Do Artificial Neural Networks Always Provide High Prediction Performance? An Experimental Study on the Insufficiency of Artificial Neural Networks in Capacitance Prediction of the 6H-SiC/MEH-PPV/Al Diode

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dc.authoridNonlaopon, Kamsing/0000-0002-7469-5402
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridShafiq, Anum/0000-0001-7186-7216
dc.contributor.authorColak, Andac Batur
dc.contributor.authorGuzel, Tamer
dc.contributor.authorShafiq, Anum
dc.contributor.authorNonlaopon, Kamsing
dc.date.accessioned2024-11-07T13:32:46Z
dc.date.available2024-11-07T13:32:46Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this paper, we study a new model that represents the symmetric connection between capacitance-voltage and Schottky diode. This model has a symmetrical shape towards the horizontal direction. In recent times, works conducted on artificial neural network structure, which is one of the greatest actual artificial intelligence apparatuses used in various fields, stated that artificial neural networks are apparatuses that proposal very high forecast performance by the side of conventional structures. In the current investigation, an artificial neural network structure has been generated to guess the capacitance voltage productions of the Schottky diode with organic polymer edge, contingent on the frequency with a symmetrical shape. Of the dataset, 130 were grouped for training, 28 for validation, and 28 for testing. In order to evaluate the effect of the number of neurons on the prediction accuracy, three different models with different neuron numbers have been developed. This study, in which an artificial neural network model, although well-trained, could not predict the output values correctly, is a first in the literature. With this aspect, the study can be considered as a pioneering study that brings a novelty to the literature.
dc.identifier.doi10.3390/sym14081511
dc.identifier.issn2073-8994
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85137360802
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/sym14081511
dc.identifier.urihttps://hdl.handle.net/11480/15606
dc.identifier.volume14
dc.identifier.wosWOS:000846589900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSymmetry-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectartificial neural network
dc.subjectMEH-PPV
dc.subjectcapacitance-voltage
dc.subjectSchottky diode
dc.subjectbarrier height
dc.titleDo Artificial Neural Networks Always Provide High Prediction Performance? An Experimental Study on the Insufficiency of Artificial Neural Networks in Capacitance Prediction of the 6H-SiC/MEH-PPV/Al Diode
dc.typeArticle

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