Mostrar el registro sencillo del ítem
dc.contributor.author
Shariat, Kourosh
dc.contributor.author
Kirsanov, Dmitry
dc.contributor.author
Olivieri, Alejandro Cesar
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.contributor.author
Parastar, Hadi
dc.date.available
2022-07-08T18:32:23Z
dc.date.issued
2022-05
dc.identifier.citation
Shariat, Kourosh; Kirsanov, Dmitry; Olivieri, Alejandro Cesar; Parastar, Hadi; Sensitivity and generalized analytical sensitivity expressions for quantitative analysis using convolutional neural networks; Elsevier Science; Analytica Chimica Acta; 1192; 338697; 5-2022; 1-9
dc.identifier.issn
0003-2670
dc.identifier.uri
http://hdl.handle.net/11336/161779
dc.description.abstract
In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, few reports have been published on their properties and characteristics of analytical figures of merit. Currently, most performance measures for these types of networks only incorporate some function of prediction error. While useful, these measures are incomplete and cannot be used as an objective comparison among different models. In this report, a new method for calculating the sensitivity of any type of neural network is proposed and studied on both simulated and real datasets. Generalized analytical sensitivity is defined and calculated for neural networks as an additional figure of merit. Moreover, the dependence of convolutional neural networks on regularization dataset size is studied and compared with other conventional calibration methods.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ANALYTICAL FIGURES OF MERIT
dc.subject
CONVOLUTIONAL NEURAL NETWORKS
dc.subject
DEEP LEARNING
dc.subject
SENSITIVITY
dc.subject.classification
Química Analítica
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
Ciencias Químicas
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.title
Sensitivity and generalized analytical sensitivity expressions for quantitative analysis using convolutional neural networks
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-07-04T19:59:31Z
dc.journal.volume
1192
dc.journal.number
338697
dc.journal.pagination
1-9
dc.journal.pais
Países Bajos
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Shariat, Kourosh. Sharif University of Technology; Irán
dc.description.fil
Fil: Kirsanov, Dmitry. Saint-Petersburg State University; Rusia
dc.description.fil
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
dc.description.fil
Fil: Parastar, Hadi. Sharif University of Technology; Irán
dc.journal.title
Analytica Chimica Acta
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.aca.2021.338697
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0003267021005237
Archivos asociados