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This book highlights recent advance in the area of Machine Learning and IoT, and their applications to solve societal issues/problems or useful for various users in the society. It is known that many smart devices are interconnected and the data generated is being analyzed and processed with machine learning models for prediction, classification, etc., to solve human needs in various sectors like health, road safety, agriculture, and education. This contributed book puts together chapters concerning the use of intelligent techniques in various aspects related to the IoT domain from protocols to applications, to give the reader an up-to-date picture of the state-of-the-art on the connection between computational intelligence, machine learning, and IoT

Intro -- Preface -- Introduction -- Contents -- About the Editors -- 1 A Review on Generative Adversarial Networks -- Introduction -- Basics of GAN -- Training Data for Discriminator -- Training the Discriminator -- The Generator -- Using the Discriminator to Train the Generator -- Attentional Generative Adversarial Network -- Control GAN -- DC-GAN -- Conditional GAN -- Cycle Consistent GAN -- FM-GAN -- Stack GAN -- MirrorGAN -- Fusion GAN -- Comparison of Different GAN Based on Inception Score -- Merits of Generative Adversarial Network -- Demerits of Generative Adversarial Network -- Future Prospects of GAN -- Conclusion -- References -- 2 Integration of Machine Learning in Education: Challenges, Issues and Trends -- Introduction -- Machine Learning Overview -- Opportunities of Machine Learning in Education -- Challenges and Issues -- Explainability -- Accountability -- Cultural Bias -- Ethical Concerns -- Existing Examples -- Conclusion -- References -- 3 IoT-Based Continuous Glucose Monitoring System for Diabetic Patients Using Sensor Technology -- Introduction -- Glucose Measurement Methods -- Existing Methods -- Internet of Things (IoT) -- General IoT-Based Continuous Glucose Measurement Architecture -- Internet of Things (IoT)-Enabled Continuous Glucose Monitoring System (CGMS) -- Results and Discussion -- Conclusion -- References -- 4 Role of Machine Learning and Cloud-Driven Platform in IoT-Based Smart Farming -- Introduction -- Literature Review -- Applications of Machines Learning in a Major IoT-Based Smart Farming Environment -- IOT-AI-Cloud in Agriculture-The Need and Implementation -- User Domain -- IoT Domain -- Cloud Domain -- Smart Farming Challenges -- Conclusion -- References -- 5 Smart Airport System to Counter COVID-19 and Future Sustainability -- Introduction -- Proposed System -- Passenger Registration Using Blockchain.