Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/74698
Title: Computational study based on supervised neural architectures for fluorescence detection of fungicides
Authors: Álvarez Romero, Yeray
Garcia Baez, Patricio 
Suarez Araujo, Carmen Paz 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Artificial neural networks
Neural ensembles
Benzimidazole fungicides
Fluorescence detection
Environment, et al
Issue Date: 2013
Journal: Lecture Notes in Computer Science 
Conference: 12th International Work-Conference on Artificial Neural Networks (IWANN) 
Abstract: Benzimidazole fungicides (BFs) are a type of pesticide of high environmental interest characterized by a heavy spectral overlap which complicates its detection in mixtures. In this paper we present a computational study based on supervised neural networks for a multi-label classification problem. Specifically, backpropagation (BPN) with data fusion and ensemble schemes is used for the simultaneous resolution of difficult multi-fungicide mixtures. We designed, optimized and compared simple BPNs, BPNs with data fusion and BPN ensembles. The information environment used is made up of synchronous and conventional BF fluorescence spectra. The mixture spectra are not used in the training stage. This study allows the use of supervised neural architectures to be compared to unsupervised ones, which have been developed in previous works, for the identification of BFs in complex multi-fungicide mixtures. The study was carried out using a new software tool, MULLPY, which was developed in Python.
URI: http://hdl.handle.net/10553/74698
ISBN: 978-3-642-38678-7
ISSN: 0302-9743
DOI: 10.1007/978-3-642-38679-4_10
Source: Advances In Computational Intelligence, Part I [ISSN 0302-9743], v. 7902, p. 114-123, (2013)
Appears in Collections:Actas de congresos
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