Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications

429 Pages·2020·15.173 MB·English

by  George A. Tsihrintzis, Lakhmi C. Jain

Learning and Analytics in Intelligent Systems 18
George A. Tsihrintzis
Lakhmi C. Jain   Editors
Machine
Learning
Paradigms
Advances in Deep Learning-based
Technological Applications
Learning and Analytics in Intelligent Systems
Volume 18
Series Editors
George A. Tsihrintzis, University of Piraeus, Piraeus, Greece
Maria Virvou, University of Piraeus, Piraeus, Greece
Lakhmi C. Jain, Faculty of Engineering and Information Technology,
Centre for Artificial Intelligence, University of Technology Sydney, NSW,
Australia;
KES International, Shoreham-by-Sea, UK;
Liverpool Hope University, Liverpool, UK
Themainaimoftheseriesistomakeavailableapublicationofbooksinhardcopy
form and soft copy form on all aspects of learning, analytics and advanced
intelligentsystemsandrelatedtechnologies.Thementioneddisciplinesarestrongly
related and complement one another significantly. Thus, the series encourages
cross-fertilization highlighting research and knowledge of common interest. The
series allows a unified/integrated approach to themes and topics in these scientific
disciplines which will result in significant cross-fertilization and research dissem-
ination. To maximize dissemination of research results and knowledge in these
disciplines, the series publishes edited books, monographs, handbooks, textbooks
and conference proceedings.
More information about this series at http://www.springer.com/series/16172
George A. Tsihrintzis Lakhmi C. Jain
(cid:129)
Editors
Machine Learning Paradigms
Advances in Deep Learning-based
Technological Applications
123
Editors
George A.Tsihrintzis LakhmiC. Jain
Department ofInformatics University of Technology
University of Piraeus Sydney,NSW,Australia
Piraeus, Greece
LiverpoolHope University
Liverpool, UK
ISSN 2662-3447 ISSN 2662-3455 (electronic)
Learning andAnalytics in Intelligent Systems
ISBN978-3-030-49723-1 ISBN978-3-030-49724-8 (eBook)
https://doi.org/10.1007/978-3-030-49724-8
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George A. Tsihrintzis and Lakhmi C. Jain
Foreword
Indisputably,theparadigmsofMachineLearninganddeeplearningwithitsplethora
ofapplicationsandadiversityoftechnologicalpursuitshavepositionedthemselves
attheforefrontofstudiesonintelligentsystems.Itisverylikelythatthisinterestwill
growovertime.Evenforanexperiencedresearcher,thebroadspectrumofdirections
and a rapidly changing landscape of the discipline create a quest to navigate effi-
ciently across current developments and gain a thorough insight into their accom-
plishments and limitations. Thisvolume addresses theevident challenges.
Professors George A. Tsihrintzis and Lakhmi C. Jain did a superb job by
bringingacollectionofcarefullyselectedandcoherentlystructuredcontributionsto
studies on recent developments. The chapters have been written by active
researchers who reported on their research agendas and timely outcomes of their
investigationsbeingpositionedattheforefrontofMachineLearningmethodologies
and its existing practices.
A set of 16 chapters delivers an impressive spectrum of authoritatively covered
timely topics by being focused on deep learning to sensing (3 chapters), Social
MediaandInternetofThings(IoT)(2chapters),healthcare(2chapters)focusedon
medical imaging and electroencephalography, systems control (2 chapters), and
forecasting and prediction (3 chapters). The last part of the book focuses on per-
formance analysis of the schemes of deep learning (3 chapters).
Invirtueoftheorganizationofthecontributionsandthewaythekeyideashave
been exposed, cohesion of exposure retained, a smooth flow of algorithmic
developments, and a wealth of valuable and far-reaching applications have been
delivered. Deep learning by itself is a well-established paradigm, however when
cast in a context of a certain problem, the domain knowledge contributes to its
enhancedusage.Thisisacruxofwellthought-outdesignprocessandherethebook
offers this important enhancement to the general design strategy.
vii
viii Foreword
Allinall,thebookisawell-roundedvolumewhichwillbeofgenuineinterestto
the academic community as well as practitioners with a vital interest in Machine
Learning. The Editors should be congratulated on bringing this timely and useful
research material to the readers.
Prof. Dr. Witold Pedrycz
Fellow IEEE
University of Alberta
Alberta, Canada
Systems Research Institute Polish Academy of Sciences
Warsaw, Poland
Preface
Artificial Intelligence [1], in general, and Machine Learning [2], in particular, are
scientific areas of very active research worldwide. However, while many
researchers continuously report on new findings and innovative applications,
skeptics warn us of potentially existential threats to humankind [3, 4]. The debate
among zealots and skeptics of Artificial Intelligence is ongoing and only in the
future we may seethis issue resolved. Forthe moment, it seems that perhaps Nick
Bostrom’s statement constitutes the most reliable view: “…artificial intelligence
(AI) is not an ongoing or imminent global catastrophic risk. Nor is it as uncon-
troversially a serious cause for concern. However, from a long term perspective,
thedevelopmentofgeneralartificialintelligenceexceedingthatofthehumanbrain
canbeseenasoneofthemainchallengestothefutureofhumanity(arguably,even
as the main challenge)” [5].
Forthemoment,itisalsocertainthataneweraisarisinginhumancivilization,
which has been called “the 4th Industrial Revolution” [6,7], and Artificial
Intelligence is one of the key technologies driving it.
Within the broad discipline of Artificial Intelligence, a sub-field of Machine
Learning, called Deep Learning, stands out due to its worldwide pace of growth
both in new theoretical results and in new technological application areas (for
example,see[8]foran-easy-to-followfirstreadonDeepLearning).Whilegrowing
at a formidable rate, Deep Learning is also achieving high scores in successful
technological applications and promises major impact in science, technology, and
the society.
The book at hand aims at exposing its reader to some of the most significant
recent advances in deep learning-based technological applications and consists of
an editorial note and an additional fifteen (15) chapters. All chapters in the book
were invited from authors who work in the corresponding chapter theme and are
recognized for their significant research contributions. In more detail, the chapters
in the book are organized into six parts, namely (1) Deep Learning in Sensing,
(2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical
Field,(4)DeepLearninginSystemsControl,(5)DeepLearninginFeatureVector
Processing, and (6) Evaluation of Algorithm Performance.
ix
x Preface
This research book is directed toward professors, researchers, scientists, engi-
neers, and students in computer science-related disciplines. It is also directed
toward readers who come from other disciplines and are interested in becoming
versed in some of the most recent deep learning-based technological applications.
An extensive list of bibliographic references at the end of each chapter guides the
readers to probe deeper into their application areas of interest. We hope that all
of them will find it useful and inspiring in their works and researches.
Wearegratefultotheauthorsandreviewersfortheirexcellentcontributionsand
visionary ideas. We are also thankful to Springer for agreeing to publish this book
initsLearningandAnalyticsinIntelligentSystemsseries.Last,butnotleast,weare
grateful to the Springer stafffor their excellent work in producing this book.
Piraeus, Greece George A. Tsihrintzis
Sydney, Australia Lakhmi C. Jain
References
1. E.Rich,K.Knight,S.B.Nair,ArtificialIntelligence,3rdedn.(TataMcGraw-HillPublishing
Company,2010)
2. J.Watt,R.Borhani,A.K.Katsaggelos,MachineLearningRefined—Foundations,Algorithms
andApplications,2ndedn.(CambridgeUniversityPress,2020)
3. J.Barrat,OurFinalInvention—ArtificialIntelligenceandtheEndoftheHumanEra,Thomas
DunnBooks,2013
4. N.Bostrom,Superintelligence—Paths,Dangers,Startegies(OxfordUniversityPress,2014)
5. N. Bostrom, M. M. Ćirković (eds.), Global Catastrophic Risks, (Oxford University Press,
2008),p.17
6. J. Toonders, Data Is the New Oil of the Digital Economy, Wired (https://www.wired.com/
insights/2014/07/data-new-oil-digital-economy/)
7. K.Schwabd,TheFourthIndustrialRevolution—WhatItMeansandHowtoRespond,Foreign
Affairs (2015), https://www.foreignaffairs.com/articles/2015-12-12/fourth-industrial-revolution
Accessed12December2015
8. J.Patterson,A.Gibson,DeepLearning—APractitioner’sApproach,O’Reilly,2017