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Need for Security in Medical IoT 9.5 Components for Enhancing Data Security in Medical IoT 9.6 Vulnerabilities in Medical IoT Environment 9.7 Solutions for IoT Healthcare Cyber-Security 9.8 Execution of Trusted Environment 9.9 Patient Registration Using Medical IoT Devices 9.10 Trusted Communication Using Block Chain 9.11 Conclusion

      16  10 Efficient Data Transmission and Remote Monitoring System for IoT Applications 10.1 Introduction 10.2 Network Configuration 10.3 Data Filtering and Predicting Processes 10.4 Experimental Setup 10.5 Conclusion

      17  11 IoT in the Current Times and its Prospective Advancements 11.1 Introduction 11.2 How IIoT Advances Industrial Engineering in Industry 4.0 Era 11.3 IoT and its Current Applications 11.4 Application Areas of IIoT 11.5 Challenges of Existing Systems 11.6 Future Advancements 11.7 Case Study of DeWalt 11.8 Conclusion

      18  12 Reliance on Artificial Intelligence, Machine Learning and Deep Learning in the Era of Industry 4.0 12.1 Introduction to Artificial Intelligence 12.2 AI and its Related Fields 12.3 What is Industry 4.0? 12.4 Industrial Revolutions 12.5 Reasons for Shifting Towards Industry 4.0 12.6 Role of AI in Industry 4.0 12.7 Role of ML in Industry 4.0 12.8 Role of Deep Learning in Industry 4.0 12.9 Applications of AI, ML, and DL in Industry 4.0 12.10 Challenges 12.11 Top Companies That Use AI to Augment Manufacturing Processes in the Era of Industry 4.0 12.12 Conclusion

      19  13 The Implementation of AI and AI-Empowered Imaging Systems to Fight Against COVID-19—A Review 13.1 Introduction 13.2 AI-Assisted Methods 13.3 Optimistic Treatments and Cures 13.4 Challenges and Future Research Issues 13.5 Conclusion

      20  14 Implementation of Machine Learning Techniques for the Analysis of Transmission Dynamics of COVID-19 14.1 Introduction 14.2 Data Analysis 14.3 Methodology 14.4 Results and Discussions 14.5 Conclusions

      21  Index

      22  End User License Agreement

      List of Illustrations

      1 Chapter 1Figure 1.1 Data mining features.Figure 1.2 Data mining classification process.Figure 1.3 Block diagram of the EEG classification.Figure 1.4 Working model of IoT-based Smart Healthcare kit.Figure 1.5 Proposed block diagram.Figure 1.6 Mindwave sensor.Figure 1.7 Home pages for EEG signal design.Figure 1.8 Pseudocode for proposed EEG prediction system.Figure 1.9 Graph between TPR vs TFR.Figure 1.10 Output result accuracy predictions for based on patient EEG data.Figure 1.11 Accuracy predictions for based on type of epilepsy.

      2 Chapter 2Figure 2.1 Technical Scenario 2, normal healthcare.Figure 2.2 Structure of transformation research. Visits to the study occur at 2,...Figure 2.3 The no. of patients visits after Post Discharge [36].

      3 Chapter 3Figure 3.1 Overall design.Figure 3.2 Kernel Function on non-linear SVM.Figure 3.3 Working of random forest algorithm.

      4 Chapter 4Figure 4.1 Home view of touch interaction.Figure 4.2 Cross language selection display for both doctor and patient.Figure 4.3 Categorical interactions initiated by doctor.Figure 4.4 Categorical interactions initiated by patient.Figure 4.5 A sample interactive interface in English.Figure 4.6 Figure 4.5’s interactions in Tamil.Figure 4.7 A sample interactive interface in English

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