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Hence, no additional time is required by the processing phase.
To further reduce the processing time, we seek to add parallel processing within the processing phase itself.
Therefore, if activation during the processing phase has occurred, it is minimal.
Yet, about 10 to 15% of the harvest was lost during the processing phase.
Depending on the processing speed, the processing phase can partially or completely happen in parallel with the sampling phase.
In our case, the processing phase happens entirely in parallel with the sampling phase.
Thus, in our terminology, the data processing phase ends before statistical analysis and reporting of results (i.e. conference presentations or drafting manuscripts).
Don't get in a hurry during the sample processing phase.
Due to the increasing amount of data that needs to be processed [10 13], feature selection can be used in the pre-processing phase before classification in order to identify important features of a dataset, with the aims of improving prediction accuracy and reducing computational complexity.
The first phase, which acts as pre-processing phase, involves the filter [48] discussed earlier.
Gaussian blurring is employed before the processing.
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