Previsão de Preço (LMP) por Redes GRNN

Pós-graduação em Engenharia Elétrica - FEIS

Projeto e implementação de redes neurais artificiais em distintos níveis de abstrações para o reconhecimento de deficiências de diversos macronutrientes e cultivares

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Reduced graphene oxide multilayers for gas and liquid phases chemical sensing

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Chitosan and alginate biopolymer membranes for remediation of contaminated water with herbicides

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Polymeric Systems as Nanodevices for siRNA Delivery

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Desenvolvimento de sensores descartáveis de ouro nanoestruturado modificados com complexo metálico visando detecção in situ de corantes semipermanentes de cabelo

Pós-graduação em Química - IQ

Structural, dielectric, ferroelectric and optical properties of PBCT, PBST and PCST complex thin films on LaNiO3 metallic conductive oxide layer coated Si substrates by the CSD technique

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Pruning methods to MLP neural networks considering proportional apparent error rate for classification problems with unbalanced data

This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. (C) 2014 Elsevier Ltd. All rights reserved.

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Aplicabilidade das equações isotermas de Freundlich e Langmuir na interação biossortiva de corantes têxteis com Saccharomyces cerevisiae liofilizada

This paper presents a study of the applicability of adsorption isotherms, known as Langmuir and Freundlich isotherm, between the biosorptive interaction of yeast lyophilized Saccharomyces cerevisiae and textile dyes. To that end, we prepared stock solutions of the textile dyes Direct Red 23 and Direct Red 75 in the concentration of 1.000μg/mL and a yeast suspension at 2,5%. We did experiments for two cases, firstly for the case that we have a fix concentration of yeast at 0,500mg/mL and an variable concentration of dye range from40, 50, 60, 80 and 100μg/mL, then for the case that we fixed the concentration of dye at 100μg/mL and the yeast concentration was variable range from 0,250, 0,500, 0,750, 1,000, 1,250mg/mL. For the dye Direct Red 23 we did analysis in the pH 2,5, 4,5 and 6,5; for the Direct Red 75, we just did for the pH 2,5. We leave the dye solution in contact with the yeast for 2 hours at a constant temperature of 30°C and then centrifuged and analyzed the sample in a spectrophotometer and finally made and analysis of parameters for the removal and study of the isotherms. After the biosorption, was observed that for the Direct Red 23 in the pH 2,5 was needed 1,407mg/mL of yeast for total removal, while for the pH 4,5 was needed 8,806mg/mL and in pH 6,5 was 9,286mg/mL; for the Direct Red 75 in pH 2,5 was needed 1,337mg/mL. This difference can be explain by the adsorption isotherms, was observed that in the case when the yeast was fix when we had in a acid pH the behavior of the system was compatible with the Langmuir isotherm, and thus, an monolayer pattern. And that when we decrease the acidity of the medium the system became more compatible with a Freundlich isotherm, and thus, a multilayer pattern; for the case that the yeast was variable this is not much evident, however for the pH 2,5 she became compatible with a Langmuir isotherm... (Complete abstract click electronic access below)