Variable Elimination (VE) is the most basic one of many Bayesian network inference algorithms. The speed and complexity reasoning mainly depend on order elimination. Finding optimal elimination a Nondeterministic Polynomial Hard (NP-Hard) problem, which often solved by heuristic search in practice. In to improve variable method, minimum, maximum potential, minimum missing edge added methods are...

one of the new research fields in plasticity is related to choosing a proper non-associated flow rule (nafr), instead of the associated one (afr), to predict the experimental results more accurately. the idea of the current research is derived from combining von mises and tresca criteria in the places of yield and plastic potential surfaces in rate-independent plasticity.  this idea is implemen...

In this paper, an extension of sensitivity based pruning (SBP) method for Nonlinear AutoRegressive models with eXogenous inputs (NARX) model is presented. Besides the inputs, input and output delays are simultaneously pruned in terms of the backward elimination. The concept is based on replacement of some regressors by their mean value, which corresponds to the removal of influence of the parti...

Feature selection is commonly employed for identifying the top n features that significantly contribute to desired prediction, example, find 50 or 100 genes responsible lung kidney cancer out of 50,000 genes. Thus, it a huge time- and resource-consuming practice. In this work, we propose divide-and-conquer technique with fuzzy backward feature elimination (FBFE) helps important quickly accurate...