ABSTRACT Background: A computer aided diagnosis system was established using the wavelet transform and neural network to differentiate malignant from benign in a   group of patients with histo-pathologically proved breast lesions based on the data derived independ­ently from time-intensity profile.   Materials and Methods: The per­formance of the artificial neural network (ANN) was evaluated u...

In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-laye...

the aim of this paper is how to predict stock return by using accounting ratios and also by using the procedure of neural network. this paper has considered the prediction of stock return by using accounting ratios with two procedures, the artificial neural network and least square regression. the independent variables in this paper are accounting ratios and dependent variable of stock return, ...

This paper presents an efficient optimization procedure to find the optimal shapes of double curvature  arch  dams  considering  fluid–structure  interaction  subject  to  earthquake  loading. The optimization is carried out using a combination of the magnetic charged system search, big bang-big crunch algorithm and artificial neural network methods. Performing the finite element  analysis  dur...

â â â â â â â  this paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and gdp of the us, as the largest oil consumer, and the uk, as the oil producer. gmdh neural network and mlff neural network approaches, which are both non-linear models, are employed to forecast gdp responses to the oil price changes. the resul...