In the present study, two different data-driven models, artificial neural network (ANN) and multiple linear regression (MLR) models, have been developed to predict the 28 days compressive strength of concrete. Seven different parameters namely 3/4 mm sand, 3/8 mm sand, cement content, gravel, maximums size of aggregate, fineness modulus, and water-cement ratio were considered as input variables...

Background: Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural ne...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...

We investigate how changes in network structure can lead to pathological oscillations similar to those observed in epileptic brain. Specifically, we conduct a bifurcation analysis of a network of two Jansen-Rit neural mass models, representing two cortical regions, to investigate different aspects of its behavior with respect to changes in the input and interconnection gains. The bifurcation di...

the work exposure conditions such as dust concentration, exposure time, use of respiratory protection devices and smoking status are effective to cause pulmonary function disorder. the objective of this study was prediction of pulmonary disorders in workers exposed to silica dust using artificial neural networks and logistic regression. a sample of 117 out of 150 workers employed in the stone c...

forecasting of macroeconomic variables has specific importance in economic topics. indeed, different models are invented to forecast variables to help economic policy makers in adopting appropriate monetary and fiscal policies. in this paper, the performance of integrated model of input-output (io) and neural network is investigated in forecasting final demand and total production and the resul...

neural networks are applicable in identification systems from input-output data. in this report, we analyze thehammerstein-wiener models and identify them. thehammerstein-wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...