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Trabalho de evento-anais periodico
Growing self-organizing maps for multi-label classification (2025)
Casarotto, Pedro Henrique; Cerri, Ricardo
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, REDES NEURAIS
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ABNT
CASAROTTO, Pedro Henrique e CERRI, Ricardo. Growing self-organizing maps for multi-label classification. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-79032-4_3. Acesso em: 05 nov. 2025. , 2025
APA
Casarotto, P. H., & Cerri, R. (2025). Growing self-organizing maps for multi-label classification. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-79032-4_3
NLM
Casarotto PH, Cerri R. Growing self-organizing maps for multi-label classification [Internet]. Lecture Notes in Artificial Intelligence. 2025 ; 15413 33-48.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-79032-4_3
Vancouver
Casarotto PH, Cerri R. Growing self-organizing maps for multi-label classification [Internet]. Lecture Notes in Artificial Intelligence. 2025 ; 15413 33-48.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-79032-4_3
Trabalho de evento-anais periodico
On the assessment of deep learning models for named entity recognition of brazilian legal documents (2023)
Albuquerque, Hidelberg Oliveira ; Souza, Ellen Polliana Ramos ; Oliveira, Adriano Lorena Inacio de ; Macêdo, David ; Zanchettin, Cleber ; Vitório, Douglas ; Silva, Nádia Felix Felipe da ; Carvalho, Andre Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Conference on Artificial Intelligence - EPIA. Unidade: ICMC
Assuntos: APRENDIZAGEM PROFUNDA, PROCESSAMENTO DE LINGUAGEM NATURAL, TRATAMENTO AUTOMÁTICO DE TEXTOS E DISCURSOS, RECUPERAÇÃO DA INFORMAÇÃO, PORTUGUÊS DO BRASIL
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ABNT
ALBUQUERQUE, Hidelberg Oliveira et al. On the assessment of deep learning models for named entity recognition of brazilian legal documents. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-49011-8_8. Acesso em: 05 nov. 2025. , 2023
APA
Albuquerque, H. O., Souza, E. P. R., Oliveira, A. L. I. de, Macêdo, D., Zanchettin, C., Vitório, D., et al. (2023). On the assessment of deep learning models for named entity recognition of brazilian legal documents. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-49011-8_8
NLM
Albuquerque HO, Souza EPR, Oliveira ALI de, Macêdo D, Zanchettin C, Vitório D, Silva NFF da, Carvalho ACP de LF de. On the assessment of deep learning models for named entity recognition of brazilian legal documents [Internet]. Lecture Notes in Artificial Intelligence. 2023 ; 14116 93-104.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-49011-8_8
Vancouver
Albuquerque HO, Souza EPR, Oliveira ALI de, Macêdo D, Zanchettin C, Vitório D, Silva NFF da, Carvalho ACP de LF de. On the assessment of deep learning models for named entity recognition of brazilian legal documents [Internet]. Lecture Notes in Artificial Intelligence. 2023 ; 14116 93-104.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-49011-8_8
Trabalho de evento-anais periodico
Exploring text decoding methods for portuguese legal text generation (2023)
Sakiyama, Kenzo ; Montanari, Raphael ; Malaquias Junior, Roseval Donisete ; Nogueira, Rodrigo Frassetto ; Romero, Roseli Aparecida Francelin
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: ICMC, EESC
Assuntos: PROCESSAMENTO DE LINGUAGEM NATURAL, TRATAMENTO AUTOMÁTICO DE TEXTOS E DISCURSOS, RECUPERAÇÃO DA INFORMAÇÃO, PORTUGUÊS DO BRASIL
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ABNT
SAKIYAMA, Kenzo et al. Exploring text decoding methods for portuguese legal text generation. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-45368-7_5. Acesso em: 05 nov. 2025. , 2023
APA
Sakiyama, K., Montanari, R., Malaquias Junior, R. D., Nogueira, R. F., & Romero, R. A. F. (2023). Exploring text decoding methods for portuguese legal text generation. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-45368-7_5
NLM
Sakiyama K, Montanari R, Malaquias Junior RD, Nogueira RF, Romero RAF. Exploring text decoding methods for portuguese legal text generation [Internet]. Lecture Notes in Artificial Intelligence. 2023 ; 14195 63-77.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-45368-7_5
Vancouver
Sakiyama K, Montanari R, Malaquias Junior RD, Nogueira RF, Romero RAF. Exploring text decoding methods for portuguese legal text generation [Internet]. Lecture Notes in Artificial Intelligence. 2023 ; 14195 63-77.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-45368-7_5
Trabalho de evento-anais periodico
UlyssesNER-Br: a corpus of brazilian legislative documents for named entity recognition (2022)
Albuquerque, Hidelberg Oliveira ; Costa, Rosimeire ; Silvestre, Gabriel Dalforno ; Souza, Ellen Polliana Ramos ; Silva, Nádia Felix Felipe da ; Vitório, Douglas ; Moriyama, Gyovana ; Martins, Lucas ; Soezima, Luiza ; Nunes, Augusto ; Siqueira, Felipe Alves ; Tarrega, João Pedro Mantovani ; Beinotti, João Vitor Pataca ; Dias, Márcio de Souza ; Silva, Matheus ; Gardini, Miguel ; Silva, Vinicius Adolfo Pereira da ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Oliveira, Adriano Lorena Inacio de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: International Conference on Computational Processing of the Portuguese Language - PROPOR. Unidades: ICMC, EESC E ICMC, IME, EESC
Assuntos: RECONHECIMENTO DE TEXTO, PROCESSAMENTO DE LINGUAGEM NATURAL, RECUPERAÇÃO DA INFORMAÇÃO, TRATAMENTO AUTOMÁTICO DE TEXTOS E DISCURSOS, CORPUS
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ABNT
ALBUQUERQUE, Hidelberg Oliveira et al. UlyssesNER-Br: a corpus of brazilian legislative documents for named entity recognition. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-98305-5_1. Acesso em: 05 nov. 2025. , 2022
APA
Albuquerque, H. O., Costa, R., Silvestre, G. D., Souza, E. P. R., Silva, N. F. F. da, Vitório, D., et al. (2022). UlyssesNER-Br: a corpus of brazilian legislative documents for named entity recognition. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-98305-5_1
NLM
Albuquerque HO, Costa R, Silvestre GD, Souza EPR, Silva NFF da, Vitório D, Moriyama G, Martins L, Soezima L, Nunes A, Siqueira FA, Tarrega JPM, Beinotti JVP, Dias M de S, Silva M, Gardini M, Silva VAP da, Carvalho ACP de LF de, Oliveira ALI de. UlyssesNER-Br: a corpus of brazilian legislative documents for named entity recognition [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13208 3-14.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-98305-5_1
Vancouver
Albuquerque HO, Costa R, Silvestre GD, Souza EPR, Silva NFF da, Vitório D, Moriyama G, Martins L, Soezima L, Nunes A, Siqueira FA, Tarrega JPM, Beinotti JVP, Dias M de S, Silva M, Gardini M, Silva VAP da, Carvalho ACP de LF de, Oliveira ALI de. UlyssesNER-Br: a corpus of brazilian legislative documents for named entity recognition [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13208 3-14.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-98305-5_1
Trabalho de evento-anais periodico
A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task (2022)
Linhares, Claudio Douglas Gouveia ; Tinoco, Claudiney Ramos ; Ponciano, Jean Roberto ; Oliveira, Gina Maira Barbosa de; Travençolo, Bruno Augusto Nassif
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: ROBÓTICA, VISÃO COMPUTACIONAL, VISUALIZAÇÃO, REDES COMPLEXAS
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ABNT
LINHARES, Claudio Douglas Gouveia et al. A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-21686-2_5. Acesso em: 05 nov. 2025. , 2022
APA
Linhares, C. D. G., Tinoco, C. R., Ponciano, J. R., Oliveira, G. M. B. de, & Travençolo, B. A. N. (2022). A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-21686-2_5
NLM
Linhares CDG, Tinoco CR, Ponciano JR, Oliveira GMB de, Travençolo BAN. A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 61-76.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_5
Vancouver
Linhares CDG, Tinoco CR, Ponciano JR, Oliveira GMB de, Travençolo BAN. A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 61-76.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_5
Trabalho de evento-anais periodico
Sequential short-text classification from multiple textual representations with weak supervision (2022)
Reis Filho, Ivan José ; Martins, Luiz Henrique Dutra ; Parmezan, Antonio Rafael Sabino ; Marcacini, Ricardo Marcondes ; Rezende, Solange Oliveira
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE TEXTO, MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS
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ABNT
REIS FILHO, Ivan José et al. Sequential short-text classification from multiple textual representations with weak supervision. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-21686-2_12. Acesso em: 05 nov. 2025. , 2022
APA
Reis Filho, I. J., Martins, L. H. D., Parmezan, A. R. S., Marcacini, R. M., & Rezende, S. O. (2022). Sequential short-text classification from multiple textual representations with weak supervision. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-031-21686-2_12
NLM
Reis Filho IJ, Martins LHD, Parmezan ARS, Marcacini RM, Rezende SO. Sequential short-text classification from multiple textual representations with weak supervision [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 165-179.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_12
Vancouver
Reis Filho IJ, Martins LHD, Parmezan ARS, Marcacini RM, Rezende SO. Sequential short-text classification from multiple textual representations with weak supervision [Internet]. Lecture Notes in Artificial Intelligence. 2022 ; 13653 165-179.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-031-21686-2_12
Trabalho de evento-anais periodico
A new genetic improvement operator based on frequency analysis for genetic algorithms applied to job shop scheduling problem (2021)
Viana, Monique Simplicio ; Contreras, Rodrigo Colnago ; Morandin Junior, Orides
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: International Conference on Artificial Intelligence and Soft Computing - ICAISC. Unidade: ICMC
Assuntos: ALGORITMOS GENÉTICOS, COMPUTAÇÃO BIOINSPIRADA, OTIMIZAÇÃO COMBINATÓRIA
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ABNT
VIANA, Monique Simplicio e CONTRERAS, Rodrigo Colnago e MORANDIN JUNIOR, Orides. A new genetic improvement operator based on frequency analysis for genetic algorithms applied to job shop scheduling problem. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-87986-0_39. Acesso em: 05 nov. 2025. , 2021
APA
Viana, M. S., Contreras, R. C., & Morandin Junior, O. (2021). A new genetic improvement operator based on frequency analysis for genetic algorithms applied to job shop scheduling problem. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-87986-0_39
NLM
Viana MS, Contreras RC, Morandin Junior O. A new genetic improvement operator based on frequency analysis for genetic algorithms applied to job shop scheduling problem [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 12854 434-450.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-87986-0_39
Vancouver
Viana MS, Contreras RC, Morandin Junior O. A new genetic improvement operator based on frequency analysis for genetic algorithms applied to job shop scheduling problem [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 12854 434-450.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-87986-0_39
Trabalho de evento-anais periodico
Speech2Phone: a novel and efficient method for training speaker recognition models (2021)
Casanova, Edresson ; Candido Junior, Arnaldo ; Shulby, Christopher Dane ; Oliveira, Frederico Santos de ; Gris, Lucas Rafael Stefanel ; Silva, Hamilton Pereira da ; Aluísio, Sandra Maria ; Ponti, Moacir Antonelli
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: PROCESSAMENTO DE LINGUAGEM NATURAL, RECONHECIMENTO DE VOZ, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CASANOVA, Edresson et al. Speech2Phone: a novel and efficient method for training speaker recognition models. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-91699-2_39. Acesso em: 05 nov. 2025. , 2021
APA
Casanova, E., Candido Junior, A., Shulby, C. D., Oliveira, F. S. de, Gris, L. R. S., Silva, H. P. da, et al. (2021). Speech2Phone: a novel and efficient method for training speaker recognition models. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-91699-2_39
NLM
Casanova E, Candido Junior A, Shulby CD, Oliveira FS de, Gris LRS, Silva HP da, Aluísio SM, Ponti MA. Speech2Phone: a novel and efficient method for training speaker recognition models [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 13074 572-585.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_39
Vancouver
Casanova E, Candido Junior A, Shulby CD, Oliveira FS de, Gris LRS, Silva HP da, Aluísio SM, Ponti MA. Speech2Phone: a novel and efficient method for training speaker recognition models [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 13074 572-585.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_39
Trabalho de evento-anais periodico
A heterogeneous network-based positive and unlabeled learning approach to detect fake news (2021)
Souza, Mariana Caravanti de ; Nogueira, Bruno Magalhães ; Rossi, Rafael Geraldeli ; Marcacini, Ricardo Marcondes ; Rezende, Solange Oliveira
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: FAKE NEWS, PROCESSAMENTO DE TEXTO, APRENDIZADO COMPUTACIONAL
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ABNT
SOUZA, Mariana Caravanti de et al. A heterogeneous network-based positive and unlabeled learning approach to detect fake news. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-91699-2_1. Acesso em: 05 nov. 2025. , 2021
APA
Souza, M. C. de, Nogueira, B. M., Rossi, R. G., Marcacini, R. M., & Rezende, S. O. (2021). A heterogeneous network-based positive and unlabeled learning approach to detect fake news. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-91699-2_1
NLM
Souza MC de, Nogueira BM, Rossi RG, Marcacini RM, Rezende SO. A heterogeneous network-based positive and unlabeled learning approach to detect fake news [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 13074 3-18.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_1
Vancouver
Souza MC de, Nogueira BM, Rossi RG, Marcacini RM, Rezende SO. A heterogeneous network-based positive and unlabeled learning approach to detect fake news [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 13074 3-18.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-91699-2_1

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