Оглавление
Группа авторов. Machine Learning Paradigm for Internet of Things Applications
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
Machine Learning Paradigm for Internet of Things Applications
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
2.3.4.1 Genetic Algorithm–Based Rice Distribution Planning
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 Group-Based 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
7.2.1.1.1 Mean Absolute Error
7.2.1.1.2 Mean Square Error
7.2.1.1.3 Root Mean Square Error
7.2.1.1.4 Hit Ratio
7.2.1.2 Decision Support Metrics
7.2.1.2.1 ROC and AUC
7.2.1.3 Rank Aware Top-N Metrics. 7.2.1.3.1 Mean Average Precision
7.2.1.3.2 Mean Reciprocal Rank
7.2.1.3.3 Normalized Discounted Cumulative Gain
7.2.1.3.4 Spearman Rank Correlation Coefficient
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. 7.3.2.1.1 Sampling
7.3.2.1.2 Dimensionality Reduction
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
11.2.3.1.1 Regression
11.2.3.1.2 Classification
11.2.3.1.3 Deep Learning
11.2.3.2 Unsupervised Learning
11.2.3.2.1 Clustering
11.2.3.2.2 Dimensionality Reduction
11.2.3.2.3 Principal Component Analysis
11.2.3.2.4 Anomaly Detection
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
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