Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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Based on such methods as a discriminant analysis and logistic regression, corporate bankruptcy prediction models have been developed as a means to determine the soundness of a company’s operational status based on its financial statements. However, such analytical methods work with binary variables, and thus, as the only outcome of machine learning, the company in question is considered either likely or unlikely to go bankrupt. However, this is insufficient for business operators who would need to know the possible risk factors of a bankruptcy, allowing them to plan and implement measures to avoid any misfortunes. We have therefore developed a prediction model that not only predicts but also identifies the financial variables that can possibly drive the company to bankruptcy.
Akira Otsuki. 2026. \u201cA Study on Machine Learning Prediction Model for Company Bankruptcy Using Features in Time Series Financial Data\u201d. Global Journal of Management and Business Research - A: Administration & Management GJMBR-A Volume 22 (GJMBR Volume 22 Issue A1): .
Crossref Journal DOI 10.17406/GJMBR
Print ISSN 0975-5853
e-ISSN 2249-4588
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Total Score: 148
Country: Japan
Subject: Global Journal of Management and Business Research - A: Administration & Management
Authors: Akira Otsuki, Shohei Narumi, Masayoshi Kawamura (PhD/Dr. count: 0)
View Count (all-time): 174
Total Views (Real + Logic): 1562
Total Downloads (simulated): 15
Publish Date: 2026 01, Fri
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Neural Networks and Rules-based Systems used to Find Rational and
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Based on such methods as a discriminant analysis and logistic regression, corporate bankruptcy prediction models have been developed as a means to determine the soundness of a company’s operational status based on its financial statements. However, such analytical methods work with binary variables, and thus, as the only outcome of machine learning, the company in question is considered either likely or unlikely to go bankrupt. However, this is insufficient for business operators who would need to know the possible risk factors of a bankruptcy, allowing them to plan and implement measures to avoid any misfortunes. We have therefore developed a prediction model that not only predicts but also identifies the financial variables that can possibly drive the company to bankruptcy.
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