Emerging Technologies for Healthcare. Группа авторов
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Table of Contents
1 Cover
4 Preface
5 Part I BASICS OF SMART HEALTHCARE 1 An Overview of IoT in Health Sectors 1.1 Introduction 1.2 Influence of IoT in Healthcare Systems 1.3 Popular IoT Healthcare Devices 1.4 Benefits of IoT 1.5 Challenges of IoT 1.6 Disadvantages of IoT 1.7 Applications of IoT 1.8 Global Smart Healthcare Market 1.9 Recent Trends and Discussions 1.10 Conclusion References 2 IoT-Based Solutions for Smart Healthcare 2.1 Introduction 2.2 IoT Smart Healthcare System 2.3 Locally and Cloud-Based IoT Architecture 2.4 Cloud Computing 2.5 Outbreak of Arduino Board 2.6 Applications of Smart Healthcare System 2.7 Smart Wearables and Apps 2.8 Deep Learning in Biomedical 2.9 Conclusion References 3 QLattice Environment and Feyn QGraph Models—A New Perspective Toward Deep Learning 3.1 Introduction 3.2 Machine Learning Model Lifecycle 3.3 A Model Deployment in Keras 3.4 QLattice Environment 3.5 Using QLattice Environment and QGraph Models for COVID-19 Impact Prediction References 4 Sensitive Healthcare Data: Privacy and Security Issues and Proposed Solutions 4.1 Introduction 4.2 Medical Sensor Networks/Medical Internet of Things/Body Area Networks/WBANs 4.3 Cloud Storage and Computing on Sensitive Healthcare Data 4.4 Blockchain for Security and Privacy Enhancement in Sensitive Healthcare Data 4.5 Artificial Intelligence, Machine Learning, and Big Data in Healthcare and Its Efficacy in Security and Privacy of Sensitive Healthcare Data 4.6 Conclusion References
6 Part II EMPLOYMENT OF MACHINE LEARNING IN DISEASE DETECTION 5 Diabetes Prediction Model Based on Machine Learning 5.1 Introduction 5.2 Literature Review 5.3 Proposed Methodology 5.4 System Implementation 5.5 Conclusion References 6 Lung Cancer Detection Using 3D CNN Based on Deep Learning 6.1 Introduction 6.2 Literature Review 6.3 Proposed Methodology 6.4 Results and Discussion 6.5 Conclusion References 7 Pneumonia Detection Using CNN and ANN Based on Deep Learning Approach 7.1 Introduction 7.2 Literature Review 7.3 Proposed Methodology 7.4 System Implementation 7.5 Conclusion References 8 Personality Prediction and Handwriting Recognition Using Machine Learning 8.1 Introduction to the System 8.2 Literature Survey 8.3 Theory 8.4 Algorithm To Be Used 8.5 Proposed Methodology 8.6 Algorithms vs. Accuracy