Repositorio Universidad del Cauca

Sistema experto basado en emparejamiento de patrones

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dc.contributor.author Lasso Sambony, Emmanuel Gerardo
dc.date.accessioned 2019-11-05T15:35:37Z
dc.date.available 2019-11-05T15:35:37Z
dc.date.issued 2016
dc.identifier.uri http://repositorio.unicauca.edu.co:8080/xmlui/handle/123456789/1355
dc.description.abstract Background: For agroindustry, crop diseases constitute one of the most common problems that generate large economic losses and low production quality. On the other hand, recent research proposes the development of expert systems to solve this problem, making use of data mining and artificial intelligence techniques. Furthermore, graphs can be used for storage of different types of variables that are present in an environment of crops, allowing the application of graph data mining techniques like graph pattern matching. Therefore, the development of an expert system for crop disease based on graph pattern matching, can generate a solution for the identification of favorable conditions for a particular disease, as a starting point for decision-making. Goals: Develop an expert system based on graph pattern matching to detect favorable conditions for coffee rust in Colombian crops. Methods: This work proposes an expert system, characterized from expert knowledge in coffee rust, as a starting point for the extraction of rules that determine conditions favorable for this disease, from induction of decision trees, applied to a dataset of monitoring and cultivation properties. These rules are expressed as patterns of graphs, which are sought within an information repository crop expressed as graphs, in order to find the similarities of these patterns, which determine the state of a crop of coffee against rust. Results: A set of predictive variables for coffee rust, defined from expert’s knowledge; a set of graph patterns to identify three favorable conditions for rust infection rates; adaptation of an algorithm for graph pattern matching and an expert system for detecting coffee rust infection rates, based on graph pattern matching. Conclusions: Expert knowledge in coffee rust allows the construction of specific predictive variables for the disease and include it within models generated by data mining techniques. From these models, can be extracted rules to be expressed as graph patterns, using their expressiveness and interpretability. Thus, the application of graph pattern matching results in the condition of a crop against disease. Moreover, the lack of a large amount of data restricts the quality of model generation process and the system validation. en
dc.language.iso spa es
dc.publisher Universidad del Cauca es
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Expert system en
dc.subject Graph en
dc.subject Pattern matching en
dc.subject Data mining en
dc.subject Crop en
dc.subject Disease en
dc.subject Agriculture en
dc.title Sistema experto basado en emparejamiento de patrones es
dc.type Tesis maestría es
dc.rights.creativecommons https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.type.driver info:eu-repo/semantics/masterThesis
dc.type.coar http://purl.org/coar/resource_type/c_bdcc
dc.publisher.faculty Facultad de Ingeniería Electrónica y Telecomunicaciones es
dc.publisher.program Maestría en Ingeniería Telemática es
dc.rights.accessrights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion
dc.coar.version http://purl.org/coar/version/c_970fb48d4fbd8a85


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