There are two broadly-defined applications of artificial neural networks (ANNs) in SAR/QSAR modeling. The first is the use of networks as preprocessors to reduce the dimensionality of chemical descriptors for use in statistical or network models. The second is to create classification models for predictive toxicology. This report discusses the use of ANNs as classifiers in SAR/QSAR modeling and...

Artificial Neural Networks (ANNs) have been successfully applied to Optical Character Recognition (OCR) yielding excellent results. This paper presents a method for segmentation of difficult handwriting with the use of conventional algorithms in conjunction with ANNs. The segmentation algorithm is heuristic in nature detecting important features which may represent a prospective segmentation po...

The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside other benefits such as minimising usage of natural resources in exploitation produce new concrete. Eventually, this will lead reducing construction waste, carbon footprints and energy consumption. This paper aims study compressive strength using Artificial Neural Network (ANN) which has ...

Artificial neural networks, ANNs, have been widely used especially in the last two decades. ANNs have a wide range of scientific applications, they are used for pattern recognition and forecasting of natural mechanisms. In water treatment, ANNs have enormous potential, especially to support workers in plant operation. In clear water plants are taking large volumes of data, especially informatio...

In this paper, we investigate the relation between Artificial Neural Networks (ANNs) and networks of populations of spiking neurons. The activity of an artificial neuron is usually interpreted as the firing rate of a neuron or neuron population. Using a model of the visual cortex, we will show that this interpretation runs into serious difficulties. We propose to interpret the activity of an ar...

this paper presents the application of three main artificial neural networks (anns) in damage detection of steel bridges. this method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. the changes in structural response is used to identify the states of structural damage. to circumvent the difficulty arising from the non-linear n...

in this paper, a fault diagnosis system based on discrete wavelet transform (dwt) and artificial neural networks (anns) is designed to diagnose different types of fault in gears. dwt is an advanced signal-processing technique for fault detection and identification. five features of wavelet transform rms, crest factor, kurtosis, standard deviation and skewness of discrete wavelet coefficients of...

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to...

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron of the desert locust, in response to...

In recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible tomodel both linear and nonlinear structures in time series by using ANNs, they are not able to handle both structures equally well. Therefore, the hybrid methodology combining ARIMA and ANN models have been used in the literature. In this study, a new hybr...