The variational segmentation methods are characterized by deriving an energy functional from some a priori mathematical model and minimizing this energy functional over all possible partitions.
Variational methods for image segmentation have had great success, which are characterized by deriving an energy functional from some a priori mathematical model and minimizing this energy functional over all possible partitions.
In order to reproduce complete constraint conditions in FEA models under loading conditions, the bottom surface of the artificial mandibular bone should be adhered completely to the worktable with adhesive; this should stabilize the experimental model and minimize minute movements during the loading process.
The quantitative rules behind the tool, which represent the group view of the elicited community of experts, are defined during a period of quiescence, thus allowing prior scrutiny of any scientific input into the model, and minimizing the external stress on scientists during an actual emergency phase.
Despite having a name which carries the connotation of uncertainty, researchers in [18, 19, 20, 21] have shown that there are limitations in the ability of type-1 fuzzy systems (T1 FSs) to model and minimize the effect of uncertainties, because type-1 fuzzy systems are certain regarding the fact that its membership grades are crisp values.
To build the mathematical model and minimize the number of experiments for the design parameters, response surface methodology with a face-centered central composite design (CCF) was defined based on the conditions found by preliminary tests that resulted in the highest refolding yield.
By using all the previous information that is available to restrict the model and minimize the number of adjustable parameters, PKQuest greatly increases the predictive power of the analysis.
To choose the appropriate smoothness of this spline model and minimize the effects of overfitting the data, we used a standard regularization method where the final smoothing parameter was selected via the so-called generalized cross-validation metric.
To construct the model, we performed a logistic regression using all variables from the univariate analysis that were associated with a different mortality as covariates; subsequently, to optimize the model and minimize an overfitting bias, an automatic stepwise backward covariate selection was performed.
