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Green Computing and Predictive Analytics for Healthcare.

By: Contributor(s): Material type: TextTextPublisher: Milton : CRC Press LLC, 2020Copyright date: ©2021Edition: 1st edDescription: 1 online resource (205 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781000224009
Subject(s): Genre/Form: Additional physical formats: Print version:: Green Computing and Predictive Analytics for HealthcareDDC classification:
  • 362.10285
LOC classification:
  • R858 .B364 2021
Online resources:
Contents:
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.
Summary: The emergent trends in Green Cloud Computing lead to new developments in various application domains, mainly in healthcare. The aim of this book is to collect innovative and high-quality research contributions related to the advances in the energy-aware cloud-enabled healthcare domain.
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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.

The emergent trends in Green Cloud Computing lead to new developments in various application domains, mainly in healthcare. The aim of this book is to collect innovative and high-quality research contributions related to the advances in the energy-aware cloud-enabled healthcare domain.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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