Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning.

Zhang, Junru; Srivatsa, Purna; Ahmadzai, Fazel Haq; Liu, Yang; Song, Xuerui; Karpatne, Anuj; Kong, Zhenyu; Johnson, Blake N

Zhang, Junru; Srivatsa, Purna; Ahmadzai, Fazel Haq; Liu, Yang; Song, Xuerui; Karpatne, Anuj; Kong, Zhenyu; Johnson, Blake N.

Afiliación

Zhang J; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
Srivatsa P; Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States.
Ahmadzai FH; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
Liu Y; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
Song X; School of Neuroscience, Virginia Tech, Blacksburg, Virginia 24061, United States.
Karpatne A; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
Kong Z; Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States.
Johnson BN; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.

ACS Sens ; 8(11): 4079-4090, 2023 11 24.

Article en En | MEDLINE | ID: mdl-37931911

Asunto(s)

Técnicas Biosensibles; Aprendizaje Automático; Técnicas Biosensibles/métodos

Palabras clave

artificial intelligence; biosensing; biosensor performance; chemical sensors; classification; false negative; false positive; measurement confidence

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Técnicas Biosensibles / Aprendizaje Automático Idioma: En Revista: ACS Sens Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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