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Trabalho de evento
Satellite imagery predicts socioeconomic indicators: experiments in Brazil (2023)
Silva, João Pedro; Rodrigues Junior, José Fernando
Source: Proceedings. Conference titles: International Conference on Transdisciplinary AI - TransAI. Unidade: ICMC
Subjects: APRENDIZAGEM PROFUNDA, REDES NEURAIS, RECONHECIMENTO DE IMAGEM, INDICADORES SOCIOECONÔMICOS
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ABNT
SILVA, João Pedro e RODRIGUES JUNIOR, José Fernando. Satellite imagery predicts socioeconomic indicators: experiments in Brazil. 2023, Anais.. Los Alamitos: IEEE, 2023. Disponível em: https://doi.org/10.1109/TransAI60598.2023.00008. Acesso em: 04 jul. 2024.
APA
Silva, J. P., & Rodrigues Junior, J. F. (2023). Satellite imagery predicts socioeconomic indicators: experiments in Brazil. In Proceedings. Los Alamitos: IEEE. doi:10.1109/TransAI60598.2023.00008
NLM
Silva JP, Rodrigues Junior JF. Satellite imagery predicts socioeconomic indicators: experiments in Brazil [Internet]. Proceedings. 2023 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/TransAI60598.2023.00008
Vancouver
Silva JP, Rodrigues Junior JF. Satellite imagery predicts socioeconomic indicators: experiments in Brazil [Internet]. Proceedings. 2023 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/TransAI60598.2023.00008
Artigo de periodico
Pay attention to evolution: time series forecasting with deep graph-evolution learning (2022)
Spadon, Gabriel ; Hong, Shenda ; Machado, Bruno Brandoli ; Matwin, Stan ; Rodrigues Junior, José Fernando ; Sun, Jimeng
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence - TPAM. Unidade: ICMC
Subjects: PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), APRENDIZADO COMPUTACIONAL, REDES NEURAIS, TEORIA DOS GRAFOS
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ABNT
SPADON, Gabriel et al. Pay attention to evolution: time series forecasting with deep graph-evolution learning. IEEE Transactions on Pattern Analysis and Machine Intelligence - TPAM, v. 44, n. 9, p. Se 2022, 2022Tradução . . Disponível em: https://doi.org/10.1109/TPAMI.2021.3076155. Acesso em: 04 jul. 2024.
APA
Spadon, G., Hong, S., Machado, B. B., Matwin, S., Rodrigues Junior, J. F., & Sun, J. (2022). Pay attention to evolution: time series forecasting with deep graph-evolution learning. IEEE Transactions on Pattern Analysis and Machine Intelligence - TPAM, 44( 9), Se 2022. doi:10.1109/TPAMI.2021.3076155
NLM
Spadon G, Hong S, Machado BB, Matwin S, Rodrigues Junior JF, Sun J. Pay attention to evolution: time series forecasting with deep graph-evolution learning [Internet]. IEEE Transactions on Pattern Analysis and Machine Intelligence - TPAM. 2022 ; 44( 9): Se 2022.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/TPAMI.2021.3076155
Vancouver
Spadon G, Hong S, Machado BB, Matwin S, Rodrigues Junior JF, Sun J. Pay attention to evolution: time series forecasting with deep graph-evolution learning [Internet]. IEEE Transactions on Pattern Analysis and Machine Intelligence - TPAM. 2022 ; 44( 9): Se 2022.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/TPAMI.2021.3076155
Trabalho de evento-anais periodico
Full motion focus: convolutional module for improved left ventricle segmentation over 4D MRI (2022)
Lima, Daniel Mário de ; Graves, Catharine de Vita ; Gutierrez, Marco Antonio ; Machado, Bruno Brandoli ; Rodrigues Junior, José Fernando
Source: Lecture Notes in Computer Science. Conference titles: International Conference on Image Analysis and Processing - ICIAP. Unidades: EP, ICMC
Subjects: APRENDIZADO COMPUTACIONAL, PROCESSAMENTO DE IMAGENS, DIAGNÓSTICO POR IMAGEM, DIAGNÓSTICO POR COMPUTADOR, TECNOLOGIAS DA SAÚDE, RESSONÂNCIA MAGNÉTICA, TESTES DE FUNÇÃO CARDÍACA
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ABNT
LIMA, Daniel Mário de et al. Full motion focus: convolutional module for improved left ventricle segmentation over 4D MRI. Lecture Notes in Computer Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-06427-2_37. Acesso em: 04 jul. 2024. , 2022
APA
Lima, D. M. de, Graves, C. de V., Gutierrez, M. A., Machado, B. B., & Rodrigues Junior, J. F. (2022). Full motion focus: convolutional module for improved left ventricle segmentation over 4D MRI. Lecture Notes in Computer Science. Cham: Springer. doi:10.1007/978-3-031-06427-2_37
NLM
Lima DM de, Graves C de V, Gutierrez MA, Machado BB, Rodrigues Junior JF. Full motion focus: convolutional module for improved left ventricle segmentation over 4D MRI [Internet]. Lecture Notes in Computer Science. 2022 ; 13231 438-450.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1007/978-3-031-06427-2_37
Vancouver
Lima DM de, Graves C de V, Gutierrez MA, Machado BB, Rodrigues Junior JF. Full motion focus: convolutional module for improved left ventricle segmentation over 4D MRI [Internet]. Lecture Notes in Computer Science. 2022 ; 13231 438-450.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1007/978-3-031-06427-2_37
Artigo de periodico
CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy (2022)
Rodrigues Junior, José Fernando ; Bailly, Sebastien ; Pepin, Jean-Louis ; Goeuriot, Lorraine ; Spadon, Gabriel ; Amer-Yahia, Sihem
Source: Applied Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, TECNOLOGIAS DA SAÚDE, TRANSTORNOS DO SONO, APNEIA DO SONO
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ABNT
RODRIGUES JUNIOR, José Fernando et al. CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy. Applied Sciences, v. 12, n. 15, p. 1-17, 2022Tradução . . Disponível em: https://doi.org/10.3390/app12157618. Acesso em: 04 jul. 2024.
APA
Rodrigues Junior, J. F., Bailly, S., Pepin, J. -L., Goeuriot, L., Spadon, G., & Amer-Yahia, S. (2022). CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy. Applied Sciences, 12( 15), 1-17. doi:10.3390/app12157618
NLM
Rodrigues Junior JF, Bailly S, Pepin J-L, Goeuriot L, Spadon G, Amer-Yahia S. CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy [Internet]. Applied Sciences. 2022 ; 12( 15): 1-17.[citado 2024 jul. 04 ] Available from: https://doi.org/10.3390/app12157618
Vancouver
Rodrigues Junior JF, Bailly S, Pepin J-L, Goeuriot L, Spadon G, Amer-Yahia S. CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy [Internet]. Applied Sciences. 2022 ; 12( 15): 1-17.[citado 2024 jul. 04 ] Available from: https://doi.org/10.3390/app12157618
Trabalho de evento
AttentionHCare: advances on computer-aided medical prognosis using attention-based neural networks (2022)
Barros, Pedro Henrique Ferracini de ; Rodrigues Junior, José Fernando
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), DIAGNÓSTICO POR COMPUTADOR, TECNOLOGIAS DA SAÚDE
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ABNT
BARROS, Pedro Henrique Ferracini de e RODRIGUES JUNIOR, José Fernando. AttentionHCare: advances on computer-aided medical prognosis using attention-based neural networks. 2022, Anais.. Piscataway: IEEE, 2022. Disponível em: https://doi.org/10.1109/IJCNN55064.2022.9892642. Acesso em: 04 jul. 2024.
APA
Barros, P. H. F. de, & Rodrigues Junior, J. F. (2022). AttentionHCare: advances on computer-aided medical prognosis using attention-based neural networks. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN55064.2022.9892642
NLM
Barros PHF de, Rodrigues Junior JF. AttentionHCare: advances on computer-aided medical prognosis using attention-based neural networks [Internet]. Proceedings. 2022 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892642
Vancouver
Barros PHF de, Rodrigues Junior JF. AttentionHCare: advances on computer-aided medical prognosis using attention-based neural networks [Internet]. Proceedings. 2022 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892642
Artigo de periodico
DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage (2021)
Machado, Bruno Brandoli ; Spadon, Gabriel ; Esau, Travis ; Hennessy, Patrick ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Amer-Yahia, Sihem ; Rodrigues Junior, José Fernando
Source: Computers and Electronics in Agriculture. Unidade: ICMC
Subjects: COMPUTAÇÃO MÓVEL, PROCESSAMENTO DE IMAGENS, AGRICULTURA DE PRECISÃO, PESTICIDAS
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ABNT
MACHADO, Bruno Brandoli et al. DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage. Computers and Electronics in Agriculture, v. 180, n. Ja 2021, p. 1-9, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2020.105906. Acesso em: 04 jul. 2024.
APA
Machado, B. B., Spadon, G., Esau, T., Hennessy, P., Carvalho, A. C. P. de L. F. de, Amer-Yahia, S., & Rodrigues Junior, J. F. (2021). DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage. Computers and Electronics in Agriculture, 180( Ja 2021), 1-9. doi:10.1016/j.compag.2020.105906
NLM
Machado BB, Spadon G, Esau T, Hennessy P, Carvalho ACP de LF de, Amer-Yahia S, Rodrigues Junior JF. DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage [Internet]. Computers and Electronics in Agriculture. 2021 ; 180( Ja 2021): 1-9.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1016/j.compag.2020.105906
Vancouver
Machado BB, Spadon G, Esau T, Hennessy P, Carvalho ACP de LF de, Amer-Yahia S, Rodrigues Junior JF. DropLeaf: a precision farming smartphone tool for real-time quantification of pesticide application coverage [Internet]. Computers and Electronics in Agriculture. 2021 ; 180( Ja 2021): 1-9.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1016/j.compag.2020.105906
Artigo de periodico
LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks (2021)
Rodrigues Junior, José Fernando ; Gutierrez, Marco Antonio ; Spadon, Gabriel ; Machado, Bruno Brandoli ; Amer-Yahia, Sihem
Source: Information Sciences. Unidades: ICMC, EP
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), PROGNÓSTICO, TECNOLOGIAS DA SAÚDE
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ABNT
RODRIGUES JUNIOR, José Fernando et al. LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. Information Sciences, v. 545, p. 813-827, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2020.09.024. Acesso em: 04 jul. 2024.
APA
Rodrigues Junior, J. F., Gutierrez, M. A., Spadon, G., Machado, B. B., & Amer-Yahia, S. (2021). LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. Information Sciences, 545, 813-827. doi:10.1016/j.ins.2020.09.024
NLM
Rodrigues Junior JF, Gutierrez MA, Spadon G, Machado BB, Amer-Yahia S. LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks [Internet]. Information Sciences. 2021 ; 545 813-827.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1016/j.ins.2020.09.024
Vancouver
Rodrigues Junior JF, Gutierrez MA, Spadon G, Machado BB, Amer-Yahia S. LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks [Internet]. Information Sciences. 2021 ; 545 813-827.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1016/j.ins.2020.09.024
Artigo de periodico
ExplorerTree: a focus+context exploration approach for 2D embeddings (2021)
Marcílio-Jr, Wilson E ; Eler, Danilo Medeiros ; Paulovich, Fernando Vieira ; Rodrigues Junior, José Fernando ; Artero, Almir Olivette
Source: Big Data Research. Unidade: ICMC
Subjects: VISUALIZAÇÃO, RECONHECIMENTO DE PADRÕES
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ABNT
MARCÍLIO-JR, Wilson E et al. ExplorerTree: a focus+context exploration approach for 2D embeddings. Big Data Research, v. 25, p. 1-12, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.bdr.2021.100239. Acesso em: 04 jul. 2024.
APA
Marcílio-Jr, W. E., Eler, D. M., Paulovich, F. V., Rodrigues Junior, J. F., & Artero, A. O. (2021). ExplorerTree: a focus+context exploration approach for 2D embeddings. Big Data Research, 25, 1-12. doi:10.1016/j.bdr.2021.100239
NLM
Marcílio-Jr WE, Eler DM, Paulovich FV, Rodrigues Junior JF, Artero AO. ExplorerTree: a focus+context exploration approach for 2D embeddings [Internet]. Big Data Research. 2021 ; 25 1-12.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1016/j.bdr.2021.100239
Vancouver
Marcílio-Jr WE, Eler DM, Paulovich FV, Rodrigues Junior JF, Artero AO. ExplorerTree: a focus+context exploration approach for 2D embeddings [Internet]. Big Data Research. 2021 ; 25 1-12.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1016/j.bdr.2021.100239
Artigo de periodico
Associative classification model for forecasting stock market trends (2021)
Tetila, Everton Castelão ; Machado, Bruno Brandoli ; Rodrigues Junior, José Fernando ; Zanoni, Diego Adania ; Belete, Nícolas Alessandro de Souza ; Zardo, Thayliny ; Constantino, Michel ; Pistori, Hemerson
Source: International Journal of Business Intelligence and Data Mining. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, RECONHECIMENTO DE PADRÕES, INTELIGÊNCIA COMPETITIVA, BOLSA DE VALORES
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ABNT
TETILA, Everton Castelão et al. Associative classification model for forecasting stock market trends. International Journal of Business Intelligence and Data Mining, v. 19, n. 1, p. 97-112, 2021Tradução . . Disponível em: https://doi.org/10.1504/IJBIDM.2021.115968. Acesso em: 04 jul. 2024.
APA
Tetila, E. C., Machado, B. B., Rodrigues Junior, J. F., Zanoni, D. A., Belete, N. A. de S., Zardo, T., et al. (2021). Associative classification model for forecasting stock market trends. International Journal of Business Intelligence and Data Mining, 19( 1), 97-112. doi:10.1504/IJBIDM.2021.115968
NLM
Tetila EC, Machado BB, Rodrigues Junior JF, Zanoni DA, Belete NA de S, Zardo T, Constantino M, Pistori H. Associative classification model for forecasting stock market trends [Internet]. International Journal of Business Intelligence and Data Mining. 2021 ; 19( 1): 97-112.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1504/IJBIDM.2021.115968
Vancouver
Tetila EC, Machado BB, Rodrigues Junior JF, Zanoni DA, Belete NA de S, Zardo T, Constantino M, Pistori H. Associative classification model for forecasting stock market trends [Internet]. International Journal of Business Intelligence and Data Mining. 2021 ; 19( 1): 97-112.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1504/IJBIDM.2021.115968
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
Source: Proceedings. Conference titles: International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, TECNOLOGIAS DA SAÚDE, PROGNÓSTICO
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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: 04 jul. 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 jul. 04 ] 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 jul. 04 ] Available from: https://doi.org/10.1109/CBMS52027.2021.00077
Artigo de periodico
DermaDL: advanced convolutional neural networks for computer-aided skin-lesion classification (2021)
Lima, Daniel Mário de ; Rodrigues Junior, José Fernando ; Machado, Bruno Brandoli ; Goeuriot, Lorraine ; Amer-Yahia, Sihem
Source: SN Computer Science. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, NEOPLASIAS CUTÂNEAS, MELANOMA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LIMA, Daniel Mário de et al. DermaDL: advanced convolutional neural networks for computer-aided skin-lesion classification. SN Computer Science, v. 2, n. 4, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.1007/s42979-021-00641-5. Acesso em: 04 jul. 2024.
APA
Lima, D. M. de, Rodrigues Junior, J. F., Machado, B. B., Goeuriot, L., & Amer-Yahia, S. (2021). DermaDL: advanced convolutional neural networks for computer-aided skin-lesion classification. SN Computer Science, 2( 4), 1-13. doi:10.1007/s42979-021-00641-5
NLM
Lima DM de, Rodrigues Junior JF, Machado BB, Goeuriot L, Amer-Yahia S. DermaDL: advanced convolutional neural networks for computer-aided skin-lesion classification [Internet]. SN Computer Science. 2021 ; 2( 4): 1-13.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1007/s42979-021-00641-5
Vancouver
Lima DM de, Rodrigues Junior JF, Machado BB, Goeuriot L, Amer-Yahia S. DermaDL: advanced convolutional neural networks for computer-aided skin-lesion classification [Internet]. SN Computer Science. 2021 ; 2( 4): 1-13.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1007/s42979-021-00641-5
Artigo de periodico
Big data and machine learning for materials science (2021)
Rodrigues Junior, José Fernando ; Florea, Larisa ; Oliveira, Maria Cristina Ferreira de ; Diamond, Dermot; Oliveira Junior, Osvaldo Novais de
Source: Discover Materials. Unidades: ICMC, IFSC
Subjects: BIG DATA, INTERNET DAS COISAS, APRENDIZADO COMPUTACIONAL
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ABNT
RODRIGUES JUNIOR, José Fernando et al. Big data and machine learning for materials science. Discover Materials, v. 1, n. 1, p. 12-1-12-27, 2021Tradução . . Disponível em: https://doi.org/10.1007/s43939-021-00012-0. Acesso em: 04 jul. 2024.
APA
Rodrigues Junior, J. F., Florea, L., Oliveira, M. C. F. de, Diamond, D., & Oliveira Junior, O. N. de. (2021). Big data and machine learning for materials science. Discover Materials, 1( 1), 12-1-12-27. doi:10.1007/s43939-021-00012-0
NLM
Rodrigues Junior JF, Florea L, Oliveira MCF de, Diamond D, Oliveira Junior ON de. Big data and machine learning for materials science [Internet]. Discover Materials. 2021 ; 1( 1): 12-1-12-27.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1007/s43939-021-00012-0
Vancouver
Rodrigues Junior JF, Florea L, Oliveira MCF de, Diamond D, Oliveira Junior ON de. Big data and machine learning for materials science [Internet]. Discover Materials. 2021 ; 1( 1): 12-1-12-27.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1007/s43939-021-00012-0
Parte de monografia/livro
O futuro da ciência e tecnologia com as máquinas inteligentes (2021)
Rodrigues Junior, José Fernando ; Oliveira, Maria Cristina Ferreira de ; Oliveira Junior, Osvaldo Novais de
Source: Inteligência artificial : avanços e tendências. Unidades: ICMC, IFSC
Subjects: APRENDIZADO COMPUTACIONAL, BIG DATA, PROCESSAMENTO DE LINGUAGEM NATURAL
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ABNT
RODRIGUES JUNIOR, José Fernando e OLIVEIRA, Maria Cristina Ferreira de e OLIVEIRA JUNIOR, Osvaldo Novais de. O futuro da ciência e tecnologia com as máquinas inteligentes. Inteligência artificial : avanços e tendências. Tradução . São Paulo: Instituto de Estudos Avançados, 2021. p. 414 . Disponível em: https://doi.org/10.11606/9786587773131. Acesso em: 04 jul. 2024.
APA
Rodrigues Junior, J. F., Oliveira, M. C. F. de, & Oliveira Junior, O. N. de. (2021). O futuro da ciência e tecnologia com as máquinas inteligentes. In Inteligência artificial : avanços e tendências (p. 414 ). São Paulo: Instituto de Estudos Avançados. doi:10.11606/9786587773131
NLM
Rodrigues Junior JF, Oliveira MCF de, Oliveira Junior ON de. O futuro da ciência e tecnologia com as máquinas inteligentes [Internet]. In: Inteligência artificial : avanços e tendências. São Paulo: Instituto de Estudos Avançados; 2021. p. 414 .[citado 2024 jul. 04 ] Available from: https://doi.org/10.11606/9786587773131
Vancouver
Rodrigues Junior JF, Oliveira MCF de, Oliveira Junior ON de. O futuro da ciência e tecnologia com as máquinas inteligentes [Internet]. In: Inteligência artificial : avanços e tendências. São Paulo: Instituto de Estudos Avançados; 2021. p. 414 .[citado 2024 jul. 04 ] Available from: https://doi.org/10.11606/9786587773131
Artigo de periodico
Real-world patient trajectory prediction from clinical notes using artificial neural networks and UMLS-based extraction of concepts (2021)
Zaghir, Jamil ; Rodrigues Junior, José Fernando ; Goeuriot, Lorraine ; Amer-Yahia, Sihem
Source: Journal of Healthcare Informatics Research. Unidade: ICMC
Subjects: REDES NEURAIS, TECNOLOGIAS DA SAÚDE, PROGNÓSTICO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ZAGHIR, Jamil et al. Real-world patient trajectory prediction from clinical notes using artificial neural networks and UMLS-based extraction of concepts. Journal of Healthcare Informatics Research, v. 5, n. 4, p. 474-496, 2021Tradução . . Disponível em: https://doi.org/10.1007/s41666-021-00100-z. Acesso em: 04 jul. 2024.
APA
Zaghir, J., Rodrigues Junior, J. F., Goeuriot, L., & Amer-Yahia, S. (2021). Real-world patient trajectory prediction from clinical notes using artificial neural networks and UMLS-based extraction of concepts. Journal of Healthcare Informatics Research, 5( 4), 474-496. doi:10.1007/s41666-021-00100-z
NLM
Zaghir J, Rodrigues Junior JF, Goeuriot L, Amer-Yahia S. Real-world patient trajectory prediction from clinical notes using artificial neural networks and UMLS-based extraction of concepts [Internet]. Journal of Healthcare Informatics Research. 2021 ; 5( 4): 474-496.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1007/s41666-021-00100-z
Vancouver
Zaghir J, Rodrigues Junior JF, Goeuriot L, Amer-Yahia S. Real-world patient trajectory prediction from clinical notes using artificial neural networks and UMLS-based extraction of concepts [Internet]. Journal of Healthcare Informatics Research. 2021 ; 5( 4): 474-496.[citado 2024 jul. 04 ] Available from: https://doi.org/10.1007/s41666-021-00100-z
Trabalho de evento
SHARq: sharing recursive queries in relational databases (2021)
Scabora, Lucas de Carvalho ; Spadon, Gabriel ; Cazzolato, Mirela Teixeira ; Kaster, Daniel dos Santos ; Traina, Agma Juci Machado ; Rodrigues Junior, José Fernando ; Traina Junior, Caetano
Source: Proceedings. Conference titles: Symposium on Applied Computing - SAC. Unidade: ICMC
Subjects: GERENCIADORES DE BANCO DE DADOS, BANCO DE DADOS RELACIONAIS, RECUPERAÇÃO DA INFORMAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SCABORA, Lucas de Carvalho et al. SHARq: sharing recursive queries in relational databases. 2021, Anais.. New York: ACM, 2021. Disponível em: https://doi.org/10.1145/3412841.3442078. Acesso em: 04 jul. 2024.
APA
Scabora, L. de C., Spadon, G., Cazzolato, M. T., Kaster, D. dos S., Traina, A. J. M., Rodrigues Junior, J. F., & Traina Junior, C. (2021). SHARq: sharing recursive queries in relational databases. In Proceedings. New York: ACM. doi:10.1145/3412841.3442078
NLM
Scabora L de C, Spadon G, Cazzolato MT, Kaster D dos S, Traina AJM, Rodrigues Junior JF, Traina Junior C. SHARq: sharing recursive queries in relational databases [Internet]. Proceedings. 2021 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1145/3412841.3442078
Vancouver
Scabora L de C, Spadon G, Cazzolato MT, Kaster D dos S, Traina AJM, Rodrigues Junior JF, Traina Junior C. SHARq: sharing recursive queries in relational databases [Internet]. Proceedings. 2021 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1145/3412841.3442078
Artigo de periodico
Aircraft fuselage corrosion detection using artificial intelligence (2021)
Machado, Bruno Brandoli ; Geus, André Reis de ; Souza, Jefferson R ; Spadon, Gabriel ; Soares, Amilcar ; Rodrigues Junior, José Fernando ; Komorowski, Jerzy ; Matwin, Stan
Source: Sensors. Unidade: ICMC
Subjects: REDES NEURAIS, RECONHECIMENTO DE IMAGEM, MANUTENÇÃO DE AERONAVES, FUSELAGEM DE AERONAVES, CORROSÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MACHADO, Bruno Brandoli et al. Aircraft fuselage corrosion detection using artificial intelligence. Sensors, v. 21, p. 1-15, 2021Tradução . . Disponível em: https://doi.org/10.3390/s21124026. Acesso em: 04 jul. 2024.
APA
Machado, B. B., Geus, A. R. de, Souza, J. R., Spadon, G., Soares, A., Rodrigues Junior, J. F., et al. (2021). Aircraft fuselage corrosion detection using artificial intelligence. Sensors, 21, 1-15. doi:10.3390/s21124026
NLM
Machado BB, Geus AR de, Souza JR, Spadon G, Soares A, Rodrigues Junior JF, Komorowski J, Matwin S. Aircraft fuselage corrosion detection using artificial intelligence [Internet]. Sensors. 2021 ; 21 1-15.[citado 2024 jul. 04 ] Available from: https://doi.org/10.3390/s21124026
Vancouver
Machado BB, Geus AR de, Souza JR, Spadon G, Soares A, Rodrigues Junior JF, Komorowski J, Matwin S. Aircraft fuselage corrosion detection using artificial intelligence [Internet]. Sensors. 2021 ; 21 1-15.[citado 2024 jul. 04 ] Available from: https://doi.org/10.3390/s21124026
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
Source: Proceedings. Conference titles: IEEE International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: 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: 04 jul. 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 jul. 04 ] 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 jul. 04 ] Available from: https://doi.org/10.1109/CBMS49503.2020.00118
Trabalho de evento
Lig-Doctor: real-world clinical prognosis using a bi-directional neural network (2020)
Rodrigues Junior, José Fernando ; Spadon, Gabriel ; Machado, Bruno Brandoli ; Amer-Yahia, Sihem
Source: Proceedings. Conference titles: IEEE International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), PROGNÓSTICO, TECNOLOGIAS DA SAÚDE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RODRIGUES JUNIOR, José Fernando et al. Lig-Doctor: real-world clinical prognosis using a bi-directional neural network. 2020, Anais.. Los Alamitos: IEEE, 2020. Disponível em: https://doi.org/10.1109/CBMS49503.2020.00113. Acesso em: 04 jul. 2024.
APA
Rodrigues Junior, J. F., Spadon, G., Machado, B. B., & Amer-Yahia, S. (2020). Lig-Doctor: real-world clinical prognosis using a bi-directional neural network. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS49503.2020.00113
NLM
Rodrigues Junior JF, Spadon G, Machado BB, Amer-Yahia S. Lig-Doctor: real-world clinical prognosis using a bi-directional neural network [Internet]. Proceedings. 2020 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/CBMS49503.2020.00113
Vancouver
Rodrigues Junior JF, Spadon G, Machado BB, Amer-Yahia S. Lig-Doctor: real-world clinical prognosis using a bi-directional neural network [Internet]. Proceedings. 2020 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/CBMS49503.2020.00113
Trabalho de evento
An extensive investigation of machine learning techniques for sleep apnea screening (2020)
Rodrigues Junior, José Fernando ; Pepin, Jean-Louis ; Goeuriot, Lorraine ; Amer-Yahia, Sihem
Source: Proceedings. Conference titles: ACM International Conference on Information & Knowledge Management - CIKM. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, TECNOLOGIAS DA SAÚDE, APNEIA DO SONO TIPO OBSTRUTIVA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RODRIGUES JUNIOR, José Fernando et al. An extensive investigation of machine learning techniques for sleep apnea screening. 2020, Anais.. New York: ACM, 2020. Disponível em: https://doi.org/10.1145/3340531.3412686. Acesso em: 04 jul. 2024.
APA
Rodrigues Junior, J. F., Pepin, J. -L., Goeuriot, L., & Amer-Yahia, S. (2020). An extensive investigation of machine learning techniques for sleep apnea screening. In Proceedings. New York: ACM. doi:10.1145/3340531.3412686
NLM
Rodrigues Junior JF, Pepin J-L, Goeuriot L, Amer-Yahia S. An extensive investigation of machine learning techniques for sleep apnea screening [Internet]. Proceedings. 2020 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1145/3340531.3412686
Vancouver
Rodrigues Junior JF, Pepin J-L, Goeuriot L, Amer-Yahia S. An extensive investigation of machine learning techniques for sleep apnea screening [Internet]. Proceedings. 2020 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1145/3340531.3412686
Trabalho de evento
DermaDL: advanced convolutional neural networks for automated melanoma detection (2020)
Rodrigues Junior, José Fernando ; Machado, Bruno Brandoli ; Amer-Yahia, Sihem
Source: Proceedings. Conference titles: IEEE International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, NEOPLASIAS CUTÂNEAS, MELANOMA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RODRIGUES JUNIOR, José Fernando e MACHADO, Bruno Brandoli e AMER-YAHIA, Sihem. DermaDL: advanced convolutional neural networks for automated melanoma detection. 2020, Anais.. Los Alamitos: IEEE, 2020. Disponível em: https://doi.org/10.1109/CBMS49503.2020.00101. Acesso em: 04 jul. 2024.
APA
Rodrigues Junior, J. F., Machado, B. B., & Amer-Yahia, S. (2020). DermaDL: advanced convolutional neural networks for automated melanoma detection. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS49503.2020.00101
NLM
Rodrigues Junior JF, Machado BB, Amer-Yahia S. DermaDL: advanced convolutional neural networks for automated melanoma detection [Internet]. Proceedings. 2020 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/CBMS49503.2020.00101
Vancouver
Rodrigues Junior JF, Machado BB, Amer-Yahia S. DermaDL: advanced convolutional neural networks for automated melanoma detection [Internet]. Proceedings. 2020 ;[citado 2024 jul. 04 ] Available from: https://doi.org/10.1109/CBMS49503.2020.00101

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