Publications by authors named "Xianxian Zeng"
Article Synopsis
- Short-cycle agricultural product sales forecasting helps reduce food waste by predicting demand accurately, matching supply with consumer needs.
- A hierarchical prediction model combining Random Forest (RF) and Extreme Gradient Boosting (XGBoost) is introduced to handle data volatility and improve prediction accuracy.
- Results show this model outperforms standalone RF and XGBoost, reducing prediction errors significantly and demonstrating effectiveness across various agricultural products, thereby optimizing supply chains and minimizing food waste.
Article Synopsis
- * It introduces a Transformer-based detector/demapper that reduces intercarrier interference (ICI) and enhances error performance by computing soft modulated symbol probabilities and mutual information for code rate allocation.
- * The results indicate that the Transformer-based approach significantly outperforms both a deep neural network (DNN)-based system and traditional methods in terms of effectiveness.
