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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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191203
Trabalho de evento
Feature selection using complex networks to support price trend forecast in energy markets (2023)
Castilho, Douglas; Santos, Moises Rocha dos ; Tinós, Renato ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Paula, Marcos Basile Saviano de; Ladeira, Lucas; Guarnier, Ewerton ; Silva Filho, Donato ; Suiama, Danilo Y; Macedo Junior, Edmur A; Alipio, Lucas P
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), ENERGIA ELÉTRICA
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ABNT
CASTILHO, Douglas et al. Feature selection using complex networks to support price trend forecast in energy markets. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/IJCNN54540.2023.10191426. Acesso em: 08 out. 2025.
APA
Castilho, D., Santos, M. R. dos, Tinós, R., Carvalho, A. C. P. de L. F. de, Paula, M. B. S. de, Ladeira, L., et al. (2023). Feature selection using complex networks to support price trend forecast in energy markets. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN54540.2023.10191426
NLM
Castilho D, Santos MR dos, Tinós R, Carvalho ACP de LF de, Paula MBS de, Ladeira L, Guarnier E, Silva Filho D, Suiama DY, Macedo Junior EA, Alipio LP. Feature selection using complex networks to support price trend forecast in energy markets [Internet]. Proceedings. 2023 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191426
Vancouver
Castilho D, Santos MR dos, Tinós R, Carvalho ACP de LF de, Paula MBS de, Ladeira L, Guarnier E, Silva Filho D, Suiama DY, Macedo Junior EA, Alipio LP. Feature selection using complex networks to support price trend forecast in energy markets [Internet]. Proceedings. 2023 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/IJCNN54540.2023.10191426
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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] 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: 08 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. 08 ] 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. 08 ] Available from: https://doi.org/10.1109/IJCNN.2019.8852379

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