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The data was divided by gender, age and day/night cycle.
Lastly, all data was divided into the calibration and validation sets by D-optimal designs and the activities of validation set were predicted.
Accurate and efficient artificial neural network was obtained by changing the number of neurons in the hidden layer, while data was divided into training, test and validation sets which contained 70, 15 and 15% of data points respectively.
The total amount of data was divided into two sets: training (or calibration) and testing.
In order to accommodate our range of experiments, the data was divided into sub-datasets.
Spectral data was divided by the normalized lamp spectrum to calibrate for variations in lamp intensity.
Data was divided into half-overlapping 4-s segments around the center of each burst.
In order to simulate the practical application of the ANN models, the data was divided into training and testing subsets.
The validation data was divided into subsets based on the availability of the data and seasonal variation.
The sample data was divided into two sub-samples one from 1970–1989 and the other from 1990–2010.
For the EM computation the data was divided into 24 h sequences, each consists of 48 measurements constructing 167 sequencesnces and 138 NO2 sequences.
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CEO of Professional Science Editing for Scientists @ prosciediting.com