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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
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] 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
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] 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
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] 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
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] 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
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] Available from: https://doi.org/10.1007/s00521-021-06601-7
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)
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] Available from: https://doi.org/10.1007/s00521-021-06263-5
Artigo de periodico
OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects (2018)
Sousa, Miguel Angelo de Abreu de; Del Moral Hernandez, Emilio ; Pires, Ricardo
Source: Neural Computing and Applications. Unidade: EP
Assunto: SISTEMAS EMBUTIDOS
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ABNT
SOUSA, Miguel Angelo de Abreu de e DEL MORAL HERNANDEZ, Emilio e PIRES, Ricardo. OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects. Neural Computing and Applications, v. 30, n. 12, p. 3759-3771, 2018Tradução . . Disponível em: https://doi.org/10.1007/s00521-017-2957-0. Acesso em: 16 jun. 2025.
APA
Sousa, M. A. de A. de, Del Moral Hernandez, E., & Pires, R. (2018). OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects. Neural Computing and Applications, 30( 12), 3759-3771. doi:10.1007/s00521-017-2957-0
NLM
Sousa MA de A de, Del Moral Hernandez E, Pires R. OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects [Internet]. Neural Computing and Applications. 2018 ; 30( 12): 3759-3771.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-017-2957-0
Vancouver
Sousa MA de A de, Del Moral Hernandez E, Pires R. OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects [Internet]. Neural Computing and Applications. 2018 ; 30( 12): 3759-3771.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-017-2957-0
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
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] Available from: https://doi.org/10.1007/s00521-017-3124-3
Artigo de periodico
Classification of electromyography signals using relevance vector machines and fractal dimension (2016)
Lima, Clodoaldo Aparecido de Moraes; Coelho, André L.V; Madeo, Renata Cristina Barros; Peres, Sarajane Marques
Source: Neural Computing and Applications. Unidade: EACH
Subjects: RECONHECIMENTO DE PADRÕES, INTELIGÊNCIA ARTIFICIAL, ANÁLISE DO MOVIMENTO HUMANO, ELETROMIOGRAFIA
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ABNT
LIMA, Clodoaldo Aparecido de Moraes et al. Classification of electromyography signals using relevance vector machines and fractal dimension. Neural Computing and Applications, v. 27, n. 3, p. 791-804, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-1953-5. Acesso em: 16 jun. 2025.
APA
Lima, C. A. de M., Coelho, A. L. V., Madeo, R. C. B., & Peres, S. M. (2016). Classification of electromyography signals using relevance vector machines and fractal dimension. Neural Computing and Applications, 27( 3), 791-804. doi:10.1007/s00521-015-1953-5
NLM
Lima CA de M, Coelho ALV, Madeo RCB, Peres SM. Classification of electromyography signals using relevance vector machines and fractal dimension [Internet]. Neural Computing and Applications. 2016 ; 27( 3): 791-804.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-1953-5
Vancouver
Lima CA de M, Coelho ALV, Madeo RCB, Peres SM. Classification of electromyography signals using relevance vector machines and fractal dimension [Internet]. Neural Computing and Applications. 2016 ; 27( 3): 791-804.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-1953-5
Artigo de periodico
Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure (2016)
Messias, Valter Rogério; Estrella, Júlio Cezar ; Ehlers, Ricardo Sandes ; Santana, Marcos José; Santana, Regina Helena Carlucci; Reiff-Marganiec, Stephan
Source: Neural Computing and Applications. Unidade: ICMC
Subjects: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE, INFERÊNCIA BAYESIANA, ESTATÍSTICA APLICADA, ALGORITMOS GENÉTICOS, MODELOS EM SÉRIES TEMPORAIS, ELASTICIDADE, COMPUTAÇÃO EM NUVEM
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ABNT
MESSIAS, Valter Rogério et al. Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure. Neural Computing and Applications, v. No 2016, n. 8, p. 2383-2406, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-2133-3. Acesso em: 16 jun. 2025.
APA
Messias, V. R., Estrella, J. C., Ehlers, R. S., Santana, M. J., Santana, R. H. C., & Reiff-Marganiec, S. (2016). Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure. Neural Computing and Applications, No 2016( 8), 2383-2406. doi:10.1007/s00521-015-2133-3
NLM
Messias VR, Estrella JC, Ehlers RS, Santana MJ, Santana RHC, Reiff-Marganiec S. Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure [Internet]. Neural Computing and Applications. 2016 ; No 2016( 8): 2383-2406.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-2133-3
Vancouver
Messias VR, Estrella JC, Ehlers RS, Santana MJ, Santana RHC, Reiff-Marganiec S. Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure [Internet]. Neural Computing and Applications. 2016 ; No 2016( 8): 2383-2406.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-2133-3
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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-1930-z

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