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The validation dataset is employed to validate the final model for accuracy and to predict the limiting velocity required to prevent sedimentation in sewer systems.
For upsampling a target size of 200% (number of instances) of the upsampled training dataset compared to the original dataset is employed.
With the first dataset, we test the ability of the methods to classify compounds, while the second dataset is employed to examine the ability to rank actives among decoys.
Using a large number of independent datasets, we showed that the stratification is stable across different datasets, regardless of which dataset is employed to derive the gene sets with which to perform the stratification.
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Four days of datasets were employed to develop the model and one day of dataset was employed to test the prediction accuracy.
A mixed multinomial logit model for each dataset was employed to examine the contributory factors.
Different from recent modeling studies, a soil dataset was employed with 200 samples of correlated soil hydraulic parameters for sandy soils.
The train dataset was used to build/simulate the model and the test dataset was employed to evaluate the performance of the built model.
To verify the efficiency of the proposed model, a dataset from approximately 3 years of cycling capacity fading experiments of various formulations using several different materials provided by Contemporary Amperex Technology Limited (CATL), as well as a NASA dataset, are employed.
To overcome the lack of large training data, the handwritten images in MNIST dataset are employed to simulate the properties of long-rang infrared images including dim targets, background clutters and low contrast.
In order to facilitate the comparison and evaluation of multiple ortholog identification methods, this dataset was employed for all analyses (except as otherwise noted).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com