Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach
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Atiea, M.A.; Reda, R.; Ataya, S.; Ibrahim, M. Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach. Processes 2025, 13, 1451. https://doi.org/10.3390/pr13051451
Atiea MA, Reda R, Ataya S, Ibrahim M. Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach. Processes. 2025; 13(5):1451. https://doi.org/10.3390/pr13051451
Chicago/Turabian StyleAtiea, Mohammed A., Reham Reda, Sabbah Ataya, and Mervat Ibrahim. 2025. "Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach" Processes 13, no. 5: 1451. https://doi.org/10.3390/pr13051451
APA StyleAtiea, M. A., Reda, R., Ataya, S., & Ibrahim, M. (2025). Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach. Processes, 13(5), 1451. https://doi.org/10.3390/pr13051451