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On the left-out COSMIC data not used for training, we achieved a classification accuracy of 85%% across 6 primary sites (with copy numbers), and 69%% across 10 primary sites (without copy numbers).
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"We achieved sport most recently, and when we applied for training, we got it first time, which is unusual.
Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a word accuracy for nonaudible murmur recognition in a clean environment.
Further study using different time domain features such as maximum absolute value (MAV), kurtosis (KUR), mean value (MV), waveform length (WL), and simple square integral (SSI) could be tested so that the requirement for training can achieve the reliable system.
We achieved MCC more than 0.6 for both train and validation sets.
Have we achieved anything for the workers?
"We achieved what we came for here".
"We achieved what we were looking for".
This is an acceptable approach which will involve the creation of a Community corps of border guards in the coming years - not straightaway but after we have fine-tuned techniques and procedures with appropriate measures for training staff and achieving cooperation between the different current corps of border guards.
Everything we achieved, we achieved together".
We achieve a ranking of 43 for training set and ranking of 48 on test set.
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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