Introduction: Automatic mognition of handwritten characters and natural patterns has been one of the most difficult problems in artificial intelligence. Artificial neural networks (ANNs) constitute an important class of computational models for handling this class of problems. Many neural techniques have been proposed to solve this problem, such as those in [l 41. We propose an eficient way of ...

Curie-point pyrolysis mass spectra were obtained from reference Propionibacterium strains and canine isolates. Artificial neural networks (ANNs) were trained by supervised learning (with the back-propagation algorithm) to recognize these strains from their pyrolysis mass spectra; all the strains isolated from dogs were identified as human wild type P. acnes. This is an important nosological dis...

Evapotranspiration (ET) is one of the major components of hydrologic cycle. Accurate estimation of this parameter is essential for studies such as water balance, irrigation system design and management, and water resources management. Generally we used climate data for calculating evapotranspiration from indirect methods. This study investigates the utility of artificial neural networks (ANNs) ...

In this paper we consider the use of Artificial Neural Networks (ANNs) in decision support in anticoagulation drug therapy. In this problem domain ANNs can be used to learn the prescribing behaviour of expert physicians or alternatively to learn the outcomes associated with such decisions. Both these possibilities are evaluated and we show how, through the prediction of outcomes that the prescr...

Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-Error-Propaga...

Due to remarkable capabilities of artificial neural networks (ANNs) such as generalization and nonlinear system modeling, ANNs have been extensively studied and applied in a wide variety of applications (Amiri et al., 2007; Davande et al., 2008). The rapid development of ANN technology in recent years has led to an entirely new approach for the solution of many data processing-based problems, u...

Minimum description length (MDL) principle is one of the wellknown solutions for overlearning problem, specifically for artificial neural networks (ANNs). Its extension is called representational MDL (RMDL) principle and takes into account that models in machine learning are always constructed within some representation. In this paper, the optimization of ANNs formalisms as information represen...

Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA) and artificial neural networks (ANNs). PCA was used to perform feature extraction in the first stage of modelling, while artif...

Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for...