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Our results indicate that the model performs very well.
The proposed model performs very well, since it provides a readily solved analytical solution with respect to the conventional MIM.
The results of the model indicate that fuzzy inference system based spatial analysis model performs very well, even with the limited and imprecise data.
Our findings suggest that a simple version of the seasonal unit root (SUROOT) model performs very well in predicting 8 of 14 variables, when the forecast horizon is 1-step ahead.
The model performs very well in identifying areas of over 40% seroprevalence (AUC = 0.96).
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But usually the model performed very well, so as time passed and confidence grew, many bankers and traders forgot the model had limitations.
In all cases, the new model performed very well.
With the designed settings, the model performed very well in the simulation of SLBs.
The model performed very well, as hold-out sample opinions were predicted at an average accuracy of 89.1%, with little variance in performance.
This model performed very well over the range of data used for training, with r-value of 0.95, mean square error (MSE) of 0.0019 and mean absolute error (MAE) of 0.024 mg/l when applied on the testing data set.
Interestingly, our model performed very well in the absence of peptides P1, P2, and P3 [SEN = 0.67 (SP) and 1.00 (XP ], but when the peptide was included in docking, there was a significant decrease in our model's ability to predict true positives (SEN = 0.33 for SP and XP).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com