Machine learning used to create a multidimensional calibration space for sensing and biosensing data (2021)
- Authors:
- USP affiliated authors: OLIVEIRA JUNIOR, OSVALDO NOVAIS DE - IFSC ; PAULOVICH, FERNANDO VIEIRA - ICMC ; POPOLIN NETO, MÁRIO - ICMC
- Unidades: IFSC; ICMC
- DOI: 10.1246/bcsj.20200359
- Subjects: BIOTECNOLOGIA; APRENDIZADO COMPUTACIONAL; SENSOR; FILMES FINOS
- Keywords: Machine learning; Interpretable artificial intelligence; Sensors classification model
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Bulletin of the Chemical Society of Japan
- ISSN: 0009-2673
- Volume/Número/Paginação/Ano: v. 94, n. 5, p. 1553-1562, May 2021
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
POPOLIN NETO, Mário et al. Machine learning used to create a multidimensional calibration space for sensing and biosensing data. Bulletin of the Chemical Society of Japan, v. 94, n. 5, p. 1553-1562, 2021Tradução . . Disponível em: https://doi.org/10.1246/bcsj.20200359. Acesso em: 03 jun. 2024. -
APA
Popolin Neto, M., Soares, A. C., Oliveira Junior, O. N. de, & Paulovich, F. V. (2021). Machine learning used to create a multidimensional calibration space for sensing and biosensing data. Bulletin of the Chemical Society of Japan, 94( 5), 1553-1562. doi:10.1246/bcsj.20200359 -
NLM
Popolin Neto M, Soares AC, Oliveira Junior ON de, Paulovich FV. Machine learning used to create a multidimensional calibration space for sensing and biosensing data [Internet]. Bulletin of the Chemical Society of Japan. 2021 ; 94( 5): 1553-1562.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1246/bcsj.20200359 -
Vancouver
Popolin Neto M, Soares AC, Oliveira Junior ON de, Paulovich FV. Machine learning used to create a multidimensional calibration space for sensing and biosensing data [Internet]. Bulletin of the Chemical Society of Japan. 2021 ; 94( 5): 1553-1562.[citado 2024 jun. 03 ] Available from: https://doi.org/10.1246/bcsj.20200359 - Explainable matrix: visualization for global and local interpretability of random forest classification ensembles
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Informações sobre o DOI: 10.1246/bcsj.20200359 (Fonte: oaDOI API)
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