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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
Fonte: Neural Computing and Applications. Unidade: ICMC
Assuntos: 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
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
Fonte: Neural Computing and Applications. Unidade: ICMC
Assuntos: 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
Fonte: Neural Computing and Applications. Unidade: ICMC
Assuntos: 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
Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction (2021)
Maciel, Leandro dos Santos ; Ballini, Rosangela ; Gomide, Fernando
Fonte: Neural Computing and Applications. Unidade: FEA
Assuntos: APRENDIZADO COMPUTACIONAL, LÓGICA FUZZY, MOEDA (ECONOMIA)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MACIEL, Leandro dos Santos e BALLINI, Rosangela e GOMIDE, Fernando. Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction. Neural Computing and Applications, v. 1, p. 1, 2021Tradução . . Disponível em: https://doi.org/10.1007/s00521-021-06263-5. Acesso em: 28 nov. 2025.
APA
Maciel, L. dos S., Ballini, R., & Gomide, F. (2021). Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction. Neural Computing and Applications, 1, 1. doi:10.1007/s00521-021-06263-5
NLM
Maciel L dos S, Ballini R, Gomide F. Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction [Internet]. Neural Computing and Applications. 2021 ; 1 1.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-021-06263-5
Vancouver
Maciel L dos S, Ballini R, Gomide F. Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction [Internet]. Neural Computing and Applications. 2021 ; 1 1.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-021-06263-5
Artigo de periodico
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms (2017)
Pereira, Luís Augusto Martins; Papa, João Paulo; Coelho, André Luís Vasconcelos; Lima, Clodoaldo Aparecido de Moraes; Pereira, Danillo Roberto; Albuquerque, Victor Hugo Costa de
Fonte: Neural Computing and Applications. Unidade: EACH
Assuntos: EPILEPSIA, ELETROENCEFALOGRAFIA, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PEREIRA, Luís Augusto Martins et al. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms. Neural Computing and Applications, n. ju 2017, p. 1-13, 2017Tradução . . Disponível em: https://doi.org/10.1007/s00521-017-3124-3. Acesso em: 28 nov. 2025.
APA
Pereira, L. A. M., Papa, J. P., Coelho, A. L. V., Lima, C. A. de M., Pereira, D. R., & Albuquerque, V. H. C. de. (2017). Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms. Neural Computing and Applications, ( ju 2017), 1-13. doi:10.1007/s00521-017-3124-3
NLM
Pereira LAM, Papa JP, Coelho ALV, Lima CA de M, Pereira DR, Albuquerque VHC de. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms [Internet]. Neural Computing and Applications. 2017 ;( ju 2017): 1-13.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-017-3124-3
Vancouver
Pereira LAM, Papa JP, Coelho ALV, Lima CA de M, Pereira DR, Albuquerque VHC de. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms [Internet]. Neural Computing and Applications. 2017 ;( ju 2017): 1-13.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s00521-017-3124-3

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