Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities

• Autore: Pradhan Devasis
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (278 pages)
• Disciplina: 610.28563
• Soggetto topico: Artificial intelligence; Medical laboratories
• Altri autori: SahuPrasanna Kumar TunHla Myo ChatterjeePrasenjit
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Introduction -- Chapter 1 Artificial Intelligence and its Application in Healthcare Systems -- 1.1. History of healthcare system -- 1.2. Literature studies -- 1.3. Evolution of AI -- 1.3.1. Advantages and disadvantages -- 1.3.2. Components of AI -- 1.4. Machine learning -- 1.4.1. Categories of ML -- 1.4.2. Supervised learning -- 1.4.3. Unsupervised learning -- 1.4.4. Reinforcement learning -- 1.5. Application of ML -- 1.6. Application of AI in healthcare -- 1.6.1. Digital health -- 1.6.2. Genetic solutions -- 1.6.3. Bio-medical visualization -- 1.7. Conclusion -- 1.8. References -- Chapter 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 -- Chapter 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 -- 3.4.1. Classification. ; 3.4.2. Clustering -- 3.4.3. K-nearest neighbors -- 3.4.4. Naive Bayes -- 3.4.5. The support vector machine -- 3.4.6. Linear regression -- 3.4.7. Random forest -- 3.4.8. K-means -- 3.4.9. Artificial neural network -- 3.4.10. Multi-layer perceptron -- 3.5. Applications in smart cities using machine learning and deep learning -- 3.5.1. Safety of the public -- 3.5.2. Intelligent traffic management -- 3.5.3. Water management strategy -- 3.5.4. Smart street lights -- 3.5.5. Intelligent parking devices -- 3.5.6. Smart waste and disposal management system -- 3.6. Future challenges and research directions -- 3.7. Conclusion -- 3.8. References -- Chapter 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 -- 4.5.1. Support vector machines (SVM) -- 4.5.2. Random forest (RF) -- 4.5.3. Stochastic gradient descent (SGD) method -- 4.5.4. Multilayer perceptron (MLP) -- 4.5.5. Naive Bayes (NB) -- 4.6. Performance parameters of ML models -- 4.6.1. Confusion matrix -- 4.6.2. Accuracy -- 4.6.3. F1-score -- 4.6.4. AUC -- 4.7. Result and analysis -- 4.8. Conclusion -- 4.9. References -- Chapter 5 3D Volume Rendering of MRI Images for Tumor Detection and Segmentation using nnUnet -- 5.1. Introduction -- 5.2. Methodology -- 5.2.1. Segmentation -- 5.2.2. Overlapping the scans -- 5.2.3. 3D reconstruction -- 5.3. Results and discussion -- 5.4. Conclusion and future scope -- 5.5. References -- Chapter 6 Implementation of Key Generation in Kyber for Post-Quantum Cryptography using VIVADO -- 6.1. Introduction -- 6.2. Methodology -- 6.2.1. RTL design -- 6.2.2. True random number generator and (SHA-512) HASH -- 6.2.3. Pseudo random number generator. ; 6.2.4. Numeric theoretic transform -- 6.2.5. Matrix multiplication -- 6.3. Results and discussion -- 6.3.1. Design outcomes -- 6.3.2. Board specifications -- 6.3.3. Interfacing with processor core using AXI -- 6.3.4. Area utilization -- 6.3.5. Power utilization -- 6.3.6. Timing utilization -- 6.4. Conclusion and future scope -- 6.5. References -- Chapter 7 Computational Intelligence and Big Data Analytics for Smart Healthcare: A Comprehensive Study -- 7.1. Introduction -- 7.1.1. Computational intelligence in smart healthcare -- 7.1.2. Big Data analytics in smart healthcare -- 7.1.3. Challenges and future directions -- 7.1.4. The landscape of smart healthcare -- 7.2. Computational intelligence techniques in healthcare -- 7.2.1. Machine learning -- 7.2.2. Natural language processing (NLP) -- 7.2.3. Expert systems -- 7.2.4. Robotic process automation (RPA) -- 7.2.5. Physical robots -- 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 -- Chapter 8 Bioinformatics, Healthcare Informatics and Analytics: An Imperative for Improved Healthcare System -- 8.1. Introduction -- 8.1.1. Justification for computational biology -- 8.1.2. Computational biology and its impact -- 8.2. Healthcare informatics -- 8.3. Health analytic -- 8.3.1. Healthcare analytics and its contribution to healthcare framework -- 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. ; Chapter 9 Natural Language Processing in Healthcare: A Systematic Review -- 9.1. Introduction -- 9.2. Materials and methods -- 9.3. Data sources and searches strategy -- 9.3.1. Requirements for inclusion -- 9.3.2. Exclusion standards -- 9.3.3. Study selection -- 9.3.4. Data extraction and synthesis -- 9.4. Results and discussion -- 9.5. Conclusion -- 9.6. References -- Chapter 10 Artificial Intelligence and Large Language Models in Mental Healthcare: A Systematic Review -- 10.1. Introduction -- 10.1.1. Objectives -- 10.1.2. Methods -- 10.1.3. Inclusion criteria -- 10.1.4. Exclusion criteria -- 10.1.5. Discussion -- 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 -- 10.7.1. Implication -- 10.7.2. Limitations -- 10.8. Suggestions for future studies -- 10.9. Conclusion -- 10.10. Appendix -- 10.11. References -- Chapter 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 -- 11.3.1. 1G to 3G (1980s-2000s) -- 11.3.2. 4G and LTE (late 2000s-early 2010s) -- 11.3.3. The path to 5G (2010s) -- 11.3.4. Key milestones towards 5G (2016-2019) -- 11.3.5. 5G deployment and expansion (2020s) -- 11.4. 5G bands -- 11.5. 5G use cases and spectrum band relevance -- 11.6. 5G for industries -- 11.7. Importance -- 11.7.1. Faster data speeds -- 11.7.2. Ultra-low latency -- 11.7.3. Massive device connectivity -- 11.7.4. Industrial automation and IoT -- 11.7.5. Healthcare advancements -- 11.7.6. Autonomous vehicles -- 11.7.7. Smart cities. ; 11.7.8. Remote work and education -- 11.7.9. Entertainment and media -- 11.7.10. Economic growth and innovation -- 11.7.11. Global competitiveness -- 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 -- EULA.
• Sommario/riassunto: This book, 'Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities,' explores the integration of artificial intelligence (AI) and cognitive computing within healthcare systems, particularly in the context of smart cities. It discusses the evolution, applications, and methodologies of AI and machine learning in various aspects of healthcare, including digital health, biomedical visualization, and genetic solutions. Additionally, the book examines the applicability of AI in medical laboratories, with a specific focus on Nigerian medical laboratories, and addresses the opportunities and challenges presented by AI. The book is intended for professionals and researchers interested in the intersection of AI, healthcare, and smart city development.
• Titolo autorizzato: Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities
• ISBN: 9781394297443; 1394297440; 9781394297429; 1394297424
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911020277203321
• Lo trovi qui: Univ. Federico II