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

{"title":"Modeling Deep Learning Neural Networks With Denotational Mathematics in UbiHealth Environment","authors":"J. Sarivougioukas, Aristides Th. Vagelatos","doi":"10.4018/ijssci.2020070102","DOIUrl":null,"url":null,"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","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Softw. Sci. Comput. Intell.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijssci.2020070102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

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

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

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

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

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

自引率

0.00%

发文量