Machine Learning Techniques for Smart City Applications: Trends and Solutions

227 Pages·2022·14.279 MB·English

by  D. Jude Hemanth

Advances in Science, Technology & Innovation
IEREK Interdisciplinary Series for Sustainable Development
D. Jude Hemanth Editor
Machine Learning
Techniques for Smart
City Applications:
Trends and Solutions
Advances in Science, Technology & Innovation
IEREK Interdisciplinary Series for Sustainable
Development
Editorial Board
Anna Laura Pisello, Department of Engineering, University of Perugia, Italy
Dean Hawkes, University of Cambridge, Cambridge, UK
Hocine Bougdah, University for the Creative Arts, Farnham, UK
Federica Rosso, Sapienza University of Rome, Rome, Italy
Hassan Abdalla, University of East London, London, UK
Sofia-Natalia Boemi, Aristotle University of Thessaloniki, Greece
Nabil Mohareb, Faculty of Architecture - Design and Built Environment,
Beirut Arab University, Beirut, Lebanon
Saleh Mesbah Elkaffas, Arab Academy for Science, Technology and Maritime Transport,
Cairo, Egypt
Emmanuel Bozonnet, University of La Rochelle, La Rochelle, France
Gloria Pignatta, University of Perugia, Italy
Yasser Mahgoub, Qatar University, Qatar
Luciano De Bonis, University of Molise, Italy
Stella Kostopoulou, Regional and Tourism Development, University of Thessaloniki,
Thessaloniki, Greece
Biswajeet Pradhan, Faculty of Engineering and IT, University of Technology Sydney,
Sydney, Australia
Md. Abdul Mannan, Universiti Malaysia Sarawak, Malaysia
Chaham Alalouch, Sultan Qaboos University, Muscat, Oman
Iman O. Gawad, Helwan University, Egypt
Anand Nayyar , Graduate School, Duy Tan University, Da Nang, Vietnam
Series Editor
Mourad Amer, International Experts for Research Enrichment and Knowledge Exchange
(IEREK), Cairo, Egypt
Advances in Science, Technology & Innovation (ASTI) is a series of peer-reviewed books
based on important emerging research that redefines the current disciplinary boundaries in
science, technology and innovation (STI) in order to develop integrated concepts for
sustainable development. It not only discusses the progress made towards securing more
resources, allocating smarter solutions, and rebalancing the relationship between nature and
people,butalsoprovidesin-depthinsightsfromcomprehensiveresearchthataddressesthe17
sustainable development goals (SDGs) as set out by the UN for 2030.
The series draws on the best research papers from various IEREK and other international
conferences to promote the creation and development of viable solutions for a sustainable
future and a positive societal transformation with the help of integrated and innovative
science-based approaches. Including interdisciplinary contributions, it presents innovative
approaches and highlights how they can best support both economic and sustainable
development, through better use of data, more effective institutions, and global, local and
individual action, for the welfare of all societies.
The series particularly features conceptual and empirical contributions from various
interrelated fields of science, technology and innovation, with an emphasis on digital
transformation,thatfocusonprovidingpracticalsolutionstoensurefood,waterandenergy
securitytoachievetheSDGs.Italsopresentsnewcasestudiesofferingconcreteexamplesof
how to resolve sustainable urbanization and environmental issues in different regions of the
world.
The series is intended for professionals in research and teaching, consultancies and
industry, and government and international organizations. Published in collaboration with
IEREK, the Springer ASTI series will acquaint readers with essential new studies in STI for
sustainabledevelopment.
ASTI series has now been accepted for Scopus (September 2020). All content published
in this series will start appearing on the Scopus site in early 2021.
D. Jude Hemanth
Editor
Machine Learning Techniques
for Smart City Applications:
Trends and Solutions
123
Editor
D.JudeHemanth
Karunya Institute of Technology andSciences
Coimbatore, India
ISSN 2522-8714 ISSN 2522-8722 (electronic)
Advances in Science, Technology &Innovation
IEREK Interdisciplinary Series for Sustainable Development
ISBN978-3-031-08858-2 ISBN978-3-031-08859-9 (eBook)
https://doi.org/10.1007/978-3-031-08859-9
©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2022
Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whetherthewholeor
part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformationstorageand
retrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynowknownorhereafter
developed.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublicationdoesnot
imply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevantprotectivelawsand
regulationsandthereforefreeforgeneraluse.
Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbookarebelieved
tobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsortheeditorsgiveawarranty,
expressedorimplied,withrespecttothematerialcontainedhereinorforanyerrorsoromissionsthatmayhavebeen
made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional
affiliations.
Coverillustration:Tostphoto/Shutterstock.com
ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
Preface
With the significant advancement in technologies, it has been always a desire of the human
race to focus towards a comfortable lifestyle. Apart from the lifestyle, technologies play a
majorroleinconservingthenaturalresourceswhichaidinabetterenvironmentforthehuman
beings to live in this world. With this background, the concept of smart cities has gained
significantattentionintoday’sscenario.Thoughthe“smartcity”conceptispopular,itisstilla
distant dream in the context of under-developed and developing countries. Though many
advancedtechnologiesarebeingexploredinthecontextofsmartcities,therearestillscopefor
improvementinthedifferentsub-conceptsofsmartcitieswhichcanhelptheruralregionsalso.
In other words, it can be mentioned that advanced technologies are not fully explored in the
context of smart cities. Another aspect is the necessity for the innovative/novel technologies
whichcanmakethedifferentdimensionsofsmartcitiespracticallyfeasible.Machinelearning
isoneoftheprimeadvancedtechnologieswhichguaranteehighperformanceforanypractical
set-up/system. The combination of machine learning and smart cities can be one of the
solutions for making this dream a reality. This book specially focuses on the application of
differentmachinelearningtechniquesonthesub-conceptsofsmartcitiessuchassmartenergy,
smart transportation, smart waste management, smart health and smart infrastructure. The
objectiveofthisbookistobringoutinnovativesolutionsintheabove-saidissuestoalleviate
the pressing difficulties of human society. A brief introduction about each chapter is as
follows.
Chapter1isalignedcloselywithsmarteducationsectorinwhichadeeplearningmodelis
usedtopredictthebehaviourofyoungadultsinthecontextoftheirparticipationinpubliclife.
This chapter also emphasizes the need for the proper involvement of young adults in the
development of any cities. Chapter 2 deals with the combination of smart infrastructure and
deep learning. A deep learning model is used in this work to analyse the electrical theft in
smart grid systems. The security issues of smart grid systems are dealt in detail which are an
inherent part of smart cities. The focal point of Chap. 3 is smart health in which a machine
learning-based assistive system is developed for the disabled community. This chapter
highlights the necessity for maintaining the quality of living of all the people in a smart city.
The availabilityof different machine learning-basedmethodsforsign languagerecognition is
described in Chap. 4. The contents of this chapter are focussed towards smart health for a
hearing-impaired person.
Chapter 5 covers the smart surveillance sector in combination with machine learning
techniques.Asmartcityisexpectedtobeundersurveillancetoavoidanyunnecessaryevents
such as the accidents. Machine learning assists in this context by analysing the behaviour
ofthedriverswhichisoneoftheprimereasonsfortheunpleasantevents.Smartdrones-based
city surveillance is the prime objective of Chap. 6. Several machine learning algorithms are
developed to classify and identify the different types of vehicles in a live traffic system.
Chapter 7 deals with an artificial neural network-based smart tracking system. The inclusion
of the concepts of antenna into machine learning adds significant weightage to the practical
feasibility of the proposed system in a smart city scenario. Ensemble machine learning
v
vi Preface
classifiersareusedtodetectthepedestrians inChap.8 with thehelp ofinfrared images.This
methodisparticularlyusefulforanightvisionsystemwhichfallsunderthecategoryofsmart
transportation systems.
Chapter 9dealswiththebroad spectrum ofsmarthealthandmachinelearningtechniques.
Machinelearningtechniquesareusedtodiagnoseandmanagethesleeppatternoftheelderly
people. It may be noted that a significant portion of the whole population in a smart city
comprises the elderly people. Smart traffic management is the emphasis of Chap. 10. In this
work,machinelearningtechniquesareusedtopredictthetime-dependenttrafficpatternwhich
canavoidthecongestionsandotherbottlenecksinatrafficsystem.Chapter11talksaboutthe
emergency department allocation in hospitals during pandemic situations. Machine learning
models are used to efficiently allot the emergency rooms to the patients who need the most.
This proposed model will avoid any chaotic situations in the hospital environment. Smart
energy is the main concept of Chap. 12. In this work, the application of machine learning
modelsforgenerationanddistributionofwindenergyisdealtindetail.Energysourcesarean
integral part of a successful smart city set-up.
Chapter 13 deals with the smart waste management system using machine learning tech-
niques. Efficient categorization and management of trash in coastal areas is the main topic of
thiswork.Deeplearningarchitecturesareusedforthisexperimentalwork.Smartclassroomin
a university set-up is discussed in Chap. 14. The integration of machine learning with the
education sector is the need of the hour in today’s online education system. Smart health
monitoringprotocolsineducationalinstitutionsamongstudentsarewelldescribedinChap.15.
This type of machine learning-based system will be useful especially during the pandemic
situation. Automation of industrial applications such as path navigation is the prime focus of
Chap.16.Autonomousrobotsalongwiththemachinelearningtechniquesarewelldescribedin
this chapter.Thistype ofmodelsis often deployed inautonomous robots-based industries.
I am thankful to the authors and reviewers for their contributions towards this book. My
special thanks go to Dr. Mourad Amer and Dr. Nabil Khelifi (Editors) for the opportunity to
organize this edited volume. Finally, I would like to thank Ms. Toka Mourad Frihy who
coordinated the entire proceedings. This edited book covers the fundamental concepts and
application areas in detail which is one of the main advantages of this book. Being an
interdisciplinary book, I hope it will be useful to a wide variety of readers and will provide
useful information to professors, researchers and students.
Coimbatore, India Dr. D. Jude Hemanth
January 2022
Contents
Applying Deep Learning to Predict Civic Purpose Development:
Within the Smart City Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Hyemin Han
Convolution Neural Network Scheme for Detection of Electricity Theft
in Smart Grids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Matthew Palmer, G. Jaspher Willsie Kathrine, and S. Jebapriya
Helping Hand: A GMM-Based Real-Time Assistive Device for Disabled
Using Hand Gestures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
S. Gnanapriya and K. Rahimunnisa
A Review on Hand Gesture and Sign Language Techniques for Hearing
Impaired Person . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Safyzan Salim, Muhammad Mahadi Abdul Jamil, Radzi Ambar,
and Mohd Helmy Abd Wahab
DriveSense: Adaptive System for Driving Behaviour Analysis and Ranking . . . . . 45
Sankar Behera, Bhavya Bhardwaj, Aurea Rose, Mohammad Hamdaan,
and M. Ganesan
Classification and Tracking of Vehicles Using Videos Captured by Unmanned
Aerial Vehicles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Jorge E. Espinosa, Jairo Espinosa, and Sergio A. Velastin
Tracking Everyone and Everything in Smart Cities with an ANN Driven
Smart Antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Herman Kunsei, Paul R. P. Hoole, K. Pirapaharan, and S. R. H. Hoole
Wavelet-Based Saliency and Ensemble Classifier for Pedestrian Detection
in Infrared Images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
R. Newlin Shebiah and S. Arivazhagan
A Survey of Emerging Applications of Machine Learning in the Diagnosis
and Management of Sleep Hygiene and Health in the Elderly Population. . . . . . . 109
B. Banu Rekha and A. Kandaswamy
Smart City Traffic Patterns Prediction Using Machine Learning . . . . . . . . . . . . . 123
David Opeoluwa Oyewola, Emmanuel Gbenga Dada,
and Muhammed Besiru Jibrin
Emergency Department Management Using Regression Models . . . . . . . . . . . . . . 135
S. Kezia, A. Hepzibah Christinal, D. Abraham Chandy,
and M. James Graham Steward
Machine Learning in Wind Energy: Generation to Supply . . . . . . . . . . . . . . . . . . 143
Bhavya Bhardwaj and M. Ganesan
vii
viii Contents
Multi-class Segmentation of Trash in Coastal Areas Using Encoder-Decoder
Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
S. Surya Prakash, V. Vengadesh, M. Vignesh, and Satheesh Kumar Gopal
Learning Analytics for Smart Classroom System in a University Campus . . . . . . 171
Tasneem Hossenally, U. Kawsar Subratty, and Soulakshmee D. Nagowah
Predictive Analytics for Smart Health Monitoring System in a University
Campus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Zakia N. S. H. Mohung, B. Unayza Boodoo, and Soulakshmee D. Nagowah
SysML-Based Design of Autonomous Multi-robot Cyber-Physical System
Using Smart IoT Modules: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Qasem Abu Al-Haija
Vulnerabilities and Ethical Issues in Machine Learning for Smart City
Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
K. Martin Sagayam, Roopa Jeyasingh, J. Jenkin Winston, and Tony Jose
Applying Deep Learning to Predict Civic
Purpose Development: Within the Smart
City Context
Hyemin Han
Abstract intended to present the prediction process and result as a
simple illustrative example demonstrating the potential val-
We applied the deep learning method, which has been
ues and benefits of using computational methods in educa-
developed in the fields of computer and data science for
tional and psychological research. Participating in political
accurate prediction, to predict political purpose develop-
activitiesduringadolescenceandearlyadulthoodisessential
mentduringemergingadulthood.Wetestedwhetherdeep
in promoting continued political engagement throughout
learning more accurately predicted Wave 2 political
one’s life (Youniss et al., 2002). Thus, it is necessary to
purpose with Wave 1 predictors compared with tradi-
empirically examine which factors significantly influence
tional regression. A convolutional neural network con-
politicalpurpose,asenseofpurposetocontinuouslyengage
sisting of two dense and dropout layers was trained to
inpoliticalactivities(Malinetal.,2015),duringthisperiod.
predict the outcome variable. For comparison, we also
Several previous studies have examined the aforementioned
estimated a multinomial logistic regression model. The
factors (Malin et al., 2017) reported that demographical and
result demonstrated that deep learning outperformed
education-related factors were significantly associated with
traditional regression in general while effectively mini-
changes in civic engagement and purpose during emerging
mizing overfitting. Moreover, from exploratory analysis,
adulthood.(Hanetal.,2021)showedthatpresenceofstrong
we found that deep learning might be able to model the
moralandpoliticalidentitypositivelypredicteddevelopment
non-linear relationship between the predictors and out-
of political purpose.
come variables. Based on the findings, we discussed the
Although the previous studies examined the factors that
implications of the present study within the context of
facilitate the development of political purpose during
improving citizens’ lives in smart cities.
emerging adulthood, (e.g., Crocetti et al., 2014; Han et al.,
2021; Malin et al., 2017) several points related to model
Keywords
(cid:1) (cid:1) prediction shall be reconsidered and improved. Recent
Deeplearning Politicalpurpose Multinomiallogistic methodological advances in data science, such as the wide
regression employment of the deep learning method, have demon-
strated that novel methods can more accurately predict out-
comes compared with traditional regression-based methods
ineducationalandpsychologicalresearch(Hanetal.,2020).
1 Introduction This advantage becomes more significant when the predic-
tionisinvolvedindiverseandcomplexpredictors(Awad&
Inthepresentstudy,weappliedthedeeplearningmethod,a Khanna, 2015). Also, previous studies that used traditional
relatively novel method for prediction and pattern classifi- regression methods did not take into account the possibility
cation in computer science, for predicting the development of overfitting (Han & Dawson, 2021; Kim et al., 2016;
of purpose to engage in civic activity, political activity in McNeish, 2015). This occurs when we attempt to predict
particular, among young adults in emerging adulthood. We outcome variables of interest with limited amount of data.
Although a trained model may well explain and predict
outcomes within the boundary of the used data, the model
H.Han(&)
might be “overfitted” to the data and not be able to perform
EducationalPsychologyProgram,UniversityofAlabama,
Box872301Tuscaloosa,AL35487,USA in reality outside of the boundary (Han & Dawson, 2021;
e-mail:[email protected]
©TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2022 1
D.J.Hemanth(ed.),MachineLearningTechniquesforSmartCityApplications:
TrendsandSolutions,AdvancesinScience,Technology&Innovation,
https://doi.org/10.1007/978-3-031-08859-9_1