Machine Learning and Internet of Things for Societal Issues

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