Machine Learning Paradigm for Internet of Things Applications

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Machine Learning Concept–Based IoT Platforms for Smart Cities’ Implementation and Requirements
	1.1 Introduction
	1.2 Smart City Structure in India
		1.2.1 Bhubaneswar City
			1.2.1.1 Specifications
			1.2.1.2 Healthcare and Mobility Services
			1.2.1.3 Productivity
		1.2.2 Smart City in Pune
			1.2.2.1 Specifications
			1.2.2.2 Transport and Mobility
			1.2.2.3 Water and Sewage Management
	1.3 Status of Smart Cities in India
		1.3.1 Funding Process by Government
	1.4 Analysis of Smart City Setup
		1.4.1 Physical Infrastructure-Based
		1.4.2 Social Infrastructure-Based
		1.4.3 Urban Mobility
		1.4.4 Solid Waste Management System
		1.4.5 Economical-Based Infrastructure
		1.4.6 Infrastructure-Based Development
		1.4.7 Water Supply System
		1.4.8 Sewage Networking
	1.5 Ideal Planning for the Sewage Networking Systems
		1.5.1 Availability and Ideal Consumption of Resources
		1.5.2 Anticipating Future Demand
		1.5.3 Transporting Networks to Facilitate
		1.5.4 Control Centers for Governing the City
		1.5.5 Integrated Command and Control Center
	1.6 Heritage of Culture Based on Modern Advancement
	1.7 Funding and Business Models to Leverage
		1.7.1 Fundings
	1.8 Community-Based Development
		1.8.1 Smart Medical Care
		1.8.2 Smart Safety for The IT
		1.8.3 IoT Communication Interface With ML
		1.8.4 Machine Learning Algorithms
		1.8.5 Smart Community
	1.9 Revolutionary Impact With Other Locations
	1.10 Finding Balanced City Development
	1.11 E-Industry With Enhanced Resources
	1.12 Strategy for Development of Smart Cities
		1.12.1 Stakeholder Benefits
		1.12.2 Urban Integration
		1.12.3 Future Scope of City Innovations
		1.12.4 Conclusion
	References
2 An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan
	2.1 Introduction
	2.2 Background
		2.2.1 Prediction of Future Paddy Harvest and Rice Consumption Demand
		2.2.2 Rice Distribution
	2.3 Methodology
		2.3.1 Requirements of the Proposed Platform
		2.3.2 Data to Evaluate the ‘isRice” Platform
		2.3.3 Implementation of Prediction Modules
			2.3.3.1 Recurrent Neural Network
			2.3.3.2 Long Short-Term Memory
			2.3.3.3 Paddy Harvest Prediction Function
			2.3.3.4 Rice Demand Prediction Function
		2.3.4 Implementation of Rice Distribution Planning Module
			Si Di Ti
			2.3.4.1 Genetic Algorithm–Based Rice Distribution Planning
			Fitness
			Si Di Ti
		2.3.5 Front-End Implementation
	2.4 Results and Discussion
		2.4.1 Paddy Harvest Prediction Function
		2.4.2 Rice Demand Prediction Function
		2.4.3 Rice Distribution Planning Module
	2.5 Conclusion
	References
3 A Collaborative Data Publishing Model with Privacy Preservation Using GroupBased Classification and Anonymity
	3.1 Introduction
	3.2 Literature Survey
	3.3 Proposed Model
	3.4 Results
	3.5 Conclusion
	References
4 Production Monitoring and Dashboard Design for Industry 4.0 Using Single-Board Computer (SBC)
	4.1 Introduction
	4.2 Related Works
	4.3 Industry 4.0 Production and Dashboard Design
	4.4 Results and Discussion
	4.5 Conclusion
	References
5 Generation of Two-Dimensional Text-Based CAPTCHA Using Graphical Operation
	5.1 Introduction
	5.2 Types of CAPTCHAs
		5.2.1 Text-Based CAPTCHA
		5.2.2 Image-Based CAPTCHA
		5.2.3 Audio-Based CAPTCHA
		5.2.4 Video-Based CAPTCHA
		5.2.5 Puzzle-Based CAPTCHA
	5.3 Related Work
	5.4 Proposed Technique
	5.5 Text-Based CAPTCHA Scheme
	5.6 Breaking Text-Based CAPTCHA’s Scheme
		5.6.1 Individual Character-Based Segmentation Method
		5.6.2 Character Width-Based Segmentation Method
	5.7 Implementation of Text-Based CAPTCHA Using Graphical Operation
		5.7.1 Graphical Operation
		5.7.2 Two-Dimensional Composite Transformation Calculation
	5.8 Graphical Text-Based CAPTCHA in Online Application
	5.9 Conclusion and Future Enhancement
	References
6 Smart IoT-Enabled Traffic Sign Recognition With High Accuracy (TSR-HA) Using Deep Learning
	6.1 Introduction
		6.1.1 Internet of Things
		6.1.2 Deep Learning
		6.1.3 Detecting the Traffic Sign With the Mask R-CNN
			6.1.3.1 Mask R-Convolutional Neural Network
			6.1.3.2 Color Space Conversion
	6.2 Experimental Evaluation
		6.2.1 Implementation Details
		6.2.2 Traffic Sign Classification
		6.2.3 Traffic Sign Detection
		6.2.4 Sample Outputs
		6.2.5 Raspberry Pi 4 Controls Vehicle Using OpenCV
			6.2.5.1 Smart IoT-Enabled Traffic Signs Recognizing With High Accuracy Using Deep Learning
		6.2.6 Python Code
	6.3 Conclusion
	References
7 Offline and Online Performance Evaluation Metrics of Recommender System: A Bird’s Eye View
	7.1 Introduction
		7.1.1 Modules of Recommender System
		7.1.2 Evaluation Structure
		7.1.3 Contribution of the Paper
		7.1.4 Organization of the Paper
	7.2 Evaluation Metrics
		7.2.1 Offline Analytics
			7.2.1.1 Prediction Accuracy Metrics
			7.2.1.2 Decision Support Metrics
			7.2.1.3 Rank Aware Top-N Metrics
		7.2.2 Item and List-Based Metrics
			7.2.2.1 Coverage
			7.2.2.2 Popularity
			7.2.2.3 Personalization
			7.2.2.4 Serendipity
			7.2.2.5 Diversity
			7.2.2.6 Churn
			7.2.2.7 Responsiveness
		7.2.3 User Studies and Online Evaluation
			7.2.3.1 Usage Log
			7.2.3.2 Polls
			7.2.3.3 Lab Experiments
			7.2.3.4 Online A/B Test
	7.3 Related Works
		7.3.1 Categories of Recommendation
		7.3.2 Data Mining Methods of Recommender System
			7.3.2.1 Data Pre-Processing
			z value mean deviation
			standard
			7.3.2.2 Data Analysis
	7.4 Experimental Setup
	7.5 Summary and Conclusions
	References
8 Deep Learning–Enabled Smart Safety Precautions and Measures in Public Gathering Places for COVID-19 Using IoT
	8.1 Introduction
	8.2 Prelims
		8.2.1 Digital Image Processing
		8.2.2 Deep Learning
		8.2.3 WSN
		8.2.4 Raspberry Pi
		8.2.5 Thermal Sensor
		8.2.6 Relay
		8.2.7 TensorFlow
		8.2.8 Convolution Neural Network (CNN)
	8.3 Proposed System
	8.4 Math Model
	8.5 Results
	8.6 Conclusion
	References
9 Route Optimization for Perishable Goods Transportation System
	9.1 Introduction
	9.2 Related Works
		9.2.1 Need for Route Optimization
	9.3 Proposed Methodology
	9.4 Proposed Work Implementation
	9.5 Conclusion
	References
10 Fake News Detection Using Machine Learning Algorithms
	10.1 Introduction
	10.2 Literature Survey
	10.3 Methodology
		10.3.1 Data Retrieval
		10.3.2 Data Pre-Processing
		10.3.3 Data Visualization
		10.3.4 Tokenization
		10.3.5 Feature Extraction
		10.3.6 Machine Learning Algorithms
			10.3.6.1 Logistic Regression
			10.3.6.2 Naïve Bayes
			10.3.6.3 Random Forest
			10.3.6.4 XGBoost
	10.4 Experimental Results
	10.5 Conclusion
	References
11 Opportunities and Challenges in Machine Learning With IoT
	11.1 Introduction
	11.2 Literature Review
		11.2.1 A Designed Architecture of ML on Big Data
		11.2.2 Machine Learning
		11.2.3 Types of Machine Learning
			11.2.3.1 Supervised Learning
			11.2.3.2 Unsupervised Learning
	11.3 Why Should We Care About Learning Representations?
	11.4 Big Data
	11.5 Data Processing Opportunities and Challenges
		11.5.1 Data Redundancy
		11.5.2 Data Noise
		11.5.3 Heterogeneity of Data
		11.5.4 Discretization of Data
		11.5.5 Data Labeling
		11.5.6 Imbalanced Data
	11.6 Learning Opportunities and Challenges
	11.7 Enabling Machine Learning With IoT
	11.8 Conclusion
	References
12 Machine Learning Effects on Underwater Applications and IoUT
	12.1 Introduction
	12.2 Characteristics of IoUT
	12.3 Architecture of IoUT
		12.3.1 Perceptron Layer
		12.3.2 Network Layer
		12.3.3 Application Layer
	12.4 Challenges in IoUT
	12.5 Applications of IoUT
	12.6 Machine Learning
	12.7 Simulation and Analysis
	12.8 Conclusion
	References
13 Internet of Underwater Things: Challenges, Routing Protocols, and ML Algorithms
	13.1 Introduction
	13.2 Internet of Underwater Things
		13.2.1 Challenges in IoUT
	13.3 Routing Protocols of IoUT
	13.4 Machine Learning in IoUT
		13.4.1 Types of Machine Learning Algorithms
	13.5 Performance Evaluation
	13.6 Conclusion
	References
14 Chest X-Ray for Pneumonia Detection
	14.1 Introduction
	14.2 Background
	14.3 Research Methodology
	14.4 Results and Discussion
		14.4.1 Results
		14.4.2 Discussion
	14.5 Conclusion
	Acknowledgment
	References
Index
EULA