Intelligent IoT Systems in Personalized Health Care delivers a significant forum for the technical advancement of IoMT learning in parallel computing environments across biomedical engineering diversified domains and its applications. Pursuing an interdisciplinary approach, the book focuses on methods used to identify and acquire valid, potentially useful knowledge sources. The book presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT and its capabilities in solving a diverse range of problems for biomedical engineering and its real-world personalized health care applications.
The book is well suited for researchers exploring the significance of IoT based architecture to perform predictive analytics of user activities in sustainable health.
- Presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT
- Illustrates state-of-the-art developments in new theories and applications of IoMT techniques as applied to parallel computing environments in biomedical engineering systems
- Presents concepts and technologies successfully used in the implementation of today's intelligent data-centric IoT systems and Edge-Cloud-Big data
| Contributors |
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ix | |
| Foreword |
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xiii | |
| Preface |
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xv | |
| Acknowledgments |
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xix | |
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1 Combining IoT architectures in next generation healthcare computing systems |
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1 | (30) |
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1 | (4) |
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2 Cloud computing architectures |
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5 | (8) |
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3 Fog computing architectures |
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13 | (7) |
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4 Edge computing architectures |
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20 | (5) |
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25 | (1) |
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26 | (5) |
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2 RFID-based unsupervised apnea detection in health care system |
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31 | (22) |
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31 | (3) |
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2 Preliminaries and challenges for RFID sensing systems |
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34 | (3) |
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37 | (13) |
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50 | (1) |
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51 | (1) |
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51 | (2) |
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3 Designing a cooperative hierarchical model of interdiction median problem with protection and its solution approach: A case study of health-care network |
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53 | (36) |
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1 Introduction and background |
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53 | (4) |
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2 Problem description and formulation |
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57 | (6) |
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3 Two hybrid algorithms-based tabu search and global approaches |
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63 | (4) |
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67 | (18) |
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5 Conclusions and future work |
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85 | (1) |
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86 | (1) |
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86 | (3) |
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4 Parallel machine learning and deep learning approaches for internet of medical things (IoMT) |
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89 | (16) |
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89 | (1) |
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2 Review of IoMT and deep learning methods |
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90 | (1) |
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3 Parallel machine learning and deep learning techniques using/(-means Hadoop frameworks |
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91 | (3) |
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4 Deep learning on internet of medical things (IoMT) |
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94 | (6) |
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5 Challenges in deep learning-based IoMT |
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100 | (1) |
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6 Applications of IoMT and deep learning |
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100 | (1) |
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7 Conclusion and future directions |
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101 | (1) |
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102 | (3) |
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5 Cloud-based IoMT framework for cardiovascular disease prediction and diagnosis in personalized E-health care |
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105 | (42) |
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105 | (3) |
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2 Fundamental concepts of cloud computing |
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108 | (3) |
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111 | (10) |
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4 The rise of cardiovascular diseases |
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121 | (1) |
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5 Taxonomy of CI techniques for CVD prediction in IoMT systems |
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121 | (7) |
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6 The rationale for choosing CI techniques for CVD prediction |
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128 | (1) |
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7 Experimental results and evaluation of CVD prediction systems |
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128 | (1) |
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8 Cloud-based IoMT framework for CVD prediction |
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129 | (3) |
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9 Practical case of CVD prediction |
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132 | (7) |
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10 Conclusion and future research direction |
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139 | (2) |
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141 | (6) |
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6 A study on security privacy issues and solutions in internet of medical things--A review |
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147 | (30) |
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147 | (4) |
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2 Internet of things applications |
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151 | (3) |
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3 Internet of medical things |
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154 | (3) |
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4 Security and privacy issues in IoMT |
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157 | (4) |
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5 Major security and privacy requirements for IoMT |
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161 | (4) |
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6 Solutions for IoMT problems |
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165 | (5) |
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7 Analysis of IoMT usage and issues |
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170 | (2) |
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172 | (1) |
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173 | (4) |
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7 Application of computational intelligence models in IoMT big data for heart disease diagnosis in personalized healthcare |
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177 | (30) |
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Oluwakemi Christiana Abikoye |
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177 | (2) |
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2 The concepts of loT and loMT |
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179 | (4) |
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3 Heart diseases and electronic health record |
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183 | (1) |
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4 Computational intelligence technique for heart disease management |
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184 | (9) |
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5 Case study on the application of ML for the diagnosis of heart diseases |
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193 | (6) |
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6 Proposed ensemble-based BDA framework for heart diseases diagnosis in personalized health care |
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199 | (2) |
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7 Conclusion and future works |
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201 | (1) |
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202 | (3) |
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205 | (2) |
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8 An improved canny detection method for detecting human flexibility |
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207 | (28) |
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207 | (2) |
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209 | (2) |
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3 Recognizing the angle of body anteflexion |
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211 | (13) |
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4 Implementation and result |
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224 | (7) |
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231 | (1) |
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232 | (1) |
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232 | (3) |
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9 Prediction and classification of diabetes mellitus using genomic data |
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235 | (58) |
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Roseline Oluwaseun Ogundokun |
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Opeyemi Emmanuel Matiluko |
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235 | (2) |
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2 Prediction and classification of DM |
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237 | (8) |
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3 Genomic data and health-care systems |
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245 | (7) |
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252 | (24) |
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5 Conclusion and future work |
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276 | (1) |
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277 | (16) |
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10 An application of cypher query-based dynamic rule-based decision tree over suicide statistics dataset with Neo4j |
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293 | (22) |
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293 | (2) |
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295 | (2) |
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297 | (2) |
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299 | (2) |
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301 | (8) |
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309 | (1) |
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309 | (3) |
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312 | (3) |
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11 Exploring the possibilities of security and privacy issues in health-care IoT |
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315 | (16) |
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315 | (2) |
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2 IoT health-care framework |
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317 | (3) |
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3 IoT health-care applications |
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320 | (1) |
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321 | (1) |
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5 Security and privacy issues in IoT health-care |
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322 | (6) |
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328 | (3) |
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329 | (2) |
| Subject Index |
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331 | |
Prof. Arun Kumar Sangaiah received his PhD from the School of Computer Science and Engineering, VIT University, Vellore, India. He is currently a Full Professor with National Yunlin University of Science and Technology, Taiwan. He is also a Professor at the School of Computing Science and Engineering, VIT University, Vellore, India. His areas of research interest include machine learning, Internet of Things, Sustainable Computing. He has published more than 300 research articles in refereed journals, 11 edited books, one patent (held and filed), as well as four projects funded by MOST-TAIWAN, one funded by Ministry of IT of India, and several international projects (CAS, Guangdong Research fund, Australian Research Council). Dr. Sangaiah has received many awards, Yushan Young Scholar, Clarivate Top 1% Highly Cited Researcher (2021,2022, 2023), Top 2% Scientist (Standord Report-2020,2021,2022, 2023), PIFI-CAS fellowship, Top-10 outstanding researcher, CSI significant Contributor etc. He is also serving as Editor-in-Chief and/or Associate Editor of various reputed ISI journals. Dr. Sangaiah is a visiting scientist (2018-2019) with Chinese Academy of Sciences (CAS), China and visiting researcher of Université Paris-Est (UPEC), France (2019-2020) and etc.
Subhas Mukhopadhyay holds a B.E.E. (gold medallist), M.E.E., Ph.D. (India) and Doctor of Engineering (Japan). He has over 30+ years of teaching, industrial and research experience.
Currently he is working as a Professor of Mechanical/Electronics Engineering, Macquarie University, Australia and is the Discipline Leader of the Mechatronics Engineering Degree Programme. Before joining Macquarie he worked as Professor of Sensing Technology, Massey University, New Zealand. His fields of interest include Smart Sensors and sensing technology, instrumentation techniques, wireless sensors and network, Internet of Things, numerical field calculation, electromagnetics etc. He has supervised over 40 postgraduate students and over 100 Honours students. He has examined over 50 postgraduate theses.
He has published over 450 papers in different international journals and conference proceedings, written eight books and forty book chapters and edited seventeen conference proceedings. He has also edited thirty books with Springer-Verlag and twenty four journal special issues. He has organized over 20 international conferences as either General Chairs/co-chairs or Technical Programme Chair. He has delivered 340 presentations including keynote, invited, tutorial and special lectures.
He is a Fellow of IEEE (USA), a Fellow of IET (UK), a Fellow of IETE (India), a Topical Editor of IEEE Sensors journal, and an associate editor of IEEE Transactions on Instrumentation and Measurements. He is a Distinguished Lecturer of the IEEE Sensors Council from 2017 to 2019. He is the Founding chair of IEEE IMS NSW chapter.
