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Trabalho de evento
APEHR: automated prognosis in electronic health records using multi-head self-attention (2021)
Florez, Alexander Ylnner Choquenaira ; Scabora, Lucas de Carvalho ; Eler, Danilo Medeiros ; Rodrigues Junior, José Fernando
Fonte: Proceedings. Nome do evento: International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, TECNOLOGIAS DA SAÚDE, PROGNÓSTICO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FLOREZ, Alexander Ylnner Choquenaira et al. APEHR: automated prognosis in electronic health records using multi-head self-attention. 2021, Anais.. Los Alamitos: IEEE, 2021. Disponível em: https://doi.org/10.1109/CBMS52027.2021.00077. Acesso em: 05 out. 2024.
APA
Florez, A. Y. C., Scabora, L. de C., Eler, D. M., & Rodrigues Junior, J. F. (2021). APEHR: automated prognosis in electronic health records using multi-head self-attention. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS52027.2021.00077
NLM
Florez AYC, Scabora L de C, Eler DM, Rodrigues Junior JF. APEHR: automated prognosis in electronic health records using multi-head self-attention [Internet]. Proceedings. 2021 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1109/CBMS52027.2021.00077
Vancouver
Florez AYC, Scabora L de C, Eler DM, Rodrigues Junior JF. APEHR: automated prognosis in electronic health records using multi-head self-attention [Internet]. Proceedings. 2021 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1109/CBMS52027.2021.00077
Trabalho de evento
Augmentation techniques for sequential clinical data to improve deep learning prediction techniques (2020)
Florez, Alexander Ylnner Choquenaira; Scabora, Lucas de Carvalho ; Amer-Yahia, Sihem ; Rodrigues Junior, José Fernando
Fonte: Proceedings. Nome do evento: IEEE International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Assuntos: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, BANCO DE DADOS, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), TECNOLOGIAS DA SAÚDE, PROGNÓSTICO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FLOREZ, Alexander Ylnner Choquenaira et al. Augmentation techniques for sequential clinical data to improve deep learning prediction techniques. 2020, Anais.. Los Alamitos: IEEE, 2020. Disponível em: https://doi.org/10.1109/CBMS49503.2020.00118. Acesso em: 05 out. 2024.
APA
Florez, A. Y. C., Scabora, L. de C., Amer-Yahia, S., & Rodrigues Junior, J. F. (2020). Augmentation techniques for sequential clinical data to improve deep learning prediction techniques. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS49503.2020.00118
NLM
Florez AYC, Scabora L de C, Amer-Yahia S, Rodrigues Junior JF. Augmentation techniques for sequential clinical data to improve deep learning prediction techniques [Internet]. Proceedings. 2020 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1109/CBMS49503.2020.00118
Vancouver
Florez AYC, Scabora L de C, Amer-Yahia S, Rodrigues Junior JF. Augmentation techniques for sequential clinical data to improve deep learning prediction techniques [Internet]. Proceedings. 2020 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1109/CBMS49503.2020.00118
Trabalho de evento
Experimental evaluation of accuracy of most common machine learning models using pulsar data set (2020)
Florez, Alexander Ylnner Choquenaira ; Vinces, Braulio Valentin Sánchez ; Arroyo, Diana Carolina Roca ; Saire, Josimar Edinson Chire ; Franco, Patrícia Batista
Fonte: Proceddings. Nome do evento: International Conference of Digital Transformation and Innovation Technology - Incodtrin. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, PULSARES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FLOREZ, Alexander Ylnner Choquenaira et al. Experimental evaluation of accuracy of most common machine learning models using pulsar data set. 2020, Anais.. Los Alamitos: IEEE, 2020. Disponível em: https://doi.org/10.1109/Incodtrin51881.2020.00027. Acesso em: 05 out. 2024.
APA
Florez, A. Y. C., Vinces, B. V. S., Arroyo, D. C. R., Saire, J. E. C., & Franco, P. B. (2020). Experimental evaluation of accuracy of most common machine learning models using pulsar data set. In Proceddings. Los Alamitos: IEEE. doi:10.1109/Incodtrin51881.2020.00027
NLM
Florez AYC, Vinces BVS, Arroyo DCR, Saire JEC, Franco PB. Experimental evaluation of accuracy of most common machine learning models using pulsar data set [Internet]. Proceddings. 2020 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1109/Incodtrin51881.2020.00027
Vancouver
Florez AYC, Vinces BVS, Arroyo DCR, Saire JEC, Franco PB. Experimental evaluation of accuracy of most common machine learning models using pulsar data set [Internet]. Proceddings. 2020 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1109/Incodtrin51881.2020.00027
Trabalho de evento
Echo state network performance analysis using non-random topologies (2020)
Arroyo, Diana Carolina Roca ; Florez, Alexander Ylnner Choquenaira ; Flores, Daniela Milón ; Romero, Roseli Aparecida Francelin ; Liang, Zhao
Fonte: Proceedings. Nome do evento: IEEE Colombian Conference on Applications of Computational Intelligence - ColCACI. Unidades: ICMC, FFCLRP
Assuntos: SISTEMAS DINÂMICOS, ANÁLISE DE SÉRIES TEMPORAIS, REDES COMPLEXAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ARROYO, Diana Carolina Roca et al. Echo state network performance analysis using non-random topologies. 2020, Anais.. Danvers: IEEE, 2020. Disponível em: https://doi.org/10.1109/ColCACI50549.2020.9248714. Acesso em: 05 out. 2024.
APA
Arroyo, D. C. R., Florez, A. Y. C., Flores, D. M., Romero, R. A. F., & Liang, Z. (2020). Echo state network performance analysis using non-random topologies. In Proceedings. Danvers: IEEE. doi:10.1109/ColCACI50549.2020.9248714
NLM
Arroyo DCR, Florez AYC, Flores DM, Romero RAF, Liang Z. Echo state network performance analysis using non-random topologies [Internet]. Proceedings. 2020 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1109/ColCACI50549.2020.9248714
Vancouver
Arroyo DCR, Florez AYC, Flores DM, Romero RAF, Liang Z. Echo state network performance analysis using non-random topologies [Internet]. Proceedings. 2020 ;[citado 2024 out. 05 ] Available from: https://doi.org/10.1109/ColCACI50549.2020.9248714
Artigo de periodico
Analysis of ENEM's attendants between 2012 and 2017 using a clustering approach (2020)
Lima, Afonso Matheus Sousa; Florez, Alexander Ylnner Choquenaira ; Lescano, Alexis Ivan Aspauza; Novaes, João Victor de Oliveira; Martins, Natalia de Fatima; Traina Junior, Caetano ; Sousa, Elaine Parros Machado de ; Rodrigues Junior, José Fernando ; Cordeiro, Robson Leonardo Ferreira
Fonte: Journal of Information and Data Management - JIDM. Unidade: ICMC
Assuntos: BANCO DE DADOS RELACIONAIS, DESCOBERTA DE CONHECIMENTO, RECONHECIMENTO DE PADRÕES, VESTIBULAR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LIMA, Afonso Matheus Sousa et al. Analysis of ENEM's attendants between 2012 and 2017 using a clustering approach. Journal of Information and Data Management - JIDM, v. 11, n. 2, p. 115-130, 2020Tradução . . Disponível em: https://periodicos.ufmg.br/index.php/jidm/article/view/24835. Acesso em: 05 out. 2024.
APA
Lima, A. M. S., Florez, A. Y. C., Lescano, A. I. A., Novaes, J. V. de O., Martins, N. de F., Traina Junior, C., et al. (2020). Analysis of ENEM's attendants between 2012 and 2017 using a clustering approach. Journal of Information and Data Management - JIDM, 11( 2), 115-130. Recuperado de https://periodicos.ufmg.br/index.php/jidm/article/view/24835
NLM
Lima AMS, Florez AYC, Lescano AIA, Novaes JV de O, Martins N de F, Traina Junior C, Sousa EPM de, Rodrigues Junior JF, Cordeiro RLF. Analysis of ENEM's attendants between 2012 and 2017 using a clustering approach [Internet]. Journal of Information and Data Management - JIDM. 2020 ; 11( 2): 115-130.[citado 2024 out. 05 ] Available from: https://periodicos.ufmg.br/index.php/jidm/article/view/24835
Vancouver
Lima AMS, Florez AYC, Lescano AIA, Novaes JV de O, Martins N de F, Traina Junior C, Sousa EPM de, Rodrigues Junior JF, Cordeiro RLF. Analysis of ENEM's attendants between 2012 and 2017 using a clustering approach [Internet]. Journal of Information and Data Management - JIDM. 2020 ; 11( 2): 115-130.[citado 2024 out. 05 ] Available from: https://periodicos.ufmg.br/index.php/jidm/article/view/24835

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