Artificial neural networks are finding many uses in the medical diagnosis application. The goal of this paper is to evaluate artificial neural network in disease diagnosis. Two cases are studied. The first one is acute nephritis disease; data is the disease symptoms. The second is the heart disease; data is on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient class...

Artificial Neural Network (ANN) learns from inputs and outputs. The values of the weights biases in ANN are updated according to Researchers have proposed algorithms train Multi-Layer Perceptron (MLP). However, classical techniques often face problems solving this optimization problem. They tend need large amounts computing time, memory. More importantly, they get stuck within local optimum pro...

These days we can get massive information and it is hard to deal with it without computers. Machine learning is effective for computers to manage massive information. Machine learning uses various learning machine models, for instance, decision trees, Bayesian Networks, Support Vector Machine, Hidden Markov Model, normal mixed distributions, neural networks and so on. Some of them are stochasti...

This paper investigates an online gradient method with innerpenalty for a novel feed forward network it is called pi-sigma network. This network utilizes product cells as the output units to indirectly incorporate the capabilities of higherorder networks while using a fewer number of weights and processing units. Penalty term methods have been widely used to improve the generalization performan...

Artificial neural networks have been used as predictive systems for variety of medical domains, but none of the systems encountered by Baxt (1995) and Dybowski & Gant (1995) in their review of the literature provided any measure of confidence in the predictions made by those systems. In a medical setting, measures of confidence are of paramount importance (Holst, Ohlsson, Peterson & Edenbrandt ...

artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. this paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. for this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. the sugg...

The paper deals with investigating approximation abilities of a special class of discrete-time dynamic neural networks. The networks considered are called locally recurrent globally feed-forward, because they are designed with dynamic neuron models which contain inner feedbacks, but interconnections between neurons are strict feed-forward ones like in the well-known multi-layer perceptron. The ...