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The experimental results show that our approach performs more effectively and accurately.
Experimental results have demonstrated that our approach performs more efficiently than existing methods with respect to accuracy, as well as alleviating the data sparsity and cold-start issues.
Furthermore, we also note that the optimized cut height approach performs more consistently, selecting the same top performing input attributes during training and testing executions on both data populations.
The results indicate that the developed approach performs more accurately and robustly in comparison with conventional region growing methods, particularly in the presence of sharp features, outliers and noise.
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That means our approach performs much more stably under noisy circumstances.
Saturation accuracy based approach performs with an accuracy more than 80% with noisy and low-resolution images of UBIRISv2 and FERET, which marks the efficiency of the proposed approach.
For data sets with very distinct separations of the clusters, the first approach performs best, while for more continuous data, a weighted sum of the local models will probably be a better choice.
It should be interesting to see how our immunization approach performs in rabbit, which exhibits a more human-like lipoprotein profile than mouse [ 4].
All sparsity-driven approaches perform much more stably, yielding high TBR up to 20 dB regardless of the decreasing SNR.
This goal of large-scale analysis of TF activity has been performed through creation of cell lines stably expressing TF reporters [8], yet a transfection-based approach could be performed more quickly and extended to a range of cell types.
Their unsupervised approach performed better than the more traditional methods of classification those based on a set of predetermined features and managed to reach up to 85.4% accuracy in the large Brazilian data set, they report today in PeerJ.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com