Application of Interpretable Artificial Intelligence for Sustainable Tax Management in the Manufacturing Industry

Sustainability, Год журнала: 2025, Номер 17(3), С. 1121 - 1121

Опубликована: Янв. 30, 2025

The long-term development of the manufacturing industry relies on sustainable tax management, which plays a key role in optimizing production costs. While artificial intelligence models have been applied to tax-related predictions, research their application for predicting management levels is quite limited, with no studies focused China. To enhance digital innovation corporate this study applies interpretable predict level, helps decision-makers maintain it within range. ratio total expense profits (ETR) used represent predicted using decision trees, random forests, linear regression, support vector and neural networks eight input features. Comparisons among developed indicate that forest model exhibits best performance terms prediction accuracy generalization capability. Additionally, Shapley additive explanations (SHAP) technique integrated interpretability its predictions. SHAP results reveal importance features also highlight dominance certain show ETR from previous year holds greatest importance, being more than twice as significant second most important factor, whereas effect board size negligible. Moreover, benefiting local interpretations values, approach aids managers making rational decisions.

Язык: Английский

A Multi-Objective Game Theory Model for Sustainable Profitability in the Tourism Supply Chain: Integrating Human Resource Management and Artificial Neural Networks

Systems and Soft Computing, Год журнала: 2025, Номер unknown, С. 200217 - 200217

Опубликована: Март 1, 2025

Язык: Английский