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In this case, the proposed method regards the result maximizing (13) as the final one.
The first phase of this method regards the search of the content, with any existing algorithm.
The method regards the known solutions as asymptotes and proposes addition to a power of such asymptotes.
This method regards each voxel value as a possible threshold and tests whether a given threshold will maximize the between-class variance [20, 29].
The proposed method regards the size of the average residual as a criterion to judge the precision of the compaction correction for the study.
Unlike the existing approaches, our method regards the satellite orbits as the analyzing objects, which avoids the underfitting performance caused by the unbalanced data distribution.
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This is an advantage of the proposed method regarding real-time applications.
It has distinct advantages over other existing method regarding sensitivity, time saving and minimum detection limits.
Therefore, a new compensation method regarding the reaction rate is suggested in this paper.
Figure 3 shows a block diagram of the proposed method regarding the estimation of the proposed weighting function.
In order to compare the effects of feature extraction, the feature extraction method of this paper is compared with the PCA feature extraction method regarding the classification result.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com