There isn’t any doubt that the problem of constructing a great prediction about an organization’s attainable failure is essential, in addition to sophisticated. A lot of fashions have been created for this very objective, of which one, the lengthy short-term reminiscence (LSTM) mannequin, holds a novel place in that it generates superb outcomes. The target of this contribution is to create a technique for the identification of an organization failure (chapter) utilizing synthetic neural networks (hereinafter known as “NN”) with no less than one lengthy short-term reminiscence (LSTM) layer. A chapter mannequin was created utilizing deep studying, for which no less than one layer of LSTM was used for the development of the NN. For the needs of this contribution, Wolfram’s Mathematica 13 (Wolfram Analysis, Champaign, Illinois) software program was used. The analysis outcomes present that LSTM NN can be utilized as a software for predicting firm failure. The target of the contribution was achieved, because the mannequin of a NN was developed, which is ready to predict the longer term improvement of an organization working within the manufacturing sector within the Czech Republic. It may be utilized to small, medium-sized and manufacturing corporations alike, in addition to utilized by monetary establishments, buyers, or auditors in its place for evaluating the monetary well being of corporations in a given area. The mannequin is versatile and might due to this fact be skilled based on a special dataset or surroundings.
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