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Promising results have been obtained after applying the method on five different data sets.
Figure 7 shows the performance of the proposed method on five other videos with the same challenges discussed above.
The performance of method is average performance of method on five sets.
A comparison also is made between PROMISCUOUS and our method on five potential drugs of hypertension (see Table 3, drugs in bold italic and underlined bold).
As our proposed model yields good performance on the PPIs data of Yeast, Human, and H. pylori, we explored our method on five other independent datasets.
To test whether this approach results in reliable estimates for N0 and N1, we applied this method on five synthetic data sets generated by the model introduced by Rocke and Durbin (2001) (also see Wang and Ethier, 2004) with increasing values for the standard deviations of the additive and multiplicative errors.
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Ozerov and Douvris tried out their catalytic method on three fluorocarbon test substrates.
We performed this method on seven cases with velopharyngeal insufficiency.
In our experiments, we illustrate the benefits of our method on two image segmentation tasks.
We applied our method on two exemplar datasets each containing a pair of sister fish species: Siniperca chuatsi vs. Sini.
We validated the performance of our method on two separate data sets from confocal and split detector AOSLO systems.
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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