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Their approach achieved good correction accuracy.
In general, our approach achieved good results, often reaching high performances very close to those achieved by fixed specialist set similarity thresholds.
Based on the result and our observation, our approach achieved good enough accuracy even some users has a bad accuracy (under 30%%).
As observed from Table 2 that the proposed approach achieved good prediction results on yeast dataset, whose average accuracies of five experiments are above 92% and average Sensitivity, Precision, and MCC of 69.25, 69.25, and 67.65% respectively.
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Overall, PCA + DFVS approach achieves good results.
Our experiments show that our approach achieves good results.
Simulation results show that the proposed approach achieves good tracking performance.
Experimental tests, using fifteen different scene categories, show that the proposed approach achieves good performances with respect to the state-of-the-art methods.
Since the similarity ratio is statistically calculated by analyzing the innate characteristics of an inter-laced screenshot, the proposed approach achieves good adaptability and does not repeatedly require new training process.
Both the escape probability and the directional reflectance approaches achieve good results, with correlation coefficients in the range 0.54 0.82, although escape probability results are usually slightly better.
It has stimulated a regional/inter-country approach, which has achieved good results and should be further developed, taking into account the information from improved STD/HIV surveillance countries in the region and the impact of improved commitment and capacity for national level programme responses.
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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