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Minimum support ith varying minimum support thresholds, from 0.02 to 0.0001, the best performing solution is stably with (f=max) (Fig. 6b) and (m=) confidence, and indifferently max or min as g.
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An interesting area of future work is to evaluate the solutions presented to determine the best performing solutions as a function of specific datasets.
Hence our best performing solution just uses an include list.
When β reaches its maximum value the algorithm stops and the best performing parameter set is used as the solution to the optimization problem.
Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets.
The best performing catalyst (Fe-AAPyr) was then tested in MFC with increasing solution conductivity from 12.4 mScm−1 to 63.1 mScm−1.
Comparisons were made to the best performing docking method BDOCKnb, filtered by metaPPI predictions, resulting in 1500 2000 docking solutions.
The best performing models are presented.
So we had the best performing shares.
Since the best-performing solution among the ones presented is the Syndrome check-based RA BCH code, the reliability of the model for this code is summarized in Table 1.
SFF is the second best performing method.
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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