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Artigo de periodico
CounterCrime: Using counterfactual explanations to explore crime reduction scenarios (2025)
Raimundo, Marcos M ; Garcia-Zanabria, Germain ; Nonato, Luis Gustavo ; Poco, Jorge
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, CRIMINALIDADE, TOMADA DE DECISÃO, ANÁLISE DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RAIMUNDO, Marcos M et al. CounterCrime: Using counterfactual explanations to explore crime reduction scenarios. IEEE Transactions on Visualization and Computer Graphics, v. 31, n. 10, p. 9008-9023, 2025Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2025.3586202. Acesso em: 19 nov. 2025.
APA
Raimundo, M. M., Garcia-Zanabria, G., Nonato, L. G., & Poco, J. (2025). CounterCrime: Using counterfactual explanations to explore crime reduction scenarios. IEEE Transactions on Visualization and Computer Graphics, 31( 10), 9008-9023. doi:10.1109/TVCG.2025.3586202
NLM
Raimundo MM, Garcia-Zanabria G, Nonato LG, Poco J. CounterCrime: Using counterfactual explanations to explore crime reduction scenarios [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10): 9008-9023.[citado 2025 nov. 19 ] Available from: https://doi.org/10.1109/TVCG.2025.3586202
Vancouver
Raimundo MM, Garcia-Zanabria G, Nonato LG, Poco J. CounterCrime: Using counterfactual explanations to explore crime reduction scenarios [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10): 9008-9023.[citado 2025 nov. 19 ] Available from: https://doi.org/10.1109/TVCG.2025.3586202
Artigo de periodico
CriPAV: street-level crime patterns analysis and visualization (2022)
Garcia-Zanabria, Germain ; Raimundo, Marcos M. M; Poco, Jorge ; Nery, Marcelo Batista ; Silva, Claudio T ; Adorno, Sérgio ; Nonato, Luis Gustavo
Source: IEEE Transactions on Visualization and Computer Graphics. Unidades: IEA, FFLCH, ICMC
Subjects: CRIMINALIDADE, ANÁLISE DE SÉRIES TEMPORAIS, ESPAÇO URBANO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GARCIA-ZANABRIA, Germain et al. CriPAV: street-level crime patterns analysis and visualization. IEEE Transactions on Visualization and Computer Graphics, v. 28, n. 12, p. 4000-4015, 2022Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2021.3111146. Acesso em: 19 nov. 2025.
APA
Garcia-Zanabria, G., Raimundo, M. M. M., Poco, J., Nery, M. B., Silva, C. T., Adorno, S., & Nonato, L. G. (2022). CriPAV: street-level crime patterns analysis and visualization. IEEE Transactions on Visualization and Computer Graphics, 28( 12), 4000-4015. doi:10.1109/TVCG.2021.3111146
NLM
Garcia-Zanabria G, Raimundo MMM, Poco J, Nery MB, Silva CT, Adorno S, Nonato LG. CriPAV: street-level crime patterns analysis and visualization [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2022 ; 28( 12): 4000-4015.[citado 2025 nov. 19 ] Available from: https://doi.org/10.1109/TVCG.2021.3111146
Vancouver
Garcia-Zanabria G, Raimundo MMM, Poco J, Nery MB, Silva CT, Adorno S, Nonato LG. CriPAV: street-level crime patterns analysis and visualization [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2022 ; 28( 12): 4000-4015.[citado 2025 nov. 19 ] Available from: https://doi.org/10.1109/TVCG.2021.3111146
Artigo de periodico
Explainable matrix: visualization for global and local interpretability of random forest classification ensembles (2021)
Popolin Neto, Mário ; Paulovich, Fernando Vieira
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: 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: 19 nov. 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 nov. 19 ] 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 nov. 19 ] Available from: https://doi.org/10.1109/TVCG.2020.3030354
Artigo de periodico
CrimAnalyzer: understanding crime patterns in São Paulo (2021)
Zanabria, Germain Garcia ; Silveira, Jaqueline Alvarenga ; Poco, Jorge ; Paiva, Afonso ; Nery, Marcelo Batista ; Silva, Claudio T; Adorno, Sérgio ; Nonato, Luis Gustavo
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: CRIMINALIDADE, ESPAÇO URBANO, APLICAÇÃO DA LEI
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ZANABRIA, Germain Garcia et al. CrimAnalyzer: understanding crime patterns in São Paulo. IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 4, p. 2313-2328, 2021Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2019.2947515. Acesso em: 19 nov. 2025.
APA
Zanabria, G. G., Silveira, J. A., Poco, J., Paiva, A., Nery, M. B., Silva, C. T., et al. (2021). CrimAnalyzer: understanding crime patterns in São Paulo. IEEE Transactions on Visualization and Computer Graphics, 27( 4), 2313-2328. doi:10.1109/TVCG.2019.2947515
NLM
Zanabria GG, Silveira JA, Poco J, Paiva A, Nery MB, Silva CT, Adorno S, Nonato LG. CrimAnalyzer: understanding crime patterns in São Paulo [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 4): 2313-2328.[citado 2025 nov. 19 ] Available from: https://doi.org/10.1109/TVCG.2019.2947515
Vancouver
Zanabria GG, Silveira JA, Poco J, Paiva A, Nery MB, Silva CT, Adorno S, Nonato LG. CrimAnalyzer: understanding crime patterns in São Paulo [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 4): 2313-2328.[citado 2025 nov. 19 ] Available from: https://doi.org/10.1109/TVCG.2019.2947515
Artigo de periodico
TopoMap: a 0-dimensional homology preserving projection of high-dimensional data (2021)
Doraiswamy, Harish ; Tierny, Julien ; Silva, Paulo J. S. ; Nonato, Luis Gustavo ; Silva, Claudio
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: TOPOLOGIA, ANÁLISE DE DADOS, PROJEÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DORAISWAMY, Harish et al. TopoMap: a 0-dimensional homology preserving projection of high-dimensional data. IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 2, p. 561-571, 2021Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2020.3030441. Acesso em: 19 nov. 2025.
APA
Doraiswamy, H., Tierny, J., Silva, P. J. S., Nonato, L. G., & Silva, C. (2021). TopoMap: a 0-dimensional homology preserving projection of high-dimensional data. IEEE Transactions on Visualization and Computer Graphics, 27( 2), 561-571. doi:10.1109/TVCG.2020.3030441
NLM
Doraiswamy H, Tierny J, Silva PJS, Nonato LG, Silva C. TopoMap: a 0-dimensional homology preserving projection of high-dimensional data [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 561-571.[citado 2025 nov. 19 ] Available from: https://doi.org/10.1109/TVCG.2020.3030441
Vancouver
Doraiswamy H, Tierny J, Silva PJS, Nonato LG, Silva C. TopoMap: a 0-dimensional homology preserving projection of high-dimensional data [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 561-571.[citado 2025 nov. 19 ] Available from: https://doi.org/10.1109/TVCG.2020.3030441

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