Artificial neural networks are promissing for separating signals from interference. However, their success depends on the amount of training data and network complexity which is prohibitively high. Pruned neural networks (PNN) are explored in this paper for the specific problem of separating artifacts from EEG signals. It is shown that PNN provides better performance than the fully interconnect...

BACKGROUND HIV voluntary counselling and testing (VCT) is an integral component of HIV prevention and treatment programmes. However, testing coverage in sub-Saharan Africa is still low, particularly among young people. As treatment becomes more widely available, strategies to expand VCT coverage are critically important. We compare VCT uptake using two delivery strategies (opt-in and opt-out) w...

Advanced Probabilistic Neural Network for the Prediction of Concrete Strength Doo Kie Kim1, Seong Kyu Chang1 and Sang Kil Chang1 Summary Accurate and realistic strength estimation before the placement of concrete is highly desirable. In this study, the advanced probabilistic neural network (APNN) was proposed to reflect the global probability density function by summing the heterogeneous local ...

This paper presents a transient power quality (PQ) disturbance classification approach based on a generalized S-transform and probabilistic neural network (PNN). Specifically, the width factor used in the generalized S-transform is feature oriented. Depending on the specific feature to be extracted from the S-transform amplitude matrix, a favorable value is determined for the width factor, with...

This paper presents transient stability assessment of a large actual 87-bus system and the IEEE 39-bus system using the probabilistic neural network (PNN) with enhanced feature selection and extraction methods. The investigated power systems are divided into smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected ...

In cloud classification from satellite imagery, temporal change in the images is one of the main factors that causes degradation in the classifier performance. In this paper, a novel temporal updating approach is developed for probabilistic neural network (PNN) classifiers that can be used to track temporal changes in a sequence of images. This is done by utilizing the temporal contextual infor...

In this paper, we proposed a multi-objective Pareto based particle swarm optimization (MOPPSO) to minimize the architectural complexity and maximize the classification accuracy of a polynomial neural network (PNN). To support this, we provide an extensive review of the literature on multi-objective particle swarm optimization and PNN. Classification using PNN can be considered as a multi-object...

Received signal strength indicator (RSSI) based indoor localization technology has its irreplaceable advantages for many location-aware applications. It is becoming obvious that in the development of fifth-generation (5G) and future communication technology, will play a key role location-based application scenarios including smart home systems, manufacturing automation, health care, robotics. C...

Se evaluó el estado de los recursos hidrobiológicos (RHB) tres Áreas Marinas Protegidas (AMPs) del Pacífico Colombia: Parque Nacional Natural (PNN) Utría, PNN Uramba Bahía Málaga y Sanquianga. Para análisis se utilizaron cuatro indicadores pesqueros: 1) Abundancia relativa dada como desembarco por unidad esfuerzo (DPUE); 2) Esfuerzo (h); 3) Talla (cm); 4) Porcentaje individuos capturados debajo...

INTRODUCTION In spite of recent advances in traffic surveillance technology and ever-growing concern over traffic safety, there have been very few research efforts establishing links between real-time traffic flow parameters and crash occurrence. This study aims at identifying patterns in the freeway loop detector data that potentially precede traffic crashes. METHOD The proposed solution ess...