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The authors defined three driving styles and achieved a classification accuracy of 77%.
For the second dataset, six channels were selected with which they achieved a classification accuracy of 86 %.
Using the GNMM proposed by the authors, 10 channels were selected in the first dataset which achieved a classification accuracy of about 80%%.
In the supervised learning experiment we achieved a classification accuracy of around 25 percentage points above baseline, a result similar to that of Vandamme et al. [13] While the classification accuracy is similar, comparing our results with theirs is difficult because of the very different feature sets and experimental setups.
They achieved a classification accuracy of about 90% [ 32].
The cross-validation achieved a classification result of 55.6%.
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For these 22 species, the network is able to achieve a classification accuracy of 86.2%.
We applied the proposed technique to the same dataset used in [6] achieving a classification rate of 84% (Figure 7).
Also, the minimum number of pcs required to achieve a classification accuracy of above 90% is 15 and 11 for N = 25, 15, respectively.
However, to date there has not been an attempt to achieve a classification of municipalities in rural-urban gradients based on socio-ecological interactions.
Tested with all the 48 recordings from the MIT/BIH arrhythmia database, the proposed method achieves a classification accuracy of 98.21%, which is comparable to the existing results.
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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