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The proposed algorithm was tested on synthetic real imagery, and the experimental results demonstrate that the proposed approach outperforms two traditional sub-pixel mapping algorithms based on a single image, and the another multiple shifted images based sub-pixel mapping method.
Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data.
We apply our approach to detect conserved protein complexes in yeast fly and yeast worm PPI networks, and show that our approach outperforms two recent approaches in most alignment performance metrics.
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We conduct sufficient experiments on the MovieLens dataset, which demonstrate that our approach outperforms five state-of-the-art approaches.
We conduct sufficient experiments on the MovieLens dataset, which demonstrate that our approach outperforms five state-of-the-art approaches. .
Its applicability to MRI data, comprising four channels: non-enhanced and contrast-enhanced T1w, T2w and FLAIR contrasts, is demonstrated on a challenging clinical data set containing brain tumors (N = 53), where our approach significantly outperforms six commonly used tools with a mean Dice score of 95.19.
As a result, according to the experiments the proposed approach in this paper outperforms two well-known benchmark ERP systems, in-house ECC 6.0, and in-cloud ByDesign.
In terms of operational speed, the approach proposed in this paper outperforms two well-known benchmark ERP systems, in-house ECC 6.0 and in-cloud ByDesign.
Our method shows good performance for reconstructing signalling networks from artificial data and outperforms two current DBN approaches.
In the 200 Kbps to 1 Mbps bitrate range, the proposed JRVIR approach outperforms the other two approaches, and the performance gap between JRVIR and the other two approaches increases with the bitrate.
Preliminary results show that the attribute-based approach outperforms the other two approaches in terms of precision and F-measure.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com