cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. in recent decades, artificial neural network model has been increasingly applied to predict survival data. this research was conducted to compare cox regression and artificial neural network models in prediction of kidney transplant survival. the prese...

In this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. Experimental data was divided into two parts (70% for training and 30% for testing). Optimal configuration of the network was obtained with minimization of prediction error on testing data. The accuracy of our proposed model was compared with four well-known empirical equations. The arti...

among the different classes of physical properties of foods, color is considered the most important visual attribute in quality perception. consumers tend to associate color with quality due to its good correlation with physical, chemical and sensorial evaluations of food quality. this study used an inexpensive method to predict sweet cherries color parameters by combining image processing and ...

in the near future the use of distributed generation systems will play a big role in the production ofelectrical energy. one of the most common types of dg technologies , fuel cells , which can be connectedto the national grid by power electronic converters or work alone studies the dynamic behavior andstability of the power grid is of crucial importance. these studies need to know the exact mo...

The neural network technology has been widely studied in the last decade. The research has already led to successful practical applications in several areas. This paper presents the technique of neural networks and how it has been applied into the area of logistics. The paper also discusses further potential for the neural network technique in this area.

In this paper, we propose a neuro-fuzzy technique for content based image retrieval. The technique is based on fuzzy interpretation of natural language, neural network learning and searching algorithms. Firstly, fuzzy logic is developed to interpret natural expressions such as mostly, many and few. Secondly, a neural network is designed to learn the meaning of mostly red, many red and few red. ...

Artificial neural networks are intelligent systems that have successfully been used for prediction in different medical fields. In this study, the efficiency of a neural network for predicting the survival of patients with acute pancreatitis is compared with days-of-survival obtained from patients. A three- layer back-propagation neural network was developed for this purpose. Clinical data (e.g...

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...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. in this study, measured bedload by helley- smith sampler was used to estimate the bedload transport of kurau river in malaysia. an artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...