Optimising the design of financial data processing models in accounting information systems based on artificial intelligence techniques DOI Creative Commons
Yang Song

Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)

Опубликована: Янв. 1, 2024

Abstract Financial assessment and early warning analysis can help enterprises find potential financial problems earlier, make timely plans take necessary measures to avoid risks. This paper uses a Bagging algorithm integrate Random Forest, Support Vector Machine, Plain Bayesian method achieve the processing classification of enterprise imbalance data. The entropy weight is used select empower indicators construct an accounting data model based on artificial intelligence technology. applied consumer electronics enterprise, Company W, analyze its situation operating level. It found that composite score from 2019 2022 60.29, 70.80, 73.11, 76.52, condition gradually improves 2019. Debt service capacity, profitability, growth capacity also show positive trend. consistent with actual development W. Accordingly. recommended W while maintaining R&D advantages, focus more long-term ability compress cycle, reduce risk repayment inventory pressure, continue enhance competitiveness enterprise. presents new ideas methods for innovation management information systems.

Язык: Английский

Ritz-least squares support vector regression technique for the system of fractional Fredholm-Volterra integro-differential equations DOI
Haniye Dehestani, Yadollah Ordokhani, Mohsen Razzaghi

и другие.

Journal of Applied Mathematics and Computing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Язык: Английский

Процитировано

1
Optimising the design of financial data processing models in accounting information systems based on artificial intelligence techniques DOI Creative Commons
Yang Song

Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)

Опубликована: Янв. 1, 2024

Abstract Financial assessment and early warning analysis can help enterprises find potential financial problems earlier, make timely plans take necessary measures to avoid risks. This paper uses a Bagging algorithm integrate Random Forest, Support Vector Machine, Plain Bayesian method achieve the processing classification of enterprise imbalance data. The entropy weight is used select empower indicators construct an accounting data model based on artificial intelligence technology. applied consumer electronics enterprise, Company W, analyze its situation operating level. It found that composite score from 2019 2022 60.29, 70.80, 73.11, 76.52, condition gradually improves 2019. Debt service capacity, profitability, growth capacity also show positive trend. consistent with actual development W. Accordingly. recommended W while maintaining R&D advantages, focus more long-term ability compress cycle, reduce risk repayment inventory pressure, continue enhance competitiveness enterprise. presents new ideas methods for innovation management information systems.

Язык: Английский

Процитировано

0