Kira Sertifikası Fiyat Değerlerinin Makine Öğrenmesi Metodu ile Tahmini

Yiğiter, Şule Yüksel; Sarı, Salim Sercan; Karabulut, Turgut; Başakın, Eyyup Ensar

doi:10.25272/ijisef.412760

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Further, Yiğiter et al. [20] used kNN algorithm to estimate the rental certificate prices. The model presented a successful performance in terms of estimating the prices for the next 1, 3 and 5 days.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms

Aydın

Yurdakul

2020

Applied Soft Computing

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The COVID-19 pandemic, which first spread to the People of Republic of China and then to other countries in a short time, affected the whole world by infecting millions of people and have been increasing its impact day by day. Hundreds of researchers in many countries are in search of a solution to end up this pandemic. This study aims to contribute to the literature by performing detailed analyses via a new three-staged framework constructed based on data envelopment analysis and machine learning algorithms to assess the performances of 142 countries against the COVID-19 outbreak. Particularly, clustering analyses were made using k-means and hierarchic clustering methods. Subsequently, efficiency analysis of countries were performed by a novel model, the weighted stochastic imprecise data envelopment analysis. Finally, parameters were analyzed with decision tree and random forest algorithms. Results have been analyzed in detail, and the classification of countries are determined by providing the most influential parameters. The analysis showed that the optimum number of clusters for 142 countries is three. In addition, while 20 countries out of 142 countries were fully effective, 36% of them were found to be effective at a rate of 90%. Finally, it has been observed that the data such as GDP, smoking rates, and the rate of diabetes patients do not affect the effectiveness level of the countries.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms

Aydın

Yurdakul

2020

Applied Soft Computing

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“…At the end of the study, it was seen that all three models could be used to capture stock market index movements, while the artificial neural network algorithm was a better classifier. Yiğiter et al (2018) tried to predict the price value of a lease certificate using machine learning techniques. Vakıf Portföy company, which issues sukuk in Turkey, was made on lease certificate prices, and daily price data were modelled using the K-Nearest Neighbors (KNN) algorithm.…”

Section: Literaturementioning

confidence: 99%

Evoluation of Financial Markets 2

Özbek,

Çalı,

Aydın

et al. 2023

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Given the importance and complexity of financial markets, it is necessary for everyone working in this field to keep up with current knowledge and theories. Sources such as "The Evolution of Financial Markets " can provide a deeper understanding of financial markets and offer readers current trends and practices. This book covers topics such as the evolution of financial markets, banking, risk management, markets, and institutions. The chapters in the book examine the functioning of financial markets worldwide and the tools used in these markets, the role of risk management and financial institutions, and the history and future of markets.

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“…The k number is very important when it comes to determining the optimum categorization or estimation. It can use trial-and-error or crossvalidation approaches to choose the correct k number [24]. The class of data is determined by averaging the k data points calculated as the closest distance of the training set.…”

Section: K-nearest Neighborhood (Knn)mentioning

confidence: 99%

Çelik Levha Arıza Tespiti için Makine Öğrenimi Algoritmalarının Karşılaştırmalı Analizi

TAŞAR

2022

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Metaller, modern zamanların en önemli yapı malzemelerinden biridir. Özellikle yassı metal sacın üretim ve işleme süreci oldukça hassastır. Üretim sürecinin kontrolü sadece ara ürünlerin değil, aynı zamanda son ürünlerinde kalitesini etkiler. Çelik levha yüzeylerinde oluşan hataların erken tespiti, endüstriyel üretimde önemli bir görevdir. Geleneksel olarak süreç kontrolü ve hata tespiti uzman kişiler tarafından manuel olarak yapılmaktadır. Ancak bu yöntem hem zaman hem de maliyet açısından uygun değildir. Sanayi devrimi IR 4.0 ile ürünlerde hata tespit problemlerini çözmek için makine öğrenimi (ML) teknikleri geliştirilmiştir. Bu çalışma, çelik yüzeyde üretim esnasında oluşabilecek altı farklı hata sınıfının tespiti için temel makine öğrenme yöntemleri geliştirmeye odaklanmıştır. Sınıflandırma problemi için beş standart ML modeli: LD, KNN, DT, SVM, RF ve bir derin öğrenme (DNN) modeli: tek boyutlu DNN geliştirilmiştir. Deneysel veri seti olarak UCI çelik plaka deformasyon veri seti kullanılmıştır. Yöntemlerin başarısını tespit etmek için beş performans kriteri: Doğruluk, Duyarlılık, Özgüllük, Kesinlik, F1 değeri kullanılmıştır. LD, KNN, DT, SVM, RF ve DNN sınıflandırma yöntemlerinin başarı oranları sırasıyla 90.136%, 91.780%, 93.013%, 93.287%, 95.479%, 96.986% olarak elde edilmiştir. Sonuçlar, makina öğrenmesi yaklaşımının çelik levha arıza teşhis problemindeki önemli etkisini gösterilmiştir.

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Kira Sertifikası Fiyat Değerlerinin Makine Öğrenmesi Metodu ile Tahmini

Cited by 5 publications

References 3 publications

Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms

Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms

Evoluation of Financial Markets 2

Çelik Levha Arıza Tespiti için Makine Öğrenimi Algoritmalarının Karşılaştırmalı Analizi

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