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Moreover, various tools for interpreting the fuzzy partition and fuzzy clusters provided by this algorithm are also presented.
The parameters estimated by this algorithm are: pore size distribution, porosity, pore symmetry, regularity and tortuosity, as well as various statistical measures.
The outcomes gained by this algorithm are in excellent concurrence with the exact solution and hence proved that this algorithm is effective and easy.
The experimental results show that the curve skeletons extracted by this algorithm are faithful and smooth, and they can be well utilized in many fields.
For the smooth temperature field both with and without obstacles, the reconstructions produced by this algorithm are good, both visually and using quantitative criteria.
The total number of real arithmetic operations required by this algorithm are shown in Table1.
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Tests made by using this algorithm are shown in Figure 9.
The key factors affecting this algorithm are analyzed by simulations.
Therefore, it was expected that the classification of the population by this algorithm was far from the medical classification.
Subsets selected by this algorithm were previously employed as external validation sets in QSAR modeling [24, 25, 41, 44].
In the case of a depth-first search algorithm [13], the first solution given by this algorithm is defined as the Babai point [25, 26].
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