Hybrid approach has a special status among Face Recognition Systems as they combine different recognition approaches in an either serial or parallel to overcome the shortcomings of individual methods. This paper explores the area of Hybrid Face Recognition using score based strategy as a combiner/fusion process. In proposed approach, the recognition system operates in two modes: training and cl...

in this paper, i develop three forecasting models: namely structural, times series, and artificial neural networks; to forecast iranian inflation rates. the structural model uses aggregate demand and aggregate supply approach, the time series model is based on the standard arlma technique, and the artificial neural network applies multi-layer back propagation model the latter, which is rooted i...

Improving model accuracy is one of the most frequently addressed issues in bankruptcy prediction. Several previous studies employed artificial neural networks (ANNs) to enhancethe at which construction company can be predicted. However, these use sample-matching technique and available quarters or years dataset, resulting sample selection biases between-class imbalances. This study integrates a...

Several researchers have underscored the importance of precise characterization of eye-tracking dysfunction (ETD) in patients with schizophrenia. This biological trait appears to be useful in estimating the probability of genetic recombination in an individual, so it may be helpful in linkage studies. This article describes a nonlinear computational model for using ETD to identify schizophrenia...

The copyright protection of digital content became a critical issue nowadays. Digital image watermarking is one of the techniques used to protect digital content. In this paper two novel approaches are compared to embed watermark into the host image using quantization based on Back Propagation Neural Network (BPNN), and Dynamic Fuzzy Inference System (DFIS). The cover image is decomposed up to ...

Soft computing methods and various sentiment indicators are employed to conduct out-of-sample predictions of the future sign of the stock market returns. In particular, we assess the performance of the probabilistic neural network (PNN) against the back-propagation neural network (BPNN) in predicting technology stocks and NYSE up and down moves. Genetic algorithms (GA) are employed to optimize ...

The suitability of Back Propagation Neural Network (BPNN) for classification of remote sensing images is explored in this paper. An approach that consists of three steps to classify IRS-1D images is proposed. In the first step, features are extracted from the firstorder histogram measures. The next step is feature classification based on BPNN, and in the finally step the results are compared wi...

Spatial variation in landscape and soil properties combined with temporal variations in weather can result in yield patterns that change annually within a field. The complexity of interactions between a number of yield-limiting factors makes it difficult to accurately attribute yield losses to conditions that occur within a field. In this research, a back-propagation neural network (BPNN) model...

A number of welding parameters are responsible for the quality of welds. The modeling of weld bead shape is important for predicting the quality of welds. In this paper, an attempt has been made to develop a back-propagation neural network (BPNN) model for the prediction of reinforcement height and width of bead in GTA bead-on plate welding process. The experimental results were used as testing...