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Trabalho de evento
Explainable NLLP: advancements in explainable AI for natural legal language processing (2025)
Resck, Lucas ; Moreno-Vera, Felipe ; Veiga, Tobias; Paucar, Gerardo; Fajreldines, Ezequiel ; Klafke, Guilherme ; Nonato, Luis Gustavo ; Poco, Jorge
Source: Proceedings. Conference titles: Workshop on Automated Semantic Analysis of Information in Legal Text - ASAIL. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, DIREITO, PROCESSAMENTO DE LINGUAGEM NATURAL, PESQUISA
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ABNT
RESCK, Lucas et al. Explainable NLLP: advancements in explainable AI for natural legal language processing. 2025, Anais.. Harvard: IAAIL, 2025. Disponível em: https://visualdslab.com/papers/ExplainableNLLP. Acesso em: 03 nov. 2025.
APA
Resck, L., Moreno-Vera, F., Veiga, T., Paucar, G., Fajreldines, E., Klafke, G., et al. (2025). Explainable NLLP: advancements in explainable AI for natural legal language processing. In Proceedings. Harvard: IAAIL. Recuperado de https://visualdslab.com/papers/ExplainableNLLP
NLM
Resck L, Moreno-Vera F, Veiga T, Paucar G, Fajreldines E, Klafke G, Nonato LG, Poco J. Explainable NLLP: advancements in explainable AI for natural legal language processing [Internet]. Proceedings. 2025 ;[citado 2025 nov. 03 ] Available from: https://visualdslab.com/papers/ExplainableNLLP
Vancouver
Resck L, Moreno-Vera F, Veiga T, Paucar G, Fajreldines E, Klafke G, Nonato LG, Poco J. Explainable NLLP: advancements in explainable AI for natural legal language processing [Internet]. Proceedings. 2025 ;[citado 2025 nov. 03 ] Available from: https://visualdslab.com/papers/ExplainableNLLP
Artigo de periodico
A street corner-level methodology to analyze the influence of points of interest on urban crime (2025)
Silva, Débora Barbosa Leite ; Vieira, Thales ; Costa, Evandro de Barros ; Paiva, Afonso ; Nonato, Luis Gustavo
Source: Socio-Economic Planning Sciences. Unidade: ICMC
Subjects: CRIMINALIDADE, ESPAÇO URBANO, ANÁLISE DE DADOS, PREDIÇÃO
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ABNT
SILVA, Débora Barbosa Leite et al. A street corner-level methodology to analyze the influence of points of interest on urban crime. Socio-Economic Planning Sciences, v. 102, p. 1-14, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.seps.2025.102297. Acesso em: 03 nov. 2025.
APA
Silva, D. B. L., Vieira, T., Costa, E. de B., Paiva, A., & Nonato, L. G. (2025). A street corner-level methodology to analyze the influence of points of interest on urban crime. Socio-Economic Planning Sciences, 102, 1-14. doi:10.1016/j.seps.2025.102297
NLM
Silva DBL, Vieira T, Costa E de B, Paiva A, Nonato LG. A street corner-level methodology to analyze the influence of points of interest on urban crime [Internet]. Socio-Economic Planning Sciences. 2025 ; 102 1-14.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1016/j.seps.2025.102297
Vancouver
Silva DBL, Vieira T, Costa E de B, Paiva A, Nonato LG. A street corner-level methodology to analyze the influence of points of interest on urban crime [Internet]. Socio-Economic Planning Sciences. 2025 ; 102 1-14.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1016/j.seps.2025.102297
Artigo de periodico
Visagreement: visualizing and exploring explanations (dis)agreement (2025)
Silva, Priscylla ; Guardieiro, Vitoria ; Barr, Brian ; Silva, Claudio; Nonato, Luis Gustavo
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, MODELAGEM DE DADOS, SIMULAÇÃO, VISUALIZAÇÃO
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ABNT
SILVA, Priscylla et al. Visagreement: visualizing and exploring explanations (dis)agreement. IEEE Transactions on Visualization and Computer Graphics, v. 31, n. 10 , p. 7862-7875, 2025Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2025.3558074. Acesso em: 03 nov. 2025.
APA
Silva, P., Guardieiro, V., Barr, B., Silva, C., & Nonato, L. G. (2025). Visagreement: visualizing and exploring explanations (dis)agreement. IEEE Transactions on Visualization and Computer Graphics, 31( 10 ), 7862-7875. doi:10.1109/TVCG.2025.3558074
NLM
Silva P, Guardieiro V, Barr B, Silva C, Nonato LG. Visagreement: visualizing and exploring explanations (dis)agreement [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10 ): 7862-7875.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2025.3558074
Vancouver
Silva P, Guardieiro V, Barr B, Silva C, Nonato LG. Visagreement: visualizing and exploring explanations (dis)agreement [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 10 ): 7862-7875.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2025.3558074
Artigo de periodico
Assessing timber trade networks and supply chains in Brazil (2025)
Nonato, Luis Gustavo ; Russo, Victor ; Costa, Bernardo; Moreno-Vera, Felipe ; Toledo, Guilherme; Jesus, Osni Brito de ; Vieira, Robson; Lentini, Marcos; Poco, Jorge
Source: Nature Sustainability. Unidade: ICMC
Subjects: FLORESTAS, AMAZÔNIA BRASILEIRA, INDÚSTRIA MADEIREIRA, CONTROLE (TEORIA DE SISTEMAS E CONTROLE)
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ABNT
NONATO, Luis Gustavo et al. Assessing timber trade networks and supply chains in Brazil. Nature Sustainability, v. 8, p. 215-220, 2025Tradução . . Disponível em: https://doi.org/10.1038/s41893-024-01491-8. Acesso em: 03 nov. 2025.
APA
Nonato, L. G., Russo, V., Costa, B., Moreno-Vera, F., Toledo, G., Jesus, O. B. de, et al. (2025). Assessing timber trade networks and supply chains in Brazil. Nature Sustainability, 8, 215-220. doi:10.1038/s41893-024-01491-8
NLM
Nonato LG, Russo V, Costa B, Moreno-Vera F, Toledo G, Jesus OB de, Vieira R, Lentini M, Poco J. Assessing timber trade networks and supply chains in Brazil [Internet]. Nature Sustainability. 2025 ; 8 215-220.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1038/s41893-024-01491-8
Vancouver
Nonato LG, Russo V, Costa B, Moreno-Vera F, Toledo G, Jesus OB de, Vieira R, Lentini M, Poco J. Assessing timber trade networks and supply chains in Brazil [Internet]. Nature Sustainability. 2025 ; 8 215-220.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1038/s41893-024-01491-8
Artigo de periodico
LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals (2025)
Resck, Lucas ; Moreno-Vera, Felipe ; Veiga, Tobias; Paucar, Gerardo; Fajreldines, Ezequiel ; Klafke, Guilherme ; Nonato, Luis Gustavo ; Poco, Jorge
Source: Artificial Intelligence and Law. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, DIREITO, PESQUISA, PROCESSAMENTO DE LINGUAGEM NATURAL
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ABNT
RESCK, Lucas et al. LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals. Artificial Intelligence and Law, 2025Tradução . . Disponível em: https://doi.org/10.1007/s10506-025-09446-w. Acesso em: 03 nov. 2025.
APA
Resck, L., Moreno-Vera, F., Veiga, T., Paucar, G., Fajreldines, E., Klafke, G., et al. (2025). LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals. Artificial Intelligence and Law. doi:10.1007/s10506-025-09446-w
NLM
Resck L, Moreno-Vera F, Veiga T, Paucar G, Fajreldines E, Klafke G, Nonato LG, Poco J. LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals [Internet]. Artificial Intelligence and Law. 2025 ;[citado 2025 nov. 03 ] Available from: https://doi.org/10.1007/s10506-025-09446-w
Vancouver
Resck L, Moreno-Vera F, Veiga T, Paucar G, Fajreldines E, Klafke G, Nonato LG, Poco J. LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals [Internet]. Artificial Intelligence and Law. 2025 ;[citado 2025 nov. 03 ] Available from: https://doi.org/10.1007/s10506-025-09446-w
Artigo de periodico
SDR-Explorer: a user-friendly visual tool to support preventing student dropouts in higher education (2025)
Ticona, Julio J ; Nonato, Luis Gustavo ; Silva, Claudio Teixeira ; Gomez-Nieto, Erick
Source: Computers & Graphics. Unidade: ICMC
Subjects: EVASÃO ESCOLAR, ENSINO SUPERIOR, LINGUAGEM NATURAL, CLUSTERS
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ABNT
TICONA, Julio J et al. SDR-Explorer: a user-friendly visual tool to support preventing student dropouts in higher education. Computers & Graphics, v. 132, p. 1-11, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.cag.2025.104375. Acesso em: 03 nov. 2025.
APA
Ticona, J. J., Nonato, L. G., Silva, C. T., & Gomez-Nieto, E. (2025). SDR-Explorer: a user-friendly visual tool to support preventing student dropouts in higher education. Computers & Graphics, 132, 1-11. doi:10.1016/j.cag.2025.104375
NLM
Ticona JJ, Nonato LG, Silva CT, Gomez-Nieto E. SDR-Explorer: a user-friendly visual tool to support preventing student dropouts in higher education [Internet]. Computers & Graphics. 2025 ; 132 1-11.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1016/j.cag.2025.104375
Vancouver
Ticona JJ, Nonato LG, Silva CT, Gomez-Nieto E. SDR-Explorer: a user-friendly visual tool to support preventing student dropouts in higher education [Internet]. Computers & Graphics. 2025 ; 132 1-11.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1016/j.cag.2025.104375
Trabalho de evento-anais periodico
Modeling and predicting crimes in the city of São Paulo using graph neural networks (2025)
Hassan, Waqar ; Cabral, Marvin Mendes ; Ramos, Thiago Rodrigo ; Castelo, Antonio ; Nonato, Luis Gustavo
Source: Lecture Notes in Artificial Intelligence - LNAI. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Subjects: SEGURANÇA PÚBLICA, MODELAGEM DE DADOS, REDES NEURAIS, CRIME
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ABNT
HASSAN, Waqar et al. Modeling and predicting crimes in the city of São Paulo using graph neural networks. Lecture Notes in Artificial Intelligence - LNAI. Cham: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/978-3-031-79035-5_26. Acesso em: 03 nov. 2025. , 2025
APA
Hassan, W., Cabral, M. M., Ramos, T. R., Castelo, A., & Nonato, L. G. (2025). Modeling and predicting crimes in the city of São Paulo using graph neural networks. Lecture Notes in Artificial Intelligence - LNAI. Cham: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. doi:10.1007/978-3-031-79035-5_26
NLM
Hassan W, Cabral MM, Ramos TR, Castelo A, Nonato LG. Modeling and predicting crimes in the city of São Paulo using graph neural networks [Internet]. Lecture Notes in Artificial Intelligence - LNAI. 2025 ; 15414 372-386.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1007/978-3-031-79035-5_26
Vancouver
Hassan W, Cabral MM, Ramos TR, Castelo A, Nonato LG. Modeling and predicting crimes in the city of São Paulo using graph neural networks [Internet]. Lecture Notes in Artificial Intelligence - LNAI. 2025 ; 15414 372-386.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1007/978-3-031-79035-5_26
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
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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: 03 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. 03 ] 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. 03 ] Available from: https://doi.org/10.1109/TVCG.2025.3586202
Artigo de periodico
TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees (2025)
Guardieiro, Vitoria ; Oliveira, Felipe Inagaki de ; Doraiswamy, Harish ; Nonato, Luis Gustavo ; Silva, Claudio
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: ANÁLISE DE DADOS, TOPOLOGIA, PROJEÇÃO
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ABNT
GUARDIEIRO, Vitoria et al. TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees. IEEE Transactions on Visualization and Computer Graphics, v. 31, n. 1, p. 229-239, 2025Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2024.3456365. Acesso em: 03 nov. 2025.
APA
Guardieiro, V., Oliveira, F. I. de, Doraiswamy, H., Nonato, L. G., & Silva, C. (2025). TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees. IEEE Transactions on Visualization and Computer Graphics, 31( 1), 229-239. doi:10.1109/TVCG.2024.3456365
NLM
Guardieiro V, Oliveira FI de, Doraiswamy H, Nonato LG, Silva C. TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 1): 229-239.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2024.3456365
Vancouver
Guardieiro V, Oliveira FI de, Doraiswamy H, Nonato LG, Silva C. TopoMap++: a faster and more space efficient technique to compute projections with topological guarantees [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2025 ; 31( 1): 229-239.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2024.3456365
Artigo de periodico
Explainable artificial intelligence (XAI): from theory to methods and applications (2024)
Ortigossa, Evandro Scudeleti ; Gonçalves, Thales ; Nonato, Luis Gustavo
Source: IEEE Access. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REPRESENTAÇÃO
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ABNT
ORTIGOSSA, Evandro Scudeleti e GONÇALVES, Thales e NONATO, Luis Gustavo. Explainable artificial intelligence (XAI): from theory to methods and applications. IEEE Access, v. 12, p. 80799-80846, 2024Tradução . . Disponível em: https://doi.org/10.1109/ACCESS.2024.3409843. Acesso em: 03 nov. 2025.
APA
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024). Explainable artificial intelligence (XAI): from theory to methods and applications. IEEE Access, 12, 80799-80846. doi:10.1109/ACCESS.2024.3409843
NLM
Ortigossa ES, Gonçalves T, Nonato LG. Explainable artificial intelligence (XAI): from theory to methods and applications [Internet]. IEEE Access. 2024 ; 12 80799-80846.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/ACCESS.2024.3409843
Vancouver
Ortigossa ES, Gonçalves T, Nonato LG. Explainable artificial intelligence (XAI): from theory to methods and applications [Internet]. IEEE Access. 2024 ; 12 80799-80846.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/ACCESS.2024.3409843
Trabalho de evento-anais periodico
Exploring the relationship between feature attribution methods and model performance (2024)
Silva, Priscylla; Silva, Claudio; Nonato, Luis Gustavo
Source: Proceedings of Machine Learning Research. Conference titles: Annual AAAI Conference on Artificial Intelligence. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, APRENDIZAGEM PROFUNDA, ANÁLISE DE DESEMPENHO, EDUCAÇÃO
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ABNT
SILVA, Priscylla e SILVA, Claudio e NONATO, Luis Gustavo. Exploring the relationship between feature attribution methods and model performance. Proceedings of Machine Learning Research. Vancouver: AAAI. Disponível em: https://proceedings.mlr.press/v257/silva24a.html. Acesso em: 03 nov. 2025. , 2024
APA
Silva, P., Silva, C., & Nonato, L. G. (2024). Exploring the relationship between feature attribution methods and model performance. Proceedings of Machine Learning Research. Vancouver: AAAI. Recuperado de https://proceedings.mlr.press/v257/silva24a.html
NLM
Silva P, Silva C, Nonato LG. Exploring the relationship between feature attribution methods and model performance [Internet]. Proceedings of Machine Learning Research. 2024 ; 257 29-38.[citado 2025 nov. 03 ] Available from: https://proceedings.mlr.press/v257/silva24a.html
Vancouver
Silva P, Silva C, Nonato LG. Exploring the relationship between feature attribution methods and model performance [Internet]. Proceedings of Machine Learning Research. 2024 ; 257 29-38.[citado 2025 nov. 03 ] Available from: https://proceedings.mlr.press/v257/silva24a.html
Trabalho de evento
A visual methodology to assess spatial graph vertex ordering algorithms (2024)
Salinas, Karelia Alexandra Vilca ; Barella, Victor Hugo ; Vieira, Thales ; Nonato, Luis Gustavo
Source: Anais. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: GRAFOLOGIA, ALGORITMOS, VISUALIZAÇÃO, ESPAÇO URBANO, ANÁLISE DE DADOS
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ABNT
SALINAS, Karelia Alexandra Vilca et al. A visual methodology to assess spatial graph vertex ordering algorithms. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/SIBGRAPI62404.2024.10716318. Acesso em: 03 nov. 2025.
APA
Salinas, K. A. V., Barella, V. H., Vieira, T., & Nonato, L. G. (2024). A visual methodology to assess spatial graph vertex ordering algorithms. In Anais. Piscataway: IEEE. doi:10.1109/SIBGRAPI62404.2024.10716318
NLM
Salinas KAV, Barella VH, Vieira T, Nonato LG. A visual methodology to assess spatial graph vertex ordering algorithms [Internet]. Anais. 2024 ;[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/SIBGRAPI62404.2024.10716318
Vancouver
Salinas KAV, Barella VH, Vieira T, Nonato LG. A visual methodology to assess spatial graph vertex ordering algorithms [Internet]. Anais. 2024 ;[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/SIBGRAPI62404.2024.10716318
Artigo de periodico
Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm (2024)
Raimundo, Marcos M ; Nonato, Luis Gustavo ; Poco, Jorge
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS
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ABNT
RAIMUNDO, Marcos M e NONATO, Luis Gustavo e POCO, Jorge. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, v. 38, p. 2942-2974, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-022-00906-4. Acesso em: 03 nov. 2025.
APA
Raimundo, M. M., Nonato, L. G., & Poco, J. (2024). Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, 38, 2942-2974. doi:10.1007/s10618-022-00906-4
NLM
Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1007/s10618-022-00906-4
Vancouver
Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1007/s10618-022-00906-4
Artigo de periodico
MOUNTAINEER: topology-driven visual analytics for comparing local explanations (2024)
Solunke, Parikshit ; Guardieiro, Vitoria ; Rulff, João ; Chan, Gromit Yeuk-Yin ; Barr, Brian ; Nonato, Luis Gustavo ; Silva, Claudio
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: MODELAGEM DE DADOS, APRENDIZADO COMPUTACIONAL, ESTATÍSTICA, SIMULAÇÃO, TOPOLOGIA
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ABNT
SOLUNKE, Parikshit et al. MOUNTAINEER: topology-driven visual analytics for comparing local explanations. IEEE Transactions on Visualization and Computer Graphics, v. 30, n. 12, p. 7763-7775, 2024Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2024.3418653. Acesso em: 03 nov. 2025.
APA
Solunke, P., Guardieiro, V., Rulff, J., Chan, G. Y. -Y., Barr, B., Nonato, L. G., & Silva, C. (2024). MOUNTAINEER: topology-driven visual analytics for comparing local explanations. IEEE Transactions on Visualization and Computer Graphics, 30( 12), 7763-7775. doi:10.1109/TVCG.2024.3418653
NLM
Solunke P, Guardieiro V, Rulff J, Chan GY-Y, Barr B, Nonato LG, Silva C. MOUNTAINEER: topology-driven visual analytics for comparing local explanations [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2024 ; 30( 12): 7763-7775.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2024.3418653
Vancouver
Solunke P, Guardieiro V, Rulff J, Chan GY-Y, Barr B, Nonato LG, Silva C. MOUNTAINEER: topology-driven visual analytics for comparing local explanations [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2024 ; 30( 12): 7763-7775.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2024.3418653
Trabalho de evento
Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data (2024)
Santos, Tiago Paulino; Souza, João Matheus Siqueira ; Vieira, Thales ; Nonato, Luis Gustavo
Source: Anais. Conference titles: Conference on Graphics, Patterns and Images - SIBGRAPI. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, TOMADA DE DECISÃO, VISUALIZAÇÃO, SEGURANÇA PÚBLICA
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ABNT
SANTOS, Tiago Paulino et al. Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/SIBGRAPI62404.2024.10716319. Acesso em: 03 nov. 2025.
APA
Santos, T. P., Souza, J. M. S., Vieira, T., & Nonato, L. G. (2024). Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data. In Anais. Piscataway: IEEE. doi:10.1109/SIBGRAPI62404.2024.10716319
NLM
Santos TP, Souza JMS, Vieira T, Nonato LG. Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data [Internet]. Anais. 2024 ;[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/SIBGRAPI62404.2024.10716319
Vancouver
Santos TP, Souza JMS, Vieira T, Nonato LG. Space-time urban explorer: a visual tool for exploring spatiotemporal crime and patrolling data [Internet]. Anais. 2024 ;[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/SIBGRAPI62404.2024.10716319
Artigo de periodico
LegalVis: exploring and inferring precedent citations in legal documents (2023)
Domingues, Lucas Emanuel Resck ; Ponciano, Jean Roberto ; Poco, Jorge ; Nonato, Luis Gustavo
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL, VISUALIZAÇÃO, SISTEMA JUDICIÁRIO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DOMINGUES, Lucas Emanuel Resck et al. LegalVis: exploring and inferring precedent citations in legal documents. IEEE Transactions on Visualization and Computer Graphics, v. 29, n. 6, p. 3105-3120, 2023Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2022.3152450. Acesso em: 03 nov. 2025.
APA
Domingues, L. E. R., Ponciano, J. R., Poco, J., & Nonato, L. G. (2023). LegalVis: exploring and inferring precedent citations in legal documents. IEEE Transactions on Visualization and Computer Graphics, 29( 6), 3105-3120. doi:10.1109/TVCG.2022.3152450
NLM
Domingues LER, Ponciano JR, Poco J, Nonato LG. LegalVis: exploring and inferring precedent citations in legal documents [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( 6): 3105-3120.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2022.3152450
Vancouver
Domingues LER, Ponciano JR, Poco J, Nonato LG. LegalVis: exploring and inferring precedent citations in legal documents [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( 6): 3105-3120.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2022.3152450
Artigo de periodico
Calibrate: interactive analysis of probabilistic model output (2023)
Xenopoulos, Peter ; Rulff, João; Nonato, Luis Gustavo ; Barr, Brian; Silva, Claudio
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, VISUALIZAÇÃO, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
XENOPOULOS, Peter et al. Calibrate: interactive analysis of probabilistic model output. IEEE Transactions on Visualization and Computer Graphics, v. 29, n. Ja 2023, p. 853-863, 2023Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2022.3209489. Acesso em: 03 nov. 2025.
APA
Xenopoulos, P., Rulff, J., Nonato, L. G., Barr, B., & Silva, C. (2023). Calibrate: interactive analysis of probabilistic model output. IEEE Transactions on Visualization and Computer Graphics, 29( Ja 2023), 853-863. doi:10.1109/TVCG.2022.3209489
NLM
Xenopoulos P, Rulff J, Nonato LG, Barr B, Silva C. Calibrate: interactive analysis of probabilistic model output [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( Ja 2023): 853-863.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2022.3209489
Vancouver
Xenopoulos P, Rulff J, Nonato LG, Barr B, Silva C. Calibrate: interactive analysis of probabilistic model output [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2023 ; 29( Ja 2023): 853-863.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1109/TVCG.2022.3209489
Artigo de periodico
Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data (2023)
Luna, Aderval Severino ; Torres, Alexandre Rodrigues ; Cunha, Camilla Lima ; Lima, Igor Conrado Alves de; Nonato, Luis Gustavo
Source: Brazilian Journal of Analytical Chemistry. Unidade: ICMC
Subjects: BIOCOMBUSTÍVEIS, ALGORITMOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LUNA, Aderval Severino et al. Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data. Brazilian Journal of Analytical Chemistry, v. 10, n. 39, p. 52-69, 2023Tradução . . Disponível em: https://doi.org/10.30744/brjac.2179-3425.AR-30-2022. Acesso em: 03 nov. 2025.
APA
Luna, A. S., Torres, A. R., Cunha, C. L., Lima, I. C. A. de, & Nonato, L. G. (2023). Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data. Brazilian Journal of Analytical Chemistry, 10( 39), 52-69. doi:10.30744/brjac.2179-3425.AR-30-2022
NLM
Luna AS, Torres AR, Cunha CL, Lima ICA de, Nonato LG. Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data [Internet]. Brazilian Journal of Analytical Chemistry. 2023 ; 10( 39): 52-69.[citado 2025 nov. 03 ] Available from: https://doi.org/10.30744/brjac.2179-3425.AR-30-2022
Vancouver
Luna AS, Torres AR, Cunha CL, Lima ICA de, Nonato LG. Employing auto-machine learning algorithms for predicting the cold filter plugging and kinematic viscosity at 40 ºC in biodiesel blends using vibrational spectroscopy data [Internet]. Brazilian Journal of Analytical Chemistry. 2023 ; 10( 39): 52-69.[citado 2025 nov. 03 ] Available from: https://doi.org/10.30744/brjac.2179-3425.AR-30-2022
Trabalho de evento
Crime prediction models in the metropolitan area of São Paulo - Brazil (2023)
Zhao, Wellington Yuanhe; Nonato, Luis Gustavo ; Russo, Cibele Maria
Source: Proceedings. Conference titles: International Workshop on Statistical Modelling. Unidades: ICMC, Interinstitucional de Pós-Graduação em Estatística
Subjects: SÃO PAULO (SP), CRIMINALIDADE, PREDIÇÃO, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ZHAO, Wellington Yuanhe e NONATO, Luis Gustavo e RUSSO, Cibele Maria. Crime prediction models in the metropolitan area of São Paulo - Brazil. 2023, Anais.. Dortmund: TU Dortmund University, 2023. Disponível em: https://iwsm2023.statistik.tu-dortmund.de. Acesso em: 03 nov. 2025.
APA
Zhao, W. Y., Nonato, L. G., & Russo, C. M. (2023). Crime prediction models in the metropolitan area of São Paulo - Brazil. In Proceedings. Dortmund: TU Dortmund University. Recuperado de https://iwsm2023.statistik.tu-dortmund.de
NLM
Zhao WY, Nonato LG, Russo CM. Crime prediction models in the metropolitan area of São Paulo - Brazil [Internet]. Proceedings. 2023 ;[citado 2025 nov. 03 ] Available from: https://iwsm2023.statistik.tu-dortmund.de
Vancouver
Zhao WY, Nonato LG, Russo CM. Crime prediction models in the metropolitan area of São Paulo - Brazil [Internet]. Proceedings. 2023 ;[citado 2025 nov. 03 ] Available from: https://iwsm2023.statistik.tu-dortmund.de
Artigo de periodico
A river flooding detection system based on deep learning and computer vision (2022)
Fernandes Junior, Francisco Erivaldo ; Nonato, Luis Gustavo ; Ueyama, Jó
Source: Multimedia Tools and Applications. Unidade: ICMC
Subjects: VISÃO COMPUTACIONAL, ENCHENTES URBANAS, SEMÂNTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERNANDES JUNIOR, Francisco Erivaldo e NONATO, Luis Gustavo e UEYAMA, Jó. A river flooding detection system based on deep learning and computer vision. Multimedia Tools and Applications, v. 81, p. 40231-40251, 2022Tradução . . Disponível em: https://doi.org/10.1007/s11042-022-12813-3. Acesso em: 03 nov. 2025.
APA
Fernandes Junior, F. E., Nonato, L. G., & Ueyama, J. (2022). A river flooding detection system based on deep learning and computer vision. Multimedia Tools and Applications, 81, 40231-40251. doi:10.1007/s11042-022-12813-3
NLM
Fernandes Junior FE, Nonato LG, Ueyama J. A river flooding detection system based on deep learning and computer vision [Internet]. Multimedia Tools and Applications. 2022 ; 81 40231-40251.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1007/s11042-022-12813-3
Vancouver
Fernandes Junior FE, Nonato LG, Ueyama J. A river flooding detection system based on deep learning and computer vision [Internet]. Multimedia Tools and Applications. 2022 ; 81 40231-40251.[citado 2025 nov. 03 ] Available from: https://doi.org/10.1007/s11042-022-12813-3

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