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As can be seen from Tables 1, 2, and 3, the SCWA samples are polyphase.
It is seen from Tables 1 and 2 that Seebeck coefficient increases with increasing A0 and 1/λ.
First, our approach could not always defeat supervised learners as it is seen from Tables 3 and 5.
However, the results are more optimistic according to other options of calculations, which can be seen from Tables 5, 6 for all rivers and stations.
It is seen from Tables 7 and 8 that the errors ΔB and Δh decrease dramatically with an increase of S. Table 7.
As seen from Tables 4 and 5, the same scale (training and testing with same scales) classification rates tend to drop with the proposed algorithm.
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These results can also be seen from Table 1.
The results can be seen from Table 6 below.
As can be seen from Table 1, the diol form (a) is themain component at 20°C.
As seen from Table 1 it provides almost 12% improvements in the mean error.
It can also be seen from Table 2 that the supercapacitor is quite nonlinear.
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