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We evaluate our approach on two well-known gender benchmarks, showing that our Local-DNN outperforms other deep learning methods also evaluated and obtains state-of-the-art results in both benchmarks.
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The AGEP method also evaluates how uniquely the tm-score categorizes the test sample among the tissues of the reference data.
To further confirm the stability and effectiveness of our proposed methods, sparse (N LMS-based estimatioN LMS-basedrestimationluated in the case of 20 dB as well as with different K, respectively.
Numerical examples are studied by the proposed method, and the method is also evaluated by comparing the results with ones from the existing method.
Two other point detection methods were also evaluated: the scale-invariant feature transform (SIFT [19] and speeded-up robust features (SURF [20] methods.
Two different charge-reducing methods were also evaluated.
The control methods are also evaluated with respect to local conditions, such as cooling water temperature and flue gas CO2 concentration.
Two 1D CFD methods are also evaluated by 3D CFD data, with the conclusion that both 1D results have some differences with the 3D data.
The methods are also evaluated by two ranking-based scores, BEDROC and ROCAUC.
As mentioned earlier, the proposed and the baseline methods are also evaluated in terms of subjective tests: MOS and ABX.
For comparison purposes, the performance of the PET-AS methods was also evaluated using the commonly used NEMA IEC body phantom with spherical plastic inserts.
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