Please use this identifier to cite or link to this item: https://repository.sustech.edu/handle/123456789/16609
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dc.contributor.authorSirElkhatim , Mohammed A.
dc.contributor.authorSalim , Naomie
dc.date.accessioned2017-04-25T07:24:33Z
dc.date.available2017-04-25T07:24:33Z
dc.date.issued2015
dc.identifier.citationSirElkhatim , Mohammed A. . Prediction of Banks Financial Distress Naomie Salim A. , Mohammed A. SirElkhatim .- Journal of Engineering and Computer Sciences (ECS) .- vol 16 , no1.- 2015.- articleen_US
dc.identifier.issnISSN 1605-427X
dc.identifier.urihttp://repository.sustech.edu/handle/123456789/16609
dc.descriptionarticleen_US
dc.description.abstractIn this research we are conducting a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then present the strengths and weaknesses of both. We come out by the fact that there is no specific type fit with all bank distress issue although we found that intelligent hybrid techniques consider the most candidates methods in term of accuracy and reputation.en_US
dc.description.sponsorshipSudan University of Science and Technologyen_US
dc.language.isoen_USen_US
dc.publisherSudan University of Science and Technologyen_US
dc.subjectBank Distress , Banks Factors , Prediction techniques ,Text Mining, Data Mining.en_US
dc.titlePrediction of Banks Financial Distressen_US
dc.typeArticleen_US
Appears in Collections:Volume 16 No. 1

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