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We performed the experiments on the above datasets: ORL, the extended Yale B, COIL-20, Isolet5 and USPS.
To integrate the above datasets into the system, we first need to tag the data with the most relevant concept of the global ontology ({mathcal{G}}).
We have reclassified the above datasets into seven categories of cropland, woodland, orchard land, grassland, water body, build up and barren lands (see Table 1).
Another issue of the above datasets is that all nodes are anonymized, which ensures protection of user privacy, but limits our ability to interpret community detection results.
We use six probability distribution functions for deriving unit hydrographs for the above datasets: two-parameter gamma, two-parameter Gumbel, two-parameter log-normal, two-parameter normal, three-parameter Pearson distribution, and two-parameter Weibull.
In addition, similar to the above datasets with Gaussian noise, besides a better reconstruction resolution, the separate searching strategy here is also faster than the exhaustive simultaneous searching strategy (Fig. 7C and 7F, Table 2).
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The global chain including foreground extraction and 3D localization, still needs to be evaluated intensively on the above dataset.
All of the proposed algorithms were run with document sets of different sizes taken from the above dataset to generate effective clustering results.
Some representative patches from the above dataset are reported in Figure 1. Figure 1 Patches of 512×512 pixels extracted from van Gogh paintings of the image set used for the study.
Then, the 'upper' FC part of the targeted CNN network, as well as the RNN hidden layers of the CNN RNN, were designed and trained with the above dataset.
To compare the proposed approach with regular genome-wide association study (GWAS), the GWAS was used to analyze the above dataset.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com