Sentence examples for models gave significantly from inspiring English sources

Neural Network models gave significantly better accuracy with normalized root mean square error of 11.42% and 11.24%.

Nevertheless, three geomagnetic field models gave significantly different geomagnetic field profiles, those points out the necessity to develop a magnetically self-consistent model for description of the inner magnetosphere geomagnetic field.

Although firm conclusions cannot be drawn from this study, the fact that all tested models gave significantly better predictions than that expected by chance alone indicates that further improvement could be possible by increasing genotyping efforts.

For the first generation of juvenile animals, nearly all the MT models gave significantly higher accuracies for trait 2, when the genetic correlation with trait 1 was 0.54 or higher (Table 5).

Table 1 summarises the results; all models give significantly increased speech intelligibility over the audio-alone case, with the rule-based model yielding the highest intelligibility score.

Zhang et al. [10] have shown that the NNs models give significantly higher accuracy than the linear regression approaches in the prediction of future AP concentrations, because it can model the complicated non-linear relationships between independent variables and objective functions.

The different statistical models give significantly different p-values, which is probably due in large part to the small number of samples that were included in this analysis as well as the use of moderated variance (Limma) versus unmoderated variance (t-test).

The ANFIS sub-models gave significantly superior results in terms of the RMSE, r2, CE and the prediction of the peak discharge, compared to other ANFIS models where the lag time was not considered.

In contrast, the modified GL 2000 model gave significantly more accurate results, regardless of the substitution level of conventional sand.

Comparing the model fit by looking at the number of cancers at screening and the following interval, the new model gave significantly (bootstrap P < 0.0001) better model fit than the classical Markov model [ 26].

The final comparison was between the response gain model and the additive model, and it is shown in Figure 5 C. The additive model gave significantly better fits to the data than the response gain model (P = 0.03, rank sum test).