the present investigation entails a procedure by which the surface tension and viscosity of liquids could be redicted.to this end, capillary experiments were performed for porous media by utilizing fifteen different liquids and powders. the time of capillary rise to a certain known height of each liquid in a particular powder was recorded. two artificial neural networks (anns) were designed and...

To improve the speech recognition performance, many ways to augment or combine HMMs (Hidden Markov Models) with other models to build hybrid architectures have been proposed. The hybrid HMM/ANN (Hidden Markov Model / Artificial Neural Network) architecture is one of the most successful approaches. In this hybrid model, ANNs (which are often multilayer perceptron neural networks MLPs) are used a...

In recent years, artificial neural networks (ANNs) have emerged as one of the potentially most successful modelling approaches in engineering. In particular, ANNs have been applied to many areas of geotechnical engineering and have demonstrated considerable success. The objective of this paper is to highlight the use of ANNs in foundation engineering. The paper describes ANN techniques and some...

In medicine at present, neural networks are a ‘hot’ research area, particularly in cardiology, radiology, urology, oncology etc. In the area of computer science, this new technology has been accepted. The purpose of a neural network is to map an input into a desired output. Combining neurons into layers permits artificial neural networks to solve highly complex classification problems. The vari...

The Stream Decision Support System (SDSS) is taking advantage of both supervised and nonsupervised artificial neural networks (ANNs) for stream assessment and prediction by an integrated approach. Non supervised ANNs were applied for patterning the natural variability in stream macroinvertebrate communities in Queensland. Supervised ANNs were developed for the prediction of the occurrence of st...

This paper focuses on the Genetic Algorithm learning paradigm applied to train the ANNs for balancing the cart-pole balancing system. The studied system is a control problem namely “cart-pole” problem. We will apply the unconventional techniques Artificial Neural Network, Genetic Algorithm and Fuzzy Logic to a classic control problem “cart-pole”. In this paper we have tried to train the Artific...

Modern data analysis often faces high-dimensional data. Nevertheless, most neural network data analysis tools are not adapted to highdimensional spaces, because of the use of conventional concepts (as the Euclidean distance) that scale poorly with dimension. This paper shows some limitations of such concepts and suggests some research directions as the use of alternative distance definitions an...

We present an automatic method, based on a neural network ensemble, for extracting multiple, diverse and complementary sets of useful classification features from highdimensional data. We demonstrate the utility of these diverse representations for an image dataset, showing good classification accuracy and a high degree of dimensionality reduction. We then outline a number of possible extension...

Artificial neural networks (ANNs) can be utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and activity. Evolutionary computation provides a means to appropriately search for the set of weights and bias terms associated with artificial neural networks that minimize selected functions of the error between the actual and...

Widespread surveillance programs using remote retinal imaging has proven to decrease the risk from diabetic retinopathy, the leading cause of blindness in the US. However, this process still requires manual verification of image quality and grading of images for level of disease by a trained human grader and will continue to be limited by the lack of such scarce resources. Computer-aided diagno...