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Our method achieved mean MinNDC 0.558 when we used three features: SIFT-Hes, MFCC, and STIP.
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It was observed that the proposed method achieved the mean classification accuracy of 90.06% using fivefold cross-validation.
It was observed that the proposed method achieved the mean classification accuracy of 84.8% using 5-fold cross-validation.
Applied to the BioCreative 2012 Triage dataset, our method achieved 0.8030 mean average precision (MAP) in the official runs, resulting in the top MAP system among participants.
Compared with [ 45, 47], the dynamic update method achieved the larger mean sensitivity (85.2%) and lower mean fpr (0.04 h−1) by a multichannel EEG feature extraction method.
On the test set, our method achieved overall macro-averaged (mean) Precision of 80%, Recall of 98%, and F-Measure of 85% on the gold standard, as well as Precision of 70%, Recall of 81%, and F-Measure of 71% on the silver standard.
In addition, also eACS-MB method achieves lower mean transmission power for FAPs location between 5 m and 7 m.
Evaluated by 3-fold cross-validation, our method achieves the mean absolute error (MAE) of 14.1% and the person correlation coefficient (PCC) of 0.75 for our new-compiled dataset.
This method achieved 87.20 % a mean agreement percentage (MAP) with five channels (compared to physician's scores) and 87.41 % MPA with four channels for full term and preterm, respectively.
It is noteworthy that none of the methods achieved a mean tree FP less than 0.95.
The elements are sorted by the overall mean rank each method achieved based on Macro-F1 for all datasets.
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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