Publication Date: 2023/06/17
Abstract: During the course of carrying out company operations, complications might often arise as a result of turbulent business operating circumstances and unforeseen abnormalities. In most cases, a number of difficulties combine to cause a lengthy decrease in the project's perceived usefulness or collapse owing to a depletion of financial resources. Preemptive evaluation of a company's failure may help anticipate potential challenges and mitigate the negative effects of such challenges by methodically planning, preparing, and carrying out a business failure prediction. For an accurate forecast of the collapse of a company, it is important to do a prediction analysis of the activities of the firm in order to detect potential problems. Methods of machine learning or deep learning that can be used for the goal of generating an accurate forecast of the collapse of a firm may effectively be used to identify these issues, and they can be used to do so successfully. This methodology will be realized by the successful use of the method of K-nearest Neighbor Clustering as well as entropy estimation, in conjunction with Long-Short Term Memory and Decision Making.
Keywords: Business Failure, K Nearest Neighbors, Entropy Estimation, Long Short Term Memory, Decision Making.
DOI: https://doi.org/10.5281/zenodo.8049727
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23MAY722.pdf
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