The elicitation method of the fuzzy membership function depends on the interpretation of the membership function. This paper applies a recently developed neural network framework, Plausible Neural Network (PNN), to generate fuzzy membership functions automatically with or without class labeling based on the similarity and likelihood measurement. The approach combining supervised and unsupervise...

A multi-classifier diagnostic system was designed for distinguishing between benign and malignant thyroid nodules from routinely taken (FNA, H&E-stained) cytological images. To construct the multi-classifier system, several combination rules and different mixtures of ensemble classifier members, employing morphological and textural nuclear features, were comparatively evaluated. Experimental re...

This paper introduces Locally Recurrent Probabilistic Neural Networks (LRPNN) as an extension of the well-known Probabilistic Neural Networks (PNN). A LRPNN, in contrast to a PNN, is sensitive to the context in which events occur, and therefore, identification of time or spatial correlations is attainable. Besides the definition of the LRPNN architecture a fast three-step training method is pro...

We propose a fast pairwise nearest neighbor (PNN)based O(N log N) time algorithm for multilevel nonparametric thresholding, where N denotes the size of the image histogram. The proposed PNN-based multilevel thresholding algorithm is considerably faster than optimal thresholding. On a set of 8 to 16 bits-per-pixel real images, experimental results also reveal that the proposed method provides be...

Automated classification of brain MRI is important for the analysis of tumor. In this paper magnetic resonance imaging (MRI) is considered to solve the problem of automatic classification of brain images. It presents the classification system with three stages. It consists of discrete wavelet decomposition of the image, texture feature extraction from the LH and HL sub bands and final classific...

Accurate identification of lithology is the basis and key process fine logging interpretation evaluation. However, reservoirs formed by different sedimentary environments tectonic movements generally have characteristics complex diverse strong heterogeneity, which brings great difficulty to reservoir lithology. This paper proposes an automatic technology for based on GWO-SVM algorithm model. Th...

In the domain of classification tasks, artificial neural nets (ANNs) are prominent data mining methods. Paradigms like learning vector quantization (LVQ) and probabilistic neural net (PNN) are suitable classifiers. In this paper, new approaches of evolutionary optimized LVQs and PNNs are proposed. Their classification accuracy is compared with results of standard PNN and LVQ. The complex real-w...

Automation of power system fault identification using information conveyed by the wavelet analysis of power system transients is proposed. Probabilistic Neural Network (PNN) for detecting the type of fault is used. The work presented in this paper is focused on identification of simple power system faults. Wavelet Transform (WT) of the transient disturbance caused as a result of occurrence of f...

La presente investigación analizó la deforestación antes y después de los acuerdos paz en territorios del Área Manejo Especial Macarena –AMEM–, pertenecientes al municipio Macarena, Meta. metodología utilizada permitió comparar el cambio área bosque 2015 a 2018 AMEM, gracias aplicación tres ecuaciones tasa deforestación, cálculo porcentaje incremento desarrollo encuestas. Como resultado, este f...

Computing and information technology has significantly increased the capabilities to collect, store, and analyze freeway traffic surveillance data. The most common forms of such data are collected using the underground loop detectors. In the recent past the potential of using these data for identification of crash-prone conditions has been explored. In the present work, application of probabili...