background: the artificial neural networks (anns) are useful in solving nonlinear processes, without the need for mathematical models of the parameters. since the relationship between the ct numbers and material compositions is not linear, ann can be used for obtaining tissue density and composition. objective: the aim of this study is to utilize ann for determination of the composition and mas...

background & aims of the study: a feed forward artificial neural network (ffann) was developed to predict the efficiency of total petroleum hydrocarbon (tph) removal from a contaminated soil, using soil washing process with tween 80. the main objective of this study was to assess the performance of developed ffann model for the estimation of   tph removal. materials and methods: several indepen...

introduction patient set-up optimization is required in radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. in this study, we aimed to develop a new method based on neural network to estimate patient geometrical setup using 4-dimensional (4d) xcat anthropomorphic phantom. materials and methods to access 4d modeling of motion...

The prediction of the ultimate bearing capacity of the pile under axial load is one of the important issues for many researches in the field of geotechnical engineering. In recent years, the use of computational intelligence techniques such as different methods of artificial neural network has been developed in terms of physical and numerical modeling aspects. In this study, a database of 100 p...

runoff is one of the major components of calculating water resource processes and is the main issue in hydrology. many concept models are used to predict the amount of runoff, which in most cases depend on topographical and hydrological data. conventional models are not appropriate for areas in which there is little hydrological data. changes in runoff are nonlinear, meaning it is time & space ...

evaporation is one of the most important components of hydrologic cycle.accurate estimation of this parameter is used for studies such as water balance,irrigation system design, and water resource management. in order to estimate theevaporation, direct measurement methods or physical and empirical models can beused. using direct methods require installing meteorological stations andinstruments ...

  Abstract: In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural network is able to pr...

Bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia. An artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...

geological facies interpretation is essential for reservoir studying. the method of classification and identification seismic traces is a powerful approach for geological facies classification and distinction. use of neural networks as classifiers is increasing in different sciences like seismic. they are computer efficient and ideal for patterns identification. they can simply learn new algori...

awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. therefore, the present study two hybrid models, wavelet- adaptive neural fuzzy interference system (wanfis) and wavelet- artificial neural network (wann) are used for flow prediction of gamasyab river (nahavand, hamedan, iran...