Abstract In this modern era, visual data transmission, processing, and analysis play a vital role in daily life. Image denoising is the process of approximately estimating original version degraded image. The presence unexpected noise (e.g., fixed, random, Gaussian) root cause degradation, which has been reduced to some extent by many linear non-linear filters based on median value. real issue ...

The ability of neural networks to closely approximate unknown functions to any degree of desired accuracy has generated considerable demand for Neural Network research in Business. The attractiveness of neural network research stems from researchers’ need to approximate models within the business environment without having a priori knowledge about the true underlying function. Gradient techniqu...

Introduction: Artificial neural networks are a type of systems that use very complex technologies and non-algorithmic solutions for problem solving. These characteristics make them suitable for various medical applications. This study set out to investigate the application of artificial neural networks for differential diagnosis of thalassemia minor and iron-deficiency anemia. Methods: It is...

This study compares the predictive performance of three neural network methods, namely the Learning Vector Quantization, the Radial Basis Function, and the Feedforward network that uses the conjugate gradient optimization algorithm, with the performance of the logistic regression and the backpropagation algorithm. All these methods are applied to a dataset of 139 matched-pairs of bankrupt and n...

In this paper, we investigate the dynamic behavior of a backpropagation neural network while learning the XOR-boolean function. It has been shown that the backpropagation algorithm exhibits chaotic behavior and this implies an highly irregular and virtually unpredictable evolution. We study the chaotic behavior as learning progresses. Our investigation indicates that chaos appears to diminish a...

Most neural networks used today rely on rigid, fixed-architecture networks and/or slow, gradient descent-based training algorithms (e. g. backpropagation). In this paper, we propose a new neural network learning architecture to counter these problems. Namely, we combine (1) flexible cascade neural networks, which dynamically adjust the size of the neural network as part of the learning process,...