Sentence examples for methods significantly better from inspiring English sources

In the other two scenarios (AO and NPO), there is no significant difference in performance between all four methods: PSO is handling the AO scenario slightly better than the other methods, while A717 performs better compared to the other methods (significantly better than DE) in the case of the NPO scenario when considering data with 5% noise.

Docking coupled with hybrid scoring 5D-QSAR methods performed significantly better than other QSAR methods in identifying agonists among these steroidal ligands (Ekins et al. 2009).

While all covariate shift adaption methods perform significantly better (p<0.05) than the baseline method without covariate shift adaption, the accuracies with the proposed method are significantly higher than with the other two tested covariate shift adaption methods (p<0.005).

Compared with other existing methods, the proposed method provided significantly better classification results for each data length of SEMG signals, which demonstrated that the RFBE method was suitable for identifying different types of forearm movements.

Our improved methods are significantly better than the MRF-Deng method in terms of identifying disease genes.

Due to the improved initial segmentation, the proposed method performs significantly better than reference method 1 and comparable to reference method 2. We use three objective measures [26] (see also [27] for other measures): color contrast, histogram distance, and motion contrast.

The proposed algorithm is also compared with an existing action recognition method in Table 14, which shows that the proposed method performs significantly better compared to other methods.

We will demonstrate that this new method performs significantly better than similar methods which do not use optical flow distributions or which do not use multiple frames.

The results demonstrate that the proposed method performs significantly better than other methods selecting relevant genes for high-dimensional, multi-category cancer diagnosis with an average of 12.82% improvement in F-score value.

Our method performs significantly better than other methods for ancestry segments of ≤1 cM, as demonstrated in both simulated and real data analysis.

We show that our CCS-based method performs significantly better than those methods in almost all settings we studied, especially in terms of precision.