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However, it still remains challenging to decide which kind of feature is suitable for a specific situation.
Hence, the signal peptide feature is suitable for our estimation method.
This feature is suitable to the in-depth analysis and study of patient samples in clinical laboratory settings.
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These features are suitable for characterizing the complicated microstructures in commercial metallic materials.
Rather, the KS-statistics demonstrate that many features are suitable for classification.
All these features are suitable for building basic structure-activity relationship (SAR) models [16].
In particular, the distance-based features are suitable for the datasets which have smaller numbers of classes, numbers of samples, and the lower dimensionality of features.
Given that the appearance of a person is dominated by its clothes, color and texture features are suitable for people description.
Therefore, this indicates that the proposed distance-based features are suitable for the datasets whose numbers of classes, numbers of samples, and the dimensionality of features are relatively small.
Although all these features should be fed within all the search algorithms to derive a more powerful conclusion, we can observe that Gaussian posteriorgram features are suitable for speech signal representation due to the best performance of system 1 when no prior knowledge of the target language is used.
It is important to note that the performance of the classifiers was similar in all cases; therefore, many combinations of features are suitable for the prognosis of cardiovascular risk as it is proposed in our work.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com