A New Approach for Improving Biodiesel Conversion Efficiency: A Stacking Ensemble Model Based on Linear Regression Approach with GAN-Enhanced
Yazarlar (2)
Dr. Öğr. Üyesi Ahmet KARAOĞLU Sinop Üniversitesi, Türkiye
Doç. Dr. Hüseyin SÖYLER Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Arabian Journal for Science and Engineering (Q2) | ||
| Dergi ISSN | 2193-567X Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 05-2025 |
| Cilt / Sayı / Sayfa | 50 / 23 / 19421–19441 | DOI | 10.1007/s13369-025-10227-5 |
| Makale Linki | https://doi.org/10.1007/s13369-025-10227-5 | ||
| UAK Araştırma Alanları |
Yakıtlar ve Yanma
Enerji
İçten Yanmalı Motorlar
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| Özet |
| This study employs a Linear Regression-based stacking ensemble learning approach as a novel method to enhance biodiesel conversion efficiency. Initially, a dataset derived from the literature was used to train an ensemble model that combines predictions from Random Forest, XGBoost, and Deep Neural Network (DNN) through a Linear Regression-based fusion approach. This model outperformed individual models (Random Forest: − 0.16, XGBoost: − 0.67, and DNN: 0.36) by achieving an R2 score of 0.45. To further improve model performance, 4900 synthetic data samples were generated and integrated into the dataset. Leveraging the stacking ensemble learning approach with this expanded dataset, the model demonstrated a significant improvement in predictive accuracy, achieving an R2 score of 0.81. This corresponds to an approximate 4% increase in performance compared to individual models … |
| Anahtar Kelimeler |
| Biodiesel conversion | Linear regression | Machine learning | Stacking ensemble | Synthetic data |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 2 |
| Google Scholar | 6 |