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Phase findmatches was applied for each set of pharmacophores.
The classification process was applied for each participant and results are shown in Table 2.
A specific combination of the evaluation methods presented above was applied for each of the themes.
To increase the accuracy of the system, a forward selection process was applied for each classification.
To predict SFC and DFC, multiple linear regression (MLR) modeling technique was applied for each type of structural surface.
A multiple baseline design across classes of questions (i.e., what, where, who and why) was applied for each child.
In addition, spatial correlation analysis was applied for each variable to select the most appropriate method of interpolation.
For better accuracy, a relatively fine mesh of triangular elements was applied for each buoy geometry that was modelled.
An education classification system that was specific to the country of origin was applied for each migrant group.
Then an FCLSMA was applied for each segment to estimate the pixel-wise fractional coverage of high-albedo material, low-albedo material, vegetation, and soil.
The same procedure was applied for each participant and the classification performance in terms of statistical parameters is show in Table 3.
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