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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/05/2012 |
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Data da última atualização: |
04/06/2012 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MASSRUHA, S. M. F. S.; RICCIOTI, R. F.; LIMA, H. P. de; MEIRA, C. A. A. |
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Afiliação: |
SILVIA MARIA FONSECA S MASSRUHA, CNPTIA; RAPHAEL FUINI RICCIOTI, Estagiário CNPTIA; HELANO POVOAS DE LIMA, CNPTIA; CARLOS ALBERTO ALVES MEIRA, CNPTIA. |
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Título: |
DiagData: A Tool for Generation of Fuzzy Inference System. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Journal of Environmental Science and Engineering B, p. 336-343, 2012. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: In this paper, it described the architecture of a tool called DiagData. This tool aims to use a large amount of data and information in the field of plant disease diagnostic to generate a disease predictive system. In this approach, techniques of data mining are used to extract knowledge from existing data. The data is extracted in the form of rules that are used in the development of a predictive intelligent system. Currently, the specification of these rules is built by an expert or data mining. When data mining on a large database is used, the number of generated rules is very complex too. The main goal of this work is minimize the rule generation time. The proposed tool, called DiagData, extracts knowledge automatically or semi-automatically from a database and uses it to build an intelligent system for disease prediction. In this work, the decision tree learning algorithm was used to generate the rules. A toolbox called Fuzzygen was used to generate a prediction system from rules generated by decision tree algorithm. The language used to implement this software was Java. The DiagData has been used in diseases prediction and diagnosis systems and in the validation of economic and environmental indicators in agricultural production systems. The validation process involved measurements and comparisons of the time spent to enter the rules by an expert with the time used to insert the same rules with the proposed tool. Thus, the tool was successfully validated, providing a reduction of time. MenosAbstract: In this paper, it described the architecture of a tool called DiagData. This tool aims to use a large amount of data and information in the field of plant disease diagnostic to generate a disease predictive system. In this approach, techniques of data mining are used to extract knowledge from existing data. The data is extracted in the form of rules that are used in the development of a predictive intelligent system. Currently, the specification of these rules is built by an expert or data mining. When data mining on a large database is used, the number of generated rules is very complex too. The main goal of this work is minimize the rule generation time. The proposed tool, called DiagData, extracts knowledge automatically or semi-automatically from a database and uses it to build an intelligent system for disease prediction. In this work, the decision tree learning algorithm was used to generate the rules. A toolbox called Fuzzygen was used to generate a prediction system from rules generated by decision tree algorithm. The language used to implement this software was Java. The DiagData has been used in diseases prediction and diagnosis systems and in the validation of economic and environmental indicators in agricultural production systems. The validation process involved measurements and comparisons of the time spent to enter the rules by an expert with the time used to insert the same rules with the proposed tool. Thus, the tool was successfully validated, pro... Mostrar Tudo |
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Palavras-Chave: |
Árvore de decisão; Fuzzy; Mineração de dados; Modelagem; Sistema de inferência fuzzy. |
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Thesaurus Nal: |
Models. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02192naa a2200229 a 4500
001 1923931
005 2012-06-04
008 2012 bl uuuu u00u1 u #d
100 1 $aMASSRUHA, S. M. F. S.
245 $aDiagData$bA Tool for Generation of Fuzzy Inference System.$h[electronic resource]
260 $c2012
520 $aAbstract: In this paper, it described the architecture of a tool called DiagData. This tool aims to use a large amount of data and information in the field of plant disease diagnostic to generate a disease predictive system. In this approach, techniques of data mining are used to extract knowledge from existing data. The data is extracted in the form of rules that are used in the development of a predictive intelligent system. Currently, the specification of these rules is built by an expert or data mining. When data mining on a large database is used, the number of generated rules is very complex too. The main goal of this work is minimize the rule generation time. The proposed tool, called DiagData, extracts knowledge automatically or semi-automatically from a database and uses it to build an intelligent system for disease prediction. In this work, the decision tree learning algorithm was used to generate the rules. A toolbox called Fuzzygen was used to generate a prediction system from rules generated by decision tree algorithm. The language used to implement this software was Java. The DiagData has been used in diseases prediction and diagnosis systems and in the validation of economic and environmental indicators in agricultural production systems. The validation process involved measurements and comparisons of the time spent to enter the rules by an expert with the time used to insert the same rules with the proposed tool. Thus, the tool was successfully validated, providing a reduction of time.
650 $aModels
653 $aÁrvore de decisão
653 $aFuzzy
653 $aMineração de dados
653 $aModelagem
653 $aSistema de inferência fuzzy
700 1 $aRICCIOTI, R. F.
700 1 $aLIMA, H. P. de
700 1 $aMEIRA, C. A. A.
773 $tJournal of Environmental Science and Engineering B, p. 336-343, 2012.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio-Norte. |
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Data corrente: |
06/03/2020 |
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Data da última atualização: |
12/12/2023 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
MARTINS, R. V. de S.; ROCHA, A. G. da; PIRES, M. de F. M.; MEDEIROS, J. C.; SOUZA, H. A. de. |
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Afiliação: |
RAPHAEL VINICIUS DE SOUZA MARTINS, ESTUDANTE DE ENGENHARIA AGRONÔMICA/UFPI, ESTAGIÁRIO NA EMBRAPA MEIO-NORTE; ANGÉLICA GOMES DA ROCHA, ESTUDANTE DE ENGENHARIA AGRONÔMICA/UFPI, ESTAGIÁRIA NA EMBRAPA MEIO-NORTE; MARIA DE FÁTIMA MARQUES PIRES, MESTRANDA DO PPG EM FITOTECNIA, UFPI BOM JESUS; JOÃO CARLOS MEDEIROS, PROFESSOR UESC; HENRIQUE ANTUNES DE SOUZA, CPAMN. |
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Título: |
Crescimento inicial da soja sob diferentes manejos do solo na Serra do Quilombo, Cerrado piauiense. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
In: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA MEIO-NORTE, 4., 2018, Teresina, PI. Anais da IV Jornada Científica da Embrapa Meio-Norte. Brasília, DF: Embrapa, 2018. |
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Páginas: |
p. 25. |
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Idioma: |
Português |
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Conteúdo: |
Assim, objetivou-se avaliar o crescimento inicial da planta de soja sob diferentes manejos do solo. |
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Thesagro: |
Escarificação de sementes; Glycine Max; Pennisetum Glaucum. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/211588/1/Quarta-Jornada-Cientifica-2018-00023.pdf
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Marc: |
LEADER 00861nam a2200205 a 4500
001 2121009
005 2023-12-12
008 2018 bl uuuu u00u1 u #d
100 1 $aMARTINS, R. V. de S.
245 $aCrescimento inicial da soja sob diferentes manejos do solo na Serra do Quilombo, Cerrado piauiense.$h[electronic resource]
260 $aIn: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA MEIO-NORTE, 4., 2018, Teresina, PI. Anais da IV Jornada Científica da Embrapa Meio-Norte. Brasília, DF: Embrapa$c2018
300 $ap. 25.
520 $aAssim, objetivou-se avaliar o crescimento inicial da planta de soja sob diferentes manejos do solo.
650 $aEscarificação de sementes
650 $aGlycine Max
650 $aPennisetum Glaucum
700 1 $aROCHA, A. G. da
700 1 $aPIRES, M. de F. M.
700 1 $aMEDEIROS, J. C.
700 1 $aSOUZA, H. A. de
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