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Comparative Study of Machine-Learning Frameworks for the Elaboration of Feed-Forward Neural Networks by Varying the Complexity of Impedimetric Datasets Synthesized Using Eddy Current Sensors for the Characterization of Bi-Metallic Coins.

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Title: Comparative Study of Machine-Learning Frameworks for the Elaboration of Feed-Forward Neural Networks by Varying the Complexity of Impedimetric Datasets Synthesized Using Eddy Current Sensors for the Characterization of Bi-Metallic Coins.
Authors: Munjal R; Professorship of Measurement and Sensor Technology, Chemnitz University of Technology, 09126 Chemnitz, Germany., Arif S; Professorship of Measurement and Sensor Technology, Chemnitz University of Technology, 09126 Chemnitz, Germany., Wendler F; Professorship of Measurement and Sensor Technology, Chemnitz University of Technology, 09126 Chemnitz, Germany., Kanoun O; Professorship of Measurement and Sensor Technology, Chemnitz University of Technology, 09126 Chemnitz, Germany.
Source: Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Feb 09; Vol. 22 (4). Date of Electronic Publication: 2022 Feb 09.
Publication Type: Journal Article
Journal Info: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
Database: MEDLINE Ultimate
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      – TitleFull: Comparative Study of Machine-Learning Frameworks for the Elaboration of Feed-Forward Neural Networks by Varying the Complexity of Impedimetric Datasets Synthesized Using Eddy Current Sensors for the Characterization of Bi-Metallic Coins.
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