Machine Learning Paradigm for Internet of Things Applications Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Next-Generation Computing and Communication Engineering Series Editors: Dr. G. R. Kanagachidambaresan and Dr. Kolla Bhanu Prakash Developmen telligence are made more challenging because the involvement of multi-domain technology creates new problems for rese p meet the challenge, this book series concentrates on next generation computing and communication methodologies involving smart and ambient environment design. It is an publishing platform for monographs, handbooks, and edited volumes on Industry 4.0, agriculture, smart city development, new computing and communication paradigms. Although the series mainly focuses on design, it also addresses analytics and investigation of industry-related real-time problems. Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected]) Machine Learning Paradigm for Internet of Things Applications Edited by Shalli Rani, R. Maheswar G. R. Kanagachidambaresan Sachin Ahuja and Deepali Gupta This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2022 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or other- wise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. 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Library of Congress Cataloging-in-Publication Data ISBN 978-1-119-76047-4 Cover image: Pixabay.Com Cover design by Russell Richardson Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1 Contents 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 v vi Contents 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 Contents vii 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 References 64 4 Production Monitoring and Dashboard Design for Industry 4.0 Using Single-Board Computer (SBC) 67 Dineshbabu V., Arul Kumar V. P. and Gowtham M. S. 4.1 Introduction 68 4.2 Related Works 69 4.3 Industry 4.0 Production and Dashboard Design 69 4.4 Results and Discussion 70 4.5 Conclusion 73 References 73 5 Generation of Two-Dimensional Text-Based CAPTCHA Using Graphical Operation 75 S. Pradeep Kumar and G. Kalpana 5.1 Introduction 75 5.2 Types of CAPTCHAs 78 5.2.1 Text-Based CAPTCHA 78 5.2.2 Image-Based CAPTCHA 80 5.2.3 Audio-Based CAPTCHA 80 5.2.4 Video-Based CAPTCHA 81 5.2.5 Puzzle-Based CAPTCHA 82 5.3 Related Work 82 5.4 Proposed Technique 82 viii Contents 5.5 Text-Based CAPTCHA Scheme 83 5.6 Breaking Text-Based CAPTCHA’s Scheme 85 5.6.1 Individual Character-Based Segmentation Method 85 5.6.2 C haracter Width-Based Segmentation Method 86 5.7 Implementation of Text-Based CAPTCHA Using Graphical Operation 87 5.7.1 Graphical Operation 87 5.7.2 Two-Dimensional Composite Transformation Calculation 89 5.8 Graphical Text-Based CAPTCHA in Online Application 91 5.9 Conclusion and Future Enhancement 93 References 94 6 Smart IoT-Enabled Traffic Sign Recognition With High Accuracy (TSR-HA) Using Deep Learning 97 Pradeep Kumar S., Jayanthi K. and Selvakumari S. 6.1 Introduction 98 6.1.1 Internet of Things 98 6.1.2 Deep Learning 98 6.1.3 Detecting the Traffic Sign With the Mask R-CNN 99 6.1.3.1 Mask R-Convolutional Neural Network 99 6.1.3.2 Color Space Conversion 100 6.2 Experimental Evaluation 101 6.2.1 Implementation Details 101 6.2.2 Traffic Sign Classification 101 6.2.3 Traffic Sign Detection 102 6.2.4 Sample Outputs 103 6.2.5 Raspberry Pi 4 Controls Vehicle Using OpenCV 103 6.2.5.1 Smart IoT-Enabled Traffic Signs Recognizing With High Accuracy Using Deep Learning 103 6.2.6 Python Code 108 6.3 Conclusion 109 References 110 7 Offline and Online Performance Evaluation Metrics of Recommender System: A Bird’s Eye View 113 R. Bhuvanya and M. Kavitha 7.1 Introduction 114 7.1.1 Modules of Recommender System 114 7.1.2 Evaluation Structure 115