Year 2020, Volume , Issue 20, Pages 280 - 286 2020-12-31

The Role of Artificial Intelligence in Productivity: A Case Study of Wine Quality Prediction
Verimlilikte Yapay Zeka’nın Rolü: Şarap Kalitesinin Tahminine Yönelik Bir Vaka Çalışması

Ramazan ÜNLÜ [1]


Artificial intelligence has been used in many areas in recent years and has achieved quite successful results. Like using artificial intelligence from healthcare to driverless vehicles, it also has often been used to increase productivity in the production sector. In this study, we tried to draw a framework for the use of artificial intelligence algorithms in a data set that is not normally distributed. Any artificial intelligence algorithm can be easily applied on normally distributed data sets, while data sets that do not normally distributed require a different operation to the data itself or it is necessary to revise the theoretical structure of the algorithm. In this regard, three different methodologies are applied in this study. Initially, Support Vector Machines, which are often used in the literature, is used. In addition, Weighted Support Vector Machines, which is the revised version of the Support Vector Machines to produce successful results in abnormal distributed data sets. Finally, the Synthetic Minority Oversampling Technique (SMOTE) is applied and the data set used was artificially converted to normal distribution. Three techniques are compared in terms of sensitivity, specificity, precision, prevalence, F-1 score, and G-Mean evaluation criteria were compared. According to the results of the study, Weighted Support Vector Machines produced the most successful results according to the evaluation criteria used.
Yapay zeka son yıllarda birçok alanda kullanılmaya başlanmış ve oldukça başarılı sonuçlar elde edilmiştir. Sağlık sektöründen sürücüsüz araçlara kadar birçok alanda kullanılan yapay zeka, üretim sektöründe de verimliliğin artırılması için sıklıkla kullanılmıştır. Bu çalışmada normal olarak dağılmamış bir veri setinde yapay zeka algoritmalarının kullanılmasına yönelik bir çerçeve çizilmeye çalışılmıştır. Normal dağılım gösteren veri setlerinde herhangi bir yapay zeka algoritması kolaylıkla uygulanabilirken normal dağılım göstermeyen veri setlerinde ya verinin kendisine farklı bir işlem uygulanması gerekir veya algoritmanın teorik yapısının revize edilmesi gerekmektedir. Bu açıdan bu çalışmada iki farklı yöntemde uygulanmıştır. İlk olarak literatürde sıklıkla kullanılan Destek Vektör Makinaları kullanılmıştır. Buna ek olarak Destek Vektör Makinalarının normal dağılmayan veri setlerinde başarılı sonuçlar vermesi için uyarlanmış şekli olan Ağırlıklandırılmış Destek Vektör Makineleri uygulanmıştır. Son olarak Sentetik Azınlık Aşırı Örnekleme Tekniği (SMOTE) tekniği uygulanmış ve kullanılan veri seti yapay olarak normal dağılıma yakınsanmıştır. Kullanılan üç teknikte duyarlılık, hassaslık, özgüllük, yaygınlık, F skor ve Geometrik Ortalama (G-Mean) değerlendirme kriterleri açısından karşılaştırılmıştır. Çalışma sonucuna göre Ağırlıklandırılmış Destek Vektör Makineleri kullanılan değerlendirMe kriterlerine göre en başarılı sonuçları vermiştir.
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Primary Language en
Subjects Engineering
Journal Section Articles
Authors

Orcid: 0000-0002-1201-195X
Author: Ramazan ÜNLÜ (Primary Author)
Institution: GÜMÜŞHANE ÜNİVERSİTESİ, GÜMÜŞHANE İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ
Country: Turkey


Dates

Publication Date : December 31, 2020

APA Ünlü, R . (2020). The Role of Artificial Intelligence in Productivity: A Case Study of Wine Quality Prediction . Avrupa Bilim ve Teknoloji Dergisi , (20) , 280-286 . DOI: 10.31590/ejosat.773736