Fusion of Machine Learning Paradigms: Theory and Applications

204 Pages·2023·4.48 MB·English

by  Ioannis K. Hatzilygeroudis, George A. Tsihrintzis, Lakhmi C. Jain

Intelligent Systems Reference Library 236
Ioannis K. Hatzilygeroudis
George A. Tsihrintzis
Lakhmi C. Jain   Editors
Fusion
of Machine
Learning
Paradigms
Theory and Applications
Intelligent Systems Reference Library
Volume 236
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· ·
Ioannis K. Hatzilygeroudis George A. Tsihrintzis
Lakhmi C. Jain
Editors
Fusion of Machine Learning
Paradigms
Theory and Applications
Editors
IoannisK.Hatzilygeroudis GeorgeA.Tsihrintzis
DepartmentofComputerEngineering DepartmentofInformatics
andInformatics UniversityofPiraeus
UniversityofPatras Piraeus,Greece
Patras,Greece
LakhmiC.Jain
KESInternational
Shoreham-by-Sea,UK
ISSN 1868-4394 ISSN 1868-4408 (electronic)
IntelligentSystemsReferenceLibrary
ISBN 978-3-031-22370-9 ISBN 978-3-031-22371-6 (eBook)
https://doi.org/10.1007/978-3-031-22371-6
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Foreword
InthesixdecadesthatfollowedArthurSamuel’sintroductionoftheterm“Machine
Learning”[1,2],severallearningparadigmshaveemergedandresearchersworld-
widehavecomeupwithvariousinnovativeapproachestolearningproblems.Indeed,
avastliteratureoftheoreticalresultsondifferentlearningmethodologiesiscurrently
available,whichhavebeensuccessfullyappliedtodiversescientificareas.Inrecent
years, however, research interest seems to be turning to the integration (fusion) of
two or more methodologies to address a learning problem [3]. For example, this
fusionmayconsistofthecombinationoftwoormoredifferentMachineLearning
paradigms,oritmaybethecombinationofaMachineLearningparadigmwithother
intelligenttechniques,suchasfuzzylogicorgeneticalgorithms.Inbothintegration
cases,thefusionresultsinhigherperformancethananyofthefusedmethodologies
ontheirownbecauseitcombinestheadvantagesofeachoneofthem.
Inthisbook,ProfessorsHatzilygeroudis,Tsihrintzis,andJainarepresentingeight
recentcasestudiesofFusionofMachineLearningParadigms.Suchabookisalways
welcomeasthisareaisconstantlyexpanding,andMachineLearningcommunities
needtobecontinuouslyupdatedonthemostrecentadvances.Morespecifically,the
casestudiesaredrawnfromthedualuse(militaryandcivilian)ofArtificialIntelli-
gence,MedicalDiagnosis,ImageRestoration,SolarPowerForecasting,Integrated
Circuit Analysis, Learning/Education Analytics, and Production Line Throughput
Estimation.Eachchapterhasbeenauthoredbyresearcherswithworldwiderecog-
nitioninitsarea.Fulldetailsofthefusionoflearningparadigmsaregiven,which
allowsthereadersofthebooktogaininsightand,moreimportantly,tobecomeable
todesignandapplyintegrationtechniquesofMachineLearningparadigmstoother
applicationareas.
Eventhoughtheeditorsundertookadifficulttask,namelytopresentarepresen-
tativeaccountofFusionofMachineLearningParadigms,thecoverageofthebook
themeisimpressiveintermsofbothbreadthanddepth.Ononehand,thepresented
casestudiesarequitediverse,and,ontheotherhand,eachcasestudyispresented
in depth. Undoubtedly, readers with a background in Machine Learning, Artificial
Intelligence,SoftwareEngineering,andComputerSciencewillfindthebookpartic-
ularlyhelpfulintheirresearches.However,Iamconfidentthatinterestwillalsobe
v
vi Foreword
stirredamonggeneralreaderswhoareseekingtobeversedincurrentmethodologies
ofFusionofMachineLearningParadigms.
I, thus, congratulate the editors for their superb job, and I highly recom-
mend this timely book to both Machine Learning/Artificial Intelligence/Software
Engineering/ComputerScienceresearchersandgeneralreaders.
NikolaosG.Bourbakis
IEEELifeFellow,AAIAFellow
DistinguishedProfessorofInformation
TechnologyandDirectoroftheCenter
ofAssistiveResearchTechnologies
(CART)
WrightStateUniversity
Dayton,OH,USA
References
1. Samuel,A.:SomeStudiesinmachinelearningusingthegameofcheckers.IBMJ.3(3),210–
229(1959)
2. https://en.wikipedia.org/wiki/Arthur_Samuel
3. Bourbakis, N.: Synergy of AI methods with Applications to Visual Impaired: Document
Reading,FaceRecognition,Navigation,AIAI-09,Thessaloniki,Greece(2009)
Preface
Originatinginaclassic1959paperbyArthurSamuelandaimingattheincorpora-
tionoflearningabilitiesintomachines,MachineLearningconstitutesasubfieldof
ArtificialIntelligencewhichenjoysintenseworldwideinterestfromresearchersin
bothacademiaandindustry.Asaresultofthissix-decade-oldresearcheffort,avast
diversityofrelevantmechanisms,methodologies,procedures,andalgorithmshave
beendevelopedandsuccessfullyappliedwhichhavealreadyeffectedamajorimpact
onscience,technology,andsocietyandpointtointensificationofthisimpactinthe
yearstocome.
At the same time, important and evolving new technological advancements in
a vast range of application areas in all sorts of human activities and professions
(e.g., collection of extensive datasets from smart sensors (“big data”), smartphone
and mobile software applications, or the Internet of Things) constantly exercise
pressureonresearcherstodeviseinnovativeandmoreefficientmethodstoaddress
thecorrespondingcomplexMachineLearningproblems.
One of the most successful and most promising Machine Learning approaches
that, in recent years, have claimed a large part of research activities is that of
Integrated or Hybrid Approaches and Methodologies. These are approaches that
combine or fuse together two or more component methodologies and include
neuro-symbolic integrations, either rule-based or logic-based, or the combination
of Machine Learning approaches with fuzzy logic, evolutionary algorithms, arti-
ficialimmunesystems,orotherstatisticalmethods.Variousimportantapplications
indicatethatfusingtogethervariousMachineLearningapproachesresultsinmethod-
ologiesthatbenefitfromeachoftheirconstituentcomponentsand,thus,outperform
them.
ThebookathandaimsatupdatingtherelevantArtificialIntelligence,Software
Engineering,andComputerScience-relatedresearchcommunities,includingprofes-
sors,researchers,scientists,engineers,andstudents,onthemostrecentadvancesin
applications of methods based on Fusing Machine Learning Paradigms. We also
present this book to general readers from other disciplines, including engineering,
medicine,andeducationandsocialsciences,asameansthatintroducesthemtothe
fascinatingfieldofMachineLearning,ingeneral,andFusionofMachineLearning
vii
viii Preface
Paradigms, in particular. At the same time and as societal demand continues to
posenewandchallengingproblemsandemergingtechnologiespressforevermore
advancedtheoriesandevermoreefficientmethodologies,tools,andsystemstobe
devisedtoaddressthem,thereadersmayexpectthatadditionalrelatedvolumeswill
appearinthenearfuture.
Patra,Greece IoannisK.Hatzilygeroudis
Piraeus,Greece GeorgeA.Tsihrintzis
Shoreham-by-Sea,UK LakhmiC.Jain
Contents
1 IntroductiontoFusionofMachineLearningParadigms ........... 1
IoannisHatzilygeroudis,GeorgeA.Tsihrintzis,andLakhmiC.Jain
1.1 Editorial .................................................. 1
References ..................................................... 4
PartI RecentApplicationAreasofFusionofMachineLearning
Paradigms
2 ArtificialIntelligenceasDual-UseTechnology ..................... 7
HarukiUeno
2.1 Introduction ............................................... 7
2.2 WhatIsDUT .............................................. 9
2.3 AI:Concepts,ModelsandTechnology ......................... 11
2.4 Agent-BasedAIandAutonomousSystem ...................... 15
2.4.1 BasicModelofAgent-BasedAI ........................ 15
2.4.2 ConceptualModelofAutonomousWeaponSystem ....... 17
2.5 Dual-UseTechnologyandDARPA ............................ 18
2.5.1 HistoricalViewandRoleofDARPA .................... 19
2.5.2 DARPA’sContributiontoDUTR&DonAI .............. 21
2.6 DARPA-LikeOrganizationsinMajorCountries ................. 24
2.7 Dual-UseDilemma ......................................... 27
2.8 ConcludingRemarks ........................................ 30
References ..................................................... 32
3 Diabetic Retinopathy Detection Using Transfer
and Reinforcement Learning with Effective Image
PreprocessingandDataAugmentationTechniques ................ 33
MariaTariq, VasilePalade, YingLiangMa,
andAbdulrahmanAltahhan
3.1 Introduction ............................................... 34
3.2 Background ............................................... 35
3.2.1 DeepLearningforDiabeticRetinopathy ................. 35
ix