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dc.contributor.authorSun, Ting
dc.contributor.authorVasarhelyi, Miklos A.
dc.date.accessioned2018-05-04T10:30:00Z
dc.date.available2018-05-04T10:30:00Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10272/14727
dc.description.abstractWhile the massive volume of text documents from multiple sources inside and outside of the company provides more information for auditors, the lack of efficient and effective technology solutions hampers the full use of text data. Powered by the emerging data analytics technology of deep learning, the value of the text can be better explored to deliver a higher quality of audit evidence and more relevant business insights. This research analyzes the usefulness of the information provided by various textual data in auditing and introduces deep learning, an evolving Artificial Intelligence approach. Furthermore, it provides a guide for auditors to implement deep learning techniques with predeveloped tools and open-source librarieses_ES
dc.language.isoenges_ES
dc.publisherUniversidad de Huelvaes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherText Analysises_ES
dc.subject.otherDeep Learninges_ES
dc.subject.otherArtificial Intelligencees_ES
dc.subject.otherBig Dataes_ES
dc.subject.otherAuditinges_ES
dc.titleEmbracing Textual Data Analytics in Auditing with Deep Learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.4192/1577-8517-v18_3
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Atribución-NoComercial-SinDerivadas 3.0 España
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