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