abstract autoregressive integrated moving average (arima) has been one of the widely used linear models in time series forecasting during the past three decades. recent studies revealed the superiority of artificial neural network (ann) over traditional linear models in forecasting. but neither arima nor anns can be adequate in modeling and forecasting time series since the first model cannot d...

modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. thisstudy made us of adaptive neuro-fuzzy ...

drying characteristics of green pea (pisum satium) with an initial moisture content of 76% (db) was studied in a fluidized bed dryer assisted by microwave heating. four drying air temperatures (30, 40, 50 and 60ºc) and five microwave powers (180, 360, 540, 720 and 900w) were adopted. several experiments were conducted to obtain data for sample moisture content versus drying time. the results sh...

Some crucial process variables in fermentation process could not be measured directly. Soft sensor technology provided an effective way to solve the problem. There has been considerable interest in modeling a soft sensor by using artificial neural network (ANN) in bioprocess. To generate a more efficient soft sensor model, we proposed a novel soft sensor model based on artificial neural network...

drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. identification of resistant phenotype is very important because it can lead to effective treatment plan. there is an interest in developing classifying models of resistance phenotype based on the multivariate data. we have investigated a vibrational spectroscopic approach in order to characterize a sen...

objective(s): a fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. artificial neural networks (anns) are strong tools for predicting nonlinear functions which are used in this paper to predict binding energy. we proposed a structure that obtains binding energy using physicochemical molecular descriptions of the se...

An artificial neural network (ANN) approach was used to develop a new predictive model for the calculation of static formation temperature (SFT) in geothermal wells. A three-layer ANN architecture was successfully trained using a geothermal borehole database, which contains ‘‘statistically normalised’’ SFT estimates. These estimates were inferred from seven analytical methods commonly used in g...

land-use change affects a number natural processes such as soil erosion, sedimentation, flooding and destruction of soil physical and chemical properties. this change of ecosystem causes the degradation of soil quality which eventually leads to a severe decrease in soil fertility. therefore, various aspects of land-use change should be taken into account in national major decision makings. this...

Trimaran hull forms have been popular recently in both commercial and military usage due to reduction resistance at high speeds, better stability, greater deck area compared conventional monohull vessels. Determination of the location side hulls is most critical get higher hydrodynamic performance. Therefore, many studies literature are related defining for trimaran Most carried out experimenta...

In this study, a three–layer \ artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of...