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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
Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction (2021)
Maciel, Leandro dos Santos ; Ballini, Rosangela ; Gomide, Fernando
Source: Neural Computing and Applications. Unidade: FEA
Subjects: 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
Source: Neural Computing and Applications. Unidade: EACH
Subjects: 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
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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