Dieses Werk, das sich umfassend mit der Einführung von maschinellem Lernen, KI und dem IoT im Gesundheitswesen beschäftigt, richtet sich an Forschende, Fachkräfte im Gesundheitswesen, Wissenschaftler und Technologen. Die Nutzung von maschinellem Lernen und künstlicher Intelligenz im Internet der Dinge (IoT) für Anwendungen im Gesundheitswesen sowie die damit einhergehenden Herausforderungen werden ausführlich erörtert. Das IoT erzeugt gewaltige Datenmengen von unterschiedlicher Qualität. Die intelligente Verarbeitung und Analyse dieser Datenmengen sind der Schlüssel zur Entwicklung…mehr
Dieses Werk, das sich umfassend mit der Einführung von maschinellem Lernen, KI und dem IoT im Gesundheitswesen beschäftigt, richtet sich an Forschende, Fachkräfte im Gesundheitswesen, Wissenschaftler und Technologen.
Die Nutzung von maschinellem Lernen und künstlicher Intelligenz im Internet der Dinge (IoT) für Anwendungen im Gesundheitswesen sowie die damit einhergehenden Herausforderungen werden ausführlich erörtert.
Das IoT erzeugt gewaltige Datenmengen von unterschiedlicher Qualität. Die intelligente Verarbeitung und Analyse dieser Datenmengen sind der Schlüssel zur Entwicklung intelligenter IoT-Anwendungen, wodurch Raum für die Nutzung des maschinellen Lernens (ML) geschaffen wird. Mit ihren Recheninstrumenten, die bei der Erledigung bestimmter Aufgaben die menschliche Intelligenz ersetzen können, macht es die künstliche Intelligenz (KI) möglich, dass Computer aus Erfahrung lernen, sich an neue Eingaben anpassen und bisher von Menschen durchgeführte Aufgaben übernehmen. Da IoT-Plattformen eine Schnittstelle bieten, um Daten von unterschiedlichen Geräten zusammenzutragen, lassen sie sich leicht mit AI/ML-Systemen verbinden. Vor diesen Hintergrund besteht der Wert der KI in ihrer Fähigkeit, schnell Erkenntnisse aus Daten zu gewinnen, automatisch Muster zu erkennen und Anomalien in den von intelligenten Sensoren und Geräten erzeugten Daten zu erkennen ? aus Angaben zu Temperatur, Druck, Luftfeuchtigkeit, Luftqualität, Schwingungen und Geräuschen ? die für eine schnelle Diagnose extrem hilfreich sein können.
Produktdetails
Produktdetails
Artificial Intelligence and Soft Computing for Industrial Transformation
Rohit Tanwar, PhD (Kurukshetra University, Kurukshetra, India) is an assistant professor in the School of Computer Science at UPES Dehradun, India. S. Balamurugan, PhD, SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels. R. K. Saini, PhD (DIT University, Dehradun, India) is an assistant professor in the Department of Computer Science & Applications at DIT University, Dehradun (Uttarakhand). Vishal Bharti, PhD is a professor in the Department of Computer Science and Engineering, Chandigarh University, India. He has published more than 75 research papers in both national & international journals. Premkumar Chithaluru, PhD is an assistant professor in the Department of SCS at the University of Petroleum and Energy Studies (UPES), Dehradun, India.
Inhaltsangabe
Preface xvii
1 Internet of Medical Things--State-of-the-Art 1 Kishor Joshi and Ruchi Mehrotra
1.1 Introduction 2
1.2 Historical Evolution of IoT to IoMT 2
1.2.1 IoT and IoMT--Market Size 4
1.3 Smart Wearable Technology 4
1.3.1 Consumer Fitness Smart Wearables 4
1.3.2 Clinical-Grade Wearables 5
1.4 Smart Pills 7
1.5 Reduction of Hospital-Acquired Infections 8
1.5.1 Navigation Apps for Hospitals 8
1.6 In-Home Segment 8
1.7 Community Segment 9
1.8 Telehealth and Remote Patient Monitoring 9
1.9 IoMT in Healthcare Logistics and Asset Management 12
1.10 IoMT Use in Monitoring During COVID-19 13
1.11 Conclusion 14
References 15
2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing 21 Hritu Raj, Mohit Kumar, Prashant Kumar, Amritpal Singh and Om Prakash Verma
2.1 Introduction 22
2.2 Related Works 23
2.3 Architecture 25
2.3.1 Device Layer 25
2.3.2 Fog Layer 26
2.3.3 Cloud Layer 26
2.4 Issues and Challenges 26
2.5 Conclusion 29
References 30
3 Study of Thyroid Disease Using Machine Learning 33 Shanu Verma, Rashmi Popli and Harish Kumar
3.1 Introduction 34
3.2 Related Works 34
3.3 Thyroid Functioning 35
3.4 Category of Thyroid Cancer 36
3.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 37
3.5.1 Decision Tree Algorithm 38
3.5.2 Support Vector Machines 39
3.5.3 Random Forest 39
3.5.4 Logistic Regression 39
3.5.5 Naïve Bayes 40
3.6 Conclusion 41
References 41
4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare 43 Abhishek Raghuvanshi, Umesh Kumar Singh and Chirag Joshi
4.1 Introduction 44
4.1.1 Introduction to IoT 44
4.1.2 Introduction to Vulnerability, Attack, and Threat 45
4.2 IoT in Healthcare 46
4.2.1 Confidentiality 46
4.2.2 Integrity 46
4.2.3 Authorization 46
4.2.4 Availability 47
4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 48
4.4 Conclusion 54
References 54
5 Methods of Lung Segmentation Based on CT Images 59 Amit Verma and Thipendra P. Singh
5.1 Introduction 59
5.2 Semi-Automated Algorithm for Lung Segmentation 60
5.2.1 Algorithm for Tracking to Lung Edge 60
5.2.2 Outlining the Region of Interest in CT Images 62
5.2.2.1 Locating the Region of Interest 62
5.2.2.2 Seed Pixels and Searching Outline 62
5.3 Automated Method for Lung Segmentation 63
5.3.1 Knowledge-Based Automatic Model for Segmentation 63
5.3.2 Automatic Method for Segmenting the Lung CT Image 64
5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 64
5.5 Conclusion 65
References 65
6 Handling Unbalanced Data in Clinical Images 69 Amit Verma
6.1 Introduction 70
6.2 Handling Imbalance Data 71
6.2.1 Cluster-Based Under-Sampling Technique 72
6.2.2 Bootstrap Aggregation (Bagging) 75
6.3 Conclusion 76
References 76
7 IoT-Based Health Monitoring System for Speech-Impaired People Using Assistive Wearable Accelerometer 81 Ishita Banerjee and Madhumathy P.
1 Internet of Medical Things--State-of-the-Art 1 Kishor Joshi and Ruchi Mehrotra
1.1 Introduction 2
1.2 Historical Evolution of IoT to IoMT 2
1.2.1 IoT and IoMT--Market Size 4
1.3 Smart Wearable Technology 4
1.3.1 Consumer Fitness Smart Wearables 4
1.3.2 Clinical-Grade Wearables 5
1.4 Smart Pills 7
1.5 Reduction of Hospital-Acquired Infections 8
1.5.1 Navigation Apps for Hospitals 8
1.6 In-Home Segment 8
1.7 Community Segment 9
1.8 Telehealth and Remote Patient Monitoring 9
1.9 IoMT in Healthcare Logistics and Asset Management 12
1.10 IoMT Use in Monitoring During COVID-19 13
1.11 Conclusion 14
References 15
2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing 21 Hritu Raj, Mohit Kumar, Prashant Kumar, Amritpal Singh and Om Prakash Verma
2.1 Introduction 22
2.2 Related Works 23
2.3 Architecture 25
2.3.1 Device Layer 25
2.3.2 Fog Layer 26
2.3.3 Cloud Layer 26
2.4 Issues and Challenges 26
2.5 Conclusion 29
References 30
3 Study of Thyroid Disease Using Machine Learning 33 Shanu Verma, Rashmi Popli and Harish Kumar
3.1 Introduction 34
3.2 Related Works 34
3.3 Thyroid Functioning 35
3.4 Category of Thyroid Cancer 36
3.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 37
3.5.1 Decision Tree Algorithm 38
3.5.2 Support Vector Machines 39
3.5.3 Random Forest 39
3.5.4 Logistic Regression 39
3.5.5 Naïve Bayes 40
3.6 Conclusion 41
References 41
4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare 43 Abhishek Raghuvanshi, Umesh Kumar Singh and Chirag Joshi
4.1 Introduction 44
4.1.1 Introduction to IoT 44
4.1.2 Introduction to Vulnerability, Attack, and Threat 45
4.2 IoT in Healthcare 46
4.2.1 Confidentiality 46
4.2.2 Integrity 46
4.2.3 Authorization 46
4.2.4 Availability 47
4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 48
4.4 Conclusion 54
References 54
5 Methods of Lung Segmentation Based on CT Images 59 Amit Verma and Thipendra P. Singh
5.1 Introduction 59
5.2 Semi-Automated Algorithm for Lung Segmentation 60
5.2.1 Algorithm for Tracking to Lung Edge 60
5.2.2 Outlining the Region of Interest in CT Images 62
5.2.2.1 Locating the Region of Interest 62
5.2.2.2 Seed Pixels and Searching Outline 62
5.3 Automated Method for Lung Segmentation 63
5.3.1 Knowledge-Based Automatic Model for Segmentation 63
5.3.2 Automatic Method for Segmenting the Lung CT Image 64
5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 64
5.5 Conclusion 65
References 65
6 Handling Unbalanced Data in Clinical Images 69 Amit Verma
6.1 Introduction 70
6.2 Handling Imbalance Data 71
6.2.1 Cluster-Based Under-Sampling Technique 72
6.2.2 Bootstrap Aggregation (Bagging) 75
6.3 Conclusion 76
References 76
7 IoT-Based Health Monitoring System for Speech-Impaired People Using Assistive Wearable Accelerometer 81 Ishita Banerjee and Madhumathy P.
7.1 Introduction 82
7.2 Literature Survey
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