This paper performed a systematic review of the various machine learning (ML) techniques
applied to assess the health condition of heritage buildings. More robust predictive models …

Soft computing (SC), due to its high abilities to solve the complex problems with uncertainty
and multiple parameters, has been widely investigated and used, especially in structural …

This study aims to develop four ensemble machine learning (ML) models, including Random
Forest (RF), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), and Extreme …

Medical disease classification using machine learning algorithms is a challenging task due
to the nature of data, which can contain incomplete, uncertain, and imprecise information …

Breast cancer rates are rising. It also remains the second principal reason for cancer-related
mortality in females, and the mortality rate is also drastically rising. In recent years …

Accurately estimating the bearing capacity of piles is an onerous task in structural design
task that requires a powerful computation model able to elucidate nonlinear impacts of …

Neuro-fuzzy systems have received considerable attention from academia in the last
decade. They can provide a tradeoff between the performance of artificial neural networks …

Built cultural heritage is exposed to various deterioration problems caused by different types
of actions. To reduce the need for major interventions, preventive conservation (PC) …

Flash flood is one of the most dangerous hydrologic and natural phenomena and is
considered as the top ranking of such events among various natural disasters due to their …

Here, we propose a new alternative machine learning method that combines the advantage
of the Random Vector Functional Link Network (RVFL) with Moth Search Algorithm (MSA) to …