Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer DOI Creative Commons
Van Giao Nguyen, Prabhu Paramasivam, Marek Dzida

и другие.

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 60, С. 104743 - 104743

Опубликована: Июнь 24, 2024

In this study, eXtreme Gradient Boosting (XGBoost) and Light (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, mass product) dependent (energy consumption size reduction) established. For energy consumption, XGBoost demonstrates superior performance R2 0.9957 during training 0.9971 testing, alongside minimal MSE 0.0034 0.0008 testing phase indicating high predictive accuracy low error rates. Conversely, LGBM shows lower values (0.9061 training, 0.8809 testing) higher 0.0747 0.0337 reflecting poorer performance. Similarly, for shrinkage prediction, outperforms LGBM, evidenced by (0.9887 0.9975 (0.2527 0.4878 testing). comparative statistics showed that regularly outperformed LightGBM. game theory-based Shapley functions revealed temperature types most influential features model. These findings illustrate practical applicability LightGBM models in food operations towards optimizing conditions, improving quality, reducing consumption.

Язык: Английский

Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer DOI Creative Commons
Van Giao Nguyen, Prabhu Paramasivam, Marek Dzida

и другие.

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 60, С. 104743 - 104743

Опубликована: Июнь 24, 2024

In this study, eXtreme Gradient Boosting (XGBoost) and Light (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, mass product) dependent (energy consumption size reduction) established. For energy consumption, XGBoost demonstrates superior performance R2 0.9957 during training 0.9971 testing, alongside minimal MSE 0.0034 0.0008 testing phase indicating high predictive accuracy low error rates. Conversely, LGBM shows lower values (0.9061 training, 0.8809 testing) higher 0.0747 0.0337 reflecting poorer performance. Similarly, for shrinkage prediction, outperforms LGBM, evidenced by (0.9887 0.9975 (0.2527 0.4878 testing). comparative statistics showed that regularly outperformed LightGBM. game theory-based Shapley functions revealed temperature types most influential features model. These findings illustrate practical applicability LightGBM models in food operations towards optimizing conditions, improving quality, reducing consumption.

Язык: Английский

Процитировано

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