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- W3195718970 abstract "Due to the difficulties experienced by the financial auditors and the managementanalyst, in order to know the financial performance of the company and the ability ofcompanies to continue and because of the inconsistency of the financial information being nottransparent so renewed the direction of accounting work to use artificial intelligence methodsand data mining techniques. In this paper, data Mining (DM) and deep learning (DL) methodswere used to detect financial distress, using Artificial Neural Networks (ANN) algorithmrepresented by the Multilayer Perception Feed Forward Neural Network Error BackPropagation Algorithm (MLP-FFNN) as well as the C4.5 algorithm and the Multi-classsupport vector machine (MSVM).The results of the analysis showed that the C4.5, ANN andMSVM algorithm had the highest rate of rating accuracy by a small margin on all scales andwere (97.98 , 96.97 , 91.92) respectively. In this study, the data of companies listed on theIraq stock exchange for 2017 were taken, including 36 companies with high financial distress,20 with medium financial distress and 43 non-distressed for a group of 99 companies ." @default.
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- W3195718970 date "2021-08-15" @default.
- W3195718970 modified "2023-09-24" @default.
- W3195718970 title "A Data Mining Approach To Detection Financial Distress In Iraqi Companies" @default.
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