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To validate the predictive ability of the suggested model, ANN method was employed to predict the concentration of three analytes in nine laboratory-prepared mixtures (validation mixtures) containing different ratios of the three drugs where satisfactory results were obtained (Table 4).
To validate the predictive ability of the suggested models, PCR and PLS methods were employed to predict the concentration of MLT in five laboratory-prepared mixtures (validation samples) containing different percentages of compound 10, where satisfactory results were obtained (Table 4).
The aim of this paper is to predict the concentration of two toxic metals namely Fe and Ni using SVM.
A phenomenological model is developed to predict the concentration of protein in foam.
Regression equations developed in this experiment may be used to predict the concentration of SID AA in canola meal.
In all cases, the importance of the model lies in its ability to predict the concentration of the low carbon number oligomers.
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In order to predict the concentrations of nutrients, DO, iron, and plankton, three models were combined.
The optimized networks were able to precisely predict the concentrations of the three cations in synthetic mixtures.
Thus, we can predict the concentrations of the four active ingredients in the excrement of rats orally administered with low, middle, and high dosages at different time points.
This, combined with the air change rate has enabled us to predict the concentrations of acetic acid in the Cartoon Gallery as about close to 100 μg m-3.
The model based on artificial neural network (ANN) could predict the concentrations of the dyestuffs removal from the aqueous solution during ozonation under the different conditions.
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