Machine Intelligence, Big Data Analytics, and IoT in Image Processing: Practical Applications

Cover
Title Page
Copyright Page
Contents
Preface
Part I: Demystifying Smart Healthcare
	Chapter 1 Deep Learning Techniques Using Transfer Learning for Classification of Alzheimer’s Disease
		1.1 Introduction
		1.2 Transfer Learning Techniques
		1.3 AD Classification Using Conventional Training Methods
		1.4 AD Classification Using Transfer Learning
		1.5 Conclusion
		References
	Chapter 2 Medical Image Analysis of Lung Cancer CT Scans Using Deep Learning with Swarm Optimization Techniques
		2.1 Introduction
		2.2 The Major Contributions of the Proposed Model
		2.3 Related Works
		2.4 Problem Statement
		2.5 Proposed Model
			2.5.1 Swarm Optimization in Lung Cancer Medical Image Analysis
			2.5.2 Deep Learning with PSO
			2.5.3 Proposed CNN Architectures
		2.6 Dataset Description
		2.7 Results and Discussions
			2.7.1 Parameters for Performance Evaluation
		2.8 Conclusion
		References
	Chapter 3 Liver Cancer Classification With Using Gray-Level Co-Occurrence Matrix Using Deep Learning Techniques
		3.1 Introduction
			3.1.1 Liver Roles in Human Body
			3.1.2 Liver Diseases
			3.1.3 Types of Liver Tumors
				3.1.3.1 Benign Tumors
				3.1.3.2 Malignant Tumors
			3.1.4 Characteristics of a Medical Imaging Procedure
			3.1.5 Problems Related to Liver Cancer Classification
			3.1.6 Purpose of the Systematic Study
		3.2 Related Works
		3.3 Proposed Methodology
			3.3.1 Gaussian Mixture Model
			3.3.2 Dataset Description
			3.3.3 Performance Metrics
				3.3.3.1 Accuracy Measures
				3.3.3.2 Key Findings
				3.3.3.3 Key Issues Addressed
		3.4 Conclusion
		References
	Chapter 4 Transforming the Technologies for Resilient and Digital Future During COVID-19 Pandemic
		4.1 Introduction
		4.2 Digital Technologies Used
			4.2.1 Artificial Intelligence
			4.2.2 Internet of Things
			4.2.3 Telehealth/Telemedicine
			4.2.4 Cloud Computing
			4.2.5 Blockchain
			4.2.6 5G
		4.3 Challenges in Transforming Digital Technology
			4.3.1 Increasing Digitalization
			4.3.2 Work From Home Culture
			4.3.3 Workplace Monitoring and Techno Stress
			4.3.4 Online Fraud
			4.3.5 Accessing Internet
			4.3.6 Internet Shutdowns
			4.3.7 Digital Payments
			4.3.8 Privacy and Surveillance
		4.4 Implications for Research
		4.5 Conclusion
		References
Part II: Plant Pathology
	Chapter 5 Plant Pathology Detection Using Deep Learning
		5.1 Introduction
		5.2 Plant Leaf Disease
		5.3 Background Knowledge
		5.4 Architecture of ResNet 512 V2
			5.4.1 Working of Residual Network
		5.5 Methodology
			5.5.1 Image Resizing
			5.5.2 Data Augmentation
				5.5.2.1 Types of Data Augmentation
			5.5.3 Data Normalization
			5.5.4 Data Splitting
		5.6 Result Analysis
			5.6.1 Data Collection
			5.6.2 Feature Extractions
			5.6.3 Plant Leaf Disease Detection
		5.7 Conclusion
		References
	Chapter 6 Smart Irrigation and Cultivation Recommendation System for Precision Agriculture Driven by IoT
		6.1 Introduction
			6.1.1 Background of the Problem
				6.1.1.1 Need of Water Management
				6.1.1.2 Importance of Precision Agriculture
				6.1.1.3 Internet of Things
				6.1.1.4 Application of IoT in Machine Learning and Deep Learning
		6.2 Related Works
		6.3 Challenges of IoT in Smart Irrigation
		6.4 Farmers’ Challenges in the Current Situation
		6.5 Data Collection in Precision Agriculture
			6.5.1 Algorithm
				6.5.1.1 Environmental Consideration on Stage Production of Crop
			6.5.2 Implementation Measures
				6.5.2.1 Analysis of Relevant Vectors
				6.5.2.2 Mean Square Error
				6.5.2.3 Potential of IoT in Precision Agriculture
			6.5.3 Architecture of the Proposed Model
		6.6 Conclusion
		References
	Chapter 7 Machine Learning-Based Hybrid Model for Wheat Yield Prediction
		7.1 Introduction
		7.2 Related Work
		7.3 Materials and Methods
			7.3.1 Methodology for the Current Work
				7.3.1.1 Data Collection for Wheat Crop
				7.3.1.2 Data Pre-Processing
				7.3.1.3 Implementation of the Proposed Hybrid Model
			7.3.2 Techniques Used for Feature Selection
				7.3.2.1 ReliefF Algorithm
				7.3.2.2 Genetic Algorithm
			7.3.3 Implementation of Machine Learning Techniques for Wheat Yield Prediction
				7.3.3.1 K-Nearest Neighbor
				7.3.3.2 Artificial Neural Network
				7.3.3.3 Logistic Regression
				7.3.3.4 Naïve Bayes
				7.3.3.5 Support Vector Machine
				7.3.3.6 Linear Discriminant Analysis
		7.4 Experimental Result and Analysis
		7.5 Conclusion
		Acknowledgment
		References
	Chapter 8 A Status Quo of Machine Learning Algorithms in Smart Agricultural Systems Employing IoT-Based WSN: Trends, Challenges and Futuristic Competences
		8.1 Introduction
		8.2 Types of Wireless Sensor for Smart Agriculture
		8.3 Application of Machine Learning Algorithms for Smart Decision Making in Smart Agriculture
		8.4 ML and WSN-Based Techniques for Smart Agriculture
		8.5 Future Scope in Smart Agriculture
		8.6 Conclusion
		References
Part III: Smart City and Villages
	Chapter 9 Impact of Data Pre-Processing in Information Retrieval for Data Analytics
		9.1 Introduction
			9.1.1 Tasks Involved in Data Pre-Processing
		9.2 Related Work
		9.3 Experimental Setup and Methodology
			9.3.1 Methodology
			9.3.2 Application of Various Data Pre-Processing Tasks on Datasets
			9.3.3 Applied Techniques
				9.3.3.1 Decision Tree
				9.3.3.2 Naive Bayes
				9.3.3.3 Artificial Neural Network
			9.3.4 Proposed Work
				9.3.4.1 PIMA Diabetes Dataset (PID)
			9.3.5 Cleveland Heart Disease Dataset
			9.3.6 Framingham Heart Study
			9.3.7 Diabetic Dataset
		9.4 Experimental Result and Discussion
		9.5 Conclusion and Future Work
		References
	Chapter 10 Cloud Computing Security, Risk, and Challenges: A Detailed Analysis of Preventive Measures and Applications
		10.1 Introduction
		10.2 Background
			10.2.1 History of Cloud Computing
				10.2.1.1 Software-as-a-Service Model
				10.2.1.2 Infrastructure-as-a-Service Model
				10.2.1.3 Platform-as-a-Service Model
			10.2.2 Types of Cloud Computing
			10.2.3 Cloud Service Model
			10.2.4 Characteristics of Cloud Computing
			10.2.5 Advantages of Cloud Computing
			10.2.6 Challenges in Cloud Computing
			10.2.7 Cloud Security
				10.2.7.1 Foundation Security
				10.2.7.2 SaaS and PaaS Host Security
				10.2.7.3 Virtual Server Security
				10.2.7.4 Foundation Security: The Application Level
				10.2.7.5 Supplier Data and Its Security
				10.2.7.6 Need of Security in Cloud
			10.2.8 Cloud Computing Applications
		10.3 Literature Review
		10.4 Cloud Computing Challenges and Its Solution
			10.4.1 Solution and Practices for Cloud Challenges
		10.5 Cloud Computing Security Issues and Its Preventive Measures
			10.5.1 General Security Threats in Cloud
			10.5.2 Preventive Measures
		10.6 Cloud Data Protection and Security Using Steganography
			10.6.1 Types of Steganography
			10.6.2 Data Steganography in Cloud Environment
			10.6.3 Pixel Value Differencing Method
		10.7 Related Study
		10.8 Conclusion
		References
	Chapter 11 Internet of Drone Things: A New Age Invention
		11.1 Introduction
		11.2 Unmanned Aerial Vehicles
			11.2.1 UAV Features and Working
			11.2.2 IoDT Architecture
		11.3 Application Areas
			11.3.1 Other Application Areas
		11.4 IoDT Attacks
			11.4.1 Counter Measures
		11.5 Fusion of IoDT With Other Technologies
		11.6 Recent Advancements in IoDT
		11.7 Conclusion
		References
	Chapter 12 Computer Vision-Oriented Gesture Recognition System for Real-Time ISL Prediction
		12.1 Introduction
		12.2 Literature Review
		12.3 System Architecture
			12.3.1 Model Development Phase
			12.3.2 Development Environment Phase
		12.4 Methodology
			12.4.1 Image Pre-Processing Phase
			12.4.2 Model Building Phase
		12.5 Implementation and Results
			12.5.1 Performance
			12.5.2 Confusion Matrix
		12.6 Conclusion and Future Scope
		References
	Chapter 13 Recent Advances in Intelligent Transportation Systems in India: Analysis, Applications, Challenges, and Future Work
		13.1 Introduction
		13.2 A Primer on ITS
		13.3 The ITS Stages
		13.4 Functions of ITS
		13.5 ITS Advantages
		13.6 ITS Applications
		13.7 ITS Across the World
		13.8 India’s Status of ITS
		13.9 Suggestions for Improving India’s ITS Position
		13.10 Conclusion
		References
	Chapter 14 Evolutionary Approaches in Navigation Systems for Road Transportation System
		14.1 Introduction
			14.1.1 Navigation System
			14.1.2 Genetic Algorithm
			14.1.3 Differential Evolution
		14.2 Related Studies
			14.2.1 Related Studies of Evolutionary Algorithms
		14.3 Navigation Based on Evolutionary Algorithm
			14.3.1 Operators and Terms Used in Evolutionary Algorithms
			14.3.2 Operator and Terms Used in Evolutionary Algorithm
		14.4 Meta-Heuristic Algorithms for Navigation
			14.4.1 Drawbacks of DE
		14.5 Conclusion
		References
	Chapter 15 IoT-Based Smart Parking System for Indian Smart Cities
		15.1 Introduction
		15.2 Indian Smart Cities Mission
		15.3 Vehicle Parking and Its Requirements in a Smart City Configuration
		15.4 Technologies Incorporated in a Vehicle Parking System in Smart Cities
		15.5 Sensors for Vehicle Parking System
			15.5.1 Active Sensors
			15.5.2 Passive Sensors
		15.6 IoT-Based Vehicle Parking System for Indian Smart Cities
			15.6.1 Guidance to the Customers Through Smart Devices
			15.6.2 Smart Parking Reservation System
		15.7 Advantages of IoT-Based Vehicle Parking System
		15.8 Conclusion
		References
	Chapter 16 Security of Smart Home Solution Based on Secure Piggybacked Key Exchange Mechanism
		16.1 Introduction
		16.2 IoT Challenges
		16.3 IoT Vulnerabilities
		16.4 Layer-Wise Threats in IoT Architecture
			16.4.1 Sensing Layer Security Issues
			16.4.2 Network Layer Security Issues
			16.4.3 Middleware Layer Security Issues
			16.4.4 Gateways Security Issues
			16.4.5 Application Layer Security Issues
		16.5 Attack Prevention Techniques
			16.5.1 IoT Authentication
			16.5.2 Session Establishment
		16.6 Conclusion
		References
	Chapter 17 Machine Learning Models in Prediction of Strength Parameters of FRP-Wrapped RC Beams
		17.1 Introduction
			17.1.1 Defining Fiber-Reinforced Polymer
			17.1.2 Types of FRP Composites
				17.1.2.1 Carbon Fiber–Reinforced Polymer
				17.1.2.2 Glass Fiber
				17.1.2.3 Aramid Fiber
				17.1.2.4 Basalt Fiber
		17.2 Strengthening of RC Beams With FRP Systems
			17.2.1 FRP-to-Concrete Bond
			17.2.2 Flexural Strengthening of Beams With FRP Composite
			17.2.3 Shear Strengthening of Beams With FRP Composite
		17.3 Machine Learning Models
			17.3.1 Prediction of Bond Strength
			17.3.2 Estimation of Flexural Strength
			17.3.3 Estimation of Shear Strength
		17.4 Conclusion
		References
	Chapter 18 Prediction of Indoor Air Quality Using Artificial Intelligence
		18.1 Introduction
		18.2 Indoor Air Quality Parameters
			18.2.1 Physical Parameters
				18.2.1.1 Humidity
				18.2.1.2 Air Changes (Ventilation)
				18.2.1.3 Air Velocity
				18.2.1.4 Temperature
			18.2.2 Particulate Matter
			18.2.3 Chemical Parameters
				18.2.3.1 Carbon Dioxide
				18.2.3.2 Carbon Monoxide
				18.2.3.3 Nitrogen Dioxide
				18.2.3.4 Sulphur Dioxide
				18.2.3.5 Ozone
				18.2.3.6 Gaseous Ammonia
				18.2.3.7 Volatile Organic Compounds
			18.2.4 Biological Parameters
		18.3 AI in Indoor Air Quality Prediction
		18.4 Conclusion
		References
Index
EULA