Volume- 10
Issue- 3
Year- 2022
DOI: 10.55524/ijircst.2022.10.3.1 |
DOI URL: https://doi.org/10.55524/ijircst.2022.10.3.1
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
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Ravindra Patel
In view of recent years' ï¬nancial information of US restaurant ï¬rms, this investigation created disappointment forecast models utilizing strategic relapse and artiï¬cial neural systems (ANNs). The discoveries demonstrate that the calculated model isn't inferior compared to the ANNs show as far as of forecast exactness. For restaurant ï¬rms, the strategic model not just gives bankruptcy prediction at a precision rate no inferior compared to that given by the ANNs demonstrate yet, in addition, shows how ï¬rms can act to lessen the opportunity of going bankrupt. Thusly, for US restaurant ï¬rms the strategic model is suggested as a favored technique for predicting restaurant ï¬rm disappointments.
Department of Computer Science, Campbellsville University, University Dr, Campbellsville, KY, USA
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