Reconnoitering the Landscape of Edge Intelligence in Healthcare
1st Edition
Published on 23. April 2024
292 pages
978-1-000-89493-6 (ISBN)
Description
The revolution in healthcare as well as demand for efficient real-time healthcare services are driving the progression of edge computing, AI-mediated techniques, deep learning, and IoT applications for healthcare industries and cloud computing. Edge computing helps to meet the demand for newer and more sophisticated healthcare systems that are more personalized and that match the speed of modern life. With applications of edge computing, automated intelligence and intuitions are incorporated into existing healthcare analysis tools for identifying, forecasting, and preventing high-risk diseases.
Reconnoitering the Landscape of Edge Intelligence in Healthcare provides comprehensive research on edge intelligence technology with the emphasis on application in the healthcare industry. It covers all the various areas of edge intelligence for data analysis in healthcare, looking at the emerging technologies such as AI-based techniques, machine learning, IoT, cloud computing, and deep learning with illustrations of the design, implementation, and management of smart and intelligent healthcare systems.
Chapters showcase the advantages and highlights of the adoption of the intelligent edge models toward smart healthcare infrastructure. The book also addresses the increased need for a high level of medical data security while transferring real-time data to cloud-based architecture, a matter of prime concern for both patient and doctor. Topics include edge intelligence for wearable sensor technologies and their applications for health monitoring, the various edge computing techniques for disease prediction, e-health services and e-security solutions through IoT devices that aim to improve the quality of care for transgender patients, smart technology in ambient assisted living, the role of edge intelligence in limiting virus spread during pandemics, neuroscience in decoding and analysis of visual perception from the neural patterns and visual image reconstruction, and more.
The technology addressed include energy aware cross-layer routing protocol (ECRP), OMKELM-IDS technique, graphical user interface (GUI), IOST (an ultra-fast, decentralized blockchain platform), etc.
This volume will be helpful to engineering students, research scholars, and manufacturing industry professionals in the fields of engineering applications initiatives on AI, machine learning, and deep learning techniques for edge computing.
Reconnoitering the Landscape of Edge Intelligence in Healthcare provides comprehensive research on edge intelligence technology with the emphasis on application in the healthcare industry. It covers all the various areas of edge intelligence for data analysis in healthcare, looking at the emerging technologies such as AI-based techniques, machine learning, IoT, cloud computing, and deep learning with illustrations of the design, implementation, and management of smart and intelligent healthcare systems.
Chapters showcase the advantages and highlights of the adoption of the intelligent edge models toward smart healthcare infrastructure. The book also addresses the increased need for a high level of medical data security while transferring real-time data to cloud-based architecture, a matter of prime concern for both patient and doctor. Topics include edge intelligence for wearable sensor technologies and their applications for health monitoring, the various edge computing techniques for disease prediction, e-health services and e-security solutions through IoT devices that aim to improve the quality of care for transgender patients, smart technology in ambient assisted living, the role of edge intelligence in limiting virus spread during pandemics, neuroscience in decoding and analysis of visual perception from the neural patterns and visual image reconstruction, and more.
The technology addressed include energy aware cross-layer routing protocol (ECRP), OMKELM-IDS technique, graphical user interface (GUI), IOST (an ultra-fast, decentralized blockchain platform), etc.
This volume will be helpful to engineering students, research scholars, and manufacturing industry professionals in the fields of engineering applications initiatives on AI, machine learning, and deep learning techniques for edge computing.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
17 Tables, black and white; 19 Illustrations, color; 41 Illustrations, black and white
ISBN-13
978-1-000-89493-6 (9781000894936)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Suneeta Satpathy | Sachi Nandan Mohanty | Sirisha Potluri
Reconnoitering the Landscape of Edge Intelligence in Healthcare
Book
04/2024
1st Edition
Apple Academic Press Inc.
€199.70
Shipment within 3-4 weeks
Persons
Suneeta Satpathy, PhD, is currently affiliated in the Faculty of Emerging Technologies SRI SRI University, Cuttack, Odisha, India. Her research interests include computer forensics, cybersecurity, data fusion, data mining, big data analysis, and decision mining. In addition to her own research, she has guided many postgraduate and graduate students. She has published papers in many international journals and conferences in repute. She holds two Indian patents. Her professional activities include roles as editorial board member and/or reviewer of the Journal of Engineering Science, Advancement of Computer Technology and Applications, Robotics and Autonomous Systems, and Computational and Structural Biotechnology Journal. She is a member of several professional societies, including CSI, ISTE, OITS, and ACM. Dr. Satpathy received her PhD from Utkal University, Bhubaneswar, Odisha, with a Directorate of Forensic Sciences, MHA scholarship from the Government of India.
Sachi Nandan Mohanty, PhD
, is affiliated with the School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India. He has edited 24 books in association with Springer and Wiley as well as over 120 papers in international journals. His research areas include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, cognition, and computational intelligence. Professor Mohanty has received several best paper awards during his PhD at IIT Kharagpur at an international conference at Beijing, China, and at the International Conference on Soft Computing Applications organized by IIT Rookee (2013). He was awarded a best thesis award (first prize) by the Computer Society of India in 2015. He has guided 10 PhD scholars. Dr. Mohanty is a Fellow of the Institute of Engineers and a senior member of IEEE Computer Society, Hyderabad chapter. He also a reviewer for the Journal of Robotics and Autonomous Systems and Computational and Structural Biotechnology Journal (both published by Elsevier) and Artificial Intelligence Review and Spatial Information Research(published by Springer). He received his postdoctoral degree from IIT Kanpur, India, and his PhD from IIT Kharagpur, India, with an MHRD scholarship from the Government of India.
Sirisha Potluri, PhD
, is affiliated in the Department of Computer Science and Engineering in the Faculty of Science and Technology at ICFAI Tech, ICFAI Foundation for Higher Education, Telangana, India. Her research areas include cloud computing, edge computing, artificial intelligence, and data analytics. She has more than eight years of teaching experience. She taught various subjects, including Computer Programming using C, Python Programming, Data Structures, Core JAVA, Advanced JAVA, OOP using C++, Operating Systems, Distributed Operating System, Human Computer Interaction, C# and .NET Programming, Computer Graphics, Web Technology, UNIX Programming, Distributed and Cloud Computing, Data Mining and Warehousing, Scripting Languages, Database Management Systems, Software Engineering, and Software Process Management. She has presented research papers at national and international conferences and published 24 research articles in international peer-reviewed journals. She has edited three books from global publishing houses and published several book chapters. She is a member of IEEE and reviewer for various journals. She has received the TN Global Research Award as "Young Scientist" in March 2022
Sachi Nandan Mohanty, PhD
, is affiliated with the School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India. He has edited 24 books in association with Springer and Wiley as well as over 120 papers in international journals. His research areas include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, cognition, and computational intelligence. Professor Mohanty has received several best paper awards during his PhD at IIT Kharagpur at an international conference at Beijing, China, and at the International Conference on Soft Computing Applications organized by IIT Rookee (2013). He was awarded a best thesis award (first prize) by the Computer Society of India in 2015. He has guided 10 PhD scholars. Dr. Mohanty is a Fellow of the Institute of Engineers and a senior member of IEEE Computer Society, Hyderabad chapter. He also a reviewer for the Journal of Robotics and Autonomous Systems and Computational and Structural Biotechnology Journal (both published by Elsevier) and Artificial Intelligence Review and Spatial Information Research(published by Springer). He received his postdoctoral degree from IIT Kanpur, India, and his PhD from IIT Kharagpur, India, with an MHRD scholarship from the Government of India.
Sirisha Potluri, PhD
, is affiliated in the Department of Computer Science and Engineering in the Faculty of Science and Technology at ICFAI Tech, ICFAI Foundation for Higher Education, Telangana, India. Her research areas include cloud computing, edge computing, artificial intelligence, and data analytics. She has more than eight years of teaching experience. She taught various subjects, including Computer Programming using C, Python Programming, Data Structures, Core JAVA, Advanced JAVA, OOP using C++, Operating Systems, Distributed Operating System, Human Computer Interaction, C# and .NET Programming, Computer Graphics, Web Technology, UNIX Programming, Distributed and Cloud Computing, Data Mining and Warehousing, Scripting Languages, Database Management Systems, Software Engineering, and Software Process Management. She has presented research papers at national and international conferences and published 24 research articles in international peer-reviewed journals. She has edited three books from global publishing houses and published several book chapters. She is a member of IEEE and reviewer for various journals. She has received the TN Global Research Award as "Young Scientist" in March 2022
Content
PART I: INTRODUCTION TO EDGE INTELLIGENCE IN HEALTHCARE 1. Edge Intelligence and Its Healthcare Applications 2. Edge Intelligence: The Cutting Edge of Healthcare PART II: EDGE INTELLIGENCE IMPLEMENTATIONS FOR SMART HEALTHCARE 3. An IoT-Based Smart Healthcare System with Edge Intelligence Computing 4. Edge Computing for Smart Healthcare Monitoring Platform Advancement 5. Application of Wearable Devices in the Medical Domain 6. Edge Computing for Smart Disease Prediction Treatment Therapy 7. IoT-Based Safety Measures and Healthcare Services for Transgender Welfare and Sustainability 8. Energy Aware Cross-Layer Routing Protocol for Body-to-Body Network in Healthcare 9. Edge Intelligence: A Smart Healthcare Scenario in Ambient Assisted Living PART III: RESEARCH CHALLENGES AND OPPORTUNITIES IN EDGE COMPUTING 10. Edge Intelligence to Smart Management and Control of EpidemicC. 11. Visual Image Reconstruction Using FMRI Analysis 12. New Research Challenges and Applications in Artificial Intelligence on Edge Computing 13. Optimal Mixed Kernel Extreme Learning Machine-Based Intrusion Detection System for Secure Intelligent Edge Computing 14. Stochastic Approach to Govern the Efficient Framework for Big Data Analytics Using Machine Learning and Edge Computing
