Big Data, IoT, and Machine Learning Internet of Everything (IoE): Security and Privacy Paradigm Series Editor Mangey Ram Professor, Graphic Era University, Uttarakhand, India IOT Security and Privacy Paradigm Edited by Souvik Pal, Vicente Garcia Diaz, and Dac-Nhuong Le Smart Innovation of Web of Things Edited by Vijender Kumar Solanki, Raghvendra Kumar, and Le Hoang Son Big Data, IoT, and Machine Learning Tools and Applications Edited by Rashmi Agrawal, Marcin Paprzycki, and Neha Gupta Internet of Everything and Big Data Major Challenges in Smart Cities Edited by Salah-ddine Krit, Mohamed Elhoseny, Valentina Emilia Balas, Rachid Benlamri, and Marius M Balas For more information about this series, please visit: https://www.crcpress.com/ Internet-of-Everything-IoE-Security-and-Privacy-Paradigm/book-series/CRCIOE SPP Big Data, IoT, and Machine Learning Tools and Applications Edited by Rashmi Agrawal, Marcin Paprzycki, Neha Gupta First edition published 2021 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2021 Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint Except as permitted under U.S Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microflming, and recording, or in any information storage or retrieval system, without written permission from the publishers For permission to photocopy or use material electronically from this work, access www.copyright com or contact the Copyright Clearance Center, Inc (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400 For works that are not available on CCC please contact mpkbookspermissions @tandf.co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identifcation and explanation without intent to infringe Library of Congress Cataloging‑in‑Publication Data Names: Kolawole, Michael O., author Title: Electronics : from classical to quantum / Michael Olorunfunmi Kolawole Description: First edition | Boca Raton, FL : CRC Press, 2020 | Includes bibliographical references and index | Summary: “This book discusses formulation and classifcation of integrated circuits, develops hierarchical structure of functional logic blocks to build more complex digital logic circuits, outlines the structure of transistors, their processing techniques, their arrangement forming logic gates and digital circuits, optimal pass transistor stages of buffered chain, and performance of designed circuits under noisy conditions It also outlines the principles of quantum electronics leading to the development of lasers, masers, reversible quantum gates and circuits and applications of quantum cells” Provided by publisher Identifers: LCCN 2020011028 (print) | LCCN 2020011029 (ebook) | ISBN 9780367512224 (hardback) | ISBN 9781003052913 (ebook) Subjects: LCSH: Electronics | Electronic circuits | Quantum electronics Classifcation: LCC TK7815 K64 2020 (print) | LCC TK7815 (ebook) | DDC 621.3815 dc23 LC record available at https://lccn.loc.gov/2020011028 LC ebook record available at https://lccn.loc.gov/2020011029 ISBN: 9780367336745 (hbk) ISBN: 9780429322990 (ebk) Typeset in Palatino by Deanta Global Publishing Services Chennai India Contents Preface vii Acknowledgement xi Editors xiii Contributors xv Section I Applications of Machine Learning Machine Learning Classifers Rachna Behl and Indu Kashyap Dimension Reduction Techniques 37 Muhammad Kashif Hanif, Shaeela Ayesha and Ramzan Talib Reviews Analysis of Apple Store Applications Using Supervised Machine Learning 51 Sarah Al Dakhil and Sahar Bayoumi Machine Learning for Biomedical and Health Informatics 79 Sanjukta Bhattacharya and Chinmay Chakraborty Meta-Heuristic Algorithms: A Concentration on the Applications in Text Mining 113 Iman Raeesi Vanani and Setareh Majidian Optimizing Text Data in Deep Learning: An Experimental Approach 133 Ochin Sharma and Neha Batra Section II Big Data, Cloud and Internet of Things Latest Data and Analytics Technology Trends That Will Change Business Perspectives 153 Kamal Gulati A Proposal Based on Discrete Events for Improvement of the Transmission Channels in Cloud Environments and Big Data 185 Reinaldo Padilha Franỗa, Yuzo Iano, Ana Carolina Borges Monteiro, Rangel Arthur and Vania V Estrela v vi Contents Heterogeneous Data Fusion for Healthcare Monitoring: A Survey 205 Shrida Kalamkar and Geetha Mary A 10 Discriminative and Generative Model Learning for Video Object Tracking 233 Vijay K Sharma, K K Mahapatra and Bibhudendra Acharya 11 Feature, Technology, Application, and Challenges of Internet of Things 255 Ayush Kumar Agrawal and Manisha Bharti 12 Analytical Approach to Sustainable Smart City Using IoT and Machine Learning 277 Syed Imtiyaz Hassan and Parul Agarwal 13 Traffc Flow Prediction with Convolutional Neural Network Accelerated by Spark Distributed Cluster 295 Yihang Tang, Melody Moh and Teng-Sheng Moh Index 317 Preface INTRODUCTION Big data, machine learning and the Internet of Things (IoT) are the most talked-about technology topics of the last few years These technologies are set to transform all areas of business, as well as everyday life At a high level, machine learning takes large amounts of data and generates useful insights that help the organisation Such insights can be related to improving processes, cutting costs, creating a better experience for the customer or opening up new business models A large number of classic data models, which are often static and of limited scalability, cannot be applied to fast-changing, fast-growing in volume, unstructured data For instance, when it comes to the IoT, it is often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of highly heterogeneous data points OBJECTIVE OF THE BOOK The idea behind this book is to simplify the journey of aspiring readers and researchers to understand big data, the IoT and machine learning It also includes various real-time/offine applications and case studies in the felds of engineering, computer science, information security, cloud computing, with modern tools and technologies used to solve practical problems Thanks to this book, readers will be enabled to work on problems involving big data, the IoT and machine learning techniques and gain from experience In this context, this book provides a high level of understanding of various techniques and algorithms used in big data, the IoT and machine learning ORGANISATION OF THE BOOK This book consists of two sections containing 13 chapters Section I, entitled “Applications of Machine Learning” contains six chapters, which describe vii viii Preface concepts and various applications of Machine Learning Section II is dedicated to “Big Data, Cloud and Internet of Things”, and contains chapters, which describe applications using integration of Big Data, cloud computing and the IoT A brief summary of each chapter follows next Section I: Applications of Machine Learning Chapter on “Machine Learning Classifers” deals with the fundamentals of machine learning Authors facilitate a detailed literature review and summary of key algorithms concerning machine learning in the area of data classifcation Chapter on “Dimension Reduction Techniques” discusses the dimension reduction problem Classical dimensional reduction techniques like principal component analysis, latent discriminant analysis, and projection pursuit, are available for pre-processing and dimension reduction before applying machine learning algorithms The dimension reduction techniques have shown viable performance gain in many application areas such as biomedical, business and life science This chapter outlines the dimension reduction problem, and presents different dimension reduction techniques to improve the accuracy and effciency of machine learning models Application distribution platforms, such as Apple Store and Google Play, enable users to search and install software applications According to statistica.com, the number of mobile application downloaded from the App Store and Google Play has increased from 17 billion in 2013 to 80 billion in 2016 Chapter 3, “Reviews Analysis of Apple Store Applications Using Supervised Machine Learning”, contains a case study, which aims at building a system that enables the classifcation of Apple Store applications, based on the user’s reviews Biomedical research areas, such as clinical informatics, image analysis, clinical informatics, precision medicine, computational neuroscience and system biology, have achieved tremendous growth and improvement using machine learning algorithms This has created remarkable outcomes, such as drug discovery, accurate analysis of disease, medical diagnosis, personalised medication and massive developments in pharmaceuticals Analysis of data in medical science is one of the important areas that can be effectively done by machine learning Here, for instance, continuous data can be effectively used in an intensive care unit, if the data can be effciently interpreted In Chapter 4, “Machine Learning for Biomedical and Health Informatics”, a detailed description of machine learning, along with its various applications in biomedical and health informatics areas, has been presented Chapter 5, “Meta-Heuristic Algorithms: A Concentration on the Applications in Text Mining”, presents a detailed literature review of metaheuristic algorithms In this chapter, 11 meta-heuristic algorithms have been introduced, and some of their applications in text mining and other areas have been pointed out Despite the fact that some of them have been widely Preface ix used in other areas, the research, which shows their application in text mining, is limited The aim of the chapter is to both introduce meta-heuristic algorithms and motivate researchers to deploy them in text mining research Deep learning is used within special forms of artifcial neural networks When using deep learning, user gets the benefts of both machine learning and artifcial intelligence The overall structure of deep learning models is based upon the structure of the brain As the brain senses input by audio, text, image or video, this input is processed and an output generated This output may trigger some action In Chapter 6, “Optimising Text Data in Deep Learning: An Experimental Approach”, challenges related to deep learning have been discussed Moreover, results of experiments conducted with textbased deep learning models, using Python, TensorFlow and Tkinter, have been presented Section II: Big Data, Cloud and Internet of Things Data and analytics technology trends will have signifcant disruptive effect over the next to years Data and analytics leaders must examine their business impacts and adjust their operating, business and strategy models accordingly Chapter 7, “Latest Data and Analytics Technology Trends That Will Change Business Perspective”, details these trends and their impact in businesses In Chapter 8, “A Proposal Based on Discrete Events for Improvement of the Transmission Channels in Cloud Environments and Big Data”, a method of data transmission, based on discrete event concepts, using the MATLAB software, is demonstrated In this method, memory consumption is evaluated, with the differential present in the use of discrete events applied in the physical layer of a transmission medium With the increasing number of sensing devices, the complexity of data fusion is also increasing Various issues, like complex distributed processing, unreliable data communication, uncertainty of data analysis and data transmission at different rates have been identifed Taking into consideration these issues, in Chapter 9, “Heterogeneous Data Fusion for Healthcare Monitoring: A Survey”, the authors review the data fusion algorithms and present some of the most important challenges materialising when handling Big Data Chapter 10, “Discriminative and Generative Model Learning for Video Object Tracking”, is devoted to video object tracking For video object tracking, a generative appearance model is constructed, using tracked targets in successive frames The purpose of the discriminative model is to construct a classifer to separate the object from the background A support vector machine (SVM) classifer performs excellently when the training samples are low and all samples are provided once In video object tracking, all examples are not available simultaneously and therefore online learning is the only way forward