Table Of ContentBig 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
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and Marius M. Balas
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Intern et- of - Ever ythin g- IoE - Secu rity- and-P rivac y -Pa radig m /b oo k -ser ies /C RCIOE
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Big Data, IoT, and
Machine Learning
Tools and Applications
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Neha Gupta
First edition published 2021
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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
1 Machine Learning Classifers ......................................................................3
Rachna Behl and Indu Kashyap
2 Dimension Reduction Techniques ............................................................37
Muhammad Kashif Hanif, Shaeela Ayesha and Ramzan Talib
3 Reviews Analysis of Apple Store Applications Using
Supervised Machine Learning .................................................................. 51
Sarah Al Dakhil and Sahar Bayoumi
4 Machine Learning for Biomedical and Health Informatics ................79
Sanjukta Bhattacharya and Chinmay Chakraborty
5 Meta-Heuristic Algorithms: A Concentration on the
Applications in Text Mining .................................................................... 113
Iman Raeesi Vanani and Setareh Majidian
6 Optimizing Text Data in Deep Learning: An Experimental
Approach ...................................................................................................... 133
Ochin Sharma and Neha Batra
Section II Big Data, Cloud and Internet of Things
7 Latest Data and Analytics Technology Trends That Will Change
Business Perspectives ................................................................................ 153
Kamal Gulati
8 A Proposal Based on Discrete Events for Improvement of the
Transmission Channels in Cloud Environments and Big Data ........ 185
Reinaldo Padilha França, Yuzo Iano, Ana Carolina Borges Monteiro,
Rangel Arthur and Vania V. Estrela
v
vi Contents
9 Heterogeneous Data Fusion for Healthcare Monitoring:
ASurvey .......................................................................................................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 pro-
cesses, 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 sen-
sor 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 dedi-
cated to “Big Data, Cloud and Internet of Things”, and contains 7 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 1 on “Machine Learning Classifers” deals with the fundamentals
of machine learning. Authors facilitate a detailed literature review and sum-
mary of key algorithms concerning machine learning in the area of data
classifcation.
Chapter 2 on “Dimension Reduction Techniques” discusses the dimension
reduction problem. Classical dimensional reduction techniques like princi-
pal 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 biomedi-
cal, 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 statis-
tica .co m, 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, person-
alised 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 effec-
tively 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 meta-
heuristic 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 min-
ing, 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 text-
based 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 3 to 5 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 evalu-
ated, 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 process-
ing, unreliable data communication, uncertainty of data analysis and data
transmission at different rates have been identifed. Taking into consider-
ation 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 track-
ing, 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.
