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Simulations are performed to evaluate the performance of the method considering one or several sources at fixed positions.
To evaluate the performance of the method, k-fold cross-validations were performed on the experimentally verified phosphorylation dataset.
To evaluate the performance of the method for stratified environments, six standard 2D and two 3D test cases are selected.
Therefore, a detailed comparison with independent temperature measurements is necessary to evaluate the performance of the method and to obtain more information about the temperature gradient.
More research is necessary in determining the quality of the response surface, in order to be able to fully evaluate the performance of the method.
From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions.
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In this section, we comprehensively evaluate the performance of the methods presented in the previous section.
Classification accuracy, sensitivity, specificity, and AUC were used to evaluate the performance of the methods.
Hence, we evaluate the performance of the methods on real-world network data.
We evaluated the performance of the method quantitatively in retrieving relevant experiments, given a query experiment.
We evaluated the performance of the method by simulations and confirmed the expectations in real data.
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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.
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CEO of Professional Science Editing for Scientists @ prosciediting.com