Probabilistic AI for Uncertainty-Aware Pharmaceutical Supply Chain Planning Aditya Narendran Shreya Venkatesh Karthik Ramanathan Volume 3, Issue 10, Pages 1-9, October 2026 Pharmaceutical supply chains operate within highly uncertain environments characterized by fluctuating medicine demand, changing healthcare requirements, supply disruptions, transportation delays, supplier constraints, inventory limitations, and product expiration. Conventional forecasting and deterministic planning approaches frequently provide single-value estimates that do not adequately ... Read More Article DOI: doi.org/10.47001/CJESR/2026.310001
Explainable At-Risk Student Prediction with SHAP-Guided Risk Segmentation and Rule-Based Intervention Recommendations P. L. Barilee D. Matthias V. I. E. Anireh Volume 3, Issue 10, Pages 10-18, October 2026 Early identification of students at risk of failure or withdrawal can support timely academic assistance, but many predictive systems provide risk labels without transparent reasons or guidance on suitable support actions. An explainable at-risk student prediction workflow combined Random Forest and XGBoost classification, SHAP-based explanation, k-means risk segmentation, and rule-based int... Read More Article DOI: doi.org/10.47001/CJESR/2026.310002
