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We show the robustness of the obtained control.
Numerical results show the robustness of the proposed method.
Extensive experiments show the robustness of our method.
Numerical examples show the robustness against measurement noise.
We evaluate our algorithm quantitatively in order to show the robustness for tracking.
The goal of this test is to show the robustness of our method against view changes.
The low values of %RSD show the robustness of the method.
Results show the robustness of PLSA, AdaBoost, and Co-Training algorithm to detect phishing.
The images have not been preprocessed in order to show the robustness of the proposed algorithm.
Experimental results show the robustness of our method against such problems.
Therefore, numerical results show the robustness and effectiveness of the proposed model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com