Green computing and predictive analytics for healthcare / / edited by Sourav Banerjee, Chinmay Chakraborty, Kousik Dasgupta

• Pubblicazione: Boca Raton : , : Chapman & Hall/CRC, , 2021
• Edizione: First edition
• Descrizione fisica: 1 online resource : portraits
• Disciplina: 362.10285
• Soggetto topico: Medical care - Data processing; Computer systems - Energy conservation
• Persona (resp. second.): BanerjeeSourav; ChakrabortyChinmay, 1984-; DasguptaKousik
• Note generali: Includes index.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- List of Contributors -- Chapter 1 Healthcare Data Monitoring under Internet of Things -- 1.1 Introduction -- 1.1.1 Healthcare Data - Efficient Storage of Big Data -- 1.2 Digitization of Healthcare-Oriented Big Data -- 1.3 Healthcare - IoT and Mobile Health -- 1.4 Management of Big Data -- 1.4.1 Electronic Medical Record (EMR) or Electronic Health Record (EHR) -- 1.4.2 Healthcare Analytics -- 1.5 Medical Data Analysis and Disease Predictions through ML -- 1.6 Applications of Big Data in the Medical Field -- 1.7 Analytics of Medical Data in the Mercantile Platform -- 1.8 Related Work -- 1.9 Challenges and Constraints Related to Healthcare-Based Big Data Concepts (Including Privacy and Security Issue) -- 1.10 Conclusion and Future Trends -- References -- Chapter 2 A Framework for Emergency Remote Care and Monitoring Using Internet of Things -- 2.1 Introduction -- 2.2 The IoT Architecture and Applications -- 2.2.1 Stage 1 (Sensors/Actuators) -- 2.2.2 Stage 2 (Data Acquisition Systems) -- 2.2.3 Stage 3 (Edge Analytics) -- 2.2.4 Stage 4 (Cloud Analytics) -- 2.3 Literature Survey -- 2.4 A Proposed Framework for Emergency Remote Care and Monitoring Using Internet of Things -- 2.4.1 Parameters for Prediction -- 2.5 Proposed Work -- 2.6 Results and Discussion -- 2.7 Conclusion and Future Work -- References -- Chapter 3 Big Data Analytics and K-Means Clustering -- 3.1 Introduction -- 3.2 Big Data -- 3.3 Predictive Analytics -- 3.4 Predictive Modeling -- 3.5 MapReduce Abstraction -- 3.6 Resilient Distributed Datasets (RDDs) -- 3.7 Computational Phenotyping -- 3.8 Clustering -- 3.9 Medicinal Oncology -- 3.10 Dimensionality Reduction -- 3.11 Patient Similarity -- 3.12 Distance Metric Learning -- 3.13 Graph-Based Similarity Learning. ; 3.14 Clustering Challenges of Big Data -- 3.15 Algorithms for Large Datasets in Clustering -- 3.16 Privacy and Security -- 3.17 Various Approaches for Predictive Analytics -- 3.18 Why Predictive Analytics and Big Data for Electronic Health Records? -- 3.19 K-Means Clustering for Analysis of EHR -- 3.20 K-Means for Very Large-Scale Dataset -- 3.20.1 Tools and Applications in the Healthcare System -- 3.20.2 Application of Big Data in Healthcare -- 3.20.3 K-Means Clustering -- 3.21 Partitioning Around Medoids (PAM) -- 3.22 Hierarchical -- 3.23 Density-Based Spatial Bunching of Applications with Noise (DBSCAN) -- 3.24 Compatibility Issues -- 3.25 Different Solutions, Supplementary Tasks? -- 3.26 Priorities Engagement toward Analytics -- 3.27 Paid, Free or Open Source Vendors? -- 3.28 Data Clustering Strategy -- 3.29 The Brilliant Future of Big Data in Healthcare -- 3.30 Fueling the Big Data Healthcare Revolution -- 3.31 Conclusion -- References -- Chapter 4 Machine Learning-Based Rapid Prediction of Sudden Cardiac Death (SCD) Using Precise Statistical Features of Heart Rate Variability for Single Lead ECG Signal -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Nature of ECG Signal -- 4.4 Matters and Methodology -- 4.4.1 Processing and Analysis of ECG Signal -- 4.4.2 Feature Extraction -- 4.4.3 Algorithm for Prediction of SCD -- 4.4.4 Classification -- 4.4.4.1 Logistic Regression -- 4.4.4.2 Support Vector Machine -- 4.5 Results and Discussion -- 4.6 Conclusion -- References -- Chapter 5 Computer Vision for Brain Tissue Segmentation -- 5.1 Introduction -- 5.2 Materials and Methods -- 5.2.1 Magnetic Resonance Imaging (MRI) -- 5.2.2 Segmentation Methods for Brain Images -- 5.2.3 Clustering Techniques -- 5.2.4 Fuzzy Clustering Method for Brain Image Segmentation -- 5.2.4.1 Fuzzy C-Means Clustering (FCM) -- 5.2.4.2 Fuzzy Local Information C-Means (FLICM). ; 5.2.4.3 Reformulated Fuzzy Information C-Means (RFLICM) -- 5.2.5 Convolution Neural Network -- 5.3 Experimental Outcomes -- 5.4 Conclusion -- References -- Chapter 6 A Study on Energy-Efficient and Green IoT for Healthcare Applications -- 6.1 Introduction -- 6.1.1 Emerging Technologies, Challenges and Issues in IoT -- 6.1.1.1 Emerging Technologies in IoT -- 6.1.1.2 Challenges and Issues in IoT -- 6.1.2 Application of IoT -- 6.1.2.1 Applications, Features and Products of IoT -- 6.1.2.2 Smart Homes -- 6.1.2.3 Wearable Technology -- 6.1.2.4 Smart City -- 6.1.2.5 Smart Grid -- 6.1.2.6 Smart Industries -- 6.1.2.7 Smart Traffic -- 6.1.2.8 Smart Healthcare -- 6.1.2.9 Smart Retail -- 6.1.2.10 Smart Supply Chain -- 6.1.2.11 Smart Agricultural -- 6.2 Green Internet of Things -- 6.2.1 Emerging Technologies, Challenges and Issues in Green IoT -- 6.2.2 Applications of Green IoT -- 6.2.2.1 Smart Green Cities -- 6.2.2.2 Smart Green Home -- 6.2.2.3 Smart Green Healthcare -- 6.2.2.4 Smart Green Grid -- 6.2.2.5 Smart Agriculture -- 6.3 Energy Efficiency in WBAN for IoT -- 6.3.1 Introduction -- 6.3.2 Energy Efficient Protocols -- 6.3.3 IEEE 802.15.4 Superframe Structure -- 6.3.3.1 Description of IEEE 802.15.4 MAC Protocol -- 6.4 Conclusion -- References -- Chapter 7 Cyber Security in Terms of IoT System and Blockchain Technologies in E-Healthcare Systems -- 7.1 Introduction -- 7.2 The IoT Device Life-Cycle -- 7.2.1 Introduction -- 7.2.2 Explanation of the Different Stages in the Life-Cycle -- 7.2.2.1 Design -- 7.2.2.2 Research and Development -- 7.2.2.3 Integration -- 7.2.2.4 Operation and Maintenance -- 7.2.2.5 Disposal -- 7.2.3 Summary -- 7.3 Aspects of Interoperability -- 7.3.1 Introduction -- 7.3.2 Discussion of Standards regarding Interoperability -- 7.3.2.1 IPSO (IP Smart Object) Alliance). ; 7.3.2.2 ETSI (European Telecommunication Standard Institute) Standardization -- 7.3.2.3 OIC (Open Interconnect Consortium) -- 7.3.3 Strength of Interoperability -- 7.3.4 Summary -- 7.4 Privacy Preservation with Trust and Authentication -- 7.4.1 Introduction -- 7.4.2 Different Aspects of Privacy Preservation with Discussion of Frameworks -- 7.4.2.1 Privacy -- 7.4.2.2 Privacy Framework -- 7.4.3 Trust: Its Properties and Objectives with Proper Management -- 7.4.3.1 Trust Properties -- 7.4.3.2 An IoT Trust Management -- 7.4.4 Authentication -- 7.4.4.1 Authentication Model Depending on Blockchain -- 7.4.5 Summary -- 7.5 Vulnerabilities, Attacks and Countermeasures in the Light of Security Engineering in IoT -- 7.5.1 Introduction -- 7.5.2 Information Assurance -- 7.5.3 Vulnerabilities -- 7.5.4 Attacks -- 7.5.5 Fault Tree and Attack Tree -- 7.5.5.1 Attack Tree -- 7.5.5.2 Fault Tree -- 7.5.5.3 Differences and Collaboration of Fault and Attack Tree -- 7.5.6 Countermeasures -- 7.6 Cryptographical Perspective of IoT Security -- 7.6.1 Introduction -- 7.6.2 Primitives of Cryptography Keeping IoT in Mind -- 7.6.2.1 Symmetric Key Cryptography -- 7.6.2.2 Public Key Encryption -- 7.6.2.3 Digital Signature -- 7.6.2.4 Hashes -- 7.7 Cloud Security -- 7.7.1 IoT Device Security Threat from Cloud Usage -- 7.7.2 Cloud IoT Security Control -- 7.7.3 Framework and Architecture -- 7.7.3.1 Fog Computing-Based Model -- 7.7.4 New Scope -- 7.7.5 Summary -- 7.8 Blockchain Technology -- 7.8.1 Introduction -- 7.8.2 Structure -- 7.8.3 Security Challenges and Probable Remedies -- 7.8.3.1 Challenges -- 7.8.3.2 A Remedy Model Using Blockchain Technology -- 7.9 Social Awareness -- 7.9.1 Introduction -- 7.9.2 Opportunistic IoT -- 7.9.3 Concern -- 7.10 Future Scope and Conclusion -- References -- Chapter 8 Domestic Medical Tourism for National Healthcare Systems -- 8.1 Introduction. ; 8.2 Medical Tourism -- 8.3 Important Factors behind the Growth of Medical Tourism -- 8.4 Medical Tourism: Emerging Trends -- 8.5 Healthcare Market Size -- 8.6 Medical Tourism Industry Perspective -- 8.7 Domestic Medical Tourism -- 8.8 Methodology -- 8.8.1 Results -- 8.8.2 Discussion of Results -- 8.9 Conclusion -- References -- Chapter 9 Study on Edge Computing Using Machine Learning Approaches in IoT Framework -- 9.1 Introduction -- 9.2 Review of IoT and Edge Computing -- 9.2.1 Internet of Things -- 9.2.1.1 Communication between Machines -- 9.2.1.2 Communication within Machine and Cloud -- 9.2.1.3 Machine-to-Gateway Communication -- 9.2.2 IoT Components -- 9.2.2.1 Sensors/Devices -- 9.2.2.2 IoT Gateways -- 9.2.2.3 Cloud-Based Core Network -- 9.2.3 Edge Computing -- 9.3 Edge Computing Paradigm in a Cloud Environment -- 9.3.1 Collection Proxy Technology -- 9.3.2 Data Validation -- 9.3.3 Annotation of Metadata -- 9.3.4 Security -- 9.3.5 Virtual IoT Device -- 9.3.6 Actuation -- 9.4 Edge Computing for Architecture -- 9.4.1 Front Structure -- 9.4.2 Near Structure -- 9.4.3 Far Structure -- 9.5 IoT and Edge Technology Integration -- 9.5.1 Overview -- 9.5.2 IoT Performance Demands -- 9.5.2.1 Transmission -- 9.5.2.2 Storage -- 9.5.2.3 Computation -- 9.6 Applications of IoT -- 9.6.1 IoT-Based Industrial Applications -- 9.6.1.1 Smart Grids -- 9.6.1.2 Manufacturing Process Monitoring -- 9.6.2 Healthcare Applications of IoT -- 9.6.2.1 IoT Health-Related Service -- 9.6.2.2 Glucose-Level Monitoring -- 9.6.2.3 Blood Pressure Monitoring -- 9.7 Advantages of Edge Computing-Based IoT -- 9.7.1 Transmission -- 9.7.2 Latency/Delay -- 9.7.3 Bandwidth -- 9.7.4 Energy -- 9.7.5 Overhead -- 9.7.6 Storage -- 9.7.6.1 Storage Balancing -- 9.7.6.2 Recovery Policy -- 9.8 Edge Computing-Based IoT Challenges -- 9.8.1 System Integration -- 9.8.2 Resource Management. ; 9.8.3 Security and Privacy.
• Sommario/riassunto: Green Computing and Predictive Analytics for Healthcare excavates the rudimentary concepts of Green Computing, Big Data and the Internet of Things along with the latest research development in the domain of healthcare. It also covers various applications and case studies in the field of computer science with state-of-the-art tools and technologies. The rapid growth of the population is a challenging issue in maintaining and monitoring various experiences of quality of service in healthcare. The coherent usage of these limited resources in connection with optimum energy consumption has been becoming more important. The major healthcare nodes are gradually becoming Internet of Things-enabled, and sensors, work data and the involvement of networking are creating smart campuses and smart houses. The book includes chapters on the Internet of Things and Big Data technologies. Features: Biomedical data monitoring under the Internet of Things Environment data sensing and analyzing Big data analytics and clustering Machine learning techniques for sudden cardiac death prediction Robust brain tissue segmentation Energy-efficient and green Internet of Things for healthcare applications Blockchain technology for the healthcare Internet of Things Advanced healthcare for domestic medical tourism system Edge computing for data analytics This book on Green Computing and Predictive Analytics for Healthcare aims to promote and facilitate the exchange of research knowledge and findings across different disciplines on the design and investigation of healthcare data analytics. It can also be used as a textbook for a master's course in biomedical engineering. This book will also present new methods for medical data evaluation and the diagnosis of different diseases to improve quality-of-life in general and for better integration of Internet of Things into society. Dr. Sourav Banerjee is an Assistant Professor at the Department of Computer Science and Engineering of Kalyani Government Engineering College, Kalyani, West Bengal, India. His research interests include Big Data, Cloud Computing, Distributed Computing and Mobile Communications. Dr. Chinmay Chakraborty is an Assistant Professor at the Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, India. His main research interests include the Internet of Medical Things, WBAN, Wireless Networks, Telemedicine, m-Health/e-Health and Medical Imaging. Dr. Kousik Dasgupta is an Assistant Professor at the Department of Computer Science and Engineering, Kalyani Government Engineering College, India. His research interests include Computer Vision, AI/ML, Cloud Computing, Big Data and Security.
• Titolo autorizzato: Green computing and predictive analytics for healthcare
• ISBN: 1-000-22394-9; 1-000-22400-7; 0-429-31722-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910860842003321
• Lo trovi qui: Univ. Federico II