大数据时代的深度学习技术与优化策略（英文）-381正式版

DeepLearningTechniquesandOptimizationStrategiesinBigDataAnalytics

J.JoshuaThomas

KDUPenangUniversityCollege,Malaysia

PinarKaragoz

MiddleEastTechnicalUniversity,Turkey

B.BazeerAhamed

BalajiInstituteofTechnologyandScience,Warangal,India

PandianVasant

UniversitiTeknologiPETRONAS,Malaysia

AvolumeintheAdvancesinSystemsAnalysis,SoftwareEngineering,andHighPerformanceComputing(ASASEHPC)BookSeries

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CrowdsourcingandProbabilisticDecision-MakinginSoftwareEngineeringEmergingResearchandOpportunities

VarunGupta(UniversityofBeiraInterior,Covilha,Portugal)

EngineeringScienceReference•©2020•182pp•H/C(ISBN:9781522596592)•US$200.00(ourprice)

MetricsandModelsforEvaluatingtheQualityandEffectivenessofERPSoftware

GeoffreyMuchiriMuketha(Murang’aUniversityofTechnology,Kenya)andElyjoyMuthoniMicheni(TechnicalUniversityofKenya,Kenya)

EngineeringScienceReference•©2020•391pp•H/C(ISBN:9781522576785)•US$225.00(ourprice)

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ArchitecturesandFrameworksforDevelopingandApplyingBlockchainTechnologyNansiShi(LogicInternationalConsultants,Singapore)

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HumanFactorsinGlobalSoftwareEngineering

MobasharRehman(UniversitiTunkuAbdulRahman,Malaysia)AamirAmin(UniversitiTunkuAbdulRahman,Malaysia)AbdulRehmanGilal(SukkurIBAUniversity,Pakistan)andManzoorAhmedHashmani(UniversityTechnologyPETRONAS,Malaysia)

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InterdisciplinaryApproachestoInformationSystemsandSoftwareEngineering

AlokBhushanMukherjee(North-EasternHillUniversityShillong,India)andAkhouriPramodKrishna(BirlaInstituteofTechnologyMesra,India)

EngineeringScienceReference•©2019•299pp•H/C(ISBN:9781522577843)•US$215.00(ourprice)

Cyber-PhysicalSystemsforSocialApplications

MayaDimitrova(BulgarianAcademyofSciences,Bulgaria)andHiroakiWagatsuma(KyushuInstituteofTech-nology,Japan)

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EditorialAdvisoryBoard

Er.Annappa,NationalInstituteofTechnology,Karnataka,IndiaVimalaA.P.Balakrishnan,UniversityofMalaya,MalaysiaSujitDas,NationalInstituteofTechnology,Warangal,IndiaUgoFiore,UniversitadegliStudidiNapoliParthenope,Italy

Y.BevishJinila,SathyabamaInstituteofScienceandTechnology,IndiaSinanKalkan,MiddleEastTechnicalUniversity,TurkeyStefanosKollias,UniversityofLincoln,UK

GilbertoPerexLechuga,AutonomousUniversityoftheHidalgoState,MexicoJanMartinovic,IT4Innovations,CzechRepublicVarunMenon,SCMSGroupofInstitutions,India

BarshaMitra,BITSPilani,India

AlevMutlu,KocaeliUniversity,Turkey

WeerakornOngsakul,AsianInstituteofTechnology,Thailand

SanjayPande,G.M.InstituteofTechnology,DavangereUniversity,India&VisvesvarayaTechnologi-calUniversity,India

N.GnaneswaraRao,VFSTRUniversity,India

CevatŞener,MiddleEastTechnicalUniversity,Turkey

VishnuSharma,GalgotiasCollegeofEngineeringTechnology,IndiaBharatSingh,BigDataLabs,Germany

KaterinaSlaninova,SilesianUniversity,CzechRepublic

R.Subhashini,SathyabamaInstituteofScienceandTechnology,IndiaIsmailHakkiToroslu,MiddleEastTechnicalUniversity,TurkeyBurcuYilmaz,GebzeTechnicalUniversity,Turkey

TableofContents

Foreword xviii

Preface xx

Acknowledgment xxv

Chapter1

ArrhythmiaDetectionBasedonHybridFeaturesofT-WaveinElectrocardiogram 1

RaghuN.,JainUniversity,India

Chapter2

AReviewonDeepLearningApplications 21

ChitraA.Dhawale,P.R.PoteCollegeofEngineeringandManagement,India

KritikaDhawale,IndianInstituteofInformationTechnology,Nagpur,India

RajeshDubey,MohanlalSukhadiaUniversity,India

Chapter3

ASurveyofNature-InspiredAlgorithmsWithApplicationtoWellPlacementOptimization 32

JahedulIslam,UniversitiTeknologiPETRONAS,Malaysia

PandianM.Vasant,UniversitiTeknologiPETRONAS,Malaysia

BerihunMamoNegash,UniversitiTeknologiPETRONAS,Malaysia

MoacyrBartholomeuLaruccia,IndependentResearcher,Malaysia

MyoMyint,UniversitiTeknologiPETRONAS,Malaysia

Chapter4

ArtificialIntelligenceApproachforPredictingTOCFromWellLogsinShaleReservoirs:A

Review 46

Md.ShokorA.Rahaman,UniversitiTeknologiPETRONAS,Malaysia

PandianVasant,UniversitiTeknologiPETRONAS,Malaysia

Chapter5

BidirectionalGRU-BasedAttentionModelforKid-SpecificURLClassification 78

RajalakshmiR.,SchoolofComputingScienceandEngineering,VelloreInstituteof

Technology,Chennai,India

HansTiwari,SchoolofElectronicsEngineering,VelloreInstituteofTechnology,Chennai,

India

JayPatel,SchoolofElectronicsEngineering,VelloreInstituteofTechnology,Chennai,India

RameshkannanR.,SchoolofComputingScienceandEngineering,VelloreInstituteof

Technology,Chennai,India

KarthikR.,VelloreInstituteofTechnology,Chennai,India

Chapter6

ClassificationofFundusImagesUsingNeuralNetworkApproach 91

AnoopBalakrishnanKadan,VimalJyothiEngineeringCollege,India

PerumalSankarSubbian,TocHInstituteofScienceandTechnology,India

JeyakrishnanV.,SaintgitsCollegeofEngineering,India

HariharanN.,AdiShankaraInstituteofEngineeringandTechnology,Ernakulam,India

RoshiniT.V.,VimalJyothiEngineeringCollege,India

SravaniS.Nath,VimalJyothiEngineeringCollege,India

Chapter7

ConvolutionalGraphNeuralNetworks:AReviewandApplicationsofGraphAutoencoderin

Chemoinformatics 107

J.JoshuaThomas,KDUPenangUniversityCollege,Malaysia

TranHuuNgocTran,KDUPenangUniversityCollege,Malaysia

GilbertoPérezLechuga,UniversidadAutónomadelEstadodeHidalgo,Mexico

BahariBelaton,UniversitiSainsMalaysia,Malaysia

Chapter8

DeepLearning:ARecentComputingPlatformforMultimediaInformationRetrieval 124

MenagaD.,B.S.AbdurRahmanCrescentInstituteofScienceandTechnology,Chennai,

India

RevathiS.,B.S.AbdurRahmanCrescentInstituteofScienceandTechnology,Chennai,

India

Chapter9

DeepLearningTechniquesandOptimizationStrategiesinBigDataAnalytics:Automated

TransferLearningofConvolutionalNeuralNetworksUsingEnasAlgorithm 142

MuruganKrishnamoorthy,AnnaUniversity,India

BazeerAhamedB.,BalajiInstitueofTechnologyandScience,India

SailakshmiSuresh,AnnaUniversity,India

SolaiappanAlagappan,AnnaUniversity,India

Chapter10

DimensionalityReductionWithMulti-FoldDeepDenoisingAutoencoder 154

PattabiramanV.,VelloreInstituteofTechnology,Chennai,India

ParvathiR.,VelloreInstituteofTechnology,Chennai,India

Chapter11

FakeNewsDetectionUsingDeepLearning:SupervisedFakeNewsDetectionAnalysisinSocial

MediaWithSemanticSimilarityMethod 166

VaralakshmiKonagala,KLHUniversity(Deemed),India

ShahanaBano,K.L.University,Vijayawada,India

Chapter12

HeuristicOptimizationAlgorithmsforPowerSystemSchedulingApplications:Multi-Objective

GenerationSchedulingWithPSO 178

AnongpunMan-Im,AsianInstituteofTechnology,Thailand

WeerakornOngsakul,AsianInstituteofTechnology,Thailand

NimalMadhuM.,AsianInstituteofTechnology,Thailand

Chapter13

MultiobjectiveOptimizationofaBiofuelSupplyChainUsingRandomMatrixGenerators 206

TimothyGanesan,RoyalBankofCanada,Canada

PandianVasant,UniversitiTeknologiPETRONAS,Malaysia

IgorLitvinchev,NuevoLeonStateUniversity,Mexico

Chapter14

OptimizedDeepLearningSystemforCropHealthClassificationStrategicallyUsingSpatialand

TemporalData 233

SaravananRadhakrishnan,VelloreInstituteofTechnology,India

VijayarajanV.,VelloreInstituteofTechnology,India

Chapter15

ProteinSecondaryStructurePredictionApproaches:AReviewWithFocusonDeepLearning

Methods 251

FawazH.H.Mahyoub,SchoolofComputerSciences,UniversitiSainsMalaysia,Malaysia

RosniAbdullah,SchoolofComputerSciences,UniversitiSainsMalaysia,Malaysia

Chapter16

RecentTrendsintheUseofGraphNeuralNetworkModelsforNaturalLanguageProcessing 274

BURCUYILMAZ,InstituteofInformationTechnologies,GebzeTechnicalUniversity

HilalGenc,DepartmentofComputerEngineering,GebzeTechnicalUniversity,Turkey

MustafaAgriman,ComputerEngineeringDepartment,MiddleEastTechnicalUniversity,

Turkey

BugraKaanDemirdover,ComputerEngineeringDepartment,MiddleEastTechnical

University,Turkey

MertErdemir,ComputerEngineeringDeptartment,MiddleEastTechnicalUniversity,

Turkey

GokhanSimsek,ComputerEngineeringDepartment,MiddleEastTechnicalUniversity,

Turkey

PinarKaragoz,ComputerEngineeringDepartment,MiddleEastTechnicalUniversity,

Turkey

Chapter17

ReviewonParticleSwarmOptimizationApproachforOptimizingWellboreTrajectory 290

KallolBiswas,UniversitiTeknologiPETRONAS,Malaysia

PandianM.Vasant,UniversitiTeknologiPETRONAS,Malaysia

MoacyrBatholomeuLaruccia,IndependentResearcher,Malaysia

JoséAntonioGámezVintaned,UniversitiTeknologiPETRONAS,Malaysia

MyoM.Myint,UniversitiTeknologiPETRONAS,Malaysia

CompilationofReferences 308

AbouttheContributors 347

Index 354

DetailedTableofContents

Foreword xviii

Preface xx

Acknowledgment xxv

Chapter1

ArrhythmiaDetectionBasedonHybridFeaturesofT-WaveinElectrocardiogram 1

RaghuN.,JainUniversity,India

Anelectrocardiogram(ECG)isusedasoneoftheimportantdiagnostictoolsforthedetectionofthehealthofaheart.Anautomaticheartabnormalityidentificationmethodssensenumerousabnormalitiesorarrhythmiaanddecreasethephysician’spressureaswellassharetheirworkload.InECGanalysis,themainfocusistoenhancedegreeofaccuracyandincludeanumberofheartdiseasesthatcanbeclassified.Inthischapter,arrhythmiaclassificationisproposedusinghybridfeaturesofT-waveinECG.Theclassificationsystemconsistsofmajorlythreephases,windowingtechnique,featureextraction,andclassification.Thisclassifiercategorizesthenormalandabnormalsignalsefficiently.Theexperimentalanalysisshowedthatthehybridfeaturesarrhythmiaclassificationperformanceofaccuracyapproximately98.3%,specificity98.0%,andsensitivity98.6%usingMIT-BIHdatabase.

Chapter2

AReviewonDeepLearningApplications 21

ChitraA.Dhawale,P.R.PoteCollegeofEngineeringandManagement,India

KritikaDhawale,IndianInstituteofInformationTechnology,Nagpur,India

RajeshDubey,MohanlalSukhadiaUniversity,India

Artificialintelligence(AI)isgoingthroughitsgoldenera.MostAIapplicationsareindeedusingmachinelearning,anditcurrentlyrepresentsthemostpromisingpathtostrongAI.Ontheotherhand,deeplearning,whichisitselfakindofmachinelearning,isbecomingmoreandmorepopularandsuccessfulatdifferentusecasesandisatthepeakofdevelopmentsbyenablingmoreaccurateforecastingandbetterplanningforcivilsociety,policymakers,andbusinesses.Asaresult,deeplearningisbecomingaleaderinthisdomain.Thischapterpresentsabriefreviewofground-breakingadvancesindeeplearningapplications.

Chapter3

ASurveyofNature-InspiredAlgorithmsWithApplicationtoWellPlacementOptimization32

JahedulIslam,UniversitiTeknologiPETRONAS,MalaysiaPandianM.Vasant,UniversitiTeknologiPETRONAS,MalaysiaBerihunMamoNegash,UniversitiTeknologiPETRONAS,MalaysiaMoacyrBartholomeuLaruccia,IndependentResearcher,MalaysiaMyoMyint,UniversitiTeknologiPETRONAS,Malaysia

Wellplacementoptimizationisoneofthemajorchallengingfactorsinthefielddevelopmentprocessintheoilandgasindustry.Thischapteraimstosurveyprominentmetaheuristictechniques,whichsolvewelltheplacementoptimizationproblem.Thewellplacementoptimizationproblemisconsideredashighdimensional,discontinuous,andmulti-modeloptimizationproblem.Moreover,thecomputationalexpensesfurthercomplicatetheissue.Overthelastdecade,bothgradient-basedandgradient-freeoptimizationmethodswereimplemented.Gradient-freeoptimization,suchastheparticleswarmoptimization,geneticalgorithm,isimplementedinthisarea.Theseoptimizationtechniquesareutilizedasstandaloneorasthehybridizationofoptimizationmethodstomaximizetheeconomicfactors.Inthischapter,theauthorssurveythetwomostpopularnature-inspiredmetaheuristicoptimizationtechniquesandtheirapplicationtomaximizetheeconomicfactors.

Chapter4

ArtificialIntelligenceApproachforPredictingTOCFromWellLogsinShaleReservoirs:A

Review 46

Md.ShokorA.Rahaman,UniversitiTeknologiPETRONAS,Malaysia

PandianVasant,UniversitiTeknologiPETRONAS,Malaysia

Totalorganiccarbon(TOC)isthemostsignificantfactorforshaleoilandgasexplorationanddevelopmentwhichcanbeusedtoevaluatethehydrocarbongenerationpotentialofsourcerock.However,estimatingTOCisachallengeforthegeologicalengineersbecausedirectmeasurementsofcoreanalysisgeochemicalexperimentsaretime-consumingandcostly.Therefore,manyAItechniquehasusedforTOCcontentpredictionintheshalereservoirwhereAItechniqueshaveimpactedpositively.Havingbothstrengthandweakness,someofthemcanexecutequicklyandhandlehighdimensionaldatawhileothershavelimitationforhandlingtheuncertainty,learningdifficulties,andunabletodealwithhighorlowdimensionaldatasetswhichremindsthe“nofreelunch”theoremwhereithasbeenproventhatnotechniqueorsystemberelevanttoallissuesinallcircumstances.So,investigatingthecutting-edgeAItechniquesisthecontributionofthisstudyastheresultinganalysisgivestoptobottomunderstandingofthedifferentTOCcontentpredictionstrategies.

Chapter5

BidirectionalGRU-BasedAttentionModelforKid-SpecificURLClassification78

RajalakshmiR.,SchoolofComputingScienceandEngineering,VelloreInstituteofTechnology,Chennai,India

HansTiwari,SchoolofElectronicsEngineering,VelloreInstituteofTechnology,Chennai,India

JayPatel,SchoolofElectronicsEngineering,VelloreInstituteofTechnology,Chennai,IndiaRameshkannanR.,SchoolofComputingScienceandEngineering,VelloreInstituteof

Technology,Chennai,India

KarthikR.,VelloreInstituteofTechnology,Chennai,India

TheGenZkidshighlyrelyoninternetforvariouspurposeslikeentertainment,sports,andschoolprojects.Thereisademandforparentalcontrolsystemstomonitorthechildrenduringtheirsurfingtime.Currentwebpageclassificationapproachesarenoteffectiveashandcraftedfeaturesareextractedfromthewebcontentandmachinelearningtechniquesareusedthatneeddomainknowledge.Hence,adeeplearningapproachisproposedtoperformURL-basedwebpageclassification.AstheURLisashorttext,themodelshouldlearntounderstandwheretheimportantinformationispresentintheURL.Theproposedsystemintegratesthestrengthofattentionmechanismwithrecurrentconvolutionalneuralnetworkforeffectivelearningofcontext-awareURLfeatures.Thisenhancedarchitectureimprovesthedesignofkids-relevantURLclassification.ByconductingvariousexperimentsonthebenchmarkcollectionOpenDirectoryProject,itisshownthatanaccuracyof0.8251wasachieved.

Chapter6

ClassificationofFundusImagesUsingNeuralNetworkApproach 91

AnoopBalakrishnanKadan,VimalJyothiEngineeringCollege,India

PerumalSankarSubbian,TocHInstituteofScienceandTechnology,India

JeyakrishnanV.,SaintgitsCollegeofEngineering,India

HariharanN.,AdiShankaraInstituteofEngineeringandTechnology,Ernakulam,India

RoshiniT.V.,VimalJyothiEngineeringCollege,India

SravaniS.Nath,VimalJyothiEngineeringCollege,India

Diabeticretinopathy(DR),whichaffectsthebloodvesselsofthehumanretina,isconsideredtobethemostseriouscomplicationprevalentamongdiabeticpatients.Ifdetectedsuccessfullyatanearlystage,theophthalmologistwouldbeabletotreatthepatientsbyadvancedlasertreatmenttopreventtotalblindness.Inthisstudy,atechniquebasedonmorphologicalimageprocessingandfuzzylogictodetecthardexudatesfromDRretinalimagesisexplored.Theproposedtechniqueistoclassifytheeyebyusinganeuralnetworkapproach(classifier)topredictwhetheritisaffectedornot.Here,aclassifierisaddedbeforethefuzzylogic.Thisfuzzywilltellhowmuchandwhereitisaffected.Theproposedtechniquewilltellwhethertheeyeisabnormalornormal.

Chapter7

ConvolutionalGraphNeuralNetworks:AReviewandApplicationsofGraphAutoencoderin

Chemoinformatics 107

J.JoshuaThomas,KDUPenangUniversityCollege,Malaysia

TranHuuNgocTran,KDUPenangUniversityCollege,Malaysia

GilbertoPérezLechuga,UniversidadAutónomadelEstadodeHidalgo,Mexico

BahariBelaton,UniversitiSainsMalaysia,Malaysia

Applyingdeeplearningtothepervasivegraphdataissignificantbecauseoftheuniquecharacteristicsofgraphs.Recently,substantialamountsofresearcheffortshavebeenkeenonthisarea,greatlyadvancinggraph-analyzingtechniques.Inthisstudy,theauthorscomprehensivelyreviewdifferentkindsofdeeplearningmethodsappliedtographs.Theydiscusswithexistingliteratureintosub-componentsoftwo:graphconvolutionalnetworks,graphautoencoders,andrecenttrendsincludingchemoinformaticsresearchareaincludingmolecularfingerprintsanddrugdiscovery.Theyfurtherexperimentwithvariationalautoencoder(VAE)analyzehowtheseapplyindrugtargetinteraction(DTI)andapplicationswithephemeraloutlineonhowtheyassistthedrugdiscoverypipelineanddiscusspotentialresearchdirections.

Chapter8

DeepLearning:ARecentComputingPlatformforMultimediaInformationRetrieval 124

MenagaD.,B.S.AbdurRahmanCrescentInstituteofScienceandTechnology,Chennai,

India

RevathiS.,B.S.AbdurRahmanCrescentInstituteofScienceandTechnology,Chennai,

India

Multimediaapplicationisasignificantandgrowingresearchareabecauseoftheadvancesintechnologyofsoftwareengineering,storagedevices,networks,anddisplaydevices.Withtheintentionofsatisfyingmultimediainformationdesiresofusers,itisessentialtobuildanefficientmultimediainformationprocess,access,andanalysisapplications,whichmaintainvarioustasks,likeretrieval,recommendation,search,classification,andclustering.Deeplearningisanemergingtechniqueinthesphereofmultimediainformationprocess,whichsolvesboththecrisisofconventionalandrecentresearches.Themainaimistoresolvethemultimedia-relatedproblemsbytheuseofdeeplearning.Thedeeplearningrevolutionisdiscussedwiththedepictionandfeature.Finally,themajorapplicationalsoexplainedwithrespecttodifferentfields.Thischapteranalyzesthecrisisofretrievalafterprovidingthesuccessfuldiscussionofmultimediainformationretrievalthatistheabilityofretrievinganobjectofeverymultimedia.

Chapter9

DeepLearningTechniquesandOptimizationStrategiesinBigDataAnalytics:Automated

TransferLearningofConvolutionalNeuralNetworksUsingEnasAlgorithm 142

MuruganKrishnamoorthy,AnnaUniversity,India

BazeerAhamedB.,BalajiInstitueofTechnologyandScience,India

SailakshmiSuresh,AnnaUniversity,India

SolaiappanAlagappan,AnnaUniversity,India

Constructionofaneuralnetworkisthecardinalsteptoanymachinelearningalgorithm.Itrequiresprofoundknowledgeforthedeveloperinassigningtheweightsandbiasestoconstructit.Andtheconstructionshouldbedoneformultipleepochstoobtainanoptimalneuralnetwork.Thismakesitcumbersomeforaninexperiencedmachinelearningaspiranttodevelopitwithease.So,anautomatedneuralnetwork

constructionwouldbeofgreatuseandprovidethedeveloperwithincrediblespeedtoprogramandrunthemachinelearningalgorithm.Thisisacrucialassistfromthedeveloper’sperspective.Thedevelopercannowfocusonlyonthelogicalportionofthealgorithmandhenceincreaseproductivity.TheuseofEnasalgorithmaidsinperformingtheautomatedtransferlearningtoconstructthecompleteneuralnetworkfromthegivensampledata.Thisalgorithmproliferatesontheincomingdata.Hence,itisveryimportanttoinculcateitwiththeexistingmachinelearningalgorithms.

Chapter10

DimensionalityReductionWithMulti-FoldDeepDenoisingAutoencoder 154

PattabiramanV.,VelloreInstituteofTechnology,Chennai,India

ParvathiR.,VelloreInstituteofTechnology,Chennai,India

Naturaldataeruptingdirectlyoutofvariousdatasources,suchastext,image,video,audio,andsensordata,comeswithaninherentpropertyofhavingverylargedimensionsorfeaturesofthedata.Whilethesefeaturesaddrichnessandperspectivestothedata,duetosparsityassociatedwiththem,itaddstothecomputationalcomplexitywhilelearning,unabletovisualizeandinterpretthem,thusrequiringlargescalecomputationalpowertomakeinsightsoutofit.Thisisfamouslycalled“curseofdimensionality.”Thischapterdiscussesthemethodsbywhichcurseofdimensionalityiscuredusingconventionalmethodsandanalyzesitsperformanceforgivencomplexdatasets.Italsodiscussestheadvantagesofnonl