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Artigo de periodico
Multi-objective PSO for control-loop tuning of DFIG wind turbines with chopper protection and reactive-current injection (2024)
Barrios Aguilar, Milton Ernesto; Coury, Denis Vinicius ; Reginatto, Romeu; Monaro, Renato Machado ; Godoy, Paulo Thiago; Jahn, Tales G
Source: Energies. Unidades: EESC, EP
Subjects: ENERGIA EÓLICA, PROTEÇÃO DE SISTEMAS ELÉTRICOS, ALGORITMOS, ENGENHARIA ELÉTRICA
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BARRIOS AGUILAR, Milton Ernesto et al. Multi-objective PSO for control-loop tuning of DFIG wind turbines with chopper protection and reactive-current injection. Energies, v. 17, n. 1, p. 1-25, 2024Tradução . . Disponível em: https://dx.doi.org/10.3390/en17010028. Acesso em: 03 nov. 2024.
APA
Barrios Aguilar, M. E., Coury, D. V., Reginatto, R., Monaro, R. M., Godoy, P. T., & Jahn, T. G. (2024). Multi-objective PSO for control-loop tuning of DFIG wind turbines with chopper protection and reactive-current injection. Energies, 17( 1), 1-25. doi:10.3390/en17010028
NLM
Barrios Aguilar ME, Coury DV, Reginatto R, Monaro RM, Godoy PT, Jahn TG. Multi-objective PSO for control-loop tuning of DFIG wind turbines with chopper protection and reactive-current injection [Internet]. Energies. 2024 ; 17( 1): 1-25.[citado 2024 nov. 03 ] Available from: https://dx.doi.org/10.3390/en17010028
Vancouver
Barrios Aguilar ME, Coury DV, Reginatto R, Monaro RM, Godoy PT, Jahn TG. Multi-objective PSO for control-loop tuning of DFIG wind turbines with chopper protection and reactive-current injection [Internet]. Energies. 2024 ; 17( 1): 1-25.[citado 2024 nov. 03 ] Available from: https://dx.doi.org/10.3390/en17010028
Artigo de periodico
Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy (2024)
Tavares, Marcos Silva ; Silva, Carlos Augusto Alves Cardoso ; Regazzo, Jamile Raquel ; Sardinha, Edson José de Souza ; Silva, Thiago Lima da ; Fiorio, Peterson Ricardo ; Baesso, Murilo Mesquita
Source: Agronomy. Unidades: FZEA, ESALQ
Subjects: ALGORITMOS, AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA INFRAVERMELHA, FEIJÃO
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TAVARES, Marcos Silva et al. Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy. Agronomy, v. 14, p. 1-14, 2024Tradução . . Disponível em: https://doi.org/10.3390/agronomy14081634. Acesso em: 03 nov. 2024.
APA
Tavares, M. S., Silva, C. A. A. C., Regazzo, J. R., Sardinha, E. J. de S., Silva, T. L. da, Fiorio, P. R., & Baesso, M. M. (2024). Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy. Agronomy, 14, 1-14. doi:10.3390/agronomy14081634
NLM
Tavares MS, Silva CAAC, Regazzo JR, Sardinha EJ de S, Silva TL da, Fiorio PR, Baesso MM. Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy [Internet]. Agronomy. 2024 ; 14 1-14.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/agronomy14081634
Vancouver
Tavares MS, Silva CAAC, Regazzo JR, Sardinha EJ de S, Silva TL da, Fiorio PR, Baesso MM. Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy [Internet]. Agronomy. 2024 ; 14 1-14.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/agronomy14081634
Artigo de periodico
Case studies with the Contiki-NG simulator to design strategies for sensors' communication optimization in an IoT-fog ecosystem (2023)
Ferreira, Antonio Marcos Almeida ; Azevedo, Leonildo José de Melo de ; Estrella, Júlio Cezar ; Delbem, Alexandre Cláudio Botazzo
Source: Sensors. Unidade: ICMC
Subjects: ALGORITMOS, TOMADA DE DECISÃO, INTERNET DAS COISAS, ANÁLISE DE DESEMPENHO
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FERREIRA, Antonio Marcos Almeida et al. Case studies with the Contiki-NG simulator to design strategies for sensors' communication optimization in an IoT-fog ecosystem. Sensors, v. 23, p. 1-18, 2023Tradução . . Disponível em: https://doi.org/10.3390/s23042300. Acesso em: 03 nov. 2024.
APA
Ferreira, A. M. A., Azevedo, L. J. de M. de, Estrella, J. C., & Delbem, A. C. B. (2023). Case studies with the Contiki-NG simulator to design strategies for sensors' communication optimization in an IoT-fog ecosystem. Sensors, 23, 1-18. doi:10.3390/s23042300
NLM
Ferreira AMA, Azevedo LJ de M de, Estrella JC, Delbem ACB. Case studies with the Contiki-NG simulator to design strategies for sensors' communication optimization in an IoT-fog ecosystem [Internet]. Sensors. 2023 ; 23 1-18.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/s23042300
Vancouver
Ferreira AMA, Azevedo LJ de M de, Estrella JC, Delbem ACB. Case studies with the Contiki-NG simulator to design strategies for sensors' communication optimization in an IoT-fog ecosystem [Internet]. Sensors. 2023 ; 23 1-18.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/s23042300
Artigo de periodico
Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms (2023)
Falcioni, Renan ; Gonçalves, João Vitor Ferreira ; Oliveira, Karym Mayara de ; Oliveira, Caio Almeida de ; Demattê, José Alexandre Melo ; Antunes, Werner Camargos ; Nanni, Marcos Rafael
Source: Plants. Unidade: ESALQ
Subjects: ALFACE, FENÓTIPOS, PIGMENTOS VEGETAIS, ESPECTROSCOPIA, INTELIGÊNCIA ARTIFICIAL, VARIEDADES VEGETAIS, ALGORITMOS
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FALCIONI, Renan et al. Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms. Plants, v. 12, p. 1-15, 2023Tradução . . Disponível em: https://doi.org/10.3390/plants12061333. Acesso em: 03 nov. 2024.
APA
Falcioni, R., Gonçalves, J. V. F., Oliveira, K. M. de, Oliveira, C. A. de, Demattê, J. A. M., Antunes, W. C., & Nanni, M. R. (2023). Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms. Plants, 12, 1-15. doi:10.3390/plants12061333
NLM
Falcioni R, Gonçalves JVF, Oliveira KM de, Oliveira CA de, Demattê JAM, Antunes WC, Nanni MR. Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms [Internet]. Plants. 2023 ; 12 1-15.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/plants12061333
Vancouver
Falcioni R, Gonçalves JVF, Oliveira KM de, Oliveira CA de, Demattê JAM, Antunes WC, Nanni MR. Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms [Internet]. Plants. 2023 ; 12 1-15.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/plants12061333
Artigo de periodico
Chemometric analysis for the prediction of biochemical compounds in leaves using UV-VIS-NIR-SWIR hyperspectroscopy (2023)
Falcioni, Renan ; Gonçalves, João Vitor Ferreira ; Oliveira, Karym Mayara de; Oliveira, Caio Almeida de; Reis, Amanda Silveira ; Crusiol, Luis Guilherme Teixeira; Furlanetto, Renato Herrig; Antunes, Werner Camargos ; Cezar, Everson ; Oliveira, Roney Berti de ; Chicati, Marcelo Luiz; Demattê, José Alexandre Melo ; Nanni, Marcos Rafael
Source: Plants. Unidade: ESALQ
Subjects: ALGORITMOS, COMPOSIÇÃO QUÍMICA, ESPECTROSCOPIA, FOLHAS (PLANTAS), GERÂNIO, HIBISCO, MÍNIMOS QUADRADOS, MODELOS MATEMÁTICOS, QUIMIOMETRIA
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FALCIONI, Renan et al. Chemometric analysis for the prediction of biochemical compounds in leaves using UV-VIS-NIR-SWIR hyperspectroscopy. Plants, v. 12, p. 1-24, 2023Tradução . . Disponível em: https://doi.org/10.3390/plants12193424. Acesso em: 03 nov. 2024.
APA
Falcioni, R., Gonçalves, J. V. F., Oliveira, K. M. de, Oliveira, C. A. de, Reis, A. S., Crusiol, L. G. T., et al. (2023). Chemometric analysis for the prediction of biochemical compounds in leaves using UV-VIS-NIR-SWIR hyperspectroscopy. Plants, 12, 1-24. doi:10.3390/plants12193424
NLM
Falcioni R, Gonçalves JVF, Oliveira KM de, Oliveira CA de, Reis AS, Crusiol LGT, Furlanetto RH, Antunes WC, Cezar E, Oliveira RB de, Chicati ML, Demattê JAM, Nanni MR. Chemometric analysis for the prediction of biochemical compounds in leaves using UV-VIS-NIR-SWIR hyperspectroscopy [Internet]. Plants. 2023 ; 12 1-24.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/plants12193424
Vancouver
Falcioni R, Gonçalves JVF, Oliveira KM de, Oliveira CA de, Reis AS, Crusiol LGT, Furlanetto RH, Antunes WC, Cezar E, Oliveira RB de, Chicati ML, Demattê JAM, Nanni MR. Chemometric analysis for the prediction of biochemical compounds in leaves using UV-VIS-NIR-SWIR hyperspectroscopy [Internet]. Plants. 2023 ; 12 1-24.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/plants12193424
Artigo de periodico
Spectral mixture modeling of an ASTER bare soil synthetic image using a representative spectral library to map soils in central-Brazil (2023)
Novais, Jean Jesus ; Poppiel, Raul Roberto ; Lacerda, Marilusa P. C ; Oliveira, Manuel P; Demattê, José Alexandre Melo
Source: AgriEngineering. Unidade: ESALQ
Subjects: ALGORITMOS, CLASSIFICAÇÃO DO SOLO, ESPECTROSCOPIA, MAPEAMENTO DO SOLO, MODELAGEM DE DADOS, PEDOLOGIA
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NOVAIS, Jean Jesus et al. Spectral mixture modeling of an ASTER bare soil synthetic image using a representative spectral library to map soils in central-Brazil. AgriEngineering, v. 5, p. 156-172, 2023Tradução . . Disponível em: https://doi.org/10.3390/agriengineering5010011. Acesso em: 03 nov. 2024.
APA
Novais, J. J., Poppiel, R. R., Lacerda, M. P. C., Oliveira, M. P., & Demattê, J. A. M. (2023). Spectral mixture modeling of an ASTER bare soil synthetic image using a representative spectral library to map soils in central-Brazil. AgriEngineering, 5, 156-172. doi:10.3390/agriengineering5010011
NLM
Novais JJ, Poppiel RR, Lacerda MPC, Oliveira MP, Demattê JAM. Spectral mixture modeling of an ASTER bare soil synthetic image using a representative spectral library to map soils in central-Brazil [Internet]. AgriEngineering. 2023 ; 5 156-172.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/agriengineering5010011
Vancouver
Novais JJ, Poppiel RR, Lacerda MPC, Oliveira MP, Demattê JAM. Spectral mixture modeling of an ASTER bare soil synthetic image using a representative spectral library to map soils in central-Brazil [Internet]. AgriEngineering. 2023 ; 5 156-172.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/agriengineering5010011
Artigo de periodico
Non−invasive assessment, classification, and prediction of biophysical parameters using reflectance hyperspectroscopy (2023)
Falcioni, Renan ; Santos, Glaucio Leboso Alemparte Abrantes dos ; Crusiol, Luis Guilherme Teixeira; Antunes, Werner Camargos ; Chicati, Marcelo Luiz ; Oliveira, Roney Berti de ; Demattê, José Alexandre Melo ; Nanni, Marcos Rafael
Source: Plants. Unidade: ESALQ
Subjects: ALGORITMOS, ANÁLISE MULTIVARIADA, CRESCIMENTO VEGETAL, ESPECTROSCOPIA INFRAVERMELHA, FUMO, MÍNIMOS QUADRADOS, MORFOLOGIA VEGETAL, SENSORIAMENTO REMOTO
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FALCIONI, Renan et al. Non−invasive assessment, classification, and prediction of biophysical parameters using reflectance hyperspectroscopy. Plants, v. 12, n. 13, p. 1-27, 2023Tradução . . Disponível em: https://doi.org/10.3390/plants12132526. Acesso em: 03 nov. 2024.
APA
Falcioni, R., Santos, G. L. A. A. dos, Crusiol, L. G. T., Antunes, W. C., Chicati, M. L., Oliveira, R. B. de, et al. (2023). Non−invasive assessment, classification, and prediction of biophysical parameters using reflectance hyperspectroscopy. Plants, 12( 13), 1-27. doi:10.3390/plants12132526
NLM
Falcioni R, Santos GLAA dos, Crusiol LGT, Antunes WC, Chicati ML, Oliveira RB de, Demattê JAM, Nanni MR. Non−invasive assessment, classification, and prediction of biophysical parameters using reflectance hyperspectroscopy [Internet]. Plants. 2023 ; 12( 13): 1-27.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/plants12132526
Vancouver
Falcioni R, Santos GLAA dos, Crusiol LGT, Antunes WC, Chicati ML, Oliveira RB de, Demattê JAM, Nanni MR. Non−invasive assessment, classification, and prediction of biophysical parameters using reflectance hyperspectroscopy [Internet]. Plants. 2023 ; 12( 13): 1-27.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/plants12132526
Artigo de periodico
Validation of hiG-flow software for simulating two-phase flows with a 3D geometric volume of fluid algorithm (2023)
Silva, Aquisson Theyllon Gomes da ; Fernandes, Célio ; Organista, Juniormar ; Souza, Leandro Franco de ; Castelo, Antonio
Source: Mathematics. Unidade: ICMC
Subjects: ESCOAMENTO BIFÁSICO, MÉTODOS NUMÉRICOS EM DINÂMICA DE FLUÍDOS, ALGORITMOS
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SILVA, Aquisson Theyllon Gomes da et al. Validation of hiG-flow software for simulating two-phase flows with a 3D geometric volume of fluid algorithm. Mathematics, v. 11, n. 18, p. 1-21, 2023Tradução . . Disponível em: https://doi.org/10.3390/math11183900. Acesso em: 03 nov. 2024.
APA
Silva, A. T. G. da, Fernandes, C., Organista, J., Souza, L. F. de, & Castelo, A. (2023). Validation of hiG-flow software for simulating two-phase flows with a 3D geometric volume of fluid algorithm. Mathematics, 11( 18), 1-21. doi:10.3390/math11183900
NLM
Silva ATG da, Fernandes C, Organista J, Souza LF de, Castelo A. Validation of hiG-flow software for simulating two-phase flows with a 3D geometric volume of fluid algorithm [Internet]. Mathematics. 2023 ; 11( 18): 1-21.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/math11183900
Vancouver
Silva ATG da, Fernandes C, Organista J, Souza LF de, Castelo A. Validation of hiG-flow software for simulating two-phase flows with a 3D geometric volume of fluid algorithm [Internet]. Mathematics. 2023 ; 11( 18): 1-21.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/math11183900
Artigo de periodico
Bayesian multi-targets strategy to track Apis mellifera movements at colony level (2022)
Oliveira Júnior, Jordão Natal de; Santos, Jônatas Cruz; Jumbo, Luis O. Viteri; Almeida, Carlos H. S.; Toledo, Pedro F. S.; Rezende, Sarah M.; Haddi, Khalid; Santana, Weyder C.; Bessani, Michel; Achcar, Jorge Alberto ; Oliveira, Eugenio E.; Maciel, Carlos Dias
Source: Insects. Unidades: FMRP, EESC
Subjects: ENTROPIA, ABELHAS, PESTICIDAS, COLÔNIAS (BIOLOGIA), ALGORITMOS, INFERÊNCIA BAYESIANA
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OLIVEIRA JÚNIOR, Jordão Natal de et al. Bayesian multi-targets strategy to track Apis mellifera movements at colony level. Insects, v. 13, n. 2, p. 1-12, 2022Tradução . . Disponível em: https://doi.org/10.3390/insects13020181. Acesso em: 03 nov. 2024.
APA
Oliveira Júnior, J. N. de, Santos, J. C., Jumbo, L. O. V., Almeida, C. H. S., Toledo, P. F. S., Rezende, S. M., et al. (2022). Bayesian multi-targets strategy to track Apis mellifera movements at colony level. Insects, 13( 2), 1-12. doi:10.3390/insects13020181
NLM
Oliveira Júnior JN de, Santos JC, Jumbo LOV, Almeida CHS, Toledo PFS, Rezende SM, Haddi K, Santana WC, Bessani M, Achcar JA, Oliveira EE, Maciel CD. Bayesian multi-targets strategy to track Apis mellifera movements at colony level [Internet]. Insects. 2022 ; 13( 2): 1-12.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/insects13020181
Vancouver
Oliveira Júnior JN de, Santos JC, Jumbo LOV, Almeida CHS, Toledo PFS, Rezende SM, Haddi K, Santana WC, Bessani M, Achcar JA, Oliveira EE, Maciel CD. Bayesian multi-targets strategy to track Apis mellifera movements at colony level [Internet]. Insects. 2022 ; 13( 2): 1-12.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/insects13020181
Artigo de periodico
Recommender system based on collaborative filtering for personalized dietary advice: a cross-sectional analysis of the ELSA-Brasil Study (2022)
Silva, Vanderlei Carneiro da; Gorgulho, Bartira; Marchioni, Dirce Maria Lobo ; Alvim, Sheila Maria; Giatti, Luana; Araujo, Tânia Aparecida de; Alonso, Angelica Castilho; Santos, Itamar de Souza ; Lotufo, Paulo Andrade ; Benseñor, Isabela Judith Martins
Source: International Journal of Environmental Research and Public Health. Unidades: HU, FM
Subjects: SISTEMAS DE RECOMENDAÇÃO, DIETA, ALGORITMOS
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SILVA, Vanderlei Carneiro da et al. Recommender system based on collaborative filtering for personalized dietary advice: a cross-sectional analysis of the ELSA-Brasil Study. International Journal of Environmental Research and Public Health, v. no 2022, n. 22, p. 14934, 2022Tradução . . Disponível em: https://doi.org/10.3390/ijerph192214934. Acesso em: 03 nov. 2024.
APA
Silva, V. C. da, Gorgulho, B., Marchioni, D. M. L., Alvim, S. M., Giatti, L., Araujo, T. A. de, et al. (2022). Recommender system based on collaborative filtering for personalized dietary advice: a cross-sectional analysis of the ELSA-Brasil Study. International Journal of Environmental Research and Public Health, no 2022( 22), 14934. doi:10.3390/ijerph192214934
NLM
Silva VC da, Gorgulho B, Marchioni DML, Alvim SM, Giatti L, Araujo TA de, Alonso AC, Santos I de S, Lotufo PA, Benseñor IJM. Recommender system based on collaborative filtering for personalized dietary advice: a cross-sectional analysis of the ELSA-Brasil Study [Internet]. International Journal of Environmental Research and Public Health. 2022 ; no 2022( 22): 14934.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/ijerph192214934
Vancouver
Silva VC da, Gorgulho B, Marchioni DML, Alvim SM, Giatti L, Araujo TA de, Alonso AC, Santos I de S, Lotufo PA, Benseñor IJM. Recommender system based on collaborative filtering for personalized dietary advice: a cross-sectional analysis of the ELSA-Brasil Study [Internet]. International Journal of Environmental Research and Public Health. 2022 ; no 2022( 22): 14934.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/ijerph192214934
Artigo de periodico
Distribution network reconfiguration considering voltage and current unbalance indexes and variable demand solved through a selective bio-inspired metaheuristic (2022)
Gerez, Cassio; Costa, Eduardo Coelho Marques da; Sguarezi Filho, Alfeu Joãozinho
Source: Energies. Unidade: EP
Subjects: REDES DE DISTRIBUIÇÃO DE ENERGIA ELÉTRICA, ALGORITMOS
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GEREZ, Cassio e COSTA, Eduardo Coelho Marques da e SGUAREZI FILHO, Alfeu Joãozinho. Distribution network reconfiguration considering voltage and current unbalance indexes and variable demand solved through a selective bio-inspired metaheuristic. Energies, v. 15, n. 6, p. 1-27, 2022Tradução . . Disponível em: https://doi.org/10.3390/en15051686. Acesso em: 03 nov. 2024.
APA
Gerez, C., Costa, E. C. M. da, & Sguarezi Filho, A. J. (2022). Distribution network reconfiguration considering voltage and current unbalance indexes and variable demand solved through a selective bio-inspired metaheuristic. Energies, 15( 6), 1-27. doi:10.3390/en15051686
NLM
Gerez C, Costa ECM da, Sguarezi Filho AJ. Distribution network reconfiguration considering voltage and current unbalance indexes and variable demand solved through a selective bio-inspired metaheuristic [Internet]. Energies. 2022 ; 15( 6): 1-27.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/en15051686
Vancouver
Gerez C, Costa ECM da, Sguarezi Filho AJ. Distribution network reconfiguration considering voltage and current unbalance indexes and variable demand solved through a selective bio-inspired metaheuristic [Internet]. Energies. 2022 ; 15( 6): 1-27.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/en15051686
Artigo de periodico
Data collection in an IoT off-grid environment systematic mapping of literature (2022)
Goulart, Ademir ; Pinto, Alex Sandro Roschildt ; Boava, Adão; Branco, Kalinka Regina Lucas Jaquie Castelo
Source: Sensors. Unidade: ICMC
Subjects: REVISÃO SISTEMÁTICA, ALGORITMOS, INTERNET DAS COISAS
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GOULART, Ademir et al. Data collection in an IoT off-grid environment systematic mapping of literature. Sensors, v. 22, n. 14, p. 1-12, 2022Tradução . . Disponível em: https://doi.org/10.3390/s22145374. Acesso em: 03 nov. 2024.
APA
Goulart, A., Pinto, A. S. R., Boava, A., & Branco, K. R. L. J. C. (2022). Data collection in an IoT off-grid environment systematic mapping of literature. Sensors, 22( 14), 1-12. doi:10.3390/s22145374
NLM
Goulart A, Pinto ASR, Boava A, Branco KRLJC. Data collection in an IoT off-grid environment systematic mapping of literature [Internet]. Sensors. 2022 ; 22( 14): 1-12.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/s22145374
Vancouver
Goulart A, Pinto ASR, Boava A, Branco KRLJC. Data collection in an IoT off-grid environment systematic mapping of literature [Internet]. Sensors. 2022 ; 22( 14): 1-12.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/s22145374
Trabalho de evento-anais periodico
Extreme learning machine to graph convolutional networks (2022)
Gonçalves, Thales ; Rodrigues, Francisco Aparecido
Source: Lecture Notes in Artificial Intelligence. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS, ALGORITMOS
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GONÇALVES, Thales e RODRIGUES, Francisco Aparecido. Extreme learning machine to graph convolutional networks. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-21689-3_42. Acesso em: 03 nov. 2024. , 2022
APA
Gonçalves, T., & Rodrigues, F. A. (2022). Extreme learning machine to graph convolutional networks. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-21689-3_42
NLM
Gonçalves T, Rodrigues FA. Extreme learning machine to graph convolutional networks [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13654 601-615.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1007/978-3-031-21689-3_42
Vancouver
Gonçalves T, Rodrigues FA. Extreme learning machine to graph convolutional networks [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13654 601-615.[citado 2024 nov. 03 ] Available from: https://doi.org/10.1007/978-3-031-21689-3_42
Artigo de periodico
An alternative promotion time cure model with overdispersed number of competing causes: an application to melanoma data (2021)
Gallardo, Diego I. ; Castro, Mário de ; Gómez, Héctor W
Source: Mathematics. Unidade: ICMC
Subjects: ANÁLISE DE SOBREVIVÊNCIA, ALGORITMOS, PROBABILIDADE APLICADA
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GALLARDO, Diego I. e CASTRO, Mário de e GÓMEZ, Héctor W. An alternative promotion time cure model with overdispersed number of competing causes: an application to melanoma data. Mathematics, v. 9, n. 15, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.3390/math9151815. Acesso em: 03 nov. 2024.
APA
Gallardo, D. I., Castro, M. de, & Gómez, H. W. (2021). An alternative promotion time cure model with overdispersed number of competing causes: an application to melanoma data. Mathematics, 9( 15), 1-14. doi:10.3390/math9151815
NLM
Gallardo DI, Castro M de, Gómez HW. An alternative promotion time cure model with overdispersed number of competing causes: an application to melanoma data [Internet]. Mathematics. 2021 ; 9( 15): 1-14.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/math9151815
Vancouver
Gallardo DI, Castro M de, Gómez HW. An alternative promotion time cure model with overdispersed number of competing causes: an application to melanoma data [Internet]. Mathematics. 2021 ; 9( 15): 1-14.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/math9151815
Artigo de periodico
MoHRiPA: an architecture for hybrid resources management of private cloud environments (2021)
Andreazi, Gabriel Tomiatti; Estrella, Júlio Cezar ; Bruschi, Sarita Mazzini ; Immich, Roger ; Guidoni, Daniel Ludovico ; Pereira Júnior, Lourenço Alves ; Meneguette, Rodolfo Ipolito
Source: Sensors. Unidade: ICMC
Subjects: COMPUTAÇÃO EM NUVEM, AVALIAÇÃO DE DESEMPENHO, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ANDREAZI, Gabriel Tomiatti et al. MoHRiPA: an architecture for hybrid resources management of private cloud environments. Sensors, v. 21, p. 1-17, 2021Tradução . . Disponível em: https://doi.org/10.3390/s21206857. Acesso em: 03 nov. 2024.
APA
Andreazi, G. T., Estrella, J. C., Bruschi, S. M., Immich, R., Guidoni, D. L., Pereira Júnior, L. A., & Meneguette, R. I. (2021). MoHRiPA: an architecture for hybrid resources management of private cloud environments. Sensors, 21, 1-17. doi:10.3390/s21206857
NLM
Andreazi GT, Estrella JC, Bruschi SM, Immich R, Guidoni DL, Pereira Júnior LA, Meneguette RI. MoHRiPA: an architecture for hybrid resources management of private cloud environments [Internet]. Sensors. 2021 ; 21 1-17.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/s21206857
Vancouver
Andreazi GT, Estrella JC, Bruschi SM, Immich R, Guidoni DL, Pereira Júnior LA, Meneguette RI. MoHRiPA: an architecture for hybrid resources management of private cloud environments [Internet]. Sensors. 2021 ; 21 1-17.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/s21206857
Artigo de periodico
Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach (2021)
Naimi, Salman ; Ayoubi, Shamsollah ; Zeraatpisheh, Mojtaba ; Dematte, Jose Alexandre Melo
Source: Remote Sensing. Unidade: ESALQ
Subjects: SALINIDADE DO SOLO, MAPEAMENTO DO SOLO, SENSORIAMENTO REMOTO, APRENDIZADO COMPUTACIONAL, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NAIMI, Salman et al. Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach. Remote Sensing, v. 13, p. 1-21, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13234825. Acesso em: 03 nov. 2024.
APA
Naimi, S., Ayoubi, S., Zeraatpisheh, M., & Dematte, J. A. M. (2021). Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach. Remote Sensing, 13, 1-21. doi:10.3390/rs13234825
NLM
Naimi S, Ayoubi S, Zeraatpisheh M, Dematte JAM. Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach [Internet]. Remote Sensing. 2021 ; 13 1-21.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/rs13234825
Vancouver
Naimi S, Ayoubi S, Zeraatpisheh M, Dematte JAM. Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach [Internet]. Remote Sensing. 2021 ; 13 1-21.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/rs13234825
Artigo de periodico
Multiscale entropy analysis of short signals: the robustness of fuzzy entropy-based variants compared to full-length long signals (2021)
Borin Junior, Airton Monte Serrat; Humeau-Heurtier, Anne; Silva, Luiz Eduardo Virgilio da ; Murta Júnior, Luiz Otávio
Source: Entropy. Unidades: FFCLRP, FMRP
Subjects: ENTROPIA, ANÁLISE DE SÉRIES TEMPORAIS, ALGORITMOS, FREQUÊNCIA CARDÍACA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BORIN JUNIOR, Airton Monte Serrat et al. Multiscale entropy analysis of short signals: the robustness of fuzzy entropy-based variants compared to full-length long signals. Entropy, v. 23, n. 12, p. 1-15, 2021Tradução . . Disponível em: https://doi.org/10.3390/e23121620. Acesso em: 03 nov. 2024.
APA
Borin Junior, A. M. S., Humeau-Heurtier, A., Silva, L. E. V. da, & Murta Júnior, L. O. (2021). Multiscale entropy analysis of short signals: the robustness of fuzzy entropy-based variants compared to full-length long signals. Entropy, 23( 12), 1-15. doi:10.3390/e23121620
NLM
Borin Junior AMS, Humeau-Heurtier A, Silva LEV da, Murta Júnior LO. Multiscale entropy analysis of short signals: the robustness of fuzzy entropy-based variants compared to full-length long signals [Internet]. Entropy. 2021 ; 23( 12): 1-15.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/e23121620
Vancouver
Borin Junior AMS, Humeau-Heurtier A, Silva LEV da, Murta Júnior LO. Multiscale entropy analysis of short signals: the robustness of fuzzy entropy-based variants compared to full-length long signals [Internet]. Entropy. 2021 ; 23( 12): 1-15.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/e23121620
Artigo de periodico
Integrating audio signal processing and deep learning algorithms for gait pattern classification in Brazilian gaited horses (2021)
Alves, Anderson Antonio Carvalho; Andrietta, Lucas Tassoni ; Lopes, Rafael Zinni; Bussiman, Fernando de Oliveira ; Silva, Fabyano Fonseca e ; Carvalheiro, Roberto ; Brito, Luiz Fernando ; Balieiro, Júlio Cesar de Carvalho ; Albuquerque, Lúcia Galvão de ; Ventura, Ricardo Vieira
Source: Frontiers in Animal Science. Unidade: FMVZ
Subjects: ALGORITMOS, CAVALO DE MARCHA, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALVES, Anderson Antonio Carvalho et al. Integrating audio signal processing and deep learning algorithms for gait pattern classification in Brazilian gaited horses. Frontiers in Animal Science, v. 2, p. 1-19, 2021Tradução . . Disponível em: https://doi.org/10.3389/fanim.2021.681557. Acesso em: 03 nov. 2024.
APA
Alves, A. A. C., Andrietta, L. T., Lopes, R. Z., Bussiman, F. de O., Silva, F. F. e, Carvalheiro, R., et al. (2021). Integrating audio signal processing and deep learning algorithms for gait pattern classification in Brazilian gaited horses. Frontiers in Animal Science, 2, 1-19. doi:10.3389/fanim.2021.681557
NLM
Alves AAC, Andrietta LT, Lopes RZ, Bussiman F de O, Silva FF e, Carvalheiro R, Brito LF, Balieiro JC de C, Albuquerque LG de, Ventura RV. Integrating audio signal processing and deep learning algorithms for gait pattern classification in Brazilian gaited horses [Internet]. Frontiers in Animal Science. 2021 ; 2 1-19.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3389/fanim.2021.681557
Vancouver
Alves AAC, Andrietta LT, Lopes RZ, Bussiman F de O, Silva FF e, Carvalheiro R, Brito LF, Balieiro JC de C, Albuquerque LG de, Ventura RV. Integrating audio signal processing and deep learning algorithms for gait pattern classification in Brazilian gaited horses [Internet]. Frontiers in Animal Science. 2021 ; 2 1-19.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3389/fanim.2021.681557
Artigo de periodico
Challenges and opportunities for integrating in silico models and adverse outcomes pathways to set and relate new biomarkers (2020)
Santos, Carlos Eduardo Matos dos; Miranda, Raul Ghiraldelli ; Oliveira, Danielle Palma de ; Dorta, Daniel Junqueira
Source: Water. Unidades: FCFRP, FFCLRP
Subjects: TOXICOLOGIA, PRODUTOS QUÍMICOS, ALGORITMOS, INTELIGÊNCIA ARTIFICIAL, ECOTOXICOLOGIA, BIOMARCADORES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Carlos Eduardo Matos dos et al. Challenges and opportunities for integrating in silico models and adverse outcomes pathways to set and relate new biomarkers. Water, v. 12, n. 12, 2020Tradução . . Disponível em: https://doi.org/10.3390/w12123549. Acesso em: 03 nov. 2024.
APA
Santos, C. E. M. dos, Miranda, R. G., Oliveira, D. P. de, & Dorta, D. J. (2020). Challenges and opportunities for integrating in silico models and adverse outcomes pathways to set and relate new biomarkers. Water, 12( 12). doi:10.3390/w12123549
NLM
Santos CEM dos, Miranda RG, Oliveira DP de, Dorta DJ. Challenges and opportunities for integrating in silico models and adverse outcomes pathways to set and relate new biomarkers [Internet]. Water. 2020 ; 12( 12):[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/w12123549
Vancouver
Santos CEM dos, Miranda RG, Oliveira DP de, Dorta DJ. Challenges and opportunities for integrating in silico models and adverse outcomes pathways to set and relate new biomarkers [Internet]. Water. 2020 ; 12( 12):[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/w12123549
Artigo de periodico
Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer (2020)
Valieris, Renan; Amaro, Lucas; Osório, Cynthia Aparecida Bueno de Toledo; Bueno, Adriana Passos; Mitrowsky, Rafael Andrés Rosales ; Carraro, Dirce Maria; Nunes, Diana Noronha; Dias-Neto, Emmanuel; Silva, Israel Tojal da
Source: Cancers. Unidade: FFCLRP
Subjects: BIOMARCADORES, NEOPLASIAS, DNA, APRENDIZADO COMPUTACIONAL, TERAPÊUTICA MÉDICA, ALGORITMOS, CARCINOGÊNESE, IMUNOTERAPIA, RECOMBINAÇÃO GENÉTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VALIERIS, Renan et al. Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer. Cancers, v. 12, n. 12, p. 1-12, 2020Tradução . . Disponível em: https://doi.org/10.3390/cancers12123687. Acesso em: 03 nov. 2024.
APA
Valieris, R., Amaro, L., Osório, C. A. B. de T., Bueno, A. P., Mitrowsky, R. A. R., Carraro, D. M., et al. (2020). Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer. Cancers, 12( 12), 1-12. doi:10.3390/cancers12123687
NLM
Valieris R, Amaro L, Osório CAB de T, Bueno AP, Mitrowsky RAR, Carraro DM, Nunes DN, Dias-Neto E, Silva IT da. Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer [Internet]. Cancers. 2020 ; 12( 12): 1-12.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/cancers12123687
Vancouver
Valieris R, Amaro L, Osório CAB de T, Bueno AP, Mitrowsky RAR, Carraro DM, Nunes DN, Dias-Neto E, Silva IT da. Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer [Internet]. Cancers. 2020 ; 12( 12): 1-12.[citado 2024 nov. 03 ] Available from: https://doi.org/10.3390/cancers12123687

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