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| 41. |  | LEITE, M. A. de A.; DUARTE, D. A.; MASSRUHÁ, S. M. F. S. Agricultura digital: levantamento junto ao produtor rural na Região Metropolitana de Campinas. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 12., 2019, Indaiatuba. Anais... Ponta Grossa: SBIAGRO, 2019. p. 440-449. Organizadores: Maria Fernanda Moura, Jayme Garcia Arnal Barbedo, Alaine Margarete Guimarães, Valter Castelhano de Oliveira. SBIAgro 2019.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 43. | | QUEIROS, L. R.; LUCHIARI JUNIOR, A.; CAMARGO NETO, J.; MASSRUHA, S. M. F.; INAMASU, R. Y. Análise das possibilidades e tendências do uso das tecnologias da informação e comunicação em agricultura de precisão. In: BERNARDI, A. C. de C.; NAIME, J. de M.; RESENDE, A. V. de; BASSOI, L. H.; INAMASU, R. Y. (Ed.). Agricultura de precisão: resultados de um novo olhar. Brasília, DF: Embrapa, 2014. p. 97-108.| Biblioteca(s): Embrapa Instrumentação. |
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| 44. | | MASSRUHA, S. M. F. S.; LEITE, M. A. de A.; BOLFE, E. L. Agro 4.0: o papel da pesquisa e perspectivas para a transformação digital na agricultura. In: DIAS, E. M.; DOURADO NETO, D.; SCOTON, M. L. R. P. D.; OLIVEIRA, D. H. de; SANTOS, I. M. G. L. dos; MENEZES, J. H. V. (org.). Agro 4.0: fundamentos, realidades e perspectivas para o Brasil. Rio de Janeiro: Autografia, 2023. cap. 3, p. 58-77.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 45. | | MASSRUHA, S. M. F. S.; SANDRI, S.; WAINER, J.; MORANDI, M. A. B. An integrated framework for clinical problem solving in agriculture. In: CONFERENCE OF THE EUROPEAN FEDERATION FOR INFORMATION TECHNOLOGY IN AGRICULTURE, FOOD AND ENVIRONMENT, 5., WORLD CONGRESS ON COMPUTERS IN AGRICULTURE AND NATURAL RESOURCES, 3., 2005, Vila Real, Portugal. [Proceedings...] Vila Real, Portugal: EFITA/WCCA, 2005. p. 1400-1407.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Meio Ambiente. |
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| 46. | | MASSRUHÁ, S. M. F. S.; SANDRI, S.; WAINER, J.; MORANDI, M. A. B. An integrated framework for clinical problem solving in agriculture. In: CONFERENCE OF THE EUROPEAN FEDERATION FOR INFORMATION TECHNOLOGY IN AGRICULTURE, FOOD AND ENVIRONMENT, 5.; WORLD CONGRESS ON COMPUTERS IN AGRICULTURE AND NATURAL RESOURCES, 3., 2005, Vila Real, Portugal. Book of abstracts... Vila Real, Portugal: Universidade de Trás-os-Montes e Alto Douro, 2005. p. 76. EFITA/WCCA 2005. Poster Sessions.| Biblioteca(s): Embrapa Agricultura Digital. |
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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
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
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. |
|
Idioma: |
Inglês |
|
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. |
|
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.
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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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