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In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms.
EASE-MM not only outperformed our previous work and other related methods, it achieved a more balanced results for different types of mutations based on the accessible surface area, secondary structure, and magnitude of stability changes.
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By taking advantages of soft-decision outputs of the channel and by employing statistical signal-processing methods, it achieves higher recognition performances than existing algorithms which are based on algebraic approaches in hard-decision situations.
Considering the average beam pattern of the PODB method, it achieves the highest gain at the direction of DCR users and the lowest to the LU.
Because the approach combines the strengths of both DFT and GRNN methods, it simultaneously achieves both efficiency and accuracy at a very low cost.
Although Sauvola's method [27] is better than Niblack's method [19] as an individual algorithm, Niblack's method is preferred as a part of a combined method since it achieves higher background precision and foreground recall.
This method provides a significant speedup over our combinatorial method, which it achieves by exploiting the observation that the independent secondary structure of an RNA molecule is mostly preserved even after it forms a joint structure with another RNA.
They compared the performance of those classifiers in differentiating AD from FTD and concluded that the PLS + KNN is the best method since it achieves the highest accuracy with leave-one-out cross-validation.
A well-known advantage of a collocation method is that it achieves high accuracy with relatively fewer spatial grid points when compared with other numerical methods.
Compared to the HOT method, it can achieve an arbitrary compromise between the superdirective beamformer and the conventional one.
By means of this method, it can achieve the dual goal of producing fine powders and maintaining the dispersiveness.
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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