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The performances of this model were evaluated using different public datasets.
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To test whether MRI data improve discrimination performance of this model, we performed Cox regression analyses with inclusion of each of the MRI variables and their combination.
Performance of this model was proved using a statistical error mostly used in hydrological modelling as given by Chai and Draxler (2014).
We studied the performance of this model.
Figure 3 depicts the simulated BER performance of this model.
The statistical performance of this model is presented in Table 1.
The predictive performance of this model was assessed in terms of average class-specific recall and precision.
The overall performance of this model is good and enabled to predict the future summer temperature of Ethiopia.
The performance of this model has been benchmarked on both classification and time series prediction problems.
The performance of this model is demonstrated through a case study.
The segmentation performance of this model is tested and evaluated with reference to 16 microarray data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com