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The results show that the proposed method achieves better performance than the baseline methods.
The results demonstrate the NAMS method achieves better performances on both clean image and low contrast and noisy images.
With good scalability for large datasets, the learning-based method achieves better classification accuracy than the rule-based one.
By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.
Experimental results demonstrate that the proposed method achieves better improvement over the state-of-the-art algorithms in terms of both subjective and objective quality.
A set of experimental results shows that the proposed method achieves better approximation accuracy than the Bezier-based method and the GA-based method.
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The results reveal that the data obtained with DIC method achieve better linearity than ESPI.
Applied on two cancer types (ovarian and glioblastoma multiforme), our method achieved better prediction power (p-value: 6.18E−03 5.15E−11) than peer methods (GE.CNAs and GE.CNAs. Lasso).
The proposed EWGP method achieved better MAE.
The pre-trained method achieved better performance.
The burst coloring method achieved better quality of colors than the nonburst method (Fig. 11b).
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CEO of Professional Science Editing for Scientists @ prosciediting.com