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Count models performed better than presence/absence, explaining 47% and 22% of the deviance for Taiwan and Japan (Table S2).
For the bubble and mountain tests, the DG models performed better.
The study indicates that hierarchical models performed better for crash frequency and severity prediction.
The regenerated and predicted storms indicate that the ANN models performed better than the classical techniques.
These results suggested that the specific models performed better than did the general models.
Comparison results indicated that the FG models performed better than the ANFIS and ANN models.
Results reveal that the ENN models performed better than the FG, ANFIS and ANN models.
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AI models perform better with more data, but that performance may plateau over time.
However, both models perform better with short term forecasting.
Simulation results revealed that the hybrid models perform better than their corresponding single-stage models.
We also show that the dynamic models perform better than the corresponding static models.
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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