Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/74698
Campo DC Valoridioma
dc.contributor.authorÁlvarez Romero, Yerayen_US
dc.contributor.authorGarcia Baez, Patricioen_US
dc.contributor.authorSuarez Araujo, Carmen Pazen_US
dc.date.accessioned2020-10-13T12:27:01Z-
dc.date.available2020-10-13T12:27:01Z-
dc.date.issued2013en_US
dc.identifier.isbn978-3-642-38678-7en_US
dc.identifier.issn0302-9743en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/74698-
dc.description.abstractBenzimidazole 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.en_US
dc.languageengen_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.sourceAdvances In Computational Intelligence, Part I [ISSN 0302-9743], v. 7902, p. 114-123, (2013)en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherArtificial neural networksen_US
dc.subject.otherNeural ensemblesen_US
dc.subject.otherBenzimidazole fungicidesen_US
dc.subject.otherFluorescence detectionen_US
dc.subject.otherEnvironmenten_US
dc.subject.otherMulti-label classificationen_US
dc.titleComputational study based on supervised neural architectures for fluorescence detection of fungicidesen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.relation.conference12th International Work-Conference on Artificial Neural Networks (IWANN)en_US
dc.identifier.doi10.1007/978-3-642-38679-4_10en_US
dc.identifier.scopus84880047905-
dc.identifier.isi000324897700010-
dc.contributor.authorscopusid55791316100-
dc.contributor.authorscopusid23476362100-
dc.contributor.authorscopusid23476354000-
dc.identifier.eissn1611-3349-
dc.description.lastpage123en_US
dc.description.firstpage114en_US
dc.relation.volume7902en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.contributor.daisngid23743799-
dc.contributor.daisngid32254292-
dc.contributor.daisngid1776211-
dc.description.numberofpages3en_US
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Romero, YA-
dc.contributor.wosstandardWOS:Baez, PG-
dc.contributor.wosstandardWOS:Araujo, CPS-
dc.date.coverdateJulio 2013en_US
dc.identifier.conferenceidevents120842-
dc.identifier.ulpgces
dc.description.sjr0,329
dc.description.sjrqQ3
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-8826-0899-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameGarcía Baez,Patricio-
crisitem.author.fullNameSuárez Araujo, Carmen Paz-
crisitem.event.eventsstartdate12-06-2013-
crisitem.event.eventsenddate14-06-2013-
Colección:Actas de congresos
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