Modeling Deep Learning Neural Networks With Denotational Mathematics in UbiHealth Environment

Int. J. Softw. Sci. Comput. Intell. Pub Date : 2020-07-01 DOI:10.4018/ijssci.2020070102

J. Sarivougioukas, Aristides Th. Vagelatos

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引用次数: 21

Abstract

Ubiquitous computing environments that are involved in healthcare applications are typically characterized bydynamically changing contexts.The contextual information must be efficiently processed in order to support medical decision making. The ubiquitous computing healthcare ecosystemmustbecapableofextractingmedicallyvaluablecharacteristics,makingprecisedecisions, andtakingmedicallyappropriateactions.Inthisframework,deeplearningnetworkscanbeused fordatafusionoflargeandcomplexsetsofinformationinordertomaketheappropriatemedical diagnoses.Thequalityofdecisionsdependsontheselectionofappropriatenetworkweights,which definea transformationof thegiven input intoadiagnosis.Denotationalmathematicsprovidea promisingframeworkformodelingdeeplearningnetworksandadjustingtheirbehaviorbyadapting theirweightsforthegiveninput.Furthermore,thefidelityofthenetwork’soutputcanbecontrolled byapplyingaregulatortotheweightsvalues.TheauthorsshowthatDenotationalMathematicscan serveasarigorousframeworkformodelingandcontrollingdeeplearningnetworks,therebyenhancing thequalityofmedicaldecisionmaking. KEyWoRDS Deep Learning Neural Networks, Denotational Mathematics, UbiComp, UbiHealth

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在UbiHealth环境下用指称数学建模深度学习神经网络

医疗保健应用程序中涉及的无所不在的计算环境通常以bydynamically不断变化的上下文为特征。The背景信息必须被有效地处理，以支持医疗决策。无所不在的计算机医疗保健ecosystemmustbecapableofextractingmedicallyvaluablecharacteristics，makingprecisedecisions, andtakingmedicallyappropriateactions。Inthisframework，deeplearningnetworkscanbeused fordatafusionoflargeandcomplexsetsofinformationinordertomaketheappropriatemedical诊断。Thequalityofdecisionsdependsontheselectionofappropriatenetworkweights，which definea transformationof thegiven input_ intoadiagnosis。Denotationalmathematicsprovidea promisingframeworkformodelingdeeplearningnetworksandadjustingtheirbehaviorbyadapting theirweightsforthegiveninput。Furthermore，thefidelityofthenetwork 'soutputcanbecontrolled byapplyingaregulatortotheweightsvalues。TheauthorsshowthatDenotationalMathematicscan serveasarigorousframeworkformodelingandcontrollingdeeplearningnetworks，therebyenhancing thequalityofmedicaldecisionmaking。关键词:深度学习神经网络，指称数学，UbiComp, UbiHealth

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Int. J. Softw. Sci. Comput. Intell.

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