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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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| Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
25/04/2023 |
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Data da última atualização: |
26/04/2023 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
STONE, L. F.; SANTOS, A. B. dos; ABREU, A. G. de; FAGERIA, N. K. |
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Afiliação: |
LUIS FERNANDO STONE, CNPAF; ALBERTO BAETA DOS SANTOS, CNPAF; ALUANA GONCALVES DE ABREU, CNPAF; NAND KUMAR FAGERIA, CNPAF. |
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Título: |
Melhoramento para tolerância à salinidade. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: FRITSCHE-NETO, R.; BORÉM, A. (ed.). Melhoramento de plantas para estresses abióticos. 2. ed. rev. ampl. Viçosa, MG: Universidade Federal de Viçosa, 2022. |
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Páginas: |
cap. 9, p. 196-219. |
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Descrição Física: |
il. |
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ISBN: |
978-65-5925-022-6 |
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Idioma: |
Português |
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Conteúdo: |
Introdução. Germoplasma e variabilidade genética. Indução do estresse e estratégias de seleção. Herança, efeito materno e relações entre caracteres. Métodos de melhoramento. Biotecnologia aplicada ao melhoramento para tolerância à salinidade. Considerações finais. Referências. |
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Palavras-Chave: |
Estresses abióticos; Tolerância à salinidade. |
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Thesagro: |
Indução; Melhoramento; Salinidade; Solo; Solo Salino. |
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Thesaurus NAL: |
Abiotic stress; Plant breeding; Saline soils; Salinity. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 01220naa a2200313 a 4500
001 2153328
005 2023-04-26
008 2022 bl uuuu u00u1 u #d
020 $a978-65-5925-022-6
100 1 $aSTONE, L. F.
245 $aMelhoramento para tolerância à salinidade.
260 $c2022
300 $acap. 9, p. 196-219.$cil.
520 $aIntrodução. Germoplasma e variabilidade genética. Indução do estresse e estratégias de seleção. Herança, efeito materno e relações entre caracteres. Métodos de melhoramento. Biotecnologia aplicada ao melhoramento para tolerância à salinidade. Considerações finais. Referências.
650 $aAbiotic stress
650 $aPlant breeding
650 $aSaline soils
650 $aSalinity
650 $aIndução
650 $aMelhoramento
650 $aSalinidade
650 $aSolo
650 $aSolo Salino
653 $aEstresses abióticos
653 $aTolerância à salinidade
700 1 $aSANTOS, A. B. dos
700 1 $aABREU, A. G. de
700 1 $aFAGERIA, N. K.
773 $tIn: FRITSCHE-NETO, R.; BORÉM, A. (ed.). Melhoramento de plantas para estresses abióticos. 2. ed. rev. ampl. Viçosa, MG: Universidade Federal de Viçosa, 2022.
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Embrapa Arroz e Feijão (CNPAF) |
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