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However, when it is interpreted as a validation problem, both fusion methods gave better FAR/FRR values regarding to individual biometrics.
To determine whether these methods gave better estimates of the vegetation composition than the original pollen proportions, the results were compared with the surveyed vegetation around each pond using the Bray Curtis Index.
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The present methods give better control performances.
When compared with voltage sensitivity index method, WOA and index vector methods gives better results.
It is shown that neuro-fuzzy methods give better results than other, more conventional, modeling approaches.
We show that neuro-fuzzy methods give better predictions than conventional modelling approaches.
The comparison shows that both probabilistic methods give better predictions than the "standard" model.
Therefore, the present methods give better prediction results than traditional methods that are based on exact solutions.
As can be seen, all the adopted weighted averaging methods give better results compared to the simple averaging (Sum) rule.
We show that the present family of methods gives better approximations and generalize all the existing finite difference and spline functions based methods up to order six.
However, for the validation problem, both fusion methods give better false acceptance rate/false rejection rate values regarding to individual biometrics.
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