Improvement of Quality Performance in Mask Production by Feature Selection and Machine Learning Methods and An Application

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dc.contributor.authorTebrizcik, Semra
dc.contributor.authorErsöz, Süleyman
dc.contributor.authorAktepe, Adnan
dc.date.accessioned2025-01-21T16:11:41Z
dc.date.available2025-01-21T16:11:41Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractWith the development of technology, large databases become more accessible thanks to automation systems that automatically keep data and allow the use of large databases in many areas. Machine learning approaches, a sub-branch of artificial intelligence, are used in making decisions about the process by analyzing the data stored in databases and converting them into information. In this paper, the body production process of the surgical (medical) mask is analyzed. As it is known, surgical masks have become a part of our lives by becoming widespread all over the world with the COVID-19 pandemic. In the surgical mask body production process, using the real data of the production factors, first of all, filtering feature selection methods and analyzes were made and the feature selection method to be used was determined. With the specified feature selection method, the factors affecting the product quality are determined. Secondly, machine learning methods were used to determine the values and value ranges of factors (features) in the production of defect-free products. The performances of the machine learning models established in the second stage were increased by feature selection analysis. In the study, together with the parameter optimizations made to machine learning algorithms, it was seen that the best algorithm to estimate the defective product rate was the Ibk algorithm with 92.3% accuracy, 91.9% F measurement and 93% AUC value. Finally, in line with the decision rules revealed in the study, it was observed that the fabric types used for the upper/middle/lower layers that make up the body part in the mask body production process greatly affect the rates of defective or defect-free products. If the rod apparatus around the nose belongs to class k, it has been determined that many masks are defective. Improvement suggestions were presented according to the application results.
dc.identifier.doi10.17134/khosbd.1298163
dc.identifier.endpage190
dc.identifier.issn1303-6831
dc.identifier.issn2148-1776
dc.identifier.issue1
dc.identifier.startpage167
dc.identifier.trdizinid1234745
dc.identifier.urihttps://doi.org/10.17134/khosbd.1298163
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1234745
dc.identifier.urihttps://hdl.handle.net/20.500.12587/21528
dc.identifier.volume20
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofSavunma Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectMühendislik
dc.subjectElektrik ve Elektronik
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.titleImprovement of Quality Performance in Mask Production by Feature Selection and Machine Learning Methods and An Application
dc.typeArticle

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