OBJECTIVE Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. METHOD A total of 383 p...

Maximum surface settlement (MSS) is an important parameter for the design and operation of earth pressure balance (EPB) shields that should determine before operate tunneling. Artificial intelligence (AI) methods are accepted as a technology that offers an alternative way to tackle highly complex problems that can’t be modeled in mathematics. They can learn from examples and they are able...

hammerstein-wiener (hw) models are capable in describing nonlinear dynamic systems. these models are nonlinear and have been widely used in a wide range of sciences due to their simplicity and having a physically-based concept. in this research, for the first time in hydrology and water resources management, three different structures of these models using daily temperature and precipitation da...

The examination of available vapor pressure data in the case of the methane, ethane, propane and butane halogenated refrigerants, allowed recommendations of standard equations for this property. In this study, three new models include a general correlation; a substance-dependent correlation and an artificial neural network (ANN) approach have been developed to estimate the saturated vapor press...

the veterinary drugs residues are also important pollutants found in milk, since veterinary drugs are commonly used in cattle management. considering the role of milk in human nutrition and its wide consumption throughout the world, it is very important to ensure the milk quality. a quantitative structure–retention relationship (qsrr) was developed using the partial least square (pls), kernel p...

BACKGROUND Several medications have proved to be useful in preventing postoperative nausea and vomiting (PONV). However, routine antiemetic prophylaxis is not cost-effective. We evaluated the accuracy and discriminating power of an artificial neural network (ANN) to predict PONV. METHODS We analysed data from 1086 in-patients who underwent various surgical procedures under general anaesthesia...

This paper explores the use of flow length and travel time as a pre-processing step for incorporating spatial precipitation information into Artificial Neural Network (ANN) models used for river flow forecasting. Spatially distributed precipitation is commonly required when modelling large basins, and it is usually incorporated in distributed physically-based hydrological modelling approaches. ...

In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...

Artificial neural network (ANN) applications in immunology include simulations of peptide binding to MHC molecules, which present peptides for recognition by the immune system. These peptides are derived from protein antigens and represent prime targets for vaccine discovery. ANN models have proven superior when compared to the alternative models. Applications of ANN models help minimise the nu...

in response to the demand for soil spatial information, the acquisition of digital auxiliary data and their matching with field soil observations is on the increase. with the harmonization of these data sets, through computer based methods, the so-called digital soil maps are increasingly being found to be as reliable as the traditional soil mapping practices, and with no prohibitive costs. the...