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dc.contributor.authorTahat, Amani
dc.contributor.authorMartí Rabassa, Jordi
dc.contributor.authorKhwaldeh, Ali
dc.contributor.authorTahat, Kaher
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Física i Enginyeria Nuclear
dc.date.accessioned2014-02-27T14:47:41Z
dc.date.created2014-02-10
dc.date.issued2014-02-10
dc.identifier.citationTahat, A. [et al.]. Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments. "Chinese Physics B", 10 Febrer 2014, vol. 23, núm. 4, p. 046101-1-046101-12.
dc.identifier.issn1674-1056
dc.identifier.urihttp://hdl.handle.net/2117/21794
dc.description.abstractIn computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing to classify the proton motion into two categories: transfer‘occurred’and transfer‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need of pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Física
dc.subject.lcshPattern perception
dc.subject.lcshData mining
dc.subject.lcshNeural networks (Computer science)
dc.subject.otherproton transfer
dc.subject.otherpattern recognition
dc.subject.otherneural networks
dc.subject.otherwater environments
dc.subject.otherchart pattern
dc.subject.otherdata mining
dc.subject.otherartificial neural network
dc.subject.otherempirical valence bond
dc.titlePattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments
dc.typeArticle
dc.subject.lemacReconeixement de formes (Informàtica)
dc.subject.lemacMineria de dades
dc.subject.lemacXarxes neuronals (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. SIMCON - First-principles approaches to condensed matter physics: quantum effects and complexity
dc.identifier.doi10.1088/1674-1056/23/4/046101
dc.relation.publisherversionhttp://cpb.iphy.ac.cn/EN/abstract/abstract58541.shtml
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac13065421
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorTahat, A.; Marti, J.; Khwaldeh, A.; Tahat, K.
local.citation.publicationNameChinese Physics B
local.citation.volume23
local.citation.number4
local.citation.startingPage046101-1
local.citation.endingPage046101-12


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