DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이상재 | ko |
dc.contributor.author | 한인구 | ko |
dc.date.accessioned | 2011-03-03T07:26:00Z | - |
dc.date.available | 2011-03-03T07:26:00Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1999-06 | - |
dc.identifier.citation | 지능정보연구, v.5, no.1, pp.35 - 48 | - |
dc.identifier.issn | 1229-4152 | - |
dc.identifier.uri | http://hdl.handle.net/10203/22393 | - |
dc.description.abstract | Many organizational contexts should be considered in designing EDI controls to make control systems effective and efficient. This paper gives a description of the neural network model for suggesting the extent of effective EDI controls for a company that has specific organizational environment. Feedforward backpropagation neural network models are designed to predict the state of 12 modes of EDI controls from the state of environment. The predictive power of the system is compared with that of multivariate regression analysis to evaluate the effectiveness of using neural network model in predicting the level of EDI controls. The results show that the neural network model outperforms regression analysis in predictive accuracy. The controls that have high estimated value in the model are likely to be critical controls and EDI auditor or management can enhance investment of IS resources to enhance these controls. | - |
dc.language | Korean | - |
dc.language.iso | ko | en |
dc.publisher | 한국지능정보시스템학회 | - |
dc.title | 인공신경망을 이용한 EDI 통제방안 설계 | - |
dc.title.alternative | The Design of EDI Controls using Neural Network | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.citation.volume | 5 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 35 | - |
dc.citation.endingpage | 48 | - |
dc.citation.publicationname | 지능정보연구 | - |
dc.contributor.localauthor | 한인구 | - |
dc.contributor.nonIdAuthor | 이상재 | - |
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