Sentence examples for datasets indicating that the from inspiring English sources

It achieved an impressive average accuracy rate higher than 97 % and high precision (times ) recall rates for all datasets indicating that the MDL classifier makes few mistakes.

The unsupervised analysis clusters together with remarkable precision sequences belonging to the same datasets, indicating that the integration sites of different vectors are defined by specific patterns of flanking TFBS motifs.

Reassuringly, 96% of the proteins identified in this filtered Q-TOF dataset are also found in the filtered LTQ datasets, indicating that the chloroplast preparations were highly reproducible and consistent with the higher sensitivity of the LTQ-Orbitrap.

In addition the overlap between cells active in C versus CI comparisons was also far greater than in reshuffled datasets, indicating that the coactivation pattern present in coactivations in general was preserved in coactivations with impinging inputs (mean = 60.2±13.4%; n = 485 movie pairs; reshuffled mean = 23.4±2.4%; different with p<0.001) (Figure 3b).

Similar dendrograms are observed for all three datasets indicating that the relative magnitude of variation within groups and between groups is similar within each dataset (see Figure 2 and Additional file 1).

AuPosSOM ROC curves for the filtered coulomb contact selection were better or comparable to those of the conventional scoring functions for eight out of nine datasets, indicating that the clustering approach is more robust than the energy-based one.

Experimental results on two large real datasets indicate that the proposed incremental slope-one recommenders can correctly reflect the increments of dynamic datasets with high computational efficiency.

All observational datasets indicate that the volumetric distribution shifts toward lower salinities, i.e. on average, fresh regions become fresher at a higher rate than saline regions become more saline (see Fig. 2 and supplementary Fig. 1).

The test results of the ISPRS datasets indicate that the proposed algorithm achieved low commission errors ranging from 1.53% to 6.91%.

An extensive experimental comparison with two public EEG datasets indicates that the MKELM method gives higher classification accuracy than those of the other competing algorithms.

Extensive experiments on several commonly used image SR testing datasets indicate that the proposed method achieves state-of-the-art image SR results.