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Models were developed using genetic function approximation technique and were extensively validated employing different validation statistics.
The obtained PLS-DA model was extensively validated employing two different validation strategies: (i) statistical validation employing a method based on permutation testing and (ii) external validation splitting the available data into two independent sub-sets.
The models were externally validated employing a test set, and the predicted values were in good agreement with the experimental results.
This equation was validated employing experimentally gathered data with a maximum deviation of ±4 % for 0.698 < n < 0.813 and 548 < N Reg < 42,800.
Based on the literature available, it appears that clinically useful proteomic biomarkers can be identified, and also validated, employing the technologies available today.
These theories must be validated employing field data, acquired by means of experiments and observations of the modelled phenomena, before the models can actually be used for sake of prediction.
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To validate employed simulating models, the simulation results have been compared with experiment results reported from MIT laboratory and also with simulation results obtained by another research team.
Good agreement between the experimental results and the simulation ones is highlighted by comparing the gear shape after cold rotary forging and the axial forging force, which validates employed model.
The accuracy of the new boundary closure is validated by employing the method of manufactured solutions.
The derived statistical model is validated by employing it to design Lloyd Max quantizers for matching pursuit coefficients.
The results were validated by employing higher-order Confirmatory Factor Analysis to determine the final PNC and CPTED measurement models.
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