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Trabalho de evento
Online event detection in streaming time series: novel metrics and practical insights (2024)
Lima, Janio; Tavares, Lucas Giusti; Pacitti, Esther; Ferreira, João Eduardo ; Santos, Ismael; Siqueira, Isabela Guimarães; Porto, Fabio; Carvalho, Diego; Coutinho, Rafaelli; Ogasawara, Eduardo
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: IME
Subjects: STREAMING, ANÁLISE DE SÉRIES TEMPORAIS
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ABNT
LIMA, Janio et al. Online event detection in streaming time series: novel metrics and practical insights. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/IJCNN60899.2024.10650809. Acesso em: 07 out. 2025.
APA
Lima, J., Tavares, L. G., Pacitti, E., Ferreira, J. E., Santos, I., Siqueira, I. G., et al. (2024). Online event detection in streaming time series: novel metrics and practical insights. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN60899.2024.10650809
NLM
Lima J, Tavares LG, Pacitti E, Ferreira JE, Santos I, Siqueira IG, Porto F, Carvalho D, Coutinho R, Ogasawara E. Online event detection in streaming time series: novel metrics and practical insights [Internet]. Proceedings. 2024 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650809
Vancouver
Lima J, Tavares LG, Pacitti E, Ferreira JE, Santos I, Siqueira IG, Porto F, Carvalho D, Coutinho R, Ogasawara E. Online event detection in streaming time series: novel metrics and practical insights [Internet]. Proceedings. 2024 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650809
Trabalho de evento
Modelling graph neural network by aggregating the activation maps of self-organizing map (2024)
Martins, Luan Vinicius de Carvalho ; Donghong, Ji; Liang, Zhao
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Subjects: REDES NEURAIS, TEORIA DOS GRAFOS
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ABNT
MARTINS, Luan Vinicius de Carvalho e DONGHONG, Ji e LIANG, Zhao. Modelling graph neural network by aggregating the activation maps of self-organizing map. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/IJCNN60899.2024.10650514. Acesso em: 07 out. 2025.
APA
Martins, L. V. de C., Donghong, J., & Liang, Z. (2024). Modelling graph neural network by aggregating the activation maps of self-organizing map. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN60899.2024.10650514
NLM
Martins LV de C, Donghong J, Liang Z. Modelling graph neural network by aggregating the activation maps of self-organizing map [Internet]. Proceedings. 2024 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650514
Vancouver
Martins LV de C, Donghong J, Liang Z. Modelling graph neural network by aggregating the activation maps of self-organizing map [Internet]. Proceedings. 2024 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650514
Trabalho de evento
Deep forests with tree-embeddings and label imputation for weak-label learning (2024)
Ilídio, Pedro ; Cerri, Ricardo ; Vens, Celine ; Nakano, Felipe Kenji
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: ICMC, IFSC
Subjects: APRENDIZADO COMPUTACIONAL, FÍSICA COMPUTACIONAL
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ABNT
ILÍDIO, Pedro et al. Deep forests with tree-embeddings and label imputation for weak-label learning. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/IJCNN60899.2024.10650456. Acesso em: 07 out. 2025.
APA
Ilídio, P., Cerri, R., Vens, C., & Nakano, F. K. (2024). Deep forests with tree-embeddings and label imputation for weak-label learning. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN60899.2024.10650456
NLM
Ilídio P, Cerri R, Vens C, Nakano FK. Deep forests with tree-embeddings and label imputation for weak-label learning [Internet]. Proceedings. 2024 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650456
Vancouver
Ilídio P, Cerri R, Vens C, Nakano FK. Deep forests with tree-embeddings and label imputation for weak-label learning [Internet]. Proceedings. 2024 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650456
Trabalho de evento
Ensemble methods for selecting single nucleotide polymorphisms associated to rice phenotypes (2024)
Funicheli, Breno Osvaldo ; Brondani, Cláudio ; Vianello, Rosana Pereira ; Cerri, Ricardo
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, MELHORAMENTO GENÉTICO, ARROZ
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ABNT
FUNICHELI, Breno Osvaldo et al. Ensemble methods for selecting single nucleotide polymorphisms associated to rice phenotypes. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/IJCNN60899.2024.10650311. Acesso em: 07 out. 2025.
APA
Funicheli, B. O., Brondani, C., Vianello, R. P., & Cerri, R. (2024). Ensemble methods for selecting single nucleotide polymorphisms associated to rice phenotypes. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN60899.2024.10650311
NLM
Funicheli BO, Brondani C, Vianello RP, Cerri R. Ensemble methods for selecting single nucleotide polymorphisms associated to rice phenotypes [Internet]. Proceedings. 2024 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650311
Vancouver
Funicheli BO, Brondani C, Vianello RP, Cerri R. Ensemble methods for selecting single nucleotide polymorphisms associated to rice phenotypes [Internet]. Proceedings. 2024 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650311
Trabalho de evento
TransGNN: a transductive graph neural network with graph dynamic embedding (2023)
Anghinoni, Leandro ; Yu-Tao, Zhu; Donghong, Ji; Liang, Zhao
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS
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ABNT
ANGHINONI, Leandro et al. TransGNN: a transductive graph neural network with graph dynamic embedding. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191134. Acesso em: 07 out. 2025.
APA
Anghinoni, L., Yu-Tao, Z., Donghong, J., & Liang, Z. (2023). TransGNN: a transductive graph neural network with graph dynamic embedding. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191134
NLM
Anghinoni L, Yu-Tao Z, Donghong J, Liang Z. TransGNN: a transductive graph neural network with graph dynamic embedding [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191134
Vancouver
Anghinoni L, Yu-Tao Z, Donghong J, Liang Z. TransGNN: a transductive graph neural network with graph dynamic embedding [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191134
Trabalho de evento
Automated keyphrase generation for brazilian legal information retrieval (2023)
Sakiyama, Kenzo ; Nogueira, Rodrigo Frassetto; Romero, Roseli Aparecida Francelin
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: RECUPERAÇÃO DA INFORMAÇÃO, TRATAMENTO AUTOMÁTICO DE TEXTOS E DISCURSOS, FRAMEWORKS
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ABNT
SAKIYAMA, Kenzo e NOGUEIRA, Rodrigo Frassetto e ROMERO, Roseli Aparecida Francelin. Automated keyphrase generation for brazilian legal information retrieval. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191598. Acesso em: 07 out. 2025.
APA
Sakiyama, K., Nogueira, R. F., & Romero, R. A. F. (2023). Automated keyphrase generation for brazilian legal information retrieval. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191598
NLM
Sakiyama K, Nogueira RF, Romero RAF. Automated keyphrase generation for brazilian legal information retrieval [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191598
Vancouver
Sakiyama K, Nogueira RF, Romero RAF. Automated keyphrase generation for brazilian legal information retrieval [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191598
Trabalho de evento
Classification of coma etiology using convolutional neural networks and long-short term memory networks (2023)
Baldo Júnior, Sérgio ; Carneiro, Murillo Guimarães; Destro-Filho, João Batista; Zhao, Liang ; Tinós, Renato
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: FFCLRP
Subjects: ELETROENCEFALOGRAFIA, COMA, APRENDIZAGEM PROFUNDA, REDES NEURAIS
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ABNT
BALDO JÚNIOR, Sérgio et al. Classification of coma etiology using convolutional neural networks and long-short term memory networks. 2023, Anais.. Queensland: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191203. Acesso em: 07 out. 2025.
APA
Baldo Júnior, S., Carneiro, M. G., Destro-Filho, J. B., Zhao, L., & Tinós, R. (2023). Classification of coma etiology using convolutional neural networks and long-short term memory networks. In Proceedings. Queensland: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1109/IJCNN54540.2023.10191203
NLM
Baldo Júnior S, Carneiro MG, Destro-Filho JB, Zhao L, Tinós R. Classification of coma etiology using convolutional neural networks and long-short term memory networks [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191203
Vancouver
Baldo Júnior S, Carneiro MG, Destro-Filho JB, Zhao L, Tinós R. Classification of coma etiology using convolutional neural networks and long-short term memory networks [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191203
Trabalho de evento
On the use of aggregation functions for semi-supervised network embedding (2023)
Moraes Junior, Marcelo Isaias de ; Marcacini, Ricardo Marcondes
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL
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ABNT
MORAES JUNIOR, Marcelo Isaias de e MARCACINI, Ricardo Marcondes. On the use of aggregation functions for semi-supervised network embedding. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191468. Acesso em: 07 out. 2025.
APA
Moraes Junior, M. I. de, & Marcacini, R. M. (2023). On the use of aggregation functions for semi-supervised network embedding. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191468
NLM
Moraes Junior MI de, Marcacini RM. On the use of aggregation functions for semi-supervised network embedding [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191468
Vancouver
Moraes Junior MI de, Marcacini RM. On the use of aggregation functions for semi-supervised network embedding [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191468
Trabalho de evento
Complex network-based data classification using minimum spanning tree metric and optimization (2023)
Saire, Josimar Edinson Chire ; Liang, Zhao
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL
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ABNT
SAIRE, Josimar Edinson Chire e LIANG, Zhao. Complex network-based data classification using minimum spanning tree metric and optimization. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191004. Acesso em: 07 out. 2025.
APA
Saire, J. E. C., & Liang, Z. (2023). Complex network-based data classification using minimum spanning tree metric and optimization. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191004
NLM
Saire JEC, Liang Z. Complex network-based data classification using minimum spanning tree metric and optimization [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191004
Vancouver
Saire JEC, Liang Z. Complex network-based data classification using minimum spanning tree metric and optimization [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191004
Trabalho de evento
A new time series framework for forest fire risk forecasting and classification (2023)
Santos, Bruna Zamith ; Soriano, Balbina Maria Araujo ; Narciso, Marcelo Gonçalves; Silva, Diego Furtado ; Cerri, Ricardo
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), FRAMEWORKS, INCÊNDIOS FLORESTAIS
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ABNT
SANTOS, Bruna Zamith et al. A new time series framework for forest fire risk forecasting and classification. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191502. Acesso em: 07 out. 2025.
APA
Santos, B. Z., Soriano, B. M. A., Narciso, M. G., Silva, D. F., & Cerri, R. (2023). A new time series framework for forest fire risk forecasting and classification. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191502
NLM
Santos BZ, Soriano BMA, Narciso MG, Silva DF, Cerri R. A new time series framework for forest fire risk forecasting and classification [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191502
Vancouver
Santos BZ, Soriano BMA, Narciso MG, Silva DF, Cerri R. A new time series framework for forest fire risk forecasting and classification [Internet]. Proceedings. 2023 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191502
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: 07 out. 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 out. 07 ] 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 out. 07 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892642
Trabalho de evento
Bayesian network for hydrological model: an inference approach (2022)
Ribeiro, Vitor P.; Cunha, Ângela Silviane Moura; Duarte, Sergio Nascimento ; Padovani, Carlos Roberto ; Marques, Patricia Algélica Alves ; Maciel, Carlos Dias ; Balestieri, José Antonio Perrella
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: ESALQ, EESC
Subjects: EVAPOTRANSPIRAÇÃO, BALANÇO HÍDRICO, INFERÊNCIA BAYESIANA, ENGENHARIA ELÉTRICA
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ABNT
RIBEIRO, Vitor P. et al. Bayesian network for hydrological model: an inference approach. 2022, Anais.. Piscataway, NJ, USA: IEEE, 2022. Disponível em: https://doi.org/10.1109/IJCNN55064.2022.9892468. Acesso em: 07 out. 2025.
APA
Ribeiro, V. P., Cunha, Â. S. M., Duarte, S. N., Padovani, C. R., Marques, P. A. A., Maciel, C. D., & Balestieri, J. A. P. (2022). Bayesian network for hydrological model: an inference approach. In Proceedings. Piscataway, NJ, USA: IEEE. doi:10.1109/IJCNN55064.2022.9892468
NLM
Ribeiro VP, Cunha ÂSM, Duarte SN, Padovani CR, Marques PAA, Maciel CD, Balestieri JAP. Bayesian network for hydrological model: an inference approach [Internet]. Proceedings. 2022 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892468
Vancouver
Ribeiro VP, Cunha ÂSM, Duarte SN, Padovani CR, Marques PAA, Maciel CD, Balestieri JAP. Bayesian network for hydrological model: an inference approach [Internet]. Proceedings. 2022 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN55064.2022.9892468
Trabalho de evento
Seasonal-trend decomposition based on Loess + machine learning: hybrid forecasting for monthly univariate time series (2021)
Silvestre, Gabriel Dalforno; Santos, Moises Rocha dos ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS)
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ABNT
SILVESTRE, Gabriel Dalforno e SANTOS, Moises Rocha dos e CARVALHO, André Carlos Ponce de Leon Ferreira de. Seasonal-trend decomposition based on Loess + machine learning: hybrid forecasting for monthly univariate time series. 2021, Anais.. Piscataway: IEEE, 2021. Disponível em: https://doi.org/10.1109/IJCNN52387.2021.9533644. Acesso em: 07 out. 2025.
APA
Silvestre, G. D., Santos, M. R. dos, & Carvalho, A. C. P. de L. F. de. (2021). Seasonal-trend decomposition based on Loess + machine learning: hybrid forecasting for monthly univariate time series. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN52387.2021.9533644
NLM
Silvestre GD, Santos MR dos, Carvalho ACP de LF de. Seasonal-trend decomposition based on Loess + machine learning: hybrid forecasting for monthly univariate time series [Internet]. Proceedings. 2021 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN52387.2021.9533644
Vancouver
Silvestre GD, Santos MR dos, Carvalho ACP de LF de. Seasonal-trend decomposition based on Loess + machine learning: hybrid forecasting for monthly univariate time series [Internet]. Proceedings. 2021 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN52387.2021.9533644
Trabalho de evento
An optimized modularity-based high level classification model (2020)
Colliri, Tiago Santos ; Weiguang, Liu; Liang, Zhao
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Subjects: REDES COMPLEXAS, RECONHECIMENTO DE PADRÕES
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ABNT
COLLIRI, Tiago Santos e WEIGUANG, Liu e LIANG, Zhao. An optimized modularity-based high level classification model. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9206755. Acesso em: 07 out. 2025.
APA
Colliri, T. S., Weiguang, L., & Liang, Z. (2020). An optimized modularity-based high level classification model. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN48605.2020.9206755
NLM
Colliri TS, Weiguang L, Liang Z. An optimized modularity-based high level classification model [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206755
Vancouver
Colliri TS, Weiguang L, Liang Z. An optimized modularity-based high level classification model [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206755
Trabalho de evento
Unveiling Parkinson's disease features from a primate model with deep neural networks (2020)
Ranieri, Caetano Mazzoni ; Moioli, Renan Cipriano ; Romero, Roseli Aparecida Francelin ; Araújo, Mariana F. P. de; Santana, Maxwell Barbosa de ; Pimentel, Jhielson Montino ; Vargas, Patrícia Amâncio
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, ELETROENCEFALOGRAFIA, DOENÇA DE PARKINSON, NEUROCIÊNCIAS
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ABNT
RANIERI, Caetano Mazzoni et al. Unveiling Parkinson's disease features from a primate model with deep neural networks. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9207180. Acesso em: 07 out. 2025.
APA
Ranieri, C. M., Moioli, R. C., Romero, R. A. F., Araújo, M. F. P. de, Santana, M. B. de, Pimentel, J. M., & Vargas, P. A. (2020). Unveiling Parkinson's disease features from a primate model with deep neural networks. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN48605.2020.9207180
NLM
Ranieri CM, Moioli RC, Romero RAF, Araújo MFP de, Santana MB de, Pimentel JM, Vargas PA. Unveiling Parkinson's disease features from a primate model with deep neural networks [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9207180
Vancouver
Ranieri CM, Moioli RC, Romero RAF, Araújo MFP de, Santana MB de, Pimentel JM, Vargas PA. Unveiling Parkinson's disease features from a primate model with deep neural networks [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9207180
Trabalho de evento
Measuring the engagement level in encrypted group conversations by using temporal networks (2020)
Cotacallapa, Moshe; Berton, Lilian; Ferreira, Leonardo N.; Quiles, Marcos Gonçalves; Liang, Zhao; Macau, Elbert E. N.; Vega-Oliveros, Didier Augusto
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: FFCLRP
Subjects: MENSAGEM, CHAT, CRIPTOLOGIA, PRIVACIDADE, FRAMEWORKS
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ABNT
COTACALLAPA, Moshe et al. Measuring the engagement level in encrypted group conversations by using temporal networks. 2020, Anais.. Los Alamitos: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9207174. Acesso em: 07 out. 2025.
APA
Cotacallapa, M., Berton, L., Ferreira, L. N., Quiles, M. G., Liang, Z., Macau, E. E. N., & Vega-Oliveros, D. A. (2020). Measuring the engagement level in encrypted group conversations by using temporal networks. In Proceedings. Los Alamitos: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1109/IJCNN48605.2020.9207174
NLM
Cotacallapa M, Berton L, Ferreira LN, Quiles MG, Liang Z, Macau EEN, Vega-Oliveros DA. Measuring the engagement level in encrypted group conversations by using temporal networks [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9207174
Vancouver
Cotacallapa M, Berton L, Ferreira LN, Quiles MG, Liang Z, Macau EEN, Vega-Oliveros DA. Measuring the engagement level in encrypted group conversations by using temporal networks [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9207174
Trabalho de evento
Uncovering human multimodal activity recognition with a deep learning approach (2020)
Ranieri, Caetano Mazzoni ; Vargas, Patrícia Amâncio; Romero, Roseli Aparecida Francelin
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, VISÃO COMPUTACIONAL, ROBÓTICA, INTERAÇÃO HOMEM-MÁQUINA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RANIERI, Caetano Mazzoni e VARGAS, Patrícia Amâncio e ROMERO, Roseli Aparecida Francelin. Uncovering human multimodal activity recognition with a deep learning approach. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9207255. Acesso em: 07 out. 2025.
APA
Ranieri, C. M., Vargas, P. A., & Romero, R. A. F. (2020). Uncovering human multimodal activity recognition with a deep learning approach. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN48605.2020.9207255
NLM
Ranieri CM, Vargas PA, Romero RAF. Uncovering human multimodal activity recognition with a deep learning approach [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9207255
Vancouver
Ranieri CM, Vargas PA, Romero RAF. Uncovering human multimodal activity recognition with a deep learning approach [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9207255
Trabalho de evento
On ensemble techniques for data stream regression (2020)
Gomes, Heitor Murilo ; Montiel, Jacob ; Mastelini, Saulo Martiello ; Pfahringer, Bernhard ; Bifet, Albert
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GOMES, Heitor Murilo et al. On ensemble techniques for data stream regression. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9206756. Acesso em: 07 out. 2025.
APA
Gomes, H. M., Montiel, J., Mastelini, S. M., Pfahringer, B., & Bifet, A. (2020). On ensemble techniques for data stream regression. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN48605.2020.9206756
NLM
Gomes HM, Montiel J, Mastelini SM, Pfahringer B, Bifet A. On ensemble techniques for data stream regression [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206756
Vancouver
Gomes HM, Montiel J, Mastelini SM, Pfahringer B, Bifet A. On ensemble techniques for data stream regression [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206756
Trabalho de evento
Effective deep reinforcement learning setups for multiple goals on visual navigation (2020)
Horita, Luiz Ricardo Takeshi ; Wolf, Denis Fernando ; Grassi Júnior, Valdir
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: ICMC, EESC
Subjects: ALGORITMOS, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HORITA, Luiz Ricardo Takeshi e WOLF, Denis Fernando e GRASSI JÚNIOR, Valdir. Effective deep reinforcement learning setups for multiple goals on visual navigation. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9206917. Acesso em: 07 out. 2025.
APA
Horita, L. R. T., Wolf, D. F., & Grassi Júnior, V. (2020). Effective deep reinforcement learning setups for multiple goals on visual navigation. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN48605.2020.9206917
NLM
Horita LRT, Wolf DF, Grassi Júnior V. Effective deep reinforcement learning setups for multiple goals on visual navigation [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206917
Vancouver
Horita LRT, Wolf DF, Grassi Júnior V. Effective deep reinforcement learning setups for multiple goals on visual navigation [Internet]. Proceedings. 2020 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9206917
Trabalho de evento
Epilepsy detection using multiclass classifier based on spectral features (2019)
Oliva, Jefferson Tales ; Rosa, João Luís Garcia
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, PROCESSAMENTO DE SINAIS BIOMÉDICOS, TECNOLOGIAS DA SAÚDE, ELETROENCEFALOGRAFIA, EPILEPSIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVA, Jefferson Tales e ROSA, João Luís Garcia. Epilepsy detection using multiclass classifier based on spectral features. 2019, Anais.. Piscataway: IEEE, 2019. Disponível em: https://doi.org/10.1109/IJCNN.2019.8852379. Acesso em: 07 out. 2025.
APA
Oliva, J. T., & Rosa, J. L. G. (2019). Epilepsy detection using multiclass classifier based on spectral features. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN.2019.8852379
NLM
Oliva JT, Rosa JLG. Epilepsy detection using multiclass classifier based on spectral features [Internet]. Proceedings. 2019 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN.2019.8852379
Vancouver
Oliva JT, Rosa JLG. Epilepsy detection using multiclass classifier based on spectral features [Internet]. Proceedings. 2019 ;[citado 2025 out. 07 ] Available from: https://doi.org/10.1109/IJCNN.2019.8852379

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