Machine Learning Paradigm for Internet of Things Applications

Inbunden, Engelska, 2022

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Beskrivning

MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS 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. 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.

Produktinformation

Utgivningsdatum:2022-03-04
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Antal sidor:304
Förlag:John Wiley & Sons Inc
ISBN:9781119760474

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Mer om författaren

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

Innehållsförteckning

Preface xiii1 Machine Learning Concept–Based IoT Platforms for Smart Cities’ Implementation and Requirements 1M. Saravanan, J. Ajayan, R. Maheswar, Eswaran Parthasarathy and K. Sumathi1.1 Introduction 21.2 Smart City Structure in India 31.2.1 Bhubaneswar City 31.2.1.1 Specifications 31.2.1.2 Healthcare and Mobility Services 31.2.1.3 Productivity 41.2.2 Smart City in Pune 41.2.2.1 Specifications 51.2.2.2 Transport and Mobility 51.2.2.3 Water and Sewage Management 51.3 Status of Smart Cities in India 51.3.1 Funding Process by Government 61.4 Analysis of Smart City Setup 71.4.1 Physical Infrastructure-Based 71.4.2 Social Infrastructure-Based 71.4.3 Urban Mobility 81.4.4 Solid Waste Management System 81.4.5 Economical-Based Infrastructure 91.4.6 Infrastructure-Based Development 91.4.7 Water Supply System 101.4.8 Sewage Networking 101.5 Ideal Planning for the Sewage Networking Systems 101.5.1 Availability and Ideal Consumption of Resources 101.5.2 Anticipating Future Demand 111.5.3 Transporting Networks to Facilitate 111.5.4 Control Centers for Governing the City 121.5.5 Integrated Command and Control Center 121.6 Heritage of Culture Based on Modern Advancement 131.7 Funding and Business Models to Leverage 141.7.1 Fundings 151.8 Community-Based Development 161.8.1 Smart Medical Care 161.8.2 Smart Safety for The IT 161.8.3 IoT Communication Interface With ML 171.8.4 Machine Learning Algorithms 171.8.5 Smart Community 181.9 Revolutionary Impact With Other Locations 181.10 Finding Balanced City Development 201.11 E-Industry With Enhanced Resources 201.12 Strategy for Development of Smart Cities 211.12.1 Stakeholder Benefits 211.12.2 Urban Integration 221.12.3 Future Scope of City Innovations 221.12.4 Conclusion 23References 242 An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan 27W. H. Rankothge2.1 Introduction 282.2 Background 292.2.1 Prediction of Future Paddy Harvest and Rice Consumption Demand 292.2.2 Rice Distribution 312.3 Methodology 312.3.1 Requirements of the Proposed Platform 322.3.2 Data to Evaluate the ‘isRice” Platform 342.3.3 Implementation of Prediction Modules 342.3.3.1 Recurrent Neural Network 352.3.3.2 Long Short-Term Memory 362.3.3.3 Paddy Harvest Prediction Function 372.3.3.4 Rice Demand Prediction Function 392.3.4 Implementation of Rice Distribution Planning Module 402.3.4.1 Genetic Algorithm–Based Rice Distribution Planning 412.3.5 Front-End Implementation 442.4 Results and Discussion 452.4.1 Paddy Harvest Prediction Function 452.4.2 Rice Demand Prediction Function 462.4.3 Rice Distribution Planning Module 462.5 Conclusion 49References 493 A Collaborative Data Publishing Model with Privacy Preservation Using Group-Based Classification and Anonymity 53Carmel Mary Belinda M. J., K. Antonykumar, S. Ravikumar and Yogesh R. Kulkarni3.1 Introduction 543.2 Literature Survey 563.3 Proposed Model 583.4 Results 613.5 Conclusion 64References 644 Production Monitoring and Dashboard Design for Industry 4.0 Using Single-Board Computer (SBC) 67Dineshbabu V., Arul Kumar V. P. and Gowtham M. S.4.1 Introduction 684.2 Related Works 694.3 Industry 4.0 Production and Dashboard Design 694.4 Results and Discussion 704.5 Conclusion 73References 735 Generation of Two-Dimensional Text-Based CAPTCHA Using Graphical Operation 75S. Pradeep Kumar and G. Kalpana5.1 Introduction 755.2 Types of CAPTCHAs 785.2.1 Text-Based CAPTCHA 785.2.2 Image-Based CAPTCHA 805.2.3 Audio-Based CAPTCHA 805.2.4 Video-Based CAPTCHA 815.2.5 Puzzle-Based CAPTCHA 825.3 Related Work 825.4 Proposed Technique 825.5 Text-Based CAPTCHA Scheme 835.6 Breaking Text-Based CAPTCHA’s Scheme 855.6.1 Individual Character-Based Segmentation Method 855.6.2 Character Width-Based Segmentation Method 865.7 Implementation of Text-Based CAPTCHA Using Graphical Operation 875.7.1 Graphical Operation 875.7.2 Two-Dimensional Composite Transformation Calculation 895.8 Graphical Text-Based CAPTCHA in Online Application 915.9 Conclusion and Future Enhancement 93References 946 Smart IoT-Enabled Traffic Sign Recognition With High Accuracy (TSR-HA) Using Deep Learning 97Pradeep Kumar S., Jayanthi K. and Selvakumari S.6.1 Introduction 986.1.1 Internet of Things 986.1.2 Deep Learning 986.1.3 Detecting the Traffic Sign With the Mask R-CNN 996.1.3.1 Mask R-Convolutional Neural Network 996.1.3.2 Color Space Conversion 1006.2 Experimental Evaluation 1016.2.1 Implementation Details 1016.2.2 Traffic Sign Classification 1016.2.3 Traffic Sign Detection 1026.2.4 Sample Outputs 1036.2.5 Raspberry Pi 4 Controls Vehicle Using OpenCV 1036.2.5.1 Smart IoT-Enabled Traffic Signs Recognizing With High Accuracy Using Deep Learning 1036.2.6 Python Code 1086.3 Conclusion 109References 1107 Offline and Online Performance Evaluation Metrics of Recommender System: A Bird’s Eye View 113R. Bhuvanya and M. Kavitha7.1 Introduction 1147.1.1 Modules of Recommender System 1147.1.2 Evaluation Structure 1157.1.3 Contribution of the Paper 1157.1.4 Organization of the Paper 1167.2 Evaluation Metrics 1167.2.1 Offline Analytics 1167.2.1.1 Prediction Accuracy Metrics 1167.2.1.2 Decision Support Metrics 1187.2.1.3 Rank Aware Top-N Metrics 1207.2.2 Item and List-Based Metrics 1227.2.2.1 Coverage 1227.2.2.2 Popularity 1237.2.2.3 Personalization 1237.2.2.4 Serendipity 1237.2.2.5 Diversity 1237.2.2.6 Churn 1247.2.2.7 Responsiveness 1247.2.3 User Studies and Online Evaluation 1257.2.3.1 Usage Log 1257.2.3.2 Polls 1267.2.3.3 Lab Experiments 1267.2.3.4 Online A/B Test 1267.3 Related Works 1277.3.1 Categories of Recommendation 1297.3.2 Data Mining Methods of Recommender System 1297.3.2.1 Data Pre-Processing 1297.3.2.2 Data Analysis 1317.4 Experimental Setup 1357.5 Summary and Conclusions 142References 1438 Deep Learning–Enabled Smart Safety Precautions and Measures in Public Gathering Places for COVID-19 Using IoT 147Pradeep Kumar S., Pushpakumar R. and Selvakumari S.8.1 Introduction 1488.2 Prelims 1488.2.1 Digital Image Processing 1488.2.2 Deep Learning 1498.2.3 WSN 1498.2.4 Raspberry Pi 1528.2.5 Thermal Sensor 1528.2.6 Relay 1528.2.7 TensorFlow 1538.2.8 Convolution Neural Network (CNN) 1538.3 Proposed System 1548.4 Math Model 1568.5 Results 1588.6 Conclusion 161References 1619 Route Optimization for Perishable Goods Transportation System 167Kowsalyadevi A. K., Megala M. and Manivannan C.9.1 Introduction 1679.2 Related Works 1689.2.1 Need for Route Optimization 1709.3 Proposed Methodology 1719.4 Proposed Work Implementation 1749.5 Conclusion 178References 17810 Fake News Detection Using Machine Learning Algorithms 181M. Kavitha, R. Srinivasan and R. Bhuvanya10.1 Introduction 18110.2 Literature Survey 18310.3 Methodology 19310.3.1 Data Retrieval 19510.3.2 Data Pre-Processing 19510.3.3 Data Visualization 19610.3.4 Tokenization 19610.3.5 Feature Extraction 19610.3.6 Machine Learning Algorithms 19710.3.6.1 Logistic Regression 19710.3.6.2 Naïve Bayes 19810.3.6.3 Random Forest 20010.3.6.4 XGBoost 20010.4 Experimental Results 20210.5 Conclusion 203References 20311 Opportunities and Challenges in Machine Learning With IoT 209Sarvesh Tanwar, Jatin Garg, Medini Gupta and Ajay Rana11.1 Introduction 20911.2 Literature Review 21011.2.1 A Designed Architecture of ML on Big Data 21011.2.2 Machine Learning 21111.2.3 Types of Machine Learning 21211.2.3.1 Supervised Learning 21211.2.3.2 Unsupervised Learning 21511.3 Why Should We Care About Learning Representations? 21711.4 Big Data 21811.5 Data Processing Opportunities and Challenges 21911.5.1 Data Redundancy 21911.5.2 Data Noise 22011.5.3 Heterogeneity of Data 22011.5.4 Discretization of Data 22011.5.5 Data Labeling 22111.5.6 Imbalanced Data 22111.6 Learning Opportunities and Challenges 22111.7 Enabling Machine Learning With IoT 22311.8 Conclusion 224References 22512 Machine Learning Effects on Underwater Applications and IoUT 229Mamta Nain, Nitin Goyal and Manni Kumar12.1 Introduction 22912.2 Characteristics of IoUT 23112.3 Architecture of IoUT 23212.3.1 Perceptron Layer 23312.3.2 Network Layer 23412.3.3 Application Layer 23412.4 Challenges in IoUT 23412.5 Applications of IoUT 23512.6 Machine Learning 24012.7 Simulation and Analysis 24112.8 Conclusion 242References 24213 Internet of Underwater Things: Challenges, Routing Protocols, and ML Algorithms 247Monika Chaudhary, Nitin Goyal and Aadil Mushtaq13.1 Introduction 24813.2 Internet of Underwater Things 24813.2.1 Challenges in IoUT 24913.3 Routing Protocols of IoUT 25013.4 Machine Learning in IoUT 25513.4.1 Types of Machine Learning Algorithms 25813.5 Performance Evaluation 25913.6 Conclusion 260References 26014 Chest X-Ray for Pneumonia Detection 265Sarang Sharma, Sheifali Gupta and Deepali Gupta14.1 Introduction 26614.2 Background 26714.3 Research Methodology 26814.4 Results and Discussion 27114.4.1 Results 27114.4.2 Discussion 27114.5 Conclusion 273Acknowledgment 273References 274Index 275