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The predicted data from the designed artificial neural network model were found to be in a good agreement with the experimental data (R2 = 0.9864).
Data obtained through this system and data obtained from an expert were compared and as a result, it was seen that data from the designed system were in line with the existing data.
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In this chapter, we concentrate on the analysis aspect of the data obtained from the designed experiments.
Empirical illustrations are provided using consumption expenditure data from the complexly designed Indian national household survey.
Model selection is considered successful when the best model is selected a posteriori from the data through the designed experiment.
Create the matrix Q calculating the features corresponding to the evaluated set and time-frequency footprint, using the data from the design set.
Create the matrix Q with the features of SET2 and the time-frequency footprint evaluated, using the data from the design set. 2.
During the risk assessment for embankment dams, engineers utilize current and historical data from the design, construction, and operation phases of these structures.
The data from the design of experiments suggested different cultivation conditions for maximum biomass concentration and PHB accumulation under nitrogen and phosphorus limitations.
The steps carried out in this experiment are the following: 1. Create the matrix Q calculating the features corresponding to the evaluated set and time-frequency footprint, using the data from the design set. 2.
As previously discussed, the preparation phase is responsible for preparing the data from previously designed workflows, allowing the execution of the sequence mining algorithm over them.
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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