An artificial neural network has been used to determine the volume flux and rejections of Ca2+ , Na+ and Cl¯, as a function of transmembrane pressure and concentrations of Ca2+, polyethyleneimine, and polyacrylic acid in water softening by nanofiltration process in presence of polyelectrolytes. The feed-forward multi-layer perceptron artificial neural network including an eight-neuron hidde...

A structure and functioning mechanisms of a neural network with competitive layers are described. The is intended to solve the character recognition task. consists several neurons. Each layer consisting number neurons represented as layer. equal recognized classes. All have one-to-one correspondence one another input raster. every mutual lateral learning connections, which weights modified duri...

Short-term water demand modeling plays a key role in urban water resources planning and management. The importance of demand prediction is even greater in countries like Iran with frequent periods of drought. Short-term water demand estimation is useful for planning and management of water and wastewater facilities such as pump scheduling, control of reservoirs and tanks volume, pressure manage...

Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...

An artificial neural network was trained to predict the presence or absence of ascites in broiler chickens. The neural network was a three-layer back-propagation neural network with an input layer of 15 neurons (defining 15 physiological variables), a hidden layer of 16 neurons, and an output layer of 2 neurons (the presence or absence of ascites). Male by-products of a breeder pullet line were...

In this paper, we study the problem of motif discoveries in unaligned DNA and protein sequences. The problem of motif identification in DNA and protein sequences has been studied for many years in the literature. Major hurdles at this point include computational complexity and reliability of the search algorithms. We propose a self-organizing neural network structure for solving the problem of ...

In this paper, we study the problem of subtle signal discoveries in unaligned DNA and protein sequences. Motifs, also known as approximate common substrings, are good examples of subtle signals in DNA and protein sequences. The problem of motif identification in DNA and protein sequences has been studied for many years in the literature. Major hurdles at this point include computational complex...