2026-10-10T07:47:47Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33975878 2026-09-23T17:34:10Z category_734 category_931 category_106 portal_5 item_type_3 month_year_09_2026

<p>Deep Neural Network Architectural Details.</p> Nusrat Jahan (399039) Jannatul Maua (23120176) Uland Rozario (25099154) Hashibul Ahsan Shoaib (24386599) Biological Sciences not elsewhere classified Information Systems not elsewhere classified Science Policy world managerial decision integrates predictive modeling integrates deep learning human resource management customer relationship management employee attrition prediction customer churn prediction support managerial decision experimental results show aware optimization problem actionable managerial insights employee attrition dataset customer churn dataset sensitive counterfactual generation churn dataset attrition dataset support systems results indicate enabling actionable counterfactual optimization unified objective proposed model practical adoption plausible recommendations paper proposes increasingly used feature attribution explanation fidelity domain setting deliver accurate counterfactual reasoning combined multi brier score baseline approaches <div><p>Artificial intelligence is increasingly used to support managerial decision-making in domains such as human resource management and customer relationship management; however, the lack of transparency in complex predictive models limits their practical adoption. This paper proposes a counterfactual-guided explainable artificial intelligence framework that integrates deep learning, feature attribution, and counterfactual reasoning to deliver accurate, interpretable, and actionable managerial insights. The proposed model is evaluated on an employee attrition dataset and a customer churn dataset, as well as in a combined multi-domain setting. Experimental results show that the framework achieves classification accuracies of up to 91.4% for employee attrition prediction and 87.3% for customer churn prediction, with corresponding ROC–AUC scores reaching 0.869 and 0.891, respectively. The model also demonstrates improved probability calibration, reducing the Brier score to 0.128 on the attrition dataset and 0.109 on the churn dataset. Furthermore, explainability analysis yields higher SHAP fidelity scores exceeding 0.90, while the counterfactual module generates valid and plausible recommendations with lower action costs compared to baseline approaches. These results indicate that the proposed framework effectively bridges predictive performance, interpretability, and actionability, making it suitable for real-world managerial decision-support systems. The framework is formulated as a constraint-aware optimization problem that integrates predictive modeling, explanation fidelity, and cost-sensitive counterfactual generation into a unified objective, enabling actionable and feasible decision support.</p></div> 2026-09-23T17:34:02Z Dataset Dataset 10.1371/journal.pone.0358643.t001 https://figshare.com/articles/dataset/_p_Deep_Neural_Network_Architectural_Details_p_/33975878 CC BY 4.0