Universidade de Pernambuco

About: Universidade de Pernambuco is a education organization based out in Recife, Brazil. It is known for research contribution in the topics: Population & Artificial neural network. The organization has 6147 authors who have published 6948 publications receiving 73648 citations.

A method for automatic stock trading combining technical analysis and nearest neighbor classification

Abstract: In this paper we propose and analyze a novel method for automatic stock trading which combines technical analysis and the nearest neighbor classification. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists of a combination of a nearest neighbor classifier and some well known tools of technical analysis, namely, stop loss, stop gain and RSI filter. For assessing the potential use of the proposed method in practice we compared the results obtained to the results that would be obtained by adopting a buy-and-hold strategy. The key performance measure in this comparison was profitability. The proposed method was shown to generate considerable higher profits than buy-and-hold for most of the companies, with few buy operations generated and, consequently, minimizing the risk of market exposure.

Modulation for Three-Phase Transformerless Z-Source Inverter to Reduce Leakage Currents in Photovoltaic Systems

Abstract: In this paper, a modified Z-source inverter (ZSI) with specific modulation techniques is proposed to reduce leakage currents in three-phase transformerless photovoltaic (PV) systems. The new topology only requires an additional fast-recovery diode when compared with the original structure. On the other hand, the pulsewidth modulation technique is entirely modified in order to reduce the leakage currents through the conduction path. Simulation results for the three-phase transformerless PV system operating in two cases, i.e., connected to a grid and connected to a grounded RL load, are presented. Experimental results of leakage currents in three-phase ZSIs connected to a RL load are obtained to validate the theoretical and simulation models.

The psychosocial effects of severe caries in 4-year-old children in Recife, Pernambuco, Brazil

Abstract: The aim of this study was to analyze the psychosocial effects of severe caries in 4-year-old children in Recife, Pernambuco, Brazil. The clinical examination was conducted by a single examiner in order to select children with severe caries and caries-free (kappa = 1). Of the 861 children examined, 77 (8.1%) had severe caries and 225 (23.6%) were caries-free. Data were collected by applying validated questionnaires answered by the parents or guardians. Most of the parents or guardians of children with severe caries reported that their children complained of toothache (72.7%), and a significant portion stated that their children had problems eating certain kinds of food (49.4%) and missed school (26.0%) because of their teeth. Most of the parents or guardians of children with severe caries (68.8%) stated that oral health affects their children's life, while the same was stated by 9.8% of the parents or guardians of the caries-free children. Severe caries was found to have a negative impact on children's oral health-related quality of life.

Comportamentos de risco, auto-avaliação do nível de saúde e percepção de estresse entre trabalhadores da indústria Health risk behaviors, health status self-assessment and stress perception among industrial workers

Abstract: #Estresse, epidemiologia. # Levantamentos epidemiologicos. # Indicadores de saude. # Estilo de vida. # Fatores de risco. Conduta de saude. Atividades de lazer. Tabagismo. Habitos alimentares. Exercicio.

Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

Abstract: Human falls are a global public health issue resulting in over 373 million severe injuries and 646,000 deaths yearly Falls result in direct financial cost to health systems and indirectly to society productivity Unsurprisingly, human fall detection and prevention are a major focus of health research In this article, we consider deep learning for fall detection in an IoT and fog computing environment We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model We evaluate its performance using three open data sets and against extant research Our approach for resolving dimensionality and modelling simplicity issues is outlined Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance The best results are achieved when using data augmentation during the training process The paper concludes with a discussion of challenges and future directions for research in this domain