At present, artificial intelligence methods have been effectively utilized for predicting the
complex performance of storage-based solar thermal technologies for cooling/heating …

Recently, accurate prediction of the distilled productivity is a decisive issue to assess and
compare the ability of these designs of solar stills to provide distilled water to a certain …

To boost the application of machine learning (ML) techniques for credit scoring models, the
blackbox problem should be addressed. The primary aim of this paper is to propose a …

The safety of urban gas pipelines is challenged by a series of adverse factors, and the
unexpected accidents may pose a catastrophic threat to humans, the environment and …

The inclusion of time-varying covariates into survival analysis has led to better predictions of
the time to default in behavioural credit scoring models. However, when these time-varying …

This paper explores alternative forecast approaches for mortgage credit risk for forward
periods of up to seven years. Using data from US prime mortgage loans from 2000 to 2016 …

Survival models with time-varying covariates (TVCs) are widely used in the literature on
credit risk prediction. However, when these covariates are endogenous, the inclusion …

Bayesian inference has gained popularity in the last half of the twentieth century thanks to
the wider applications in numerous fields such as economics, finance, physics, engineering …

Due to the randomness and complexity of the discontinuity distribution, it is always
complicated to analyse the three-dimensional stability problem of discontinuity-controlled …

A new Bayesian informative prior specification method (BAF method–Bayesian priors using
ARIMA forecasts) is proposed to introduce additional information into credit risk modelling …