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Artigo de periodico
Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space (2022)
Soares, Juliana Coatrini ; Soares, Andrey Coatrini ; Popolin Neto, Mário ; Paulovich, Fernando Vieira ; Oliveira Junior, Osvaldo Novais de ; Mattoso, Luiz Henrique Capparelli
Fonte: Sensors and Actuators Reports. Unidades: ICMC, IFSC
Assuntos: APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA, MASTITE ANIMAL, PECUÁRIA LEITEIRA, STAPHYLOCOCCUS
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ABNT
SOARES, Juliana Coatrini et al. Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space. Sensors and Actuators Reports, v. No 2022, p. 100083-1-100083-10, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.snr.2022.100083. Acesso em: 08 out. 2025.
APA
Soares, J. C., Soares, A. C., Popolin Neto, M., Paulovich, F. V., Oliveira Junior, O. N. de, & Mattoso, L. H. C. (2022). Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space. Sensors and Actuators Reports, No 2022, 100083-1-100083-10. doi:10.1016/j.snr.2022.100083
NLM
Soares JC, Soares AC, Popolin Neto M, Paulovich FV, Oliveira Junior ON de, Mattoso LHC. Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space [Internet]. Sensors and Actuators Reports. 2022 ; No 2022 100083-1-100083-10.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.snr.2022.100083
Vancouver
Soares JC, Soares AC, Popolin Neto M, Paulovich FV, Oliveira Junior ON de, Mattoso LHC. Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space [Internet]. Sensors and Actuators Reports. 2022 ; No 2022 100083-1-100083-10.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.snr.2022.100083
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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ABNT
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: 08 out. 2025.
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 2025 out. 08 ] 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 2025 out. 08 ] Available from: https://doi.org/10.1007/s12650-021-00809-4
Artigo de periodico
Explainable matrix: visualization for global and local interpretability of random forest classification ensembles (2021)
Popolin Neto, Mário ; Paulovich, Fernando Vieira
Fonte: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Assuntos: VISUALIZAÇÃO, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
POPOLIN NETO, Mário e PAULOVICH, Fernando Vieira. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles. IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 2, p. 1427-1437, 2021Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2020.3030354. Acesso em: 08 out. 2025.
APA
Popolin Neto, M., & Paulovich, F. V. (2021). Explainable matrix: visualization for global and local interpretability of random forest classification ensembles. IEEE Transactions on Visualization and Computer Graphics, 27( 2), 1427-1437. doi:10.1109/TVCG.2020.3030354
NLM
Popolin Neto M, Paulovich FV. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 1427-1437.[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/TVCG.2020.3030354
Vancouver
Popolin Neto M, Paulovich FV. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 1427-1437.[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/TVCG.2020.3030354
Artigo de periodico
Random forest similarity maps: a scalable visual representation for global and local interpretation (2021)
Mazumdar, Dipankar ; Popolin Neto, Mário ; Paulovich, Fernando Vieira
Fonte: Electronics. Unidade: ICMC
Assuntos: VISUALIZAÇÃO, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAZUMDAR, Dipankar e POPOLIN NETO, Mário e PAULOVICH, Fernando Vieira. Random forest similarity maps: a scalable visual representation for global and local interpretation. Electronics, v. 10, p. 1-20, 2021Tradução . . Disponível em: https://doi.org/10.3390/electronics10222862. Acesso em: 08 out. 2025.
APA
Mazumdar, D., Popolin Neto, M., & Paulovich, F. V. (2021). Random forest similarity maps: a scalable visual representation for global and local interpretation. Electronics, 10, 1-20. doi:10.3390/electronics10222862
NLM
Mazumdar D, Popolin Neto M, Paulovich FV. Random forest similarity maps: a scalable visual representation for global and local interpretation [Internet]. Electronics. 2021 ; 10 1-20.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/electronics10222862
Vancouver
Mazumdar D, Popolin Neto M, Paulovich FV. Random forest similarity maps: a scalable visual representation for global and local interpretation [Internet]. Electronics. 2021 ; 10 1-20.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/electronics10222862
Artigo de periodico
Machine learning used to create a multidimensional calibration space for sensing and biosensing data (2021)
Popolin Neto, Mário ; Soares, Andrey Coatrini ; Oliveira Junior, Osvaldo Novais de ; Paulovich, Fernando Vieira
Fonte: Bulletin of the Chemical Society of Japan. Unidades: IFSC, ICMC
Assuntos: BIOTECNOLOGIA, APRENDIZADO COMPUTACIONAL, SENSOR, FILMES FINOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
POPOLIN NETO, Mário et al. Machine learning used to create a multidimensional calibration space for sensing and biosensing data. Bulletin of the Chemical Society of Japan, v. 94, n. 5, p. 1553-1562, 2021Tradução . . Disponível em: https://doi.org/10.1246/bcsj.20200359. Acesso em: 08 out. 2025.
APA
Popolin Neto, M., Soares, A. C., Oliveira Junior, O. N. de, & Paulovich, F. V. (2021). Machine learning used to create a multidimensional calibration space for sensing and biosensing data. Bulletin of the Chemical Society of Japan, 94( 5), 1553-1562. doi:10.1246/bcsj.20200359
NLM
Popolin Neto M, Soares AC, Oliveira Junior ON de, Paulovich FV. Machine learning used to create a multidimensional calibration space for sensing and biosensing data [Internet]. Bulletin of the Chemical Society of Japan. 2021 ; 94( 5): 1553-1562.[citado 2025 out. 08 ] Available from: https://doi.org/10.1246/bcsj.20200359
Vancouver
Popolin Neto M, Soares AC, Oliveira Junior ON de, Paulovich FV. Machine learning used to create a multidimensional calibration space for sensing and biosensing data [Internet]. Bulletin of the Chemical Society of Japan. 2021 ; 94( 5): 1553-1562.[citado 2025 out. 08 ] Available from: https://doi.org/10.1246/bcsj.20200359

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