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At the modelling stage the most adequate models were determined by using systematic approach.
Predictive models were determined, able to obtain satisfactory fittings of the experimental data, with coefficient of determination (R2) values higher than 0.95.
The models were determined by process identification.
Best-fit models were determined by quadratic regression.
Parameters of both models were determined by least squares method.
The best nucleotide substitution models were determined by Modeltest v2.7 (Posada and Crandall 1998).
Receiver operating characteristic curves for the maternal and fetal models were determined.
The models were determined by process identification techniques and were validated with a PI algorithm.
Imperial equations and prediction models were determined using regression analysis and neural networking, respectively.
The inertial and geometric parameters of the models were determined from published anthropometric data.
The hyper-parameters of the ANN and SVM models were determined using a 10-fold cross-validation scheme.
More suggestions(16)
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models were launched
models were analyzed
models were wrong
models were sent
models were interspersed
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