Sterilization process stages estimation for an autoclave using logistic regression models

L. Angel; J. Viola; M. Vega; R. Restrepo

doi:10.1109/STSIVA.2016.7743337

Sterilization process stages estimation for an autoclave using logistic regression models

L. Angel, J. Viola, M. Vega, R. Restrepo

Producción científica: Capítulo del libro/informe/acta de congreso › Ponencia publicada en las memorias del evento con ISBN › revisión exhaustiva

3 Citas (Scopus)

Resumen

This paper presents a methodology for an autoclave sterilization process stages estimation using logistic regression models. The Autoclave sterilization process has four stages Pre-Vacuum, Rising Temperature, Sterilizing and Vacuum-Drying, which are classified employing the one vs all algorithm. The logistic regression model employed as variables the Autoclave absolute temperature and pressure. Data from 35 sterilization process were employed to find the logistic regression coefficients. As performance indexes, the precision, coverage and harmonic mean were employed. Results shown that the classification algorithm reached an efficiency of 81% to estimate the sterilization process stages.

Idioma original	Inglés
Título de la publicación alojada	2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
Editores	Miguel Altuve
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781509037971
DOI	https://doi.org/10.1109/STSIVA.2016.7743337
Estado	Publicada - 14 nov. 2016
Publicado de forma externa	Sí
Evento	21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 - Bucaramanga, Colombia Duración: 30 ago. 2016 → 2 sep. 2016

Serie de la publicación

Nombre	2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016

Conferencia

Conferencia	21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016
País/Territorio	Colombia
Ciudad	Bucaramanga
Período	30/08/16 → 2/09/16

Nota bibliográfica

Publisher Copyright:
© 2016 IEEE.

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10.1109/STSIVA.2016.7743337

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Angel, L., Viola, J., Vega, M., & Restrepo, R. (2016). Sterilization process stages estimation for an autoclave using logistic regression models. En M. Altuve (Ed.), 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 Artículo 7743337 (2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/STSIVA.2016.7743337

Angel, L. ; Viola, J. ; Vega, M. et al. / Sterilization process stages estimation for an autoclave using logistic regression models. 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016. editor / Miguel Altuve. Institute of Electrical and Electronics Engineers Inc., 2016. (2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016).

@inproceedings{b7c621f9315945e4bf929f198158223b,

title = "Sterilization process stages estimation for an autoclave using logistic regression models",

abstract = "This paper presents a methodology for an autoclave sterilization process stages estimation using logistic regression models. The Autoclave sterilization process has four stages Pre-Vacuum, Rising Temperature, Sterilizing and Vacuum-Drying, which are classified employing the one vs all algorithm. The logistic regression model employed as variables the Autoclave absolute temperature and pressure. Data from 35 sterilization process were employed to find the logistic regression coefficients. As performance indexes, the precision, coverage and harmonic mean were employed. Results shown that the classification algorithm reached an efficiency of 81% to estimate the sterilization process stages.",

author = "L. Angel and J. Viola and M. Vega and R. Restrepo",

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year = "2016",

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Angel, L, Viola, J, Vega, M & Restrepo, R 2016, Sterilization process stages estimation for an autoclave using logistic regression models. En M Altuve (ed.), 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016., 7743337, 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016, Institute of Electrical and Electronics Engineers Inc., 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016, Bucaramanga, Colombia, 30/08/16. https://doi.org/10.1109/STSIVA.2016.7743337

Sterilization process stages estimation for an autoclave using logistic regression models. / Angel, L.; Viola, J.; Vega, M. et al.
2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016. ed. / Miguel Altuve. Institute of Electrical and Electronics Engineers Inc., 2016. 7743337 (2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016).

Producción científica: Capítulo del libro/informe/acta de congreso › Ponencia publicada en las memorias del evento con ISBN › revisión exhaustiva

TY - GEN

T1 - Sterilization process stages estimation for an autoclave using logistic regression models

AU - Angel, L.

AU - Viola, J.

AU - Vega, M.

AU - Restrepo, R.

PY - 2016/11/14

Y1 - 2016/11/14

N2 - This paper presents a methodology for an autoclave sterilization process stages estimation using logistic regression models. The Autoclave sterilization process has four stages Pre-Vacuum, Rising Temperature, Sterilizing and Vacuum-Drying, which are classified employing the one vs all algorithm. The logistic regression model employed as variables the Autoclave absolute temperature and pressure. Data from 35 sterilization process were employed to find the logistic regression coefficients. As performance indexes, the precision, coverage and harmonic mean were employed. Results shown that the classification algorithm reached an efficiency of 81% to estimate the sterilization process stages.

AB - This paper presents a methodology for an autoclave sterilization process stages estimation using logistic regression models. The Autoclave sterilization process has four stages Pre-Vacuum, Rising Temperature, Sterilizing and Vacuum-Drying, which are classified employing the one vs all algorithm. The logistic regression model employed as variables the Autoclave absolute temperature and pressure. Data from 35 sterilization process were employed to find the logistic regression coefficients. As performance indexes, the precision, coverage and harmonic mean were employed. Results shown that the classification algorithm reached an efficiency of 81% to estimate the sterilization process stages.

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M3 - Ponencia publicada en las memorias del evento con ISBN

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T3 - 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016

BT - 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016

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PB - Institute of Electrical and Electronics Engineers Inc.

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Angel L, Viola J, Vega M, Restrepo R. Sterilization process stages estimation for an autoclave using logistic regression models. En Altuve M, editor, 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7743337. (2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016). doi: 10.1109/STSIVA.2016.7743337

Sterilization process stages estimation for an autoclave using logistic regression models

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