Mining unstructured processes: an exploratory study on a distance learning domain (2017)
- Authors:
- USP affiliated authors: PERES, SARAJANE MARQUES - EACH ; FANTINATO, MARCELO - EACH
- Unidade: EACH
- DOI: 10.1109/IJCNN.2017.7966261
- Subjects: MINERAÇÃO DE DADOS; GESTÃO POR PROCESSOS; EDUCAÇÃO A DISTÂNCIA; INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher place: Piscataway
- Date published: 2017
- Source:
- Título do periódico: International Joint Conference on Neural Networks. Proceedings
- ISSN: 1098-7576
- Volume/Número/Paginação/Ano: v. 2017, p. 3240-3247, mai./jun. 2017
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
MAITA, Ana Rocío Cárdenas et al. Mining unstructured processes: an exploratory study on a distance learning domain. International Joint Conference on Neural Networks. Proceedings, v. 2017, n. mai./ju 2017, p. 3240-3247, 2017Tradução . . Disponível em: https://doi.org/10.1109/IJCNN.2017.7966261. Acesso em: 29 mar. 2024. -
APA
Maita, A. R. C., Fantinato, M., Peres, S. M., Thom, L. H., & Hung, P. C. K. (2017). Mining unstructured processes: an exploratory study on a distance learning domain. International Joint Conference on Neural Networks. Proceedings, 2017( mai./ju 2017), 3240-3247. doi:10.1109/IJCNN.2017.7966261 -
NLM
Maita ARC, Fantinato M, Peres SM, Thom LH, Hung PCK. Mining unstructured processes: an exploratory study on a distance learning domain [Internet]. International Joint Conference on Neural Networks. Proceedings. 2017 ; 2017( mai./ju 2017): 3240-3247.[citado 2024 mar. 29 ] Available from: https://doi.org/10.1109/IJCNN.2017.7966261 -
Vancouver
Maita ARC, Fantinato M, Peres SM, Thom LH, Hung PCK. Mining unstructured processes: an exploratory study on a distance learning domain [Internet]. International Joint Conference on Neural Networks. Proceedings. 2017 ; 2017( mai./ju 2017): 3240-3247.[citado 2024 mar. 29 ] Available from: https://doi.org/10.1109/IJCNN.2017.7966261 - Enhancing completion time prediction through attribute selection
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Informações sobre o DOI: 10.1109/IJCNN.2017.7966261 (Fonte: oaDOI API)
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