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| Registros recuperados : 104 | |
| 4. |  | SANTOS, S. A.; LIMA, H. P. de; MASSRUHA, S. M. F. S.; ABREU, U. G. P. de; TOMAS, W. M.; SALIS, S. M. de; CARDOSO, E. L.; OLIVEIRA, M. D. de; SOARES, M. T. S.; SANTOS JÚNIOR, A.; OLIVEIRA, L. O. F. de; CALHEIROS, D. F.; CRISPIM, S. M. A.; SORIANO, B. M. A.; AMANCIO, C. O. da G.; NUNES, A. P.; PELLEGRIN, L. A. A fuzzy logic-based tool to assess beef cattle ranching sustainability in complex environmental systems. Journal of Environmental Management, v. 198, part 2, p. 95-106, 2017.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Agrobiologia; Embrapa Pantanal. |
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| 7. | | SOUZA, K. X. S. de; OLIVEIRA, S. R. de M.; MACÁRIO, C. G. do N.; ESQUERDO, J. C. D. M.; MOURA, M. F.; LEITE, M. A. de A.; LIMA, H. P. de; CASTRO, A. de; TERNES, S.; YANO, I. H.; SANTOS, E. H. dos. Agricultura digital: definições e tecnologias. In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (Ed.). Agricultura digital: pesquisa, desenvolvimento e inovação nas cadeias produtivas. Brasília, DF: Embrapa, 2020. cap. 2, p. 46-66.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 8. | | LIMA, H. P. de; ABREU, U. G. P. de; SANTOS, S. A.; MASSRUHA, S. M. F. S. Análise de indicadores econômicos em fazendas no Pantanal utilizando inferência Fuzzy: ferramentas, construção e validação. Corumbá, 2012. 13p. In: CONGRESSO BRASILEIRO DE SISTEMAS FUZZY - CBSF, 2., 2012, Natal, RN. Anais... Natal: 2012.| Biblioteca(s): Embrapa Pantanal. |
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| 11. | | SANTOS, S. A.; LIMA, H. P. de; TOMAS, W. M.; MASSRUHA, S. M. F. S.; SALIS, S. M. de; ABREU, U. G. P. de; CARDOSO, E. L.; OLIVEIRA, M. D. de; OLIVEIRA, L. O. F. de; SOARES, M. T. S.; CALHEIROS, D. F.; CRISPIM, S. M. A.; SORIANO, B. M. A.; AMANCIO, C. O. da G.; ARAUJO, M. T. B. D. Como tornar uma fazenda pantaneira mais sustentável? Revista Ciência Pantanal, v. 2, n. 1, 2016. p. 18-21. Artigo de divulgação na Mídia.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 12. | | SANTOS, S. A.; LIMA, H. P. de; TOMAS, W. M.; MASSRUHA, S. M. F. S.; SALIS, S. M. de; ABREU, U. G. P. de; CARDOSO, E. L.; OLIVEIRA, M. D. de; OLIVEIRA, L. O. F. de; SOARES, M. T. S.; CALHEIROS, D. F.; CRISPIM, S. M. A.; SORIANO, B. M. A.; AMANCIO, C. O. da G.; ARAUJO, M. T. B. D. Como tornar uma fazenda pantaneira mais sustentável? Revista Ciência Pantanal, v. 2, n. 1, 2016. p. 18-21 Artigo de divulgação na Mídia.| Biblioteca(s): Embrapa Pantanal. |
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| 13. | | TERNES, S.; MOURA, M. F.; SOUZA, K. X. S. de; VAZ, G. J.; OLIVEIRA, S. R. de M.; HIGA, R. H.; LIMA, H. P. de; TAKEMURA, C. M.; COELHO, E. A.; BARBOSA, F. F. L.; VISOLI, M. C.; MENEZES, G. R. de O.; SILVA, L. O. C. da; SANTOS, S. A.; MASSRUHÁ, S. M. F. S.; ABREU, U. G. P. de; SORIANO, B. M. A.; SALIS, S. M.; OLIVEIRA, M. D. de; TOMAS, W. M. Computação científica na agricultura. In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (Ed.). Agricultura digital: pesquisa, desenvolvimento e inovação nas cadeias produtivas. Brasília, DF: Embrapa, 2020. cap. 5, p. 120-144. Na publicação: Enilda Coelho, Suzana Maria Salis.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Milho e Sorgo; Embrapa Pantanal; Embrapa Territorial. |
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| 14. | | SANTOS, S. A.; CARDOSO, E. L.; TAKAHASHI, F.; LIMA, H. P. de; FLORES, C. P.; FERNANDES, A. H. B. M.; FERNANDES, F. A.; SORIANO, B. M. A.; TOMAS, W. M.; SALIS, S. M.; URBANETZ, C.; COMASTRI FILHO, J. A.; PAIVA, L. M. Conservação e manejo adaptativo de pastagens nativas. In: SOTTA, E. D.; SAMPAIO, F. G.; MARZALL, K.; SILVA, W. G. da (org.). Estratégias de adaptação às mudanças do clima dos sistemas agropecuários brasileiros. Brasília, DF: MAPA, 2021. p. 76-77.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Pantanal. |
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| 15. | | SANTOS, S. A.; CARDOSO, E. L.; TAKAHASHI, F.; LIMA, H. P. de; FLORES, C. P.; FERNANDES, A. H. B. M.; FERNANDES, F. A.; SORIANO, B. M. A.; TOMAS, W. M.; SALIS, S. M.; URBANETZ, C.; COMASTRI FILHO, J. A.; PAIVA, L. M. Conservation and adaptive management of native pastures. In: SOTTA, E. D.; SAMPAIO, F. G.; MARZALL, K.; SILVA, W . G. da (ed.). Adapting to climate change: strategies for Brazilian agricultural and livestock systems. Brasília, DF: MAPA, 2021. p. 76-77.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Pantanal. |
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| Registros recuperados : 104 | |
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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. |
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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Circulação/Nível: |
B - 4 |
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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 |
|
Marc: |
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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
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700 1 $aMEIRA, C. A. A.
773 $tJournal of Environmental Science and Engineering B, p. 336-343, 2012.
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