This project focuses on applying neural networks to the classification of biological state based on gene expression data. In order to take advantage of the non-linear classification abilities of neural networks, a genetic algorithm is employed as a “wrapper” feature selector. Results indicate that the genetic algorithm effectively identifies features that allow successful neural network trainin...

357 T HE field of neural networks was known to only a small group of scientists just a decade ago, but after the development of the back-propagation algorithm in 1984, the field cxperienced incredible growth. By early 1989, the Joint Conference on Neural Networks held in San Diego was more of a religious experience than a scientific conference. With the press looking on, the 1,600 true believer...

ABSTRACT-Determining hydraulic conductivity of soil is difficult, expensive, and time-consuming. In this study, Algorithm Genetic and geostatistical analysis and Neural Networks method are used to estimate soil saturated hydraulic conductivity using the properties of particle size distribution. The data were gathered from 134soil profiles from soil and lander form studies of the Ardabil Agricul...

One billion people worldwide experience intermittent water supply (IWS), in which piped is delivered for limited durations. Households with IWS must invest storage infrastructure and often rely on multiple sources of water; therefore, these household-level purchasing decisions a critical component access. Informed by interviews households, we use radial basis function networks, type artificial ...

there are three major strategies to form neural network ensembles. the simplest one is the cross validation strategy in which all members are trained with the same training data. bagging and boosting strategies pro-duce perturbed sample from training data. this paper provides an ideal model based on two important factors: activation function and number of neurons in the hidden layer and based u...