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Next, the measures of predictive performance of trained models are Precision (Prec) = TP/ TP+FP), Sensitivity (Sn) = TP/ TP+FN), Specificity (Sp) = TN/ TN+FP), and Accuracy (Acc) = (TP + TN / TP+FP+TN+FN), where TP, TN, FP, and FN are the true positive, true negative, false positive, and false negative predictions, respectively.
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Finite element models are high precision method in simulation of the pile soil interaction problems however these analyses are usually complex and computationally expensive.
It shows that GSSR models are higher in precision and more complex compared with GSR models.
Finally the scaled models are fabricated by precision casting for static and dynamic tests.
The models are of sufficient precision to compare the architecture of the different pili in detail.
By accounting for changes in gene regulations between comparable conditions, graphical statistical models are expected to improve prediction precision.
However, it has become clear that more sophisticated transgenic animal models are required to determine with precision the receptor-mediated signalling pathways that underlie physiological GPCR responses.
Note that model precision is not given for use of LiDAR without age (panel a) as these models are not feasible.
Therefore, the models were calibrated, and precision of estimation was increased (the values of R 2 were less than 0.90 for only ten provinces in the modified models).
Performance of the models was measured using precision and recall after application of the various class-specific binary thresholds (previously calculated) for each target.
Another widely used metric for the effectiveness of IR models is the mean average precision (MAP).
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