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Artigo de periodico
How do financial time series enhance the detection of news significance in market movements?: A study using graph neural networks with heterogeneous representations (2025)
Reis Filho, Ivan José dos ; Gôlo, Marcos Paulo Silva ; Marcacini, Ricardo Marcondes ; Rezende, Solange Oliveira
Source: Neural Computing and Applications. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), MERCADO FINANCEIRO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
REIS FILHO, Ivan José dos et al. How do financial time series enhance the detection of news significance in market movements?: A study using graph neural networks with heterogeneous representations. Neural Computing and Applications, v. 37, n. Ja 2025, p. 1307-1319, 2025Tradução . . Disponível em: https://doi.org/10.1007/s00521-024-10418-5. Acesso em: 28 nov. 2025.
APA
Reis Filho, I. J. dos, Gôlo, M. P. S., Marcacini, R. M., & Rezende, S. O. (2025). How do financial time series enhance the detection of news significance in market movements?: A study using graph neural networks with heterogeneous representations. Neural Computing and Applications, 37( Ja 2025), 1307-1319. doi:10.1007/s00521-024-10418-5
NLM
Reis Filho IJ dos, Gôlo MPS, Marcacini RM, Rezende SO. How do financial time series enhance the detection of news significance in market movements?: A study using graph neural networks with heterogeneous representations [Internet]. Neural Computing and Applications. 2025 ; 37( Ja 2025): 1307-1319.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-024-10418-5
Vancouver
Reis Filho IJ dos, Gôlo MPS, Marcacini RM, Rezende SO. How do financial time series enhance the detection of news significance in market movements?: A study using graph neural networks with heterogeneous representations [Internet]. Neural Computing and Applications. 2025 ; 37( Ja 2025): 1307-1319.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-024-10418-5
Artigo de periodico
Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers (2024)
Rocha Filho, Geraldo Pereira ; Meneguette, Rodolfo Ipolito ; Mendonça, Fabio Lucio Lopes de ; Enamoto, Liriam Michi ; Pessin, Gustavo ; Gonçalves, Vinicius Pereira
Source: Neural Computing and Applications. Unidade: ICMC
Subjects: EMOÇÕES, VOZ, REDES NEURAIS, RECONHECIMENTO DE VOZ
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ROCHA FILHO, Geraldo Pereira et al. Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers. Neural Computing and Applications, v. 36, p. 19939–19950, 2024Tradução . . Disponível em: https://doi.org/10.1007/s00521-024-10249-4. Acesso em: 28 nov. 2025.
APA
Rocha Filho, G. P., Meneguette, R. I., Mendonça, F. L. L. de, Enamoto, L. M., Pessin, G., & Gonçalves, V. P. (2024). Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers. Neural Computing and Applications, 36, 19939–19950. doi:10.1007/s00521-024-10249-4
NLM
Rocha Filho GP, Meneguette RI, Mendonça FLL de, Enamoto LM, Pessin G, Gonçalves VP. Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers [Internet]. Neural Computing and Applications. 2024 ; 36 19939–19950.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-024-10249-4
Vancouver
Rocha Filho GP, Meneguette RI, Mendonça FLL de, Enamoto LM, Pessin G, Gonçalves VP. Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers [Internet]. Neural Computing and Applications. 2024 ; 36 19939–19950.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-024-10249-4
Artigo de periodico
Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis (2023)
Vargas, Dionathan Luan de ; Oliva, Jefferson Tales ; Teixeira, Marcelo ; Casanova, Dalcimar ; Rosa, João Luís Garcia
Source: Neural Computing and Applications. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ELETROENCEFALOGRAFIA, EPILEPSIA, DIAGNÓSTICO POR COMPUTADOR, TECNOLOGIAS DA SAÚDE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VARGAS, Dionathan Luan de et al. Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis. Neural Computing and Applications, v. 35, n. 16, p. 12195-12219, 2023Tradução . . Disponível em: https://doi.org/10.1007/s00521-023-08350-1. Acesso em: 28 nov. 2025.
APA
Vargas, D. L. de, Oliva, J. T., Teixeira, M., Casanova, D., & Rosa, J. L. G. (2023). Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis. Neural Computing and Applications, 35( 16), 12195-12219. doi:10.1007/s00521-023-08350-1
NLM
Vargas DL de, Oliva JT, Teixeira M, Casanova D, Rosa JLG. Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis [Internet]. Neural Computing and Applications. 2023 ; 35( 16): 12195-12219.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-023-08350-1
Vancouver
Vargas DL de, Oliva JT, Teixeira M, Casanova D, Rosa JLG. Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis [Internet]. Neural Computing and Applications. 2023 ; 35( 16): 12195-12219.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-023-08350-1
Artigo de periodico
A classification and quantification approach to generate features in soundscape ecology using neural networks (2022)
Dias, Fabio Felix ; Ponti, Moacir Antonelli ; Minghim, Rosane
Source: Neural Computing and Applications. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ACÚSTICA, MONITORAMENTO AMBIENTAL, PÁSSAROS, ANURA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DIAS, Fabio Felix e PONTI, Moacir Antonelli e MINGHIM, Rosane. A classification and quantification approach to generate features in soundscape ecology using neural networks. Neural Computing and Applications, v. 34, n. 3, p. 1923-1937, 2022Tradução . . Disponível em: https://doi.org/10.1007/s00521-021-06501-w. Acesso em: 28 nov. 2025.
APA
Dias, F. F., Ponti, M. A., & Minghim, R. (2022). A classification and quantification approach to generate features in soundscape ecology using neural networks. Neural Computing and Applications, 34( 3), 1923-1937. doi:10.1007/s00521-021-06501-w
NLM
Dias FF, Ponti MA, Minghim R. A classification and quantification approach to generate features in soundscape ecology using neural networks [Internet]. Neural Computing and Applications. 2022 ; 34( 3): 1923-1937.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-021-06501-w
Vancouver
Dias FF, Ponti MA, Minghim R. A classification and quantification approach to generate features in soundscape ecology using neural networks [Internet]. Neural Computing and Applications. 2022 ; 34( 3): 1923-1937.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-021-06501-w
Artigo de periodico
Robust image features for classification and zero-shot tasks by merging visual and semantic attributes (2022)
Resende, Damares Crystina Oliveira de ; Ponti, Moacir Antonelli
Source: Neural Computing and Applications. Unidade: ICMC
Subjects: RECONHECIMENTO DE IMAGEM, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RESENDE, Damares Crystina Oliveira de e PONTI, Moacir Antonelli. Robust image features for classification and zero-shot tasks by merging visual and semantic attributes. Neural Computing and Applications, v. 34, n. 6, p. 4459-4471, 2022Tradução . . Disponível em: https://doi.org/10.1007/s00521-021-06601-7. Acesso em: 28 nov. 2025.
APA
Resende, D. C. O. de, & Ponti, M. A. (2022). Robust image features for classification and zero-shot tasks by merging visual and semantic attributes. Neural Computing and Applications, 34( 6), 4459-4471. doi:10.1007/s00521-021-06601-7
NLM
Resende DCO de, Ponti MA. Robust image features for classification and zero-shot tasks by merging visual and semantic attributes [Internet]. Neural Computing and Applications. 2022 ; 34( 6): 4459-4471.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-021-06601-7
Vancouver
Resende DCO de, Ponti MA. Robust image features for classification and zero-shot tasks by merging visual and semantic attributes [Internet]. Neural Computing and Applications. 2022 ; 34( 6): 4459-4471.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-021-06601-7
Artigo de periodico
Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil (2016)
Furquim, Gustavo; Pessin, Gustavo; Faiçal, Bruno S; Mendiondo, Eduardo Mario ; Ueyama, Jó
Source: Neural Computing and Applications. Unidades: EESC, ICMC
Subjects: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FURQUIM, Gustavo et al. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil. Neural Computing and Applications, v. 27, p. 1129-1141, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-1930-z. Acesso em: 28 nov. 2025.
APA
Furquim, G., Pessin, G., Faiçal, B. S., Mendiondo, E. M., & Ueyama, J. (2016). Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil. Neural Computing and Applications, 27, 1129-1141. doi:10.1007/s00521-015-1930-z
NLM
Furquim G, Pessin G, Faiçal BS, Mendiondo EM, Ueyama J. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil [Internet]. Neural Computing and Applications. 2016 ; 27 1129-1141.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-015-1930-z
Vancouver
Furquim G, Pessin G, Faiçal BS, Mendiondo EM, Ueyama J. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil [Internet]. Neural Computing and Applications. 2016 ; 27 1129-1141.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-015-1930-z

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