Authors : Md Tanvir Rahman Tarafder, Md Eahia Ansari, Md Ariful Alam, Sanjib Kumar Shil, Rakibul Islam, Khandakar Rabbi Ahmed
Publication date : 2026/12
Journal : Discover Artificial Intelligence
Volume : 6
Issue : 1
Pages : 56
Publisher : Springer International Publishing
Description : In this study, an integrated, AI-driven framework is recommended for enhancing Management Information Systems (MIS) capabilities to fulfill the demands of studying sustainable supply chain optimization and environmental impact analysis. To address the three core tasks, research utilizes both classical machine learning regression models (Linear Regression, Random Forest, Gradient Boosting, XGBoost, SVR) as well as a deep learning-based Gated TabTransformer (GTT) to predict GHG emissions, a Variational Graph Autoencoder (VGAE) for unsupervised anomaly detection, and a hybrid interpretability model constructed with Explainable Boosting Machines (EBM) and SHapley Additive exPlanations (SHAP) for explainable AI. The results show that the Linear Regression and ensemble models (eg, Gradient Boosting with R 2= 0.9995 and MAE= 0.0036) clearly outperform the existing methods both in terms of …
Total citations : Cited by 17
Scholar articles :
MTR Tarafder, ME Ansari, MA Alam, SK Shil, R Islam… – Discover Artificial Intelligence, 2026