Support vector machine (SVM) is a powerful machine learning technique relying on the
structural risk minimization principle. The applications of SVM in structural reliability analysis …

In this paper, an effective hybrid optimization strategy by incorporating the adaptive
optimization of particle swarm optimization (PSO) into genetic algorithm (GA), namely …

Assessing rare event probabilities still suffers from its computational cost despite some
available methods widely accepted by researchers and engineers. For low to moderately …

Several lifetime distributions have played an important role to fit survival data. However, for
some of these models, the computation of maximum likelihood estimators is quite difficult …

As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent
major efforts on better understanding the characteristics of the virus and possible means to …

In this paper, multilayer feedforward neural networks based on multi-valued neurons
(MLMVN), a specific type of complex valued neural networks, are proposed to be applied to …

In this paper, a novel multi-kernel support vector machine (MKSVM) combining global and
local characteristics of the input data is proposed. Along with, a parameter tuning approach …

The reliability estimation of engineered components is fundamental for many optimization
policies in a production process. The main goal of this paper is to study how machine …

Support vector regression (SVR) has been applied to time series prediction and some works
have demonstrated the feasibility of its use to forecast system reliability. For accuracy of …

Landslides occur frequently in the Three Gorges in China, posing threats to human life and
the normal operation of the Three Gorges Dam. A number of preexisting landslides have …