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Machine Learning Paradigm for Internet of Things Applications

Edited by Shalli Rani, R. Maheswar, G. R. Kanagachidambaresan, Sachin Ahuja and Deepali Gupta
Series: Next-Generation Computing and Communication Engineering
Copyright: 2022   |   Status: Published
ISBN: 9781119760474  |  Hardcover  |  
286 pages | 116 illustrations
Price: $195 USD
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One Line Description
As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT’s potential and this book brings clarity to the issue.

Audience
Scholars and scientists working in artificial intelligence and electronic engineering, industry engineers, software and computer hardware specialists.

Description
Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems.
Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’, and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.

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Author / Editor Details
Shalli Rani, PhD is an associate professor in the Department of CSE, Chitkara University, Punjab, India.

R. Maheswar, PhD is the Dean and associate professor, School of EEE, VIT Bhopal University, Madya Pradesh, India.

G. R. Kanagachidambaresan, PhD associate professor, Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India.

Sachin Ahuja, PhD is a professor in the Department of CSE, Chitkara University, Punjab, India.

Deepali Gupta, PhD is a professor, Department of CSE, Chitkara University, Punjab, India.

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Table of Contents
Preface
1. Machine Learning Concept–Based IoT Platforms for Smart Cities’ Implementation and Requirements
M. Saravanan, J. Ajayan, R. Maheswar, Eswaran Parthasarathy and K. Sumathi
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
W. H. Rankothge
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
Carmel Mary Belinda M. J., K. Antonykumar, S. Ravikumar and Yogesh R. Kulkarni
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)
Dineshbabu V., Arul Kumar V. P. and Gowtham M. S.
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
S. Pradeep Kumar and G. Kalpana
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
Pradeep Kumar S., Jayanthi K. and Selvakumari S.
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
R. Bhuvanya and M. Kavitha
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
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
Pradeep Kumar S., Pushpakumar R. and Selvakumari S.
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 Kowsalyadevi A. K., Megala M. and Manivannan C.
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
M. Kavitha, R. Srinivasan and R. Bhuvanya
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
Sarvesh Tanwar, Jatin Garg, Medini Gupta and Ajay Rana
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
Mamta Nain, Nitin Goyal and Manni Kumar
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
Monika Chaudhary, Nitin Goyal and Aadil Mushtaq
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
Sarang Sharma, Sheifali Gupta and Deepali Gupta
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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Description
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