PRECISION GROWTH MODELING OF ESCHERICHIA COLI IN SPARSE DATA SCENARIOS: A MULTI-STAGE LEARNING APPROACH

Authors : Hamit Armağan, Ulas Yamanci

Pages : 1178-1187

Doi:10.21923/jesd.1778201

View : 110 | Download : 151

Publication Date : 2025-12-30

Article Type : Research Paper

Abstract :This study investigates the prediction of Escherichia coli growth in environments where experimental data are limited, by integrating mathematical curve fitting with machine learning regression models. Two hybrid frameworks are developed: Fourier Series Curve Fitting combined with Gaussian Process Regression (FSCF-GPR), and Gaussian Curve Fitting integrated with Support Vector Machine Regression (GCF-SVMR). The raw dataset, initially composed of only 10 experimental measurements, was expanded to 114 data points through mathematical smoothing, providing a richer basis for model training. Model performance was assessed using Root Mean Square Error (RMSE), Mean Squared Error (MSE), Coefficient of Determination (R²), and Mean Absolute Error (MAE). Results demonstrate that the FSCF-GPR framework achieved outstanding predictive accuracy with an R² of 0.9999, while GCF-SVMR also showed strong performance with an R² of 0.9934. These findings highlight that data augmentation via curve fitting can substantially enhance the accuracy and robustness of machine learning approaches in microbiological growth prediction under data-scarce conditions.
Keywords : Mikrobiyal büyüme modellemesi, Veri artırma, Makine öğrenmesi, Regresyon model

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