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Artigo de periodico
Brazilian forest dataset: a new dataset to model local biodiversity (2022)
Rios, Ricardo Araújo ; Rios, Tatiane Nogueira; Palma, Gabriel Rodrigues ; Mello, Rodrigo Fernandes de
Fonte: Journal of Experimental and Theoretical Artificial Intelligence. Unidades: ICMC, ESALQ
Assuntos: APRENDIZADO COMPUTACIONAL, BIODIVERSIDADE, FLORESTAS TROPICAIS, MODELAGEM DE DADOS
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RIOS, Ricardo Araújo et al. Brazilian forest dataset: a new dataset to model local biodiversity. Journal of Experimental and Theoretical Artificial Intelligence, v. 34, p. 327-354, 2022Tradução . . Disponível em: https://doi.org/10.1080/0952813X.2021.1871972. Acesso em: 25 set. 2024.
APA
Rios, R. A., Rios, T. N., Palma, G. R., & Mello, R. F. de. (2022). Brazilian forest dataset: a new dataset to model local biodiversity. Journal of Experimental and Theoretical Artificial Intelligence, 34, 327-354. doi:10.1080/0952813X.2021.1871972
NLM
Rios RA, Rios TN, Palma GR, Mello RF de. Brazilian forest dataset: a new dataset to model local biodiversity [Internet]. Journal of Experimental and Theoretical Artificial Intelligence. 2022 ; 34 327-354.[citado 2024 set. 25 ] Available from: https://doi.org/10.1080/0952813X.2021.1871972
Vancouver
Rios RA, Rios TN, Palma GR, Mello RF de. Brazilian forest dataset: a new dataset to model local biodiversity [Internet]. Journal of Experimental and Theoretical Artificial Intelligence. 2022 ; 34 327-354.[citado 2024 set. 25 ] Available from: https://doi.org/10.1080/0952813X.2021.1871972
Artigo de periodico
Neural network training fingerprint: visual analytics of the training process in classification neural networks (2022)
Ferreira, Martha Dais ; Cantareira, Gabriel Dias ; Mello, Rodrigo Fernandes de ; Paulovich, Fernando Vieira
Fonte: Journal of Visualization. Unidade: ICMC
Assuntos: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, VISUALIZAÇÃO
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FERREIRA, Martha Dais et al. Neural network training fingerprint: visual analytics of the training process in classification neural networks. Journal of Visualization, v. 25, n. 3, p. 593-612, 2022Tradução . . Disponível em: https://doi.org/10.1007/s12650-021-00809-4. Acesso em: 25 set. 2024.
APA
Ferreira, M. D., Cantareira, G. D., Mello, R. F. de, & Paulovich, F. V. (2022). Neural network training fingerprint: visual analytics of the training process in classification neural networks. Journal of Visualization, 25( 3), 593-612. doi:10.1007/s12650-021-00809-4
NLM
Ferreira MD, Cantareira GD, Mello RF de, Paulovich FV. Neural network training fingerprint: visual analytics of the training process in classification neural networks [Internet]. Journal of Visualization. 2022 ; 25( 3): 593-612.[citado 2024 set. 25 ] Available from: https://doi.org/10.1007/s12650-021-00809-4
Vancouver
Ferreira MD, Cantareira GD, Mello RF de, Paulovich FV. Neural network training fingerprint: visual analytics of the training process in classification neural networks [Internet]. Journal of Visualization. 2022 ; 25( 3): 593-612.[citado 2024 set. 25 ] Available from: https://doi.org/10.1007/s12650-021-00809-4
Artigo de periodico
Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination (2022)
Aradhya, Abhay M. S ; Ashfahani, Andri; Angelina, Fienny; Pratama, Mahardhika ; Mello, Rodrigo Fernandes de ; Sundaram, Suresh
Fonte: Information Sciences. Unidade: ICMC
Assuntos: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, COMPUTAÇÃO EVOLUTIVA
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ARADHYA, Abhay M. S et al. Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination. Information Sciences, v. 607, p. 638-653, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2022.05.100. Acesso em: 25 set. 2024.
APA
Aradhya, A. M. S., Ashfahani, A., Angelina, F., Pratama, M., Mello, R. F. de, & Sundaram, S. (2022). Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination. Information Sciences, 607, 638-653. doi:10.1016/j.ins.2022.05.100
NLM
Aradhya AMS, Ashfahani A, Angelina F, Pratama M, Mello RF de, Sundaram S. Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination [Internet]. Information Sciences. 2022 ; 607 638-653.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.ins.2022.05.100
Vancouver
Aradhya AMS, Ashfahani A, Angelina F, Pratama M, Mello RF de, Sundaram S. Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination [Internet]. Information Sciences. 2022 ; 607 638-653.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.ins.2022.05.100
Artigo de periodico
Multi-keyword ranked search based on mapping set matching in cloud ciphertext storage system (2021)
Xiao, Tingting; Han, Dezhi ; He, Junhui; Li, Kuan-Ching ; Mello, Rodrigo Fernandes de
Fonte: Connection Science. Unidade: ICMC
Assuntos: COMPUTAÇÃO EM NUVEM, RECUPERAÇÃO DA INFORMAÇÃO, CRIPTOLOGIA
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XIAO, Tingting et al. Multi-keyword ranked search based on mapping set matching in cloud ciphertext storage system. Connection Science, v. 33, n. 1, p. 95-112, 2021Tradução . . Disponível em: https://doi.org/10.1080/09540091.2020.1753175. Acesso em: 25 set. 2024.
APA
Xiao, T., Han, D., He, J., Li, K. -C., & Mello, R. F. de. (2021). Multi-keyword ranked search based on mapping set matching in cloud ciphertext storage system. Connection Science, 33( 1), 95-112. doi:10.1080/09540091.2020.1753175
NLM
Xiao T, Han D, He J, Li K-C, Mello RF de. Multi-keyword ranked search based on mapping set matching in cloud ciphertext storage system [Internet]. Connection Science. 2021 ; 33( 1): 95-112.[citado 2024 set. 25 ] Available from: https://doi.org/10.1080/09540091.2020.1753175
Vancouver
Xiao T, Han D, He J, Li K-C, Mello RF de. Multi-keyword ranked search based on mapping set matching in cloud ciphertext storage system [Internet]. Connection Science. 2021 ; 33( 1): 95-112.[citado 2024 set. 25 ] Available from: https://doi.org/10.1080/09540091.2020.1753175
Artigo de periodico
Time complexity evaluation of cover song identification algorithms (2021)
Ferreira, Martha Dais ; Mello, Rodrigo Fernandes de
Fonte: Applied Acoustics. Unidade: ICMC
Assuntos: RECUPERAÇÃO DA INFORMAÇÃO, RECONHECIMENTO DE PADRÕES, ALGORITMOS ÚTEIS E ESPECÍFICOS, MÚSICA
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FERREIRA, Martha Dais e MELLO, Rodrigo Fernandes de. Time complexity evaluation of cover song identification algorithms. Applied Acoustics, v. 175, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.apacoust.2020.107777. Acesso em: 25 set. 2024.
APA
Ferreira, M. D., & Mello, R. F. de. (2021). Time complexity evaluation of cover song identification algorithms. Applied Acoustics, 175, 1-11. doi:10.1016/j.apacoust.2020.107777
NLM
Ferreira MD, Mello RF de. Time complexity evaluation of cover song identification algorithms [Internet]. Applied Acoustics. 2021 ; 175 1-11.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.apacoust.2020.107777
Vancouver
Ferreira MD, Mello RF de. Time complexity evaluation of cover song identification algorithms [Internet]. Applied Acoustics. 2021 ; 175 1-11.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.apacoust.2020.107777
Artigo de periodico
Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII (2021)
Lopes, Tiago José da Silva ; Rios, Ricardo Araújo ; Nogueira, Tatiane ; Mello, Rodrigo Fernandes de
Fonte: Scientific Reports. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, HEMOFILIA, PROTEÍNAS
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LOPES, Tiago José da Silva et al. Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII. Scientific Reports, v. 11, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1038/s41598-021-92201-3. Acesso em: 25 set. 2024.
APA
Lopes, T. J. da S., Rios, R. A., Nogueira, T., & Mello, R. F. de. (2021). Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII. Scientific Reports, 11, 1-11. doi:10.1038/s41598-021-92201-3
NLM
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII [Internet]. Scientific Reports. 2021 ; 11 1-11.[citado 2024 set. 25 ] Available from: https://doi.org/10.1038/s41598-021-92201-3
Vancouver
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII [Internet]. Scientific Reports. 2021 ; 11 1-11.[citado 2024 set. 25 ] Available from: https://doi.org/10.1038/s41598-021-92201-3
Artigo de periodico
Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series (2021)
Ferreira, Marcos Vinícius dos Santos; Rios, Ricardo Araújo ; Mello, Rodrigo Fernandes de ; Rios, Tatiane Nogueira
Fonte: Applied Soft Computing. Unidade: ICMC
Assuntos: ANÁLISE DE SÉRIES TEMPORAIS, FUZZY (INTELIGÊNCIA ARTIFICIAL)
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FERREIRA, Marcos Vinícius dos Santos et al. Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series. Applied Soft Computing, v. 113, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2021.108011. Acesso em: 25 set. 2024.
APA
Ferreira, M. V. dos S., Rios, R. A., Mello, R. F. de, & Rios, T. N. (2021). Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series. Applied Soft Computing, 113, 1-13. doi:10.1016/j.asoc.2021.108011
NLM
Ferreira MV dos S, Rios RA, Mello RF de, Rios TN. Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series [Internet]. Applied Soft Computing. 2021 ; 113 1-13.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.asoc.2021.108011
Vancouver
Ferreira MV dos S, Rios RA, Mello RF de, Rios TN. Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series [Internet]. Applied Soft Computing. 2021 ; 113 1-13.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.asoc.2021.108011
Artigo de periodico
Prediction of hemophilia A severity using a small-input machine-learning framework (2021)
Lopes, Tiago José da Silva ; Rios, Ricardo Araújo ; Nogueira, Tatiane ; Mello, Rodrigo Fernandes de
Fonte: npj Systems Biology and Applications. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, HEMOFILIA, PROTEÍNAS
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LOPES, Tiago José da Silva et al. Prediction of hemophilia A severity using a small-input machine-learning framework. npj Systems Biology and Applications, v. 7, p. 1-8, 2021Tradução . . Disponível em: https://doi.org/10.1038/s41540-021-00183-9. Acesso em: 25 set. 2024.
APA
Lopes, T. J. da S., Rios, R. A., Nogueira, T., & Mello, R. F. de. (2021). Prediction of hemophilia A severity using a small-input machine-learning framework. npj Systems Biology and Applications, 7, 1-8. doi:10.1038/s41540-021-00183-9
NLM
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Prediction of hemophilia A severity using a small-input machine-learning framework [Internet]. npj Systems Biology and Applications. 2021 ; 7 1-8.[citado 2024 set. 25 ] Available from: https://doi.org/10.1038/s41540-021-00183-9
Vancouver
Lopes TJ da S, Rios RA, Nogueira T, Mello RF de. Prediction of hemophilia A severity using a small-input machine-learning framework [Internet]. npj Systems Biology and Applications. 2021 ; 7 1-8.[citado 2024 set. 25 ] Available from: https://doi.org/10.1038/s41540-021-00183-9
Artigo de periodico
Coarse-refinement dilemma: on generalization bounds for data clustering (2021)
Vaz, Yule; Mello, Rodrigo Fernandes de ; Grossi, Carlos Henrique
Fonte: Expert Systems with Applications. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, HOMOLOGIA, ESPAÇOS TOPOLÓGICOS
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VAZ, Yule e MELLO, Rodrigo Fernandes de e GROSSI, Carlos Henrique. Coarse-refinement dilemma: on generalization bounds for data clustering. Expert Systems with Applications, v. 184, p. 1-28, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2021.115399. Acesso em: 25 set. 2024.
APA
Vaz, Y., Mello, R. F. de, & Grossi, C. H. (2021). Coarse-refinement dilemma: on generalization bounds for data clustering. Expert Systems with Applications, 184, 1-28. doi:10.1016/j.eswa.2021.115399
NLM
Vaz Y, Mello RF de, Grossi CH. Coarse-refinement dilemma: on generalization bounds for data clustering [Internet]. Expert Systems with Applications. 2021 ; 184 1-28.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.eswa.2021.115399
Vancouver
Vaz Y, Mello RF de, Grossi CH. Coarse-refinement dilemma: on generalization bounds for data clustering [Internet]. Expert Systems with Applications. 2021 ; 184 1-28.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.eswa.2021.115399
Artigo de periodico
Cross-modality co-attention networks for visual question answering (2021)
Han, Dezhi ; Zhou, Shuli; Li, Kuan-Ching ; Mello, Rodrigo Fernandes de
Fonte: Soft Computing. Unidade: ICMC
Assuntos: VISÃO COMPUTACIONAL, APRENDIZADO COMPUTACIONAL
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HAN, Dezhi et al. Cross-modality co-attention networks for visual question answering. Soft Computing, v. 25, n. 7, p. 5411-5421, 2021Tradução . . Disponível em: https://doi.org/10.1007/s00500-020-05539-7. Acesso em: 25 set. 2024.
APA
Han, D., Zhou, S., Li, K. -C., & Mello, R. F. de. (2021). Cross-modality co-attention networks for visual question answering. Soft Computing, 25( 7), 5411-5421. doi:10.1007/s00500-020-05539-7
NLM
Han D, Zhou S, Li K-C, Mello RF de. Cross-modality co-attention networks for visual question answering [Internet]. Soft Computing. 2021 ; 25( 7): 5411-5421.[citado 2024 set. 25 ] Available from: https://doi.org/10.1007/s00500-020-05539-7
Vancouver
Han D, Zhou S, Li K-C, Mello RF de. Cross-modality co-attention networks for visual question answering [Internet]. Soft Computing. 2021 ; 25( 7): 5411-5421.[citado 2024 set. 25 ] Available from: https://doi.org/10.1007/s00500-020-05539-7
Trabalho de evento
Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos (2021)
Nazaré, Tiago Santana de ; Mello, Rodrigo Fernandes de ; Ponti, Moacir Antonelli
Fonte: Proceedings. Nome do evento: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISIGRAPP. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, RECONHECIMENTO DE IMAGEM, VÍDEO
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NAZARÉ, Tiago Santana de e MELLO, Rodrigo Fernandes de e PONTI, Moacir Antonelli. Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos. 2021, Anais.. Lisboa: SciTePress, 2021. Disponível em: https://doi.org/10.5220/0010347907530762. Acesso em: 25 set. 2024.
APA
Nazaré, T. S. de, Mello, R. F. de, & Ponti, M. A. (2021). Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos. In Proceedings. Lisboa: SciTePress. doi:10.5220/0010347907530762
NLM
Nazaré TS de, Mello RF de, Ponti MA. Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos [Internet]. Proceedings. 2021 ;[citado 2024 set. 25 ] Available from: https://doi.org/10.5220/0010347907530762
Vancouver
Nazaré TS de, Mello RF de, Ponti MA. Investigating 3D convolutional layers as feature extractors for anomaly detection systems applied to surveillance videos [Internet]. Proceedings. 2021 ;[citado 2024 set. 25 ] Available from: https://doi.org/10.5220/0010347907530762
Artigo de periodico
Country transition index based on hierarchical clustering to predict next COVID-19 waves (2021)
Rios, Ricardo Araújo ; Nogueira, Tatiane ; Coimbra, Danilo Barbosa; Lopes, Tiago José da Silva ; Abraham, Ajith ; Mello, Rodrigo Fernandes de
Fonte: Scientific Reports. Unidade: ICMC
Assuntos: ANÁLISE DE SÉRIES TEMPORAIS, INTELIGÊNCIA ARTIFICIAL, SURTOS DE DOENÇAS, COVID-19
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RIOS, Ricardo Araújo et al. Country transition index based on hierarchical clustering to predict next COVID-19 waves. Scientific Reports, v. 11, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.1038/s41598-021-94661-z. Acesso em: 25 set. 2024.
APA
Rios, R. A., Nogueira, T., Coimbra, D. B., Lopes, T. J. da S., Abraham, A., & Mello, R. F. de. (2021). Country transition index based on hierarchical clustering to predict next COVID-19 waves. Scientific Reports, 11, 1-13. doi:10.1038/s41598-021-94661-z
NLM
Rios RA, Nogueira T, Coimbra DB, Lopes TJ da S, Abraham A, Mello RF de. Country transition index based on hierarchical clustering to predict next COVID-19 waves [Internet]. Scientific Reports. 2021 ; 11 1-13.[citado 2024 set. 25 ] Available from: https://doi.org/10.1038/s41598-021-94661-z
Vancouver
Rios RA, Nogueira T, Coimbra DB, Lopes TJ da S, Abraham A, Mello RF de. Country transition index based on hierarchical clustering to predict next COVID-19 waves [Internet]. Scientific Reports. 2021 ; 11 1-13.[citado 2024 set. 25 ] Available from: https://doi.org/10.1038/s41598-021-94661-z
Artigo de periodico
Materials selection of furnace linings with multi-component refractory ceramics based on an evolutionary screening procedure (2020)
Santos, Dulce P; Pelissari, Pedro Ivo Batistel Galiote Brossi ; Oliveira, B. S. de; Leiva, Daniel Rodrigo ; Mello, Rodrigo Fernandes de ; Pandolfelli, Victor Carlos
Fonte: Ceramics International. Unidade: ICMC
Assuntos: ALGORITMOS GENÉTICOS, MÉTODO DOS ELEMENTOS FINITOS, REFRATÁRIOS, FORNOS METALÚRGICOS
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SANTOS, Dulce P et al. Materials selection of furnace linings with multi-component refractory ceramics based on an evolutionary screening procedure. Ceramics International, v. 46, n. 4, p. 4113-4125, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ceramint.2019.10.127. Acesso em: 25 set. 2024.
APA
Santos, D. P., Pelissari, P. I. B. G. B., Oliveira, B. S. de, Leiva, D. R., Mello, R. F. de, & Pandolfelli, V. C. (2020). Materials selection of furnace linings with multi-component refractory ceramics based on an evolutionary screening procedure. Ceramics International, 46( 4), 4113-4125. doi:10.1016/j.ceramint.2019.10.127
NLM
Santos DP, Pelissari PIBGB, Oliveira BS de, Leiva DR, Mello RF de, Pandolfelli VC. Materials selection of furnace linings with multi-component refractory ceramics based on an evolutionary screening procedure [Internet]. Ceramics International. 2020 ; 46( 4): 4113-4125.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.ceramint.2019.10.127
Vancouver
Santos DP, Pelissari PIBGB, Oliveira BS de, Leiva DR, Mello RF de, Pandolfelli VC. Materials selection of furnace linings with multi-component refractory ceramics based on an evolutionary screening procedure [Internet]. Ceramics International. 2020 ; 46( 4): 4113-4125.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.ceramint.2019.10.127
Trabalho de evento-resumo
Random projections and α-shape to support the kernel design (2020)
Cestari, Daniel Moreira ; Mello, Rodrigo Fernandes de
Fonte: Proceedings. Nome do evento: AAAI Conference on Artificial Intelligence - AAAI. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, RECONHECIMENTO DE PADRÕES
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ABNT
CESTARI, Daniel Moreira e MELLO, Rodrigo Fernandes de. Random projections and α-shape to support the kernel design. 2020, Anais.. Palo Alto: AAAI Press, 2020. Disponível em: https://doi.org/10.1609/aaai.v34i10.7211. Acesso em: 25 set. 2024.
APA
Cestari, D. M., & Mello, R. F. de. (2020). Random projections and α-shape to support the kernel design. In Proceedings. Palo Alto: AAAI Press. doi:10.1609/aaai.v34i10.7211
NLM
Cestari DM, Mello RF de. Random projections and α-shape to support the kernel design [Internet]. Proceedings. 2020 ;[citado 2024 set. 25 ] Available from: https://doi.org/10.1609/aaai.v34i10.7211
Vancouver
Cestari DM, Mello RF de. Random projections and α-shape to support the kernel design [Internet]. Proceedings. 2020 ;[citado 2024 set. 25 ] Available from: https://doi.org/10.1609/aaai.v34i10.7211
Artigo de periodico
Time series clustering using stochastic and deterministic influences (2020)
Silva, Mirlei M. da; Mello, Rodrigo Fernandes de ; Rios, Ricardo A
Fonte: International Journal of Computational Science and Engineering. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES
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SILVA, Mirlei M. da e MELLO, Rodrigo Fernandes de e RIOS, Ricardo A. Time series clustering using stochastic and deterministic influences. International Journal of Computational Science and Engineering, v. 21, n. 3, p. 394-417, 2020Tradução . . Disponível em: https://doi.org/10.1504/IJCSE.2020.106063. Acesso em: 25 set. 2024.
APA
Silva, M. M. da, Mello, R. F. de, & Rios, R. A. (2020). Time series clustering using stochastic and deterministic influences. International Journal of Computational Science and Engineering, 21( 3), 394-417. doi:10.1504/IJCSE.2020.106063
NLM
Silva MM da, Mello RF de, Rios RA. Time series clustering using stochastic and deterministic influences [Internet]. International Journal of Computational Science and Engineering. 2020 ; 21( 3): 394-417.[citado 2024 set. 25 ] Available from: https://doi.org/10.1504/IJCSE.2020.106063
Vancouver
Silva MM da, Mello RF de, Rios RA. Time series clustering using stochastic and deterministic influences [Internet]. International Journal of Computational Science and Engineering. 2020 ; 21( 3): 394-417.[citado 2024 set. 25 ] Available from: https://doi.org/10.1504/IJCSE.2020.106063
Artigo de periodico
Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification (2020)
Canário, João Paulo ; Mello, Rodrigo Fernandes de ; Curilem, Millaray ; Huenupan, Fernando ; Rios, Ricardo Araújo
Fonte: Data in Brief. Unidade: ICMC
Assuntos: ANÁLISE DE SÉRIES TEMPORAIS, REDES NEURAIS, VULCÕES
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ABNT
CANÁRIO, João Paulo et al. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification. Data in Brief, v. 30, p. 1-6, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.dib.2020.105627. Acesso em: 25 set. 2024.
APA
Canário, J. P., Mello, R. F. de, Curilem, M., Huenupan, F., & Rios, R. A. (2020). Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification. Data in Brief, 30, 1-6. doi:10.1016/j.dib.2020.105627
NLM
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Data in Brief. 2020 ; 30 1-6.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.dib.2020.105627
Vancouver
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. Llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Data in Brief. 2020 ; 30 1-6.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.dib.2020.105627
Artigo de periodico
In-depth comparison of deep artificial neural network architectures on seismic events classification (2020)
Canário, João Paulo ; Mello, Rodrigo Fernandes de ; Curilem, Millaray ; Huenupan, Fernando ; Rios, Ricardo Araújo
Fonte: Journal of Volcanology and Geothermal Research. Unidade: ICMC
Assuntos: ANÁLISE DE SÉRIES TEMPORAIS, REDES NEURAIS, VULCÕES, ANÁLISE DE ONDALETAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CANÁRIO, João Paulo et al. In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, v. 401, p. Se 2020, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.jvolgeores.2020.106881. Acesso em: 25 set. 2024.
APA
Canário, J. P., Mello, R. F. de, Curilem, M., Huenupan, F., & Rios, R. A. (2020). In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, 401, Se 2020. doi:10.1016/j.jvolgeores.2020.106881
NLM
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ; 401 Se 2020.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881
Vancouver
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ; 401 Se 2020.[citado 2024 set. 25 ] Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881
Artigo de periodico
Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework (2020)
Santos, Denise P ; Pelissari, Pedro Ivo Batistel Galiote Brossi ; Mello, Rodrigo Fernandes de ; Pandolfelli, Victor Carlos
Fonte: Journal of Applied Physics. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, MÉTODO DOS ELEMENTOS FINITOS, FORNO ELÉTRICO, TRANSFERÊNCIA DE CALOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Denise P et al. Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework. Journal of Applied Physics, v. 127, n. 21, p. 215104-1-215104-7, 2020Tradução . . Disponível em: https://doi.org/10.1063/5.0004395. Acesso em: 25 set. 2024.
APA
Santos, D. P., Pelissari, P. I. B. G. B., Mello, R. F. de, & Pandolfelli, V. C. (2020). Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework. Journal of Applied Physics, 127( 21), 215104-1-215104-7. doi:10.1063/5.0004395
NLM
Santos DP, Pelissari PIBGB, Mello RF de, Pandolfelli VC. Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework [Internet]. Journal of Applied Physics. 2020 ; 127( 21): 215104-1-215104-7.[citado 2024 set. 25 ] Available from: https://doi.org/10.1063/5.0004395
Vancouver
Santos DP, Pelissari PIBGB, Mello RF de, Pandolfelli VC. Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework [Internet]. Journal of Applied Physics. 2020 ; 127( 21): 215104-1-215104-7.[citado 2024 set. 25 ] Available from: https://doi.org/10.1063/5.0004395
Trabalho de evento-anais periodico
Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection (2020)
Santos, Victor M. G. dos; Mello, Rodrigo Fernandes de ; Nogueira, Tatiane ; Rios, Ricardo A
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, MÍDIAS SOCIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Victor M. G. dos et al. Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_44. Acesso em: 25 set. 2024. , 2020
APA
Santos, V. M. G. dos, Mello, R. F. de, Nogueira, T., & Rios, R. A. (2020). Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_44
NLM
Santos VMG dos, Mello RF de, Nogueira T, Rios RA. Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 650-664.[citado 2024 set. 25 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_44
Vancouver
Santos VMG dos, Mello RF de, Nogueira T, Rios RA. Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 650-664.[citado 2024 set. 25 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_44
Artigo de periodico
Enhancing the sensor node localization algorithm based on improved DV-Hop and DE algorithms in wireless sensor networks (2020)
Han, Dezhi ; Yu, Yunping; Li, Kuan-Ching ; Mello, Rodrigo Fernandes de
Fonte: Sensors. Unidade: ICMC
Assuntos: WIRELESS, ALGORITMOS GENÉTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HAN, Dezhi et al. Enhancing the sensor node localization algorithm based on improved DV-Hop and DE algorithms in wireless sensor networks. Sensors, v. 20, p. 1-24, 2020Tradução . . Disponível em: https://doi.org/10.3390/s20020343. Acesso em: 25 set. 2024.
APA
Han, D., Yu, Y., Li, K. -C., & Mello, R. F. de. (2020). Enhancing the sensor node localization algorithm based on improved DV-Hop and DE algorithms in wireless sensor networks. Sensors, 20, 1-24. doi:10.3390/s20020343
NLM
Han D, Yu Y, Li K-C, Mello RF de. Enhancing the sensor node localization algorithm based on improved DV-Hop and DE algorithms in wireless sensor networks [Internet]. Sensors. 2020 ; 20 1-24.[citado 2024 set. 25 ] Available from: https://doi.org/10.3390/s20020343
Vancouver
Han D, Yu Y, Li K-C, Mello RF de. Enhancing the sensor node localization algorithm based on improved DV-Hop and DE algorithms in wireless sensor networks [Internet]. Sensors. 2020 ; 20 1-24.[citado 2024 set. 25 ] Available from: https://doi.org/10.3390/s20020343

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