rel="nofollow" href="#ulink_d6bff945-d506-5be5-b92a-3eccfc965cdb">9.1 Introduction
9.2 Literature Survey
9.3 System Requirements
9.4 System Design
9.5 Implementation
9.6 Results and Discussions
9.7 Conclusion
References
14 10 Cloud-IoT Secured Prediction System for Processing and Analysis of Healthcare Data Using Machine Learning Techniques 10.1 Introduction 10.2 Literature Survey 10.3 Medical Data Classification 10.4 Data Analysis 10.5 ML Methods Used in Healthcare 10.6 Probability Distributions 10.7 Evaluation Metrics 10.8 Proposed Methodology 10.9 Experimental Results 10.10 Conclusion References
15 11 CloudIoT-Driven Healthcare: Review, Architecture, Security Implications, and Open Research Issues 11.1 Introduction 11.2 Background Elements 11.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 11.4 CloudIoT Health System Framework 11.5 Security Challenges and Vulnerabilities 11.6 Security Countermeasures and Considerations 11.7 Open Research Issues and Security Challenges 11.8 Discussion and Analysis 11.9 Conclusion References
16 12 A Novel Usage of Artificial Intelligence and Internet of Things in RemoteBased Healthcare Applications 12.1 Introduction Machine Learning 12.2 Importance of Machine Learning 12.3 Procedure 12.4 Feature Extraction 12.5 Experimental Methods 12.6 Experiments 12.7 Framework for EEG Signal Classification 12.8 Detection of the Preictal State 12.9 Determination of the Seizure Prediction Horizon 12.10 Dynamic Classification Over Time 12.11 Conclusion References
17 13 Use of Machine Learning in Healthcare 13.1 Introduction 13.2 Uses of Machine Learning in Pharma and Medicine 13.3 The Ongoing Preferences of ML in Human Services 13.4 The Morals of the Use of Calculations in Medicinal Services 13.5 Opportunities in Healthcare Quality Improvement 13.6 A Team-Based Care Approach Reduces Waste 13.7 Conclusion References
18 14 Methods of MRI Brain Tumor Segmentation 14.1 Introduction 14.2 Generative and Descriptive Models 14.3 Conclusion References
19 15 Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network–Based Model 15.1 Introduction 15.2 Data Set 15.3 Feature Engineering 15.4 Framework for Early Detection of Disease 15.5 Result 15.6 Conclusion References
20 16 A Comprehensive Analysis on Masked Face Detection Algorithms 16.1 Introduction 16.2 Literature Review 16.3 Implementation Approach 16.4 Observation and Analysis 16.5
