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Each model showed high assessment accuracy.
The validated model showed high internal and external predictive power.
The developed model showed high predictions during training, cross-validation and testing.
The neural network model showed high validity on predicting compressive strength, expansion and weight loss due to sulfate attack.
The present model showed high statistical quality as confirmed by its robust predictive potential and satisfactory descriptive capability.
The only exception being quad-helix model showed high stress levels around pterygo-maxillary suture whereas minimal stress around pterygo-maxillary suture was noticed after NPE2 activation.
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But in group II, 7-mm implant height model showed highest stresses on the posterior teeth.
The smallest model showed higher deviations for the measurements, specifically for the LE-RE and the RE-N distances.
In the experiments, the proposed model showed higher F1-scores than independent models: 6.8% higher in speech act identification, 6.2% higher in predicator identification, and 4.9% higher in sentiment identification.
Compared with the control experimental curves, the post-stapedotomy FE model showed higher values for the RW membrane displacement amplitudes for frequencies from 0.5 to 1.0 kHz.
In both sets of subjects the expression model showed higher specificity and negative predictive value (NPV) versus sensitivity and positive predictive value (PPV).
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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