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Registro Completo |
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
10/01/2023 |
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Data da última atualização: |
10/01/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
SILVA, M. A. S. da; MATOS, L. N.; SANTOS, F. E. DE O.; DOMPIERI, M. H. G.; MOURA, F. R. DE. |
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Afiliação: |
MARCOS AURELIO SANTOS DA SILVA, CPATC; LEONARDO NOGUEIRA MATOS, UNIVERSIDADE FEDERAL DE SERGIPE; FLÁVIO EMANUEL DE OLIVEIRA SANTOS, UNIVERSIDADE FEDERAL DE SERGIPE; MARCIA HELENA GALINA DOMPIERI, CNPM; FÁBIO RODRIGUES DE MOURA, UNIVERSIDADE FEDERAL DE SERGIPE. |
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Título: |
Feature extraction of spatial panel data with autoencoders for clustering the Brazilian agricultural diversity. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: BRAZILIAN SYMPOSIUM ON GEOINFORMATICS, 22., 2022, São José dos Campos. Proceedings... São José dos Campos: MCTIC/INPE, 2022. p. 27-38. |
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Idioma: |
Inglês |
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Notas: |
GEOINFO 2022. |
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Conteúdo: |
ABSTRACT - Brazilian agricultural production presents a high degree of spatial diversity, which challenges designing territorial public policies to promote sustainable development. This article proposes a new approach to cluster Brazilian municipalities according to their agricultural production. It combines a feature extraction mechanism using Deep Learning based on Autoencoders and clustering based on k-means and Self-Organizing Maps. We used the panel data from IBGE?s annual estimates of Brazilian agricultural production between 1999 and 2018. Different structures of simple stacked undercomplete autoencoders were analyzed, varying the number of layers and neurons in each of them, including the latent layer. We evaluated the asymmetric exponential linear loss function to cope with the sparse data. The results show that in comparison with the ground truth adopted, the autoencoder model combined with the Self-Organizing Maps and the k-means algorithm presented a better result than the clustering of the raw data from the k-means, demonstrating the ability of simple stacked autoencoders to reduce the dimensionality and create a new space of features in their latent layer where the data can be analyzed and clustered. Although the general accuracy is low, the results are promising, considering that we can add new improvements to the Deep Clustering process. |
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Palavras-Chave: |
Clustering process. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1150828/1/6082.pdf
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Marc: |
LEADER 02062nam a2200181 a 4500
001 2150828
005 2023-01-10
008 2022 bl uuuu u00u1 u #d
100 1 $aSILVA, M. A. S. da
245 $aFeature extraction of spatial panel data with autoencoders for clustering the Brazilian agricultural diversity.$h[electronic resource]
260 $aIn: BRAZILIAN SYMPOSIUM ON GEOINFORMATICS, 22., 2022, São José dos Campos. Proceedings... São José dos Campos: MCTIC/INPE, 2022. p. 27-38.$c2022
500 $aGEOINFO 2022.
520 $aABSTRACT - Brazilian agricultural production presents a high degree of spatial diversity, which challenges designing territorial public policies to promote sustainable development. This article proposes a new approach to cluster Brazilian municipalities according to their agricultural production. It combines a feature extraction mechanism using Deep Learning based on Autoencoders and clustering based on k-means and Self-Organizing Maps. We used the panel data from IBGE?s annual estimates of Brazilian agricultural production between 1999 and 2018. Different structures of simple stacked undercomplete autoencoders were analyzed, varying the number of layers and neurons in each of them, including the latent layer. We evaluated the asymmetric exponential linear loss function to cope with the sparse data. The results show that in comparison with the ground truth adopted, the autoencoder model combined with the Self-Organizing Maps and the k-means algorithm presented a better result than the clustering of the raw data from the k-means, demonstrating the ability of simple stacked autoencoders to reduce the dimensionality and create a new space of features in their latent layer where the data can be analyzed and clustered. Although the general accuracy is low, the results are promising, considering that we can add new improvements to the Deep Clustering process.
653 $aClustering process
700 1 $aMATOS, L. N.
700 1 $aSANTOS, F. E. DE O.
700 1 $aDOMPIERI, M. H. G.
700 1 $aMOURA, F. R. DE
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Embrapa Territorial (CNPM) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
12/10/2021 |
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Data da última atualização: |
12/10/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
NASCIMENTO, E. C. do; SABINO, M. C.; CORGUINHA, L. da R.; TARGINO, B. N.; LANGE, C. C.; PINTO, C. L. de O.; PINTO, M. de F.; VIDIGAL, P. M. P.; SANT'ANA, A. S.; HUNGARO, H. M. |
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Afiliação: |
EDILANE CRISTINA DO NASCIMENTO, Universidade Federal de Juiz de Fora; MELISSA CORREA SABINO, Universidade Federal de Juiz de Fora; LUCAS DA ROZA CORGUINHA, Universidade Federal de Juiz de Fora; BRENDA NERES TARGINO, Universidade Federal de Juiz de Fora; CARLA CHRISTINE LANGE, CNPGL; CLAUDIA LUCIA DE OLIVEIRA PINTO, Empresa Mineira de Pesquisa Agropecuária; PRISCILA DE FARIA PINTO, Universidade Federal de Juiz de Fora; PEDRO MARCUS PEREIRA VIDIGAL, Universidade Federal de Viçosa; ANDERSON S. SANT'ANA, Unicamp; HUMBERTO MOREIRA HUNGARO, Universidade Federal de Juiz de Fora. |
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Título: |
Lytic bacteriophages UFJF_PfDIW6 and UFJF_PfSW6 prevent Pseudomonas fluorescens growth in vitro and the proteolytic-caused spoilage of raw milk during chilled storage . |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Food Microbiology, v. 101, 103892, 2022 |
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Idioma: |
Inglês |
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Conteúdo: |
In this study, P. fluorescens-infecting phages were isolated, characterized, and evaluated to their potential to control the bacterial counts and, consequently, the proteolytic spoilage of raw milk during cold storage. The UFJF_PfDIW6 and UFJF_PfSW6 phages showed titers of 9.7 and 7.6 log PFU/ml; latent period of 115 and 25 min, and burst size of 145 and 25 PFU/infected cell, respectively. They also were highly specific to the host bacterium, morphologically classified as the Podoviridae family, stable at pH 5 to 11 and were not inactivated at 63 degrees Celsius or 72 degrees Celsiusfor 30 min. These phages found to be effective against P. fluorescens, reducing bacterial count throughout the entire exponential growth phase in broth formulated with milk at both 4 degrees Celsius and 10 degrees Celsius. This effect on bacteria growth led to inhibition by at least 2 days in proteases production, delaying the degradation of milk proteins. When applied together in raw milk stored at 4 degrees Celsius, they reduced the total bacteria, psychrotrophic, and Pseudomonas by 3 log CFU/ml. This study?s findings indicate that these phages have a great potential to prevent the growth of Pseudomonas and, consequently, to retard proteolytic spoilage of raw milk during chilled storage. |
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Palavras-Chave: |
Dairy food; Milk spoilage; Shelf-life. |
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Thesagro: |
Armazenamento; Bactéria; Laticínio; Leite. |
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Thesaurus NAL: |
Psychrotrophic bacteria; Storage. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 02292naa a2200337 a 4500
001 2135249
005 2021-10-12
008 2022 bl uuuu u00u1 u #d
100 1 $aNASCIMENTO, E. C. do
245 $aLytic bacteriophages UFJF_PfDIW6 and UFJF_PfSW6 prevent Pseudomonas fluorescens growth in vitro and the proteolytic-caused spoilage of raw milk during chilled storage .$h[electronic resource]
260 $c2022
520 $aIn this study, P. fluorescens-infecting phages were isolated, characterized, and evaluated to their potential to control the bacterial counts and, consequently, the proteolytic spoilage of raw milk during cold storage. The UFJF_PfDIW6 and UFJF_PfSW6 phages showed titers of 9.7 and 7.6 log PFU/ml; latent period of 115 and 25 min, and burst size of 145 and 25 PFU/infected cell, respectively. They also were highly specific to the host bacterium, morphologically classified as the Podoviridae family, stable at pH 5 to 11 and were not inactivated at 63 degrees Celsius or 72 degrees Celsiusfor 30 min. These phages found to be effective against P. fluorescens, reducing bacterial count throughout the entire exponential growth phase in broth formulated with milk at both 4 degrees Celsius and 10 degrees Celsius. This effect on bacteria growth led to inhibition by at least 2 days in proteases production, delaying the degradation of milk proteins. When applied together in raw milk stored at 4 degrees Celsius, they reduced the total bacteria, psychrotrophic, and Pseudomonas by 3 log CFU/ml. This study?s findings indicate that these phages have a great potential to prevent the growth of Pseudomonas and, consequently, to retard proteolytic spoilage of raw milk during chilled storage.
650 $aPsychrotrophic bacteria
650 $aStorage
650 $aArmazenamento
650 $aBactéria
650 $aLaticínio
650 $aLeite
653 $aDairy food
653 $aMilk spoilage
653 $aShelf-life
700 1 $aSABINO, M. C.
700 1 $aCORGUINHA, L. da R.
700 1 $aTARGINO, B. N.
700 1 $aLANGE, C. C.
700 1 $aPINTO, C. L. de O.
700 1 $aPINTO, M. de F.
700 1 $aVIDIGAL, P. M. P.
700 1 $aSANT'ANA, A. S.
700 1 $aHUNGARO, H. M.
773 $tFood Microbiology$gv. 101, 103892, 2022
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