Machine Learning Paradigm for Internet of Things Applications

Herausgegeben:Rani, Shalli; Maheswar, R.; Kanagachidambaresan, G. R.; Ahuja, Sachin; Gupta, Deepali

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Wiley & Sons / Wiley-Scrivener
• Artikelnr. des Verlages: 1W119760470
• 1. Auflage
• Seitenzahl: 304
• Erscheinungstermin: 2. März 2022
• Englisch
• Abmessung: 231mm x 152mm x 23mm
• Gewicht: 546g
• ISBN-13: 9781119760474
• ISBN-10: 111976047X
• Artikelnr.: 60407613

Autorenporträt

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.

Inhaltsangabe

Preface xiii

1 Machine Learning Concept-Based IoT Platforms for Smart Cities' Implementation and Requirements 1
M. Saravanan, J. Ajayan, R. Maheswar, Eswaran Parthasarathy and K. Sumathi

1.1 Introduction 2

1.2 Smart City Structure in India 3

1.2.1 Bhubaneswar City 3

1.2.1.1 Specifications 3

1.2.1.2 Healthcare and Mobility Services 3

1.2.1.3 Productivity 4

1.2.2 Smart City in Pune 4

1.2.2.1 Specifications 5

1.2.2.2 Transport and Mobility 5

1.2.2.3 Water and Sewage Management 5

1.3 Status of Smart Cities in India 5

1.3.1 Funding Process by Government 6

1.4 Analysis of Smart City Setup 7

1.4.1 Physical Infrastructure-Based 7

1.4.2 Social Infrastructure-Based 7

1.4.3 Urban Mobility 8

1.4.4 Solid Waste Management System 8

1.4.5 Economical-Based Infrastructure 9

1.4.6 Infrastructure-Based Development 9

1.4.7 Water Supply System 10

1.4.8 Sewage Networking 10

1.5 Ideal Planning for the Sewage Networking Systems 10

1.5.1 Availability and Ideal Consumption of Resources 10

1.5.2 Anticipating Future Demand 11

1.5.3 Transporting Networks to Facilitate 11

1.5.4 Control Centers for Governing the City 12

1.5.5 Integrated Command and Control Center 12

1.6 Heritage of Culture Based on Modern Advancement 13

1.7 Funding and Business Models to Leverage 14

1.7.1 Fundings 15

1.8 Community-Based Development 16

1.8.1 Smart Medical Care 16

1.8.2 Smart Safety for The IT 16

1.8.3 IoT Communication Interface With ML 17

1.8.4 Machine Learning Algorithms 17

1.8.5 Smart Community 18

1.9 Revolutionary Impact With Other Locations 18

1.10 Finding Balanced City Development 20

1.11 E-Industry With Enhanced Resources 20

1.12 Strategy for Development of Smart Cities 21

1.12.1 Stakeholder Benefits 21

1.12.2 Urban Integration 22

1.12.3 Future Scope of City Innovations 22

1.12.4 Conclusion 23

References 24

2 An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan 27
W. H. Rankothge

2.1 Introduction 28

2.2 Background 29

2.2.1 Prediction of Future Paddy Harvest and Rice Consumption Demand 29

2.2.2 Rice Distribution 31

2.3 Methodology 31

2.3.1 Requirements of the Proposed Platform 32

2.3.2 Data to Evaluate the 'isRice" Platform 34

2.3.3 Implementation of Prediction Modules 34

2.3.3.1 Recurrent Neural Network 35

2.3.3.2 Long Short-Term Memory 36

2.3.3.3 Paddy Harvest Prediction Function 37

2.3.3.4 Rice Demand Prediction Function 39

2.3.4 Implementation of Rice Distribution Planning Module 40

2.3.4.1 Genetic Algorithm-Based Rice Distribution Planning 41

2.3.5 Front-End Implementation 44

2.4 Results and Discussion 45

2.4.1 Paddy Harvest Prediction Function 45

2.4.2 Rice Demand Prediction Function 46

2.4.3 Rice Distribution Planning Module 46

2.5 Conclusion 49

References 49

3 A Collaborative Data Publishing Model with Privacy Preservation Using Group-Based Classification and Anonymity 53
Carmel Mary Belinda M. J., K. Antonykumar, S. Ravikumar and Yogesh R. Kulkarni

3.1 Introduction 54

3.2 Literature Survey 56

3.3 Proposed Model 58

3.4 Results 61

3.5 Conclusion 64

Referen