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Trabalho de evento
MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data (2024)
Aguiar, Erikson Júlio de ; Traina, Agma Juci Machado ; Helal, Sumi
Source: Proceedings. Conference titles: IEEE International Conference on Big Data - Big Data. Unidade: ICMC
Subjects: BANCO DE DADOS MULTIMÍDIA, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR IMAGEM, TECNOLOGIAS DA SAÚDE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
AGUIAR, Erikson Júlio de e TRAINA, Agma Juci Machado e HELAL, Sumi. MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/BigData62323.2024.10826044. Acesso em: 08 out. 2025.
APA
Aguiar, E. J. de, Traina, A. J. M., & Helal, S. (2024). MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data. In Proceedings. Piscataway: IEEE. doi:10.1109/BigData62323.2024.10826044
NLM
Aguiar EJ de, Traina AJM, Helal S. MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data [Internet]. Proceedings. 2024 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData62323.2024.10826044
Vancouver
Aguiar EJ de, Traina AJM, Helal S. MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data [Internet]. Proceedings. 2024 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData62323.2024.10826044
Trabalho de evento
TgraphSpot: fast and effective anomaly detection for time-evolving graphs (2022)
Cazzolato, Mirela Teixeira ; Vijayakumar, Saranya; Zheng, Xinyi; Park, Namyong; Lee, Meng-Chieh; Fidalgo, Pedro; Lages, Bruno; Traina, Agma Juci Machado ; Faloutsos, Christos
Source: Proceedings. Conference titles: IEEE International Conference on Big Data - Big Data. Unidade: ICMC
Subjects: RECUPERAÇÃO DA INFORMAÇÃO, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAZZOLATO, Mirela Teixeira et al. TgraphSpot: fast and effective anomaly detection for time-evolving graphs. 2022, Anais.. Piscataway: IEEE, 2022. Disponível em: https://doi.org/10.1109/BigData55660.2022.10020898. Acesso em: 08 out. 2025.
APA
Cazzolato, M. T., Vijayakumar, S., Zheng, X., Park, N., Lee, M. -C., Fidalgo, P., et al. (2022). TgraphSpot: fast and effective anomaly detection for time-evolving graphs. In Proceedings. Piscataway: IEEE. doi:10.1109/BigData55660.2022.10020898
NLM
Cazzolato MT, Vijayakumar S, Zheng X, Park N, Lee M-C, Fidalgo P, Lages B, Traina AJM, Faloutsos C. TgraphSpot: fast and effective anomaly detection for time-evolving graphs [Internet]. Proceedings. 2022 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData55660.2022.10020898
Vancouver
Cazzolato MT, Vijayakumar S, Zheng X, Park N, Lee M-C, Fidalgo P, Lages B, Traina AJM, Faloutsos C. TgraphSpot: fast and effective anomaly detection for time-evolving graphs [Internet]. Proceedings. 2022 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData55660.2022.10020898
Trabalho de evento
Understanding the state of the art in animal detection and classification using computer vision technologies (2021)
Ferrante, Gabriel Souto ; Rodrigues, Felipe Maciel ; Andrade, Fernando Roberto Hebeler; Goularte, Rudinei ; Meneguette, Rodolfo Ipolito
Source: Proceedings. Conference titles: IEEE International Conference on Big Data - Big Data. Unidade: ICMC
Subjects: VISÃO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, ANIMAIS, AMBIENTES URBANOS, REVISÃO SISTEMÁTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERRANTE, Gabriel Souto et al. Understanding the state of the art in animal detection and classification using computer vision technologies. 2021, Anais.. Piscataway: IEEE, 2021. Disponível em: https://doi.org/10.1109/BigData52589.2021.9672049. Acesso em: 08 out. 2025.
APA
Ferrante, G. S., Rodrigues, F. M., Andrade, F. R. H., Goularte, R., & Meneguette, R. I. (2021). Understanding the state of the art in animal detection and classification using computer vision technologies. In Proceedings. Piscataway: IEEE. doi:10.1109/BigData52589.2021.9672049
NLM
Ferrante GS, Rodrigues FM, Andrade FRH, Goularte R, Meneguette RI. Understanding the state of the art in animal detection and classification using computer vision technologies [Internet]. Proceedings. 2021 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData52589.2021.9672049
Vancouver
Ferrante GS, Rodrigues FM, Andrade FRH, Goularte R, Meneguette RI. Understanding the state of the art in animal detection and classification using computer vision technologies [Internet]. Proceedings. 2021 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData52589.2021.9672049
Trabalho de evento
Embedding propagation over heterogeneous event networks for link prediction (2021)
Carmo, Paulo Ricardo Viviurka do ; Marcacini, Ricardo Marcondes
Source: Proceedings. Conference titles: IEEE International Conference on Big Data - Big Data. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, RECONHECIMENTO DE TEXTO, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CARMO, Paulo Ricardo Viviurka do e MARCACINI, Ricardo Marcondes. Embedding propagation over heterogeneous event networks for link prediction. 2021, Anais.. Piscataway: IEEE, 2021. Disponível em: https://doi.org/10.1109/BigData52589.2021.9671645. Acesso em: 08 out. 2025.
APA
Carmo, P. R. V. do, & Marcacini, R. M. (2021). Embedding propagation over heterogeneous event networks for link prediction. In Proceedings. Piscataway: IEEE. doi:10.1109/BigData52589.2021.9671645
NLM
Carmo PRV do, Marcacini RM. Embedding propagation over heterogeneous event networks for link prediction [Internet]. Proceedings. 2021 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData52589.2021.9671645
Vancouver
Carmo PRV do, Marcacini RM. Embedding propagation over heterogeneous event networks for link prediction [Internet]. Proceedings. 2021 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData52589.2021.9671645

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