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dc.contributor.authorSOUZA, K. X. S. D.eng
dc.contributor.authorMASSRUHÁ, S. M. F. S.eng
dc.contributor.authorFERNANDES, V. M.eng
dc.date.accessioned2020-01-15T18:07:48Z-
dc.date.available2020-01-15T18:07:48Z-
dc.date.created2009-07-21
dc.date.issued2009
dc.identifier.citationIn: EFITA CONFERENCE, 7., 2009, Wageningen. Papers... Wageningen: Wagenigen Academic, 2009.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/83225-
dc.descriptionDiagnostic reasoning either automated or conducted by diagnosticians relies on having a set of symptoms and trying to match them against a (sparse) matrix containing the complete set of diseases and their corresponding symptioms. Whenever a symptom manifests itself, the diagnostician activates in this matrix several possible diseases which could manifest that symptom. The reasoning proceeds up to the point in which a very small subset of diseases (ideally one) are known to cause those symptoms.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectOntologiaeng
dc.subjectRepresentação do conhecimentoeng
dc.subjectOntologyeng
dc.subjectKnowledge representationeng
dc.subjectLatticeeng
dc.subjectFormal Concept Analysiseng
dc.subjectConcept Latticeseng
dc.titleUsing formal concept analysis to calculate similarities among corn diseases.eng
dc.typeArtigo em anais e proceedingseng
dc.date.updated2020-01-15T18:07:48Z
dc.subject.thesagroDoençaeng
dc.subject.thesagroMilhoeng
dc.subject.thesagroDoença de Plantaeng
dc.subject.nalthesaurusPlant diseases and disorderseng
dc.subject.nalthesaurusCorneng
dc.description.notesEfita 2009.eng
dc.format.extent2p. 41-47.eng
riaa.ainfo.id83225eng
riaa.ainfo.lastupdate2020-01-15
dc.contributor.institutionKLEBER XAVIER SAMPAIO DE SOUZA, CNPTIA; SILVIA MARIA FONSECA SILVEIRA MASSRUHÁ, CNPTIA; VINÍCIUS M. FERNANDES, Bolsista/CNPTIA.eng
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