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Lastly, the authors have evaluated the propose technique using machine learning and obtained more than 90% accuracy.
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We evaluated the proposed proCMF on FER.
We evaluated the proposed method in [9].
We evaluated the proposed algorithm in all the data sets.
We have evaluated the proposed algorithm on several depth sequences.
We have also evaluated the proposed method through case studies.
We have also evaluated the proposed method through experimental study.
After that, we evaluated the proposed update method with the one previously proposed in the literature.
We also evaluated the proposed method using the identification rate of the referents.
We evaluated the proposed approach on the self-report data using leave-one-out cross validation.
We first evaluated the proposed models on the UniRef50 datasets.
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