Article citationsMore>>

G. Brion, C. Viswanathan, T. R. Neelakantan, S. Lingireddy, R. Girones, D. Lees, A. Allard and A. Vantarakis, “Artificial Neural Network Prediction of Viruses in Shellfish,” Applied and Environmental Microbiology, Vol. 71, No. 9, 2005, pp. 5244-5253. doi:10.1128/AEM.71.9.5244-5253.2005

has been cited by the following article:

• TITLE: Genetic Optimization of Artificial Neural Networks to Forecast Virioplankton Abundance from Cytometric Data

  AUTHORS: Gilberto C. Pereira, Marilia M. F.de Oliveira, Nelson F. F. Ebecken

  KEYWORDS: Virioplankton Prediction; Flow Cytometry; Neural Networks; Genetic Algorithm; Trophic Gradients

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.1, February 26, 2013

  ABSTRACT: Since viruses are able to influence the trophic status and community structure they should be accessed and accounted in ecosystem functioning and management models. So, this work met a set of biological, chemical and physical time series in order to explore the correlations with marine virioplankton community across different trophic gradients. The case studied is the Arraial do Cabo upwelling system, northeast of Rio de Janeiro State in Southeast coast of Brazil. The main goal is to evolve three type of artificial neural network (ANN) by genetic algorithm (GA) optimization to predict virioplankton abundance and dynamic. The input variables range from the abundance of phytoplankton, bacterioplankton and its ratios acquired by one in situ and another ex situ flow cytometers. These data were collected with weekly frequency from August 2006 to June 2007. Our results show viruses being highly correlated to their host, and that GA provided an efficient method of optimizing ANN architectures to predict the virioplankton abundance. The RBF-NN model presented the best performance to an accuracy of 97% for any period in the year. A discussion and ecological interpretations about the system behavior is also provided.