Closed-Loop Deep Learning Improves E-Commerce Pricing and Forecasting
A new closed-loop framework using Transformer-based forecasting and Proximal Policy Optimization improves e-commerce dynamic pricing and demand accuracy by integrating ...
A new closed-loop framework using Transformer-based forecasting and Proximal Policy Optimization improves e-commerce dynamic pricing and demand accuracy by integrating ...
A comprehensive four-year mixed-methods evaluation across primary healthcare facilities in mainland Tanzania has revealed a moderate overall level of implementation ...
A case study demonstrated that an artificial intelligence digital twin using interpretable regression matched complex machine learning models for energy ...
A simulation study published in Discover Computing reports that a hybrid framework combining a sorting-inspired data prioritization score with neural ...
A new benchmark finds that prompt-engineered contextual embeddings improved Random Forest but failed to outperform a strong tabular XGBoost model ...
A systematic review of 78 papers examines how machine learning methods support demand forecasting and inventory decision-making.
A one-year Port of Ancona modelling study found that autoregressive forecasting of cold-ironing demand could support 95th-percentile infrastructure sizing and ...
A lean XAI framework combining SHAP, SHAPTime, and rolling-origin validation delivers stable, horizon-aware explanations for supply-chain demand forecasts on the ...
Researchers have built an AI framework that forecasts demand for individual live-streaming shopping sessions and uses reinforcement learning to automatically ...
A new benchmark study shows that an uncertainty-aware AI decision support system significantly improves small-business supply chain resilience, but its ...
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© 2026 Scienmag - Science Magazine