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Firstly, to evaluate the performance of the nose tip detection method, we test our method on the Bosphorus database.
In the second method, we test the plausibility of existing reconstructions by comparing their C∗ and C̃ against the modern minimum bounds.
To evaluate the performance of our method, we test it on a large dataset provided by a local hospital, courtesy of the SiWei medical company (Shanghai, China) and SiWei Remote ECG diagnostic center.
After introducing the method, we test its performance on artificial benchmark graphs, comparing it with the performances of the best algorithms currently available.
To analyse the influence of the parameters of the proposed plane detection method, we test it with the first half of the packaging data set.
These weights will be fitted during an optimisation process, described in Section 7. To prove the robustness of our proposed method, we test on two different sports datasets.
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As a proof-of-principle of this method, we tested it with recombinant antigen (human recombinant interleukin 8).
To validate the method, we tested it with the feature algorithm known as speeded up robust features (SURF), one of the most efficient approaches for feature extraction.
The method we tested is direct measurement of CO2 concentrations with a portable air-quality meter, which allows a large number of samples.
Using this method, we tested a total of 2 327 cattle from three dairy farms for the presence of BVDV persistently infected (PI) animals.
For the SVM-based method, we tested each of the five set features built on GO terms, EC numbers, amino acid sequences, secondary structures, and solvent accessibilities.
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