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Trabalho de evento
Medical dialogue audio transcription: dataset and benchmarking of ASR models (2025)
Gassenn, Aline Elí ; Andrade, Luis Gustavo Modelli de ; Teodoro, Douglas ; Rodrigues Junior, José Fernando
Source: Anais. Conference titles: Simpósio Brasileiro de Bancos de Dados - SBBD. Unidade: ICMC
Subjects: RECONHECIMENTO DA FALA, TRATAMENTO AUTOMÁTICO DE TEXTOS E DISCURSOS, PROCESSAMENTO DE LINGUAGEM NATURAL, BENCHMARKS, CORPUS, TECNOLOGIAS DA SAÚDE
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ABNT
GASSENN, Aline Elí et al. Medical dialogue audio transcription: dataset and benchmarking of ASR models. 2025, Anais.. Porto Alegre: SBC, 2025. Disponível em: https://doi.org/10.5753/dsw.2025.248010. Acesso em: 04 dez. 2025.
APA
Gassenn, A. E., Andrade, L. G. M. de, Teodoro, D., & Rodrigues Junior, J. F. (2025). Medical dialogue audio transcription: dataset and benchmarking of ASR models. In Anais. Porto Alegre: SBC. doi:10.5753/dsw.2025.248010
NLM
Gassenn AE, Andrade LGM de, Teodoro D, Rodrigues Junior JF. Medical dialogue audio transcription: dataset and benchmarking of ASR models [Internet]. Anais. 2025 ;[citado 2025 dez. 04 ] Available from: https://doi.org/10.5753/dsw.2025.248010
Vancouver
Gassenn AE, Andrade LGM de, Teodoro D, Rodrigues Junior JF. Medical dialogue audio transcription: dataset and benchmarking of ASR models [Internet]. Anais. 2025 ;[citado 2025 dez. 04 ] Available from: https://doi.org/10.5753/dsw.2025.248010
Trabalho de evento
Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences (2025)
Alam, Md Mahbub ; Soares, Amilcar ; Rodrigues Junior, José Fernando ; Spadon, Gabriel
Source: Proceedings. Conference titles: International Symposium on Spatial and Temporal Data - SSTD. Unidade: ICMC
Subjects: REDES NEURAIS, NAVEGAÇÃO MARÍTIMA
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ABNT
ALAM, Md Mahbub et al. Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences. 2025, Anais.. New York: ACM, 2025. Disponível em: https://doi.org/10.1145/3748777.3748779. Acesso em: 04 dez. 2025.
APA
Alam, M. M., Soares, A., Rodrigues Junior, J. F., & Spadon, G. (2025). Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences. In Proceedings. New York: ACM. doi:10.1145/3748777.3748779
NLM
Alam MM, Soares A, Rodrigues Junior JF, Spadon G. Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences [Internet]. Proceedings. 2025 ;[citado 2025 dez. 04 ] Available from: https://doi.org/10.1145/3748777.3748779
Vancouver
Alam MM, Soares A, Rodrigues Junior JF, Spadon G. Physics-informed neural networks for vessel trajectory prediction: learning time-discretized kinematic dynamics via finite differences [Internet]. Proceedings. 2025 ;[citado 2025 dez. 04 ] Available from: https://doi.org/10.1145/3748777.3748779
Trabalho de evento
AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data (2025)
Silva, João Pedro da; Aguiar, Erikson Júlio de ; Spadon, Gabriel ; Traina, Agma Juci Machado ; Rodrigues Junior, José Fernando
Source: Proceedings. Conference titles: IEEE International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, SENSORIAMENTO REMOTO, INDICADORES SOCIOECONÔMICOS, SAÚDE PÚBLICA, BRASIL
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ABNT
SILVA, João Pedro da et al. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data. 2025, Anais.. Los Alamitos: IEEE, 2025. Disponível em: https://doi.org/10.1109/CBMS65348.2025.00180. Acesso em: 04 dez. 2025.
APA
Silva, J. P. da, Aguiar, E. J. de, Spadon, G., Traina, A. J. M., & Rodrigues Junior, J. F. (2025). AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS65348.2025.00180
NLM
Silva JP da, Aguiar EJ de, Spadon G, Traina AJM, Rodrigues Junior JF. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data [Internet]. Proceedings. 2025 ;[citado 2025 dez. 04 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180
Vancouver
Silva JP da, Aguiar EJ de, Spadon G, Traina AJM, Rodrigues Junior JF. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data [Internet]. Proceedings. 2025 ;[citado 2025 dez. 04 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180
Artigo de periodico
Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil (2024)
Silva, João Pedro; Rodrigues Junior, José Fernando
Source: International Journal of Semantic Computing. Unidade: ICMC
Subjects: APRENDIZAGEM PROFUNDA, REDES NEURAIS, INDICADORES SOCIOECONÔMICOS, IMAGEAMENTO DE SATÉLITE, SENSORIAMENTO REMOTO
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ABNT
SILVA, João Pedro e RODRIGUES JUNIOR, José Fernando. Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil. International Journal of Semantic Computing, v. 18, n. 3, p. 329-345, 2024Tradução . . Disponível em: https://doi.org/10.1142/S1793351X24420017. Acesso em: 04 dez. 2025.
APA
Silva, J. P., & Rodrigues Junior, J. F. (2024). Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil. International Journal of Semantic Computing, 18( 3), 329-345. doi:10.1142/S1793351X24420017
NLM
Silva JP, Rodrigues Junior JF. Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil [Internet]. International Journal of Semantic Computing. 2024 ; 18( 3): 329-345.[citado 2025 dez. 04 ] Available from: https://doi.org/10.1142/S1793351X24420017
Vancouver
Silva JP, Rodrigues Junior JF. Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil [Internet]. International Journal of Semantic Computing. 2024 ; 18( 3): 329-345.[citado 2025 dez. 04 ] Available from: https://doi.org/10.1142/S1793351X24420017
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 dez. 2025.
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 2025 dez. 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 2025 dez. 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 dez. 2025.
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 2025 dez. 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 2025 dez. 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 dez. 2025. , 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 2025 dez. 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 2025 dez. 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 dez. 2025.
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 2025 dez. 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 2025 dez. 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 dez. 2025.
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 2025 dez. 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 2025 dez. 04 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892642
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 dez. 2025.
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 2025 dez. 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 2025 dez. 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 dez. 2025.
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 2025 dez. 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 2025 dez. 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
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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 dez. 2025.
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 2025 dez. 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 2025 dez. 04 ] Available from: https://doi.org/10.1007/s42979-021-00641-5
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 dez. 2025.
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 2025 dez. 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 2025 dez. 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
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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 dez. 2025.
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 2025 dez. 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 2025 dez. 04 ] Available from: https://doi.org/10.1007/s41666-021-00100-z
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 dez. 2025.
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 2025 dez. 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 2025 dez. 04 ] Available from: https://doi.org/10.1007/s43939-021-00012-0
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 dez. 2025.
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 2025 dez. 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 2025 dez. 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 dez. 2025.
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 2025 dez. 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 2025 dez. 04 ] Available from: https://doi.org/10.3390/s21124026

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