Shahrzad Saremi

Temporal feature engineering for agricultural commodity price forecasting in Nigeria: evaluating machine learning, deep learning and time-series approaches

Purpose This study investigates the effectiveness of machine learning, deep learning and traditional time-series approaches for predicting agricultural commodity prices in Nigeria, focussing on improving forecasting reliability through temporal feature engineering and time-aware...

SELM-CTR: a stacking ensemble deep learning model with SHAP-based analysis for large-scale click-through rate prediction

Purpose Click-through rate (CTR) prediction is a core challenge in digital advertising, as forecasting whether a user will click on an advertisement directly determines ad placement decisions and revenue outcomes. Standard machine learning and deep learning approaches achieve rea...