Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities delves into the transformative potential of artificial and cognitive computing in the realm of healthcare systems, maintaining a specific emphasis on sustainability. By exploring the integration of advanced technologies in smart cities, the authors examine and discuss how AI and cognitive computing can be harnessed to enhance healthcare delivery. The book provides focused navigation through innovative solutions and strategies that contribute to the creation of sustainable healthcare ecosystems within the dynamic environment of smart cities. From optimizing resource utilization to improving patient outcomes, this comprehensive exploration provides insight for readers with an interest in the future of healthcare within the era of intelligent urban development.
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Seitenzahl: 363
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright Page
Preface
Introduction
1 Artificial Intelligence and its Application in Healthcare Systems
1.1. History of healthcare system
1.2. Literature studies
1.3. Evolution of AI
1.4. Machine learning
1.5. Application of ML
1.6. Application of AI in healthcare
1.7. Conclusion
1.8. References
2 Medical Laboratory Artificial Intelligence: The Applicability in Nigerian Medical Laboratories
2.1. Introduction
2.2. Historical trend of artificial intelligence (AI)
2.3. AI in medical science/medical laboratory science in history
2.4. Medical Laboratory Information Management System, centralized data and WWW
2.5. Artificial intelligence methodologies and their application in medical laboratory science
2.6. Nigerian medical laboratory intelligence before, now and future
2.7. Medical laboratory services where AI is used in Nigeria
2.8. AI and Internet of medical laboratory things
2.9. Opportunities and challenges of AI for Nigerian medical laboratories
2.10. Risks/limitations and challenges associated with AI in Nigerian medical laboratories
2.11. AI and digitalization of Nigerian medical laboratories
2.12. Conclusion
2.13. References
3 Machine Learning and Deep Learning for Smart City Services
3.1. Introduction
3.2. Basics of machine learning and its implications in smart cities
3.3. Basics of deep learning and its implications in smart cities
3.4. Algorithms of machine learning and deep learning
3.5. Applications in smart cities using machine learning and deep learning
3.6. Future challenges and research directions
3.7. Conclusion
3.8. References
4 An Intelligent Healthcare System Based on Machine Learning Models for Accurate Detection of Heart Disease
4.1. Introduction
4.2. Literature survey
4.3. Features of the dataset
4.4. Proposed system
4.5. ML models used for the experimental work
4.6. Performance parameters of ML models
4.7. Result and analysis
4.8. Conclusion
4.9. References
5 3D Volume Rendering of MRI Images for Tumor Detection and Segmentation using nnUnet
5.1. Introduction
5.2. Methodology
5.3. Results and discussion
5.4. Conclusion and future scope
5.5. References
6 Implementation of Key Generation in Kyber for Post-Quantum Cryptography using VIVADO
6.1. Introduction
6.2. Methodology
6.3. Results and discussion
6.4. Conclusion and future scope
6.5. References
7 Computational Intelligence and Big Data Analytics for Smart Healthcare: A Comprehensive Study
7.1. Introduction
7.2. Computational intelligence techniques in healthcare
7.3. Applications of intelligence and Big Data analytics in healthcare
7.4. Benefits of computational intelligence and Big Data analytics in healthcare
7.5. Challenges in implementing computational intelligence and Big Data analytics
7.6. Future aspect of computational intelligence and Big Data analytics in smart healthcare
7.7. Conclusion
7.8. References
8 Bioinformatics, Healthcare Informatics and Analytics: An Imperative for Improved Healthcare System
8.1. Introduction
8.2. Healthcare informatics
8.3. Health analytic
8.4. The intersection amidst bioinformatics, healthcare informatics and analytics
8.5. Future prospects of healthcare informatics and analytics
8.6. Conclusion
8.7. References
9 Natural Language Processing in Healthcare: A Systematic Review
9.1. Introduction
9.2. Materials and methods
9.3. Data sources and searches strategy
9.4. Results and discussion
9.5. Conclusion
9.6. References
10 Artificial Intelligence and Large Language Models in Mental Healthcare: A Systematic Review
10.1. Introduction
10.2. AI as an advantage for users
10.3. Ethical implications of AI
10.4. AI chatbot and its functions in diagnosing and intervention
10.5. Machine learning as a base of AI for mental healthcare
10.6. Forms of AI as a mental healthcare support mechanism
10.7. AI as a support for mental health professional
10.8. Suggestions for future studies
10.9. Conclusion
10.10. Appendix
10.11. References
11 Unleashing the Future: Exploring the Transformative Potential of 5G Technology in Healthcare
11.1. Introduction to 5G technology
11.2. Definition of 5G
11.3. History of 5G evolution
11.4. 5G bands
11.5. 5G use cases and spectrum band relevance
11.6. 5G for industries
11.7. Importance
11.8. Key features of 5G
11.9. Intel 5G technologies and solutions
11.10. Healthcare
11.11. 5G technology’s impact on healthcare: a comprehensive overview
11.12. Conclusion
11.13. References
List of Authors
Index
Other titles from iSTE in Computer Engineering
End User License Agreement
List of Tables
Chapter 1
Table 1.1. Training and testing dataset split
Table 1.2. Performance analysis
Chapter 2
Table 2.1. Some AI equipment and parameters of interest in medical laboratorie...
Table 2.2. Some AI applications of interest in medical laboratory diagnosis
Chapter 3
Table 3.1. Machine learning techniques
Chapter 4
Table 4.1. Features of the dataset used for the experimental work
Table 4.2. Values of confusion matrix parameters of ML models
Table 4.3. Values of ML model performance parameters
Table 4.4. Performance comparison of the proposed model with the existing mode...
Chapter 5
Table 5.1. Loss functions
Table 5.2. Performance metrics
Chapter 8
Table 8.1. Statistics and impact for various aspect
Table 8.2. Aspect versus impact
Table 8.3. Aspect versus impact inline to healthcare analytics
Chapter 9
Table 9.1. Literature sources from selected journals
Table 9.2. Literature sources from conference proceedings
Table 9.3. Summary of approaches and contribution
Table 9.4. Summary of quantity of studies per initiative
Chapter 11
Table 11.1. Spectrum bands of 5G. Source: https://stl.tech/blog/the-evolution-...
Table 11.2. 5G use cases and spectrum band relevance. Source: https://stl.tech...
Table 11.3. Benefits of mid-band 5G services. Source: https://stl.tech/blog/th...
List of Illustrations
Chapter 1
Figure 1.1. AI–ML–DL
Figure 1.2. DNN architecture
Figure 1.3. Supervised learning
Figure 1.4. Unsupervised learning
Figure 1.5. Reinforced learning
Figure 1.6. Malaria detection using CV
Figure 1.7. Performance graph
Chapter 2
Figure 2.1. Processes of machine learning technique as associated with artific...
Figure 2.2. The work process of the medical laboratory AI system
Figure 2.3. Scanbo AI and electrocardiogram glucose device
Figure 2.4. Computer-assisted semen analyzers (CASAs) in use in Nigeria
Figure 2.5. AI semen analysis machine, parts and accessories (LensHooke™) in u...
Chapter 3
Figure 3.1. Machine learning algorithms and their categorization
Figure 3.2. Multi-layer perception
Figure 3.3. Applications of ML and DL for smart cities
Chapter 4
Figure 4.1. HD detection using random forest (see: https://www.who.int/news-ro...
Figure 4.2. Number of male and female patients suffering from heart disease
Figure 4.3. Count plot for the number of patients suffering from different typ...
Figure 4.4. Number of patients with normal ECG signals and abnormal ECG signal...
Figure 4.5. Count plot for the number of patients with different types of ST_s...
Figure 4.6. Number of patients suffering from angina problem
Figure 4.7. Schematic block diagram for HD detection
Figure 4.8. Architecture of MLP diagram
Figure 4.9. Confusion matrix parameter comparison of ML models
Figure 4.10. Testing accuracy comparison of ML models
Figure 4.11. ROC–AUC curve of the SVM model
Chapter 5
Figure 5.1. Flow diagram
Figure 5.2. Loss function – skull stripping
Figure 5.3. Loss function – tumor segmentation
Figure 5.4. MRI scan
Figure 5.5. Segmentations
Figure 5.6. 3D model
Chapter 6
Figure 6.1. Alice and Bob communication
Figure 6.2. RTL block design
Figure 6.3. sk generation
Figure 6.4. pk generation
Figure 6.5. A key generation implementation with processor subsystem
Figure 6.6. Area utilization report (post-synthesis)
Figure 6.7. Area utilization report (post-implementation)
Figure 6.8. Power utilization
Figure 6.9. Setup report
Figure 6.10. Hold report
Figure 6.11. Pulse width report
Chapter 7
Figure 7.1. Computational intelligence techniques in healthcare
Figure 7.2. Physical robots
Chapter 9
Figure 9.1. Methodology for the review
Figure 9.2. Flowchart that details article extraction, screening and inclusion
Figure 9.3. Summary of quantity of studies per initiative discovered.
Chapter 10
Figure 10.1. Prisma flowchart.
Chapter 11
Figure 11.1. Evolution of 5G. Source: https://link.springer.com/article/10.100...
Figure 11.2. Applications of the 5G network. Source: https://stl.tech/blog/the...
Figure 11.3. Major proponents of a digital network. Source: https://stl.tech/b...
Figure 11.4. Comparative analysis of 4G and 5G. Source: https://stl.tech/blog/...
Guide
Cover Page
Table of Contents
Title Page
Copyright Page
Preface
Introduction
Begin Reading
List of Authors
Index
Other titles from iSTE in Computer Engineering
WILEY END USER LICENSE AGREEMENT
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International Perspectives in Decision Analytics and Operations Research Set
coordinated byPrasenjit Chatterjee
Volume 3
Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities
Edited by
Devasis Pradhan
Prasanna Kumar Sahu
Hla Myo Tun
Prasenjit Chatterjee
First published 2024 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2024The rights of Devasis Pradhan, Prasanna Kumar Sahu, Hla Myo Tun and Prasenjit Chatterjee to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.
Library of Congress Control Number: 2023951338
British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78630-864-1
Preface
Welcome to the intriguing world of “Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities”. In an era where our lives are becoming increasingly entwined with technology, the fusion of artificial intelligence and cognitive computing with healthcare in the context of smart cities represents a groundbreaking frontier. This preface sets the stage for a compelling journey through the pages of this book, which explores the transformative potential of these cutting-edge technologies for the betterment of healthcare systems in urban environments.
Smart cities are on the rise, and with their rapid urbanization come a unique set of challenges and opportunities. These urban centers are harnessing the power of data, connectivity and intelligent systems to enhance the quality of life for their citizens. One of the most critical domains where this transformation is taking place is healthcare. The demand for efficient, sustainable and patient-centric healthcare services is escalating in tandem with urban growth, and this book explores how artificial and cognitive computing can serve as catalysts for positive change.
In this book, you will find a comprehensive exploration of the fusion of artificial intelligence, machine learning and cognitive computing with healthcare systems. We bring together experts and thought leaders from various fields to share their insights and experiences. Their contributions offer diverse perspectives on the challenges and potential solutions in the realm of sustainable healthcare within smart cities. As you navigate through these chapters, you will gain a brief understanding of how AI-driven systems can be leveraged to mitigate healthcare disparities, promote preventive care, and create more resilient healthcare infrastructures in urban environments.
The topics covered within these pages include the ethical considerations of AI in healthcare, the implementation of IoT devices, the development of data-driven healthcare policies and the human-centered design of smart city healthcare services. Our aim is to inspire and equip readers to harness the potential of these technologies for creating sustainable, accessible and efficient healthcare systems that cater to the needs of a rapidly urbanizing world. We hope that this book fosters a deeper appreciation of the exciting possibilities that lie ahead and empower you to be an active participant in the journey towards a future where healthcare in smart cities is not only intelligent but also deeply compassionate.
Thank you for embarking on this intellectual journey with us. We invite you to continue reading to explore the transformative world of “Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities”.
January 2024
Note
Chapter written by Devasis PRADHAN and Prasanna Kumar SAHU.
Introduction
In the 21st century, the world is undergoing an unprecedented transformation. Our cities are evolving into smart, connected ecosystems, where data flows like the lifeblood of urban existence. As these urban centers swell with population and complexity, so do the challenges they face, particularly in the realm of healthcare. The need for sustainable, efficient and accessible healthcare systems in smart cities has never been more pressing. It is in this context that we embark on a journey of discovery in “Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities”. Smart cities represent the nexus of technological innovation, urbanization and the ever-evolving field of healthcare. These cities are not merely clusters of buildings and streets, but intricate networks of data and technology, designed to enhance the quality of life for their residents. At the heart of this transformation lies the intersection of artificial intelligence and cognitive computing with healthcare; a convergence that promises to revolutionize the way we receive, access and experience healthcare services.
This book is your portal into this exciting and dynamic world. In the pages that follow, we will explore the profound impact of artificial intelligence (AI) and cognitive computing on the healthcare landscape within smart cities. We will delve into the realms of data analytics, machine learning and advanced algorithms to understand how these technologies are changing the way healthcare is delivered, managed and experienced by patients. Our exploration encompasses the full spectrum of healthcare, from diagnostics to treatment, from patient records to population health management. We recognize that the implementation of artificial and cognitive computing in healthcare is not without its challenges. Ethical considerations, data security and the need to ensure equitable access to healthcare services are integral to this discussion. In these pages, we will scrutinize these vital issues, providing a well-rounded perspective on the complex interplay between technology and humanity.
This book serves as a comprehensive guide for healthcare professionals, policymakers, technologists, researchers and anyone intrigued by the potential of AI and cognitive computing in the healthcare sector. It is a resource that equips you with the knowledge and insights required to navigate the ever-evolving landscape of healthcare in smart cities. As we journey through the chapters ahead, we invite you to ponder the possibilities and implications of artificial and cognitive computing for healthcare in smart cities. This is a realm where technology not only meets human needs but anticipates them, where cities are not only smart but also compassionate, and where healthcare is not just a service but a right.
Join us as we embark on a transformative exploration of “Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities”. Together, let us imagine a future where healthcare is not only a system but also a force for healthier, happier urban living.
Note
Chapter written by Devasis PRADHAN, Prasanna Kumar SAHU, Hla Myo TUN and Prasenjit CHATTERJEE.
