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In this work we develop a methodology to drive this selection process towards the optimal choices by modelling the accuracy and the performance of the solution algorithm.
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Over time, as more of these factors were identified and fed into the model, the accuracy of the forecast improved.
Considering relative simplicity of the model, the accuracy of the PAFRAG code predictions is rather remarkable.
Including phoneme probabilities into the recognition process (unigram language model), the accuracy of the recognition improves significantly.
For solving above kinetic models, the accuracy of the estimated result is dependent on the calculated method.
When the probability confidence map is used as the prediction model, the accuracy of SVM classifier cannot achieve 100%.
In reservoir modeling, the accuracy of permeability estimation is very important because it will greatly affect the fluid flow.
By using a 4-gram language model, the accuracy of pitch accent detection was substantially improved from 74.1%to80.1%1%.
This paper presents the NN methodology used in developing the models, the accuracy of the predictions, and some sample results.
Given that histological evaluation of disease lesions is semi-quantitative and subjective, and the small sample numbers used for predictive modeling, the accuracy of the predictions is encouraging.
When we applied the genomic selection models, the accuracy of predicted traits increased with spatial adjustment.
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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