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The comparison between the SVM predictions and the CFD results showed that the SVM models could predict the numerical data with a good accuracy.
Both proposed models could predict bed shear stress values very close to experimental data.
The multiple linear regression equation models could predict 91.7, 90.9 and 94.8% of each response, respectively.
The proposed GRF estimation models could predict full and partial GRF with high accuracy.
These α1A pharmacophore models could predict compounds well, both in the training set and the test set.
This showed that simple canopy models could predict transpiration in data scarce regions where only L was measured.
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Our prediction model could predict the primary outcome, one-year U-RTW, in 'intermediate' and 'high' risk patients, but with less precision in 'low' risk patients.
The initial prediction result indicated that the established model could predict the hot cracking rate adequately within the range of welding parameters being used.
For a decision threshold of 0.33, sensitivity and negative predictive values were 100%, which indicated that this model could predict insignificant fibrosis with the highest accuracy.
The extended model could predict the indentation loading and unloading response accurately and provided improved predictions of the residual state.
A visual predictive check per study did, however, show that the present model could predict EPS after haloperidol both in the open-label study and when used as comparator (data not shown).
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Justyna Jupowicz-Kozak
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