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We explore the complementarity of datasets considering the pathogen in its host, the host and human cases in the context of Puumala orthohantavirus infection in Germany.
These model variants were extensively tested under unsteady and steady flow conditions using the numerical experiments, and USGS laboratory and real-world river application datasets considering the popular MIKE11-AD model as the benchmark.
Experiments were conducted on three image datasets, considering different visual descriptors (shape, color, and texture descriptors).
Figure 8 shows the results for accuracy of each of the ten generated bases from both datasets, considering a semi-supervision of 1, 3 and (10~%).
This experimental phase was carried out on publicly available face datasets, considering different working strategies (i.e., the balancing/unbalancing of the training dataset and the numerical range of the input measurements).
Compared to existing research, the present study has unique significance in terms of research methods, for it adopted the way of matching the separate datasets considering logical and concrete variables of individuals.
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These datasets, considered representative of state-of-the-art for isotherm experiments, had been previously analyzed using trial-and-error, linearization, or non-linear regression methods.
The standard datasets considered are partitioned using the tenfold cross-validation procedure [67].
Tenfold cross-validation is performed in all the six datasets considered for evaluation.
As mentioned above, the CSWL is one of the datasets considered for the purposes of this research in Spain.
It should be noted that the evaluation datasets considered in [45, 48, 50] are private datasets comprising 11, 70, and 90 signals, respectively.
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