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In clean signal conditions the proposed method achieved average recognition rates of 76% for four emotions and 66.5% for all six emotions.
The method achieved average above 93% agreement with Bergey's taxonomy in quartet topologies on these datasets.
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The GLEG and GPF methods achieved average accuracies of 63.24% and 61.83%, respectively, among the four above-mentioned test data sets.
Furthermore, these ICA and PCA RotBoost ensemble methods achieve average kappa values of 0.48 and 0.55, respectively.
The RST-based classifiers demonstrate average AUC of 69.74 % with MODLEM and 71.73 % with VC-DomLEM, while the compared methods achieve average AUC of 74.21 % for logistic regression, 73.52 % for support vector machines, 74.59 % for random forests, and 70.88 % for C4.5.
Compared with the MVDR method, the achieved average DI of the GSMDS method can reach 16.3 dB at d/λ = 0.19, which is also higher than that of the DAS method.
The method achieved 7.1 average channels, 0.011 average false negative, 0.48 average false positive, and 9.54 s average latency time.
The GSS-based method with MFT achieved average relative word error reduction rates of 32.6 and 11.4% compared to conventional CMN and the original proposed method, respectively.
Their method achieved an average of 87% sensitivity, 0.24/h false prediction rate, and in average a 27-min warning time ahead the ictal.
For example, models with 3 internal nodes compared to models with 2 internal nodes offer an average increase in accuracy of 1.60%, while the accuracy of the bootstrap method achieved an average improvement of 1.39%.
The average target detection F-score obtained for the baseline algorithm was 0.32 while our proposed method achieved an average F-score of 0.45.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com