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The extensive experiments demonstrate that our method achieves significantly better performance compared with the state-of-the-art interpolation methods.
From Table 3, it is observed that the proposed method achieves significantly higher speedup values compared to those obtained by other researchers [6 9].
The results show that the proposed method achieves significantly better performance than the traditional signal forecasting models both on one-step and multi-step wind speed forecasting with at least 40% average performance promotion over all the seven competitors.
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As shown in Figure 5, our method achieved significantly better performance than the baseline method.
Furthermore, our method achieved significantly higher module scores in the GO-based reference networks than the k-means algorithm (Fig 9(c)).
The results demonstrate that proposed feature selection approach outperformed traditional methods achieving significantly higher classification accuracy of 96.6% and 94.4% with BPANN and SVM classifiers respectively.
Both methods achieved significantly higher accuracy in the classification of catastrophic, severe and strong epidemics compared to moderate, mild and milder epidemics.
Combined with the RCS and FCS scores, this method can achieve significantly lower alignment errors in comparison to an exhaustive sampling method.
RAP-MAC achieves significantly higher goodput compared to OPT-MAC.
The simulation scheme introduced here achieves significantly improved efficiencies.
In comparison with Nelder-Mead and MPSO methods, TSPSO achieved significantly lower MAEs at all max_ FEs levels (all P < 0.05, except for the comparison with MPSO at max_ FEs = 2000).
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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