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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
16/02/1998 |
Data da última atualização: |
11/12/2007 |
Autoria: |
PASSOS, E.; VALENTE, R. |
Título: |
An automatic adaptive neurocomputing algorithm for time series prediction. |
Ano de publicação: |
1995 |
Fonte/Imprenta: |
In: BRAZILIAN SYMPOSIUM ON ARTIFICIAL INTELLIGENCE, 12., 1995, Campinas. Advances in artificial intelligence: proceedings. Berlin: Springer, 1995. |
Páginas: |
p.210-218 |
Série: |
(Lecture Notes in Artificial Intelligence, 991; Lecture Notes in Computer Science). |
ISBN: |
3-540-60436-7 |
Idioma: |
Inglês |
Notas: |
SBIA'95. Ed. by Jacques Wainer and Ariadne Carvalho. |
Conteúdo: |
This work proposes a new algorithm called KNNN (k-nearest neighbours network) and demonstrates its use in a prediction task. The algorithm constructs estimators arranged in layers, using cross validation and kernel smoothing to achieve (with weight-elimination) algorithm in the prediction of future behaviour of the benchmark sunspot series. The results show that KNNN can be applied sucessfully as an estimator. |
Palavras-Chave: |
Inteligencia artificial. |
Thesaurus Nal: |
artificial intelligence. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01187naa a2200205 a 4500 001 1005963 005 2007-12-11 008 1995 bl uuuu u00u1 u #d 020 $a3-540-60436-7 100 1 $aPASSOS, E. 245 $aAn automatic adaptive neurocomputing algorithm for time series prediction. 260 $c1995 300 $ap.210-218 490 $a(Lecture Notes in Artificial Intelligence, 991; Lecture Notes in Computer Science). 500 $aSBIA'95. Ed. by Jacques Wainer and Ariadne Carvalho. 520 $aThis work proposes a new algorithm called KNNN (k-nearest neighbours network) and demonstrates its use in a prediction task. The algorithm constructs estimators arranged in layers, using cross validation and kernel smoothing to achieve (with weight-elimination) algorithm in the prediction of future behaviour of the benchmark sunspot series. The results show that KNNN can be applied sucessfully as an estimator. 650 $aartificial intelligence 653 $aInteligencia artificial 700 1 $aVALENTE, R. 773 $tIn: BRAZILIAN SYMPOSIUM ON ARTIFICIAL INTELLIGENCE, 12., 1995, Campinas. Advances in artificial intelligence: proceedings. Berlin: Springer, 1995.
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Embrapa Agricultura Digital (CNPTIA) |
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Registro Completo
Biblioteca(s): |
Embrapa Café; Embrapa Recursos Genéticos e Biotecnologia. |
Data corrente: |
17/12/2021 |
Data da última atualização: |
27/01/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
ALMEIDA, J. D. de; MOTTA, I. O.; VIDAL, L. A.; NASCIMENTO, E. F. M. B.; BILIO, J.; PUPE, J. M.; VEIGA, A. D.; CARVALHO, C. H. S. de; LOPES, R. B.; ROCHA, T. L.; SILVA, L. P. da; PUJOL-LUZ, J. R.; FREIRE, E. V. S. A. |
Afiliação: |
JULIANA DANTAS DE ALMEIDA, Cenargen; ISABELA O. MOTTA; LEONARDO A. VIDAL; ELIZA F. M. B. NASCIMENTO; JOÃO BILIO; JÚLIA M. PUPE; ADRIANO DELLY VEIGA, CPAC; CARLOS HENRIQUE S DE CARVALHO, CNPCa; ROGERIO BIAGGIONI LOPES, Cenargen; THALES LIMA ROCHA, Cenargen; LUCIANO PAULINO DA SILVA, Cenargen; JOSÉ R. PUJOL-LUZ, UNB; ERIKA VALERIA SALIBA ALBUQUERQUE FR, Cenargen. |
Título: |
A comprehensive review of the coffee leaf miner leucoptera coffeella (lepidoptera: lyonetiidae): a major pest for the coffee crop in Brazil and others neotropical countries. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Insects, v. 12, n. 12, 2021. |
DOI: |
https://doi.org/10.3390/insects12121130 |
Idioma: |
Inglês |
Conteúdo: |
The coffee leaf miner (CLM) Leucoptera coffeella moth is a major threat to coffee production. Insect damage is related to the feeding behavior of the larvae on the leaf. During the immature life stages, the insect feeds in the mesophyll triggering necrosis and causing loss of photosynthetic capacity, defoliation and significant yield loss to coffee crops. Chemical control is used to support the coffee production chain, though market requirements move toward conscious consumption claiming for more sustainable methods. In this overview, we discuss aspects about the CLM concerning biology, history, geographical distribution, economic impacts, and the most relevant control strategies in progress. Insights to develop an integrated approach for a safer and eco-friendly control of the CLM are discussed here, including bio-extracts, nanotechnology, pheromones, and tolerant cultivars. |
Palavras-Chave: |
Biopesticide; CLM; Cultivar; Life cycle; Resistance. |
Thesaurus NAL: |
Biological control; Biopesticides; Chemical control; Varietal resistance; Varieties. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230564/1/A-Comprehensive-Review-of-the-Coffee-Leaf.pdf
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Marc: |
LEADER 02067naa a2200397 a 4500 001 2139369 005 2022-01-27 008 2021 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3390/insects12121130$2DOI 100 1 $aALMEIDA, J. D. de 245 $aA comprehensive review of the coffee leaf miner leucoptera coffeella (lepidoptera$blyonetiidae): a major pest for the coffee crop in Brazil and others neotropical countries.$h[electronic resource] 260 $c2021 520 $aThe coffee leaf miner (CLM) Leucoptera coffeella moth is a major threat to coffee production. Insect damage is related to the feeding behavior of the larvae on the leaf. During the immature life stages, the insect feeds in the mesophyll triggering necrosis and causing loss of photosynthetic capacity, defoliation and significant yield loss to coffee crops. Chemical control is used to support the coffee production chain, though market requirements move toward conscious consumption claiming for more sustainable methods. In this overview, we discuss aspects about the CLM concerning biology, history, geographical distribution, economic impacts, and the most relevant control strategies in progress. Insights to develop an integrated approach for a safer and eco-friendly control of the CLM are discussed here, including bio-extracts, nanotechnology, pheromones, and tolerant cultivars. 650 $aBiological control 650 $aBiopesticides 650 $aChemical control 650 $aVarietal resistance 650 $aVarieties 653 $aBiopesticide 653 $aCLM 653 $aCultivar 653 $aLife cycle 653 $aResistance 700 1 $aMOTTA, I. O. 700 1 $aVIDAL, L. A. 700 1 $aNASCIMENTO, E. F. M. B. 700 1 $aBILIO, J. 700 1 $aPUPE, J. M. 700 1 $aVEIGA, A. D. 700 1 $aCARVALHO, C. H. S. de 700 1 $aLOPES, R. B. 700 1 $aROCHA, T. L. 700 1 $aSILVA, L. P. da 700 1 $aPUJOL-LUZ, J. R. 700 1 $aFREIRE, E. V. S. A. 773 $tInsects$gv. 12, n. 12, 2021.
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Embrapa Café (CNPCa) |
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