life style and life expectancy of inhabitants have been affected by the increase of particulate matter 10 micrometers or less in diameter (pm10) in cities and this is why maximum pm10 concentrations have received extensive attention. an early notice system for pm10 concentrations necessitates an accurate forecasting of the pollutant. in the current study an artificial neural network was used t...

This research intends to develop a method based on the Artificial Neural Network (ANN) to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation o...

Problem statement: A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Approach: Most commercial iris recognition systems use patented algorithms developed by Daugman and these algorithms are able to ...

Functionally similar pathways are often seen in biological systems, forming feed-forward controls. The robustness in network motifs such as feed-forward loops (FFLs) has been reported previously. In this work, we studied noise propagation in a development network that has multiple interlinked FFLs. A FFL has the potential of asymmetric noise-filtering (i.e., it works at either the "ON" or the "...

In this paper, vapor pressure for pure compounds is estimated using the Artificial Neural Networks and a simple Group Contribution Method (ANN–GCM). For model comprehensiveness, materials were chosen from various families. Most of materials are from 12 families. Vapor pressure data of 100 compounds is used to train, validate and test the ANN-GCM model. Va...

Modularity and hierarchy are fundamental notions in structured system design. By subdividing a large and unstructured problem into smaller and tractable chunks, design automation becomes possible. In this paper we discuss the use of modularity and hierarchy for functional specialization during the development of neural networks. We study the behavioral differences and requirements for back-prop...

This paper presents a segmental durations’ model applied to the European Portuguese language for TTS purposes. The model is based on a feed-forward neural network, trained with a back-propagation algorithm, and has as input a set of phonological and contextual features, automatically extracted from the text. The relative importance of each feature, concerning the correlation with segmental dura...

An attempt has been made to simulate the temperature distribution in prostate cancer tissue during laser ablation using finite element approach. Parameter studies have been carried out. The results have been consolidated using tool of artificial intelligence-Artificial Neural Network. Feed forward back propagation network has been used for this purpose. It has been found that artificial neural ...

Product unit neural networks with exponential weights (PUNNs) can provide more powerful internal representation capability than traditional feed-forward neural networks. In this paper, a convergence result of the back-propagation (BP) algorithm for training PUNNs is presented. The monotonicity of the error function in the training iteration process is also guaranteed. A numerical example is giv...

This paper investigates the potential of applying the feed forward neural network architecture for the classification of breast cancer. Back-propagation algorithm is used for training multi-layer artificial neural network. Missing values are replaced with median method before the construction of the network. This paper presents the results of a comparison among ten different hidden neuron initi...