Thèses en ligne de l'université 8 Mai 1945 Guelma

Pattern recognition using collaborative neural networks

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dc.contributor.author SIOUDA, Roguia
dc.date.accessioned 2022-11-17T09:29:52Z
dc.date.available 2022-11-17T09:29:52Z
dc.date.issued 2022-11-10
dc.identifier.uri http://dspace.univ-guelma.dz/jspui/handle/123456789/13979
dc.description.abstract Artificial neuralnetworkshavebeensuccessfullyappliedtoawiderangeofproblems.Inpat- tern recognition,theyhavebeenusedinseveraltasks,suchasfeatureextraction,dimension reduction, andclassification.Inthiswork,weproposetwoECGheartbeatclassificationmodels based oncollaboratingdifferenttypesofneuralnetworks.Themainaimistocombinetheir complementaryproperties. The firstmodelusesastackedsparseautoencoder(SSAE)asfeatureextractorandasystem of multipleMulti-layeredperceptrons(MLP)asaclassifier.Inthismodel,theentireproblem is dividedintosimplerparts,whichareresolvedusingdifferentMLPs.Thesecondmodelalso uses aSSAEtoextractfeaturesinadditiontotwootherdynamicfeatures.Inthismodel,the classification isperformedbyahybridneuralmodelbasedoncombiningrandomandRBFneural networks. The proposedmodelsareevaluatedontheMIT-BIHarrhythmiadataset.Thetestsarebasedon the inter-patientparadigm,inwhichthetrainingandtestdataaretakenfromdifferentpatients. The obtainedresultsarecomparedwithsomeofthestate-of-the-artmethods. en_US
dc.language.iso en en_US
dc.subject Patternrecognition,classification,neuralnetworks,machinelearning,deeplearn- ing, ECGdataset. iv en_US
dc.title Pattern recognition using collaborative neural networks en_US
dc.type Thesis en_US


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