The probabilistic nearest neighbour (PNN) method for pattern recognition was introduced to overcome a number of perceived shortcomings of the nearest neighbour (NN) classifiers namely the lack of any probabilistic semantics when making predictions of class membership. In addition the NN method possesses no inherent principled framework for inferring the number of neighbours, K, nor indeed assoc...

Sprague-Dawley (SD) rats' normal and abnormal pancreatic tissues are determined directly by attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy method. In order to diagnose earlier stage of SD rats pancreatic cancer rate with FT-IR, a novel method of extraction of FT-IR feature using discrete wavelet transformation (DWT) analysis and classification with the probabil...

The Probabilistic Neural Network (PNN) algorithm represents the likelihood function of a given class as the sum of identical, isotropic Gaussians. In practice, PNN is often an excellent pattern classifier, outperforming other classifiers including backpropagation. However, it is not robust with respect to affine transformations of feature space, and this can lead to poor performance on certain ...

Today the whole world suffers and fears epidemic of Coronavirus developed waves in it, as we have now reached fourth wave, this is a serious matter. Where statistics current data showed that 213 countries are affected by epidemic, about 6 millions deaths recorded. This virus spreads rapidly, patients mainly suffer from breathing. The patient who pre-existing health problems will be more likely ...

A novel condensed polynomial neural network using particle swarm optimization (PSO) technique is proposed for the task of classification in this paper. In solving classification task classical algorithms such as polynomial neural network (PNN) and its variants need more computational time as the partial descriptions (PDs) grow over the training period layer-by-layer and make the network very co...

This study proposes an Artificial Neural Network (ANN) and Genetic Algorithm model for diagnostic risk factors selection in medicine. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Networks (PNNs) were used to face a medical disease prediction. Genetic Algorithm (GA) was used for pruning t...

In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN) and a Probabilistic Neural Network (PNN) in differentiation of commonly encountered features in indoor environments. The stability-plasticity behaviors of both networks have been investigated. The experimental result shows that MRAN possesses lower network complexity but experien...

Abstract Accurately obtaining the distribution of open-channel velocity field in hydraulic engineering is extremely important, which helpful for better calculation flow and analysis water characteristics. In recent years, machine learning has been used prediction. However, effective training data-driven models heavily depends on diversity quantity data. this paper, a CFD-based pre-training neur...

In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and perturbed by the variation rotor resistance load changes. system has two stages. identification stage, group method data-handling (GMDH) neural network (NN) was designed online modeling IM. GMDH-NN applied to compensate impacts disturbances uncertainties. stability shown Lya...