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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
07/05/2012 |
Data da última atualização: |
04/06/2012 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
MASSRUHA, S. M. F. S.; RICCIOTI, R. F.; LIMA, H. P. de; MEIRA, C. A. A. |
Afiliação: |
SILVIA MARIA FONSECA S MASSRUHA, CNPTIA; RAPHAEL FUINI RICCIOTI, Estagiário CNPTIA; HELANO POVOAS DE LIMA, CNPTIA; CARLOS ALBERTO ALVES MEIRA, CNPTIA. |
Título: |
DiagData: A Tool for Generation of Fuzzy Inference System. |
Ano de publicação: |
2012 |
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 |
Palavras-Chave: |
Árvore de decisão; Fuzzy; Mineração de dados; Modelagem; Sistema de inferência fuzzy. |
Thesaurus Nal: |
Models. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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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Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia. |
Data corrente: |
20/02/2013 |
Data da última atualização: |
06/03/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
TONIETTO, A.; PETRIZ, B. A.; ARAÚJO, W. C.; REIS, A. M. dos; MAGALHÃES, B. S.; FRANCO, O. L. |
Afiliação: |
ÂNGELA TONIETTO, UCB; BERNARDO A. PETRIZ, UCB; WÉRIKA C. ARAÚJO, UCB; ANGELA MEHTA DOS REIS, CENARGEN; BEATRIZ S. MAGALHÃES, UCB; OCTÁVIO L. FRANCO, UCB. |
Título: |
Comparative proteomics between natural Microcystis isolates with a focus on microcystin synthesis. |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
Proteome Science, v. 10, 2012. Article 38. (Open Access). |
Idioma: |
Inglês |
Palavras-Chave: |
Cyanotoxin. |
Thesaurus NAL: |
Cyanobacteria; Microcystis aeruginosa; proteome. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/156255/1/art3A10.11862F1477-5956-10-38.pdf
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Marc: |
LEADER 00662naa a2200217 a 4500 001 1950118 005 2023-03-06 008 2012 bl uuuu u00u1 u #d 100 1 $aTONIETTO, A. 245 $aComparative proteomics between natural Microcystis isolates with a focus on microcystin synthesis.$h[electronic resource] 260 $c2012 650 $aCyanobacteria 650 $aMicrocystis aeruginosa 650 $aproteome 653 $aCyanotoxin 700 1 $aPETRIZ, B. A. 700 1 $aARAÚJO, W. C. 700 1 $aREIS, A. M. dos 700 1 $aMAGALHÃES, B. S. 700 1 $aFRANCO, O. L. 773 $tProteome Science$gv. 10, 2012. Article 38. (Open Access).
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