Machine Learning and Internet of Things for Societal Issues

Description

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. 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 Blockchain for Data Storage Blockchain Data Storage Benefits of Storing Data Using Blockchain Passenger Verification and Reports Management in Hospitals Passenger Authentication at Airport Recognizing Faces with CNN Dataset of Facial Images MTCNN System for Face Detection Post-Processing of Images Facial Feature Extraction Facial Authentication Wearable Smart Band Assessing Risk Level and Acceptance Maintaining Health and Hygiene Maintaining Clean and Hygiene Places Smart Soap Dispensers Smart Toilet Paper Dispenser Smart Garbage Bin Conclusion References 6 Early Prediction of COVID-19 Using Modified Convolutional Neural Networks Introduction Literature Review Existing System and Their Implementation Details Existing System Proposed System System Design and Implementation Implementation and Experimentation Details System Modules ECNN Algorithm Results Evaluation Methods Conclusions and Future Possibilities References 7 A Cyber Physical System Model for Autonomous Tolling Booths Introduction Problem Definition Cyber Physical System for Autonomous Toll Booths (CPSATB) Input Layer of CPSATB Control Layer of CPSATB Cloud Model Output Layer of CPSATB A Proof of Concept for CPSATB Conclusion References 8 Sentiment Extraction from English-Telugu Code Mixed Tweets Using Lexicon Based and Machine Learning Approaches Introduction Related Work Methodology Experiments and Results Conclusion and Future Work References 9 An Integrated Approach for Medical Image Classification Using Potential Shape Signature and Neural Network Introduction Methodology Results and Discussions Conclusion References 10 Brain Tumour Segmentation Using Hybrid Approach Introduction Related Work Proposed Methodology Experimental Results Conclusions References 11 Identification of Brain Tumors Using Deep Learning Techniques Introduction Literature Study Proposed Method Results and Discussions Conclusion References 12 Implementation of ANN for Examining the Electrical Parameters of Cadmium Sulfide Solar Cell Introduction Working Model of CdS Thin-Film Solar Cell Artificial Neural Network Modeling of CdS Thin-Film Solar Cell Results and Discussion Impact of Hidden Neurons: Modeling of CdS Solar Cell Conclusion References 13 Deduplication of IoT Data in Cloud Storage Introduction Background and Related Work Proposed System Model Results After Experiments Done Conclusion References