiruchirappalli, India
Michael Sheng
Michael Sheng is a full Professor and Head of Department of Computing at Macquarie University, Sydney, Australia. Before moving to Macquarie University, Michael spent 10 years at School of Computer Science, the University of Adelaide (UoA). Prof. Sheng has more than 400 publications as edited books and proceedings, refereed book chapters, and refereed technical papers in journals and conferences. He is ranked by Microsoft Academic as one of the Top Authors in Services Computing (ranked the 5th of All Time worldwide). He is the recipient of the AMiner Most Influential Scholar Award on IoT (2007-2017), ARC Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003).
Affiliations and Expertise
Professor and Head of Department of Computing, Macquarie University, Sydney, Australia
Rajan Shankaran
Rajan Shankaran is a Senior Lecturer in the Department of Computing at Macquarie University. He leads the 'Security, Communications, and Networks Research Group'. He obtained his PhD in Wireless and Mobile communications from the Western Sydney University (Sydney, Australia). He has previously held an appointment at Western Sydney University. His research focuses on Internetworking security and Quality of service with a special focus on Body Area Networks and Medical Implants, Cognitive Radio, Internet of Things and Vehicular Networks and D2D Communications. He has supervised several Master by Research (MRES) and PhD students to successful completion in these areas. He has published in reputed journals such as IEEE Transactions on Vehicular communications and IEEE Sensors as well as in several prestigious conferences. He has also acted as a reviewer for many SCI journals. He has also served in Technical Program Committees for several conferences in computer networking and security.
Affiliations and Expertise
Senior Lecturer, Department of Computing, Macquarie University, Sydney, Australia
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