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On the PIM subset the multi-task approaches achieved a significantly lower MSE compared to the tSVM for all targets.
Moreover, the CRF, LSTM, and SVM approaches achieved a reduction of 0.23, 0.38, and 1.07% in average WER over a conventional perplexity-based approach, respectively.
On 3 targets GRMT and the 1SVM perform considerably better, whereas the top-down approaches achieved a better MSE for 4 targets.
For all subsets, but the MAPK subset, the multi-task approaches achieved a significantly better mean performance than the baseline methods 1SVM and tSVM.
It is clear that for the three scenarios considered, the proposed approaches achieved a significant reduction of control packets compared to the other evaluated schemes.
A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives.
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We can see that all the three approaches achieve a throughput of 100% in all cases.
Experimental results showed that our automatic figure segmentation approach surpasses pure caption-based and image-based approaches, achieving a 96.64% accuracy.
That approach achieved a high overall accuracy of 100%%.
Our suggested approach achieved a remarkable ranking performance at top-5 and top-10 retrieval results.
This approach achieved a 93.33% accuracy with a true positive rate of 86.67% and a false positive rate of 0.00%.
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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