Evaluation of activated sludge settling characteristics from microscopy images with deep convolutional neural networks and transfer learning (2024)
- Authors:
- USP affiliated authors: BRUNO, ODEMIR MARTINEZ - IFSC ; SCABINI, LEONARDO FELIPE DOS SANTOS - IFSC
- Unidade: IFSC
- DOI: 10.1016/j.jwpe.2024.105692
- Subjects: APRENDIZADO COMPUTACIONAL; VISÃO COMPUTACIONAL; REDES NEURAIS; TRATAMENTO DE ÁGUA
- Keywords: Wastewater treatment plant; Filamentous bulking; Convolutional neural networks; Transfer learning; Microscopy images; Eigen-CAM
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Water Process Engineering
- ISSN: 2214-7144
- Volume/Número/Paginação/Ano: v. 64, p. 105692-1-105692-13, Jul. 2024
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
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: 14 fev. 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 fev. 14 ] 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 fev. 14 ] Available from: https://doi.org/10.1016/j.jwpe.2024.105692 - Artificial neural networks and complex networks: an integrative study of topological properties and pattern recognition
- Artificial neural networks and complex networks: an integrative study of topological properties and pattern recognition
- IFSC/USP desenvolve “RADAM”: IA para padrões complexos - Primeira no mundo: Uma IA que treina outra IA. [Depoimento à Rui Sintra]
- Improving deep neural network random initialization through neuronal rewiring
- Structure and functioning of neural networks: the complex network properties of artificial neurons
- IA desenvolvida na USP é a melhor do mundo para reconhecer texturas [Depoimento a Ivan Conterno]
- Local complex features learned by randomized neural networks for texture analysis
- RADAM: texture recognition through randomized aggregated encoding of deep activation maps
- Randomized encoding ensemble: a new approach for texture representation
- Exploring neighborhood variancy for rule search optimization in life-like network automata
Informações sobre o DOI: 10.1016/j.jwpe.2024.105692 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 |
3202171.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
