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Fig. 11 Denoising methods performance: the performance of the denoising methods for the four images at various noise levels.

To characterize the method performance, the developed method was partly validated in-house as a quantitative confirmatory method according to Commission Decision 2002/657/EC [ 10].

Bar charts are used in Fig. 12 to represent the methods performance when the noise level is low σ=10.

*PPV = Positive predictive value +NPV = Negative predictive value A summary of the three test methods performance on the different specimens tested is presented in Table 3.

Open image in new window Fig. 10 Hybrid ANFIS methods' performance in the training and testing datasets.

Brute force grid search was used to identify the value of α that gave the optimal AUC0.1 (see material and methods) performance measured on the balanced training set.

Lastly, in the classification of seasonal influenza epidemics based on ILI data from the CDC, the methods' performance was comparable.

Our first goal was to check the influence of the tolerance parameter on the methods' performance.

Among these, the meta-heuristic approaches have many parameters that guide the search and consequently influence the methods' performance.

The charts in Fig. 13 show the methods performance when σ=20.

Expectedly, the method performance reflects the availability of training structures over the different loop length ranges (Detailed results are provided in Supplementary Table S2).