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A cross-validation R2 value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets.
On the other hand, the high specificity yielded by the model signifies that the selected functional domains are meaningful since they do not exist in most of the non-toxin proteins.
Moreover, the use of a linear-threshold model signifies that the effect of heat on mortality is assumed constant for the whole temperature range above the threshold, simplifying the actual relationship, and the effect on mortality of temperatures below the threshold was not analysed.
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Therefore, the comparison between the best ANFIS-GA and ANFIS-DE models signifies that despite the models' close performance in the testing dataset, the training dataset performance of the ANFIS-GA with RMSE of 0.040 is most likely trapped in over-fitting, and the ANFIS-DE result with RMSE of 0.057 in the training dataset is more reliable.
The results of application of the developed novel model signify that forecasting could be effectively carried out under uncertain conditions and hence, positions the model as a distinctly superior tool for accident forecasting investigations.
Therefore, when a DMU is inefficient in CCR models and turns out to be efficient in BCC models, signifying that the dominant source of inefficiency is due to scale efficiency.
In contrast, DIC values for M1 were very high when compared to other models, signifying that for this study, structured spatial effects alone do not give a true reflection of the variation in the prevalence of childhood diseases and that other factors should be taken into account.
This signifies that Bluetooth is turned on.
The better performance of a non-linear model signifies the fact that the relationship between sunshine duration and clearness index does not follow a straight line.
When the data corresponds with the Rasch model, it signifies that a fundamental underlying dimension exists that is shared by the various healthcare technologies examined.
After all, the existing version of Windows Phone is being left behind in favor of a more powerful, unified platform — one would imagine they would use a much loftier model number (not unlike "1001") to signify that transition.
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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