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We first show the convergence performance of the objective function J of FCM in Figure 7a.
In order to test the performance of the objective algorithms, we computed the Spearman Rank Order Correlation Coefficient (SROCC), the Pearson correlation coefficient, the RMSE, and the Outlier Ratio (OR) [34].
The performance of the objective models is evaluated using the Spearman Rank Order Correlation Coefficient, the Pearson Linear Correlation Coefficient, the Root-Mean-Square Error (RMSE) and the Outlier Ratio.
A large beam diameter at the objective pupil was needed to ensure the best performance of the objective (the smallest spot on the sample).
The performance of the objective functions in predicting the accumulation of the mixture of storage compounds on two different carbon sources (glucose and acetate) under N-limited conditions were compared with our experimental observations using a Bayesian approach.
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Figure 8 shows the relative performance loss of the objective function values from the REF value when using three and seven time slices, for varying ε.
A fivefold cross-validation strategy was used to evaluate the generalization performance of the trained objective assessment system; the LIVE database provided the dataset.
The sub-optimal performance of the aspheric objective lens also limited the field depth to 56 µm.
The results show better performance of the multi-objective approach independently of the strategy adopted.
Comparison metrics: It is common to compare the performance of the multi-objective algorithms' performance by means of some specific comparison metrics; to compare proposed algorithms with each other, three comparison metrics are employed (Rabbani et al. 2016a, b). 1. Number of Pareto solutions (NPS): The quantity of non-dominated solutions that every algorithm can discover.
From the standpoint of electrical performance, the objective of a wire conductor is to transmit signals efficiently from one point to another.
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