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Filtros : "Torfs, Elena" Limpar

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  • Artigo de periodico

    Morphology-informed deep learning for risk assessment of filamentous bulking in a full-scale industrial wastewater treatment plant (2026)

    Borzooei, Sina ; Scabini, Leonardo Felipe dos Santos ; Zhu, Jun-Jie; Daneshgar, Saba ; Deblieck, Lukas; Broeck, Elias Van Den; Torfs, Elena

    Source: Environmental Research. Unidade: IFSC

    Subjects: LODO, NANOCOMPÓSITOS

    PrivadoAcesso à fonteDOIHow to cite
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    • ABNT

      BORZOOEI, Sina et al. Morphology-informed deep learning for risk assessment of filamentous bulking in a full-scale industrial wastewater treatment plant. Environmental Research, v. 301, p. 124520-1-124520-12 + supplementary data, 2026Tradução . . Disponível em: https://doi.org/10.1016/j.envres.2026.124520. Acesso em: 04 maio 2026.

    • APA

      Borzooei, S., Scabini, L. F. dos S., Zhu, J. -J., Daneshgar, S., Deblieck, L., Broeck, E. V. D., & Torfs, E. (2026). Morphology-informed deep learning for risk assessment of filamentous bulking in a full-scale industrial wastewater treatment plant. Environmental Research, 301, 124520-1-124520-12 + supplementary data. doi:10.1016/j.envres.2026.124520

    • NLM

      Borzooei S, Scabini LF dos S, Zhu J-J, Daneshgar S, Deblieck L, Broeck EVD, Torfs E. Morphology-informed deep learning for risk assessment of filamentous bulking in a full-scale industrial wastewater treatment plant [Internet]. Environmental Research. 2026 ; 301 124520-1-124520-12 + supplementary data.[citado 2026 maio 04 ] Available from: https://doi.org/10.1016/j.envres.2026.124520

    • Vancouver

      Borzooei S, Scabini LF dos S, Zhu J-J, Daneshgar S, Deblieck L, Broeck EVD, Torfs E. Morphology-informed deep learning for risk assessment of filamentous bulking in a full-scale industrial wastewater treatment plant [Internet]. Environmental Research. 2026 ; 301 124520-1-124520-12 + supplementary data.[citado 2026 maio 04 ] Available from: https://doi.org/10.1016/j.envres.2026.124520

  • Artigo de periodico

    Evaluation of activated sludge settling characteristics from microscopy images with deep convolutional neural networks and transfer learning (2024)

    Borzooei, Sina ; Scabini, Leonardo ; Miranda, Gisele Helena Barboni ; Daneshgar, Saba ; Deblieck, Lukas; Bruno, Odemir Martinez ; Langhe, Piet De; Baets, Bernard De ; Nopens, Ingmar; Torfs, Elena

    Source: Journal of Water Process Engineering. Unidade: IFSC

    Subjects: APRENDIZADO COMPUTACIONAL, VISÃO COMPUTACIONAL, REDES NEURAIS, TRATAMENTO DE ÁGUA

    PrivadoAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      BORZOOEI, Sina et al. Evaluation of activated sludge settling characteristics from microscopy images with deep convolutional neural networks and transfer learning. Journal of Water Process Engineering, v. 64, p. 105692-1-105692-13, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.jwpe.2024.105692. Acesso em: 04 maio 2026.

    • APA

      Borzooei, S., Scabini, L., Miranda, G. H. B., Daneshgar, S., Deblieck, L., Bruno, O. M., et al. (2024). Evaluation of activated sludge settling characteristics from microscopy images with deep convolutional neural networks and transfer learning. Journal of Water Process Engineering, 64, 105692-1-105692-13. doi:10.1016/j.jwpe.2024.105692

    • NLM

      Borzooei S, Scabini L, Miranda GHB, Daneshgar S, Deblieck L, Bruno OM, Langhe PD, Baets BD, Nopens I, Torfs E. Evaluation of activated sludge settling characteristics from microscopy images with deep convolutional neural networks and transfer learning [Internet]. Journal of Water Process Engineering. 2024 ; 64 105692-1-105692-13.[citado 2026 maio 04 ] Available from: https://doi.org/10.1016/j.jwpe.2024.105692

    • Vancouver

      Borzooei S, Scabini L, Miranda GHB, Daneshgar S, Deblieck L, Bruno OM, Langhe PD, Baets BD, Nopens I, Torfs E. Evaluation of activated sludge settling characteristics from microscopy images with deep convolutional neural networks and transfer learning [Internet]. Journal of Water Process Engineering. 2024 ; 64 105692-1-105692-13.[citado 2026 maio 04 ] Available from: https://doi.org/10.1016/j.jwpe.2024.105692
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