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We should note that in our proposed method, the improvement of classification accuracy on difficult classes is the result of calculating the local weights for kernels which could address the problem of high intra class variance.
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For the second-order method, the improvements can be comparable to the improvements resulting from a less diffusive limiter.
This set is updated by using the Combination Method and the Improvement Method until it does not change.
Compared with reference methods, the improvement of spatial quality with JSPQ is mainly manifest in the rear region.
Comparing our method with other feature extraction methods, the improvement of the overall prediction accuracy ranged from 5.5%to21.2%2%.
In comparison with the existing methods, the improvement of the proposed method over the existing results is shown via two numerical examples.
In this case, APCpred outperformed the other three methods, the improvement were 6/387 for ABCpred, 8/387 for BCpred, and 13/387 for AAPBCpred.
Our analyses show that PAGAN's phylogenetic approach clearly outperforms most alternative methods, the improvement being especially striking in the alignment of short sequence fragments.
From the simulation results, the proposed method shows the improvement to the settling and disturbance rejection.
Finally, numerical examples are given to show the effectiveness of the proposed method and the improvement over some existing results.
It is therefore, imperative for engineering of materials and optimization of the fabrication method for the improvement of QDSSCs performance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com