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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).
It is evident from the results that our proposed method achieved better objective and subjective denoising quality.
Results from simulations and lake experiment showed that the proposed LP-ETAM method achieved better angular resolution than ETAM.
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The results show that the proposed method achieves better performance than the baseline methods.
The results reveal that the data obtained with DIC method achieve better linearity than ESPI.
By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.
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.
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