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Dergiler / International Journal of 3D Printing Technologies and Digital Industry / 2026 / Cilt: 9 Sayı: 2

PREDICTION PERFORMANCE OF DECISION TREE INDUCERS ON AUGMENTED BACILLUS CEREUS GROWTH DATA

Hamit Armağan(Suleyman Demirel University, İsparta, Turkey)0000-0002-8948-1546
Sayfa
352–362
Yayın Tarihi
2025-08-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In general, ensemble methods offer significant advantages in prediction accuracy.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Accurate prediction of microbial growth is of great importance in critical areas such as food safety and environmental sciences.
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Temel Bulgular & Veri:We evaluate each model with performance metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), R² and Mean Absolute Error (MAE).
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Sonuç & Katkı:In general, ensemble methods offer significant advantages in prediction accuracy.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Data Augmentation · Machine Learning · Microbial Growth Prediction

Özet

Accurate prediction of microbial growth is of great importance in critical areas such as food safety and environmental sciences. In this study, a hybrid of mathematical methods and machine learning-based approaches are used to model the growth dynamics of foodborne pathogen Bacillus cereus. Since the use of mathematical models alone does not sufficiently cover the non-linear data structure of bacterial systems, better results are obtained when hybrids are used together with machine learning methods. We examine the results of five different tree-based models for predicting the growth of Bacillus cereus, namely Fine Tree, Medium Tree, Coarse Tree, Ensemble Boosted Trees and Ensemble Bagged Trees. We evaluate each model with performance metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), R² and Mean Absolute Error (MAE). The results show that the Ensemble Bagged Trees model performs the best, with a validation RMSE of 0.0094 and an R² value of 0.9995. Also, the Fine Tree model has an R² value of 0.9990. In general, ensemble methods offer significant advantages in prediction accuracy.

Abstract

Accurate prediction of microbial growth is of great importance in critical areas such as food safety and environmental sciences. In this study, a hybrid of mathematical methods and machine learning-based approaches are used to model the growth dynamics of foodborne pathogen Bacillus cereus. Since the use of mathematical models alone does not sufficiently cover the non-linear data structure of bacterial systems, better results are obtained when hybrids are used together with machine learning methods. We examine the results of five different tree-based models for predicting the growth of Bacillus cereus, namely Fine Tree, Medium Tree, Coarse Tree, Ensemble Boosted Trees and Ensemble Bagged Trees. We evaluate each model with performance metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), R² and Mean Absolute Error (MAE). The results show that the Ensemble Bagged Trees model performs the best, with a validation RMSE of 0.0094 and an R² value of 0.9995. Also, the Fine Tree model has an R² value of 0.9990. In general, ensemble methods offer significant advantages in prediction accuracy.

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Bu Makaleye Atıf Yap

Armağan, H. (2025). PREDICTION PERFORMANCE OF DECISION TREE INDUCERS ON AUGMENTED BACILLUS CEREUS GROWTH DATA. International Journal of 3D Printing Technologies and Digital Industry, Cilt: 9 Sayı: 2, 352–362. https://doi.org/10.46519/ij3dptdi.1726410

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Kaynakça (18)

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