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http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10015
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DC Field | Value | Language |
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dc.contributor.author | Mukunthan, T. | - |
dc.contributor.author | Brian, G. | - |
dc.contributor.author | Roberto La, R. | - |
dc.contributor.author | Anil, F. | - |
dc.date.accessioned | 2023-12-29T05:54:16Z | - |
dc.date.available | 2023-12-29T05:54:16Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10015 | - |
dc.description.abstract | PredictingAntimicrobialResistance(AMR)fromgenomicsequencedatahasbecomea significantcomponentofovercomingtheAMRchallenge,especiallygivenitspotentialforfacilitatingmore rapiddiagnosticsandpersonalisedantibiotictreatments.Withtherecentadvancesinsequencingtechnologies andcomputingpower,deeplearningmodelsforgenomicsequencedatahavebeenwidelyadoptedtopredict AMRmorereliablyanderror-free.TherearemanydifferenttypesofAMR;therefore,anypracticalAMR predictionsystemmustbeabletoidentifymultipleAMRspresentinagenomicsequence.Unfortunately, mostgenomicsequencedatasetsdonothaveallthelabelsmarked,therebymakingadeeplearningmodelling approachchallengingowingtoitsrelianceonlabelsforreliabilityandaccuracy.Thispaperaddresses thisissuebypresentinganeffectivedeeplearningsolution,Mask-Loss1Dconvolutionneuralnetwork (ML-ConvNet),forAMRpredictionondatasetswithmanymissinglabels.Thecorecomponentof ML-ConvNetutilisesamaskedlossfunctionthatovercomestheeffectofmissinglabelsinpredicting AMR.TheproposedML-ConvNetisdemonstratedtooutperformstate-of-the-artmethodsintheliteratureby 10.5%,accordingtotheF1score.Theproposedmodel’sperformanceisevaluatedusingdifferentdegrees ofthemissinglabelandisfoundtooutperformtheconventionalapproachby76%intheF1scorewhen 86.68%oflabelsaremissing.Furthermore,theML-ConvNetwasestablishedwithanexplainableartificial intelligence(XAI)pipeline,therebymakingitideallysuitedforhospitalandhealthcaresettings,wheremodel interpretabilityisanessentialrequirement. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Multi label classification | en_US |
dc.subject | Deep neural network | en_US |
dc.subject | Multi-drug AMR | en_US |
dc.subject | Missing labels | en_US |
dc.subject | Explainable AI | en_US |
dc.title | Explainable Deep Learning Approach for Multi label Classification of Antimicrobial Resistance with Missing Labels | en_US |
dc.type | Article | en_US |
Appears in Collections: | Electrical & Electronic Engineering |
Files in This Item:
File | Description | Size | Format | |
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Explainable_Deep_Learning_Approach_for_Multilabel_Classification_of_Antimicrobial_Resistance_With_Missing_Labels.pdf | 7.39 MB | Adobe PDF | View/Open |
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