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A Deep Learning Approach to Organic Pollutants Classification Using Voltammetry.
Molinara, Mario; Cancelliere, Rocco; Di Tinno, Alessio; Ferrigno, Luigi; Shuba, Mikhail; Kuzhir, Polina; Maffucci, Antonio; Micheli, Laura.
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  • Molinara M; Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy.
  • Cancelliere R; Department of Chemical Science and Technologies, University of Rome "Tor Vergata", 00133 Rome, Italy.
  • Di Tinno A; Department of Chemical Science and Technologies, University of Rome "Tor Vergata", 00133 Rome, Italy.
  • Ferrigno L; Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy.
  • Shuba M; Center of Physical Science and Technologies, 10257 Vilnius, Lithuania.
  • Kuzhir P; Institute of Photonics, Department of Physics and Mathematics, University of Eastern Finland, 80101 Joensuu, Finland.
  • Maffucci A; Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy.
  • Micheli L; INFN, Italian National Institute for Nuclear Physics, 00044 Frascati, Italy.
Sensors (Basel) ; 22(20)2022 Oct 21.
Article en En | MEDLINE | ID: mdl-36298383
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Nanotubos de Carbono / Contaminantes Ambientales / Aprendizaje Profundo Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Italia
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Nanotubos de Carbono / Contaminantes Ambientales / Aprendizaje Profundo Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Italia