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During the reduction phase, the n MPI-processes exchange their local results with each other.
Therefore, feature reduction phase is necessary and important for our task.
The dimensionality reduction phase was performed by extracting the nonlinear principal components.
This better performance is due to taking into consideration of the ANN classifier in the feature reduction phase.
Also, a novel approach, the generic algorithm (GA -based method, is proposed for the feature reduction phase.
Further, we also eliminate redundancies in the original MDP in order to speed up the model reduction phase.
As for the previous experiment, the dimensionality reduction phase is performed by a grid search of the best AANN topology.
Principal Components Analysis, followed by sensitivity analysis with Monte Carlo simulation were used in the data reduction phase.
Further, some fragments of much smaller GnP particles were seen entrapped on the basal plane which may occur due to size reduction phase involving pulverization process.
Analyzing the values of quality indexes in Table5, it can be noted that the dimensionality reduction phase, performed through the NLPCA, does not produced any relevant distortion.
In the columns, Algorithm ?? + Algorithm ?? refers to the algorithm used in reduction phase (Algorithm ??) and the algorithm used to solve the reduced MDP (Algorithm ??).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com