Fuzzy Logic and Deep Learning Integration in Likert Type Data

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dc.contributor.authorÜnal, Zeynep
dc.contributor.authorÇetin, Emre İpekçi
dc.date.accessioned2024-11-07T13:19:14Z
dc.date.available2024-11-07T13:19:14Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractDeep learning networks have many modern applications and demonstrate a high-performance level. As the applications of deep learning networks to real-world problems continues to spread, the reason why they are effective remains unknown. However, it is possible to make some judgments by examining the behaviour of the network in experiments. The main aim of this study is to analyse the performance of deep learning techniques in the form of a 5-point Likert-type scale by converting the artificial data sets into a fuzzy form using triangular or trapezium fuzzy numbers.To test the performance of the proposed model, which is the integration of deep learning and fuzzy logic techniques, the satisfaction estimation problem was chosen. Data sets consisting of fuzzy numbers which reach at least three or four times more parameters than normal data sets. Thus, it decreases the possibility of falling into the local optimum trap in optimization studies with big data. In the analysis conducted with deep learning, in accordance with the fuzzification examples in the literature, the defuzzification was carried out with separate results for peak, maximum, and minimum values. In contrast to the literature, the performances of the deep learning model were investigated by suggesting that fuzzy numbers produce a single result series.
dc.identifier.doi10.35414/akufemubid.1019671
dc.identifier.endpage125
dc.identifier.issn2149-3367
dc.identifier.issue1
dc.identifier.startpage112
dc.identifier.trdizinid517449
dc.identifier.urihttps://doi.org/10.35414/akufemubid.1019671
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/517449
dc.identifier.urihttps://hdl.handle.net/11480/12977
dc.identifier.volume22
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofAfyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241107
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectİstatistik ve Olasılık
dc.subjectDeep Learning
dc.titleFuzzy Logic and Deep Learning Integration in Likert Type Data
dc.typeArticle

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