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    Comparative analysis of neural networks in the diagnosis of emerging diseases based on COVID-19
    (Konuralp Journal of Mathematics, 2021) Kirişci, Murat; Demir, İbrahim; Şimşek, Necip
    Dermatological diseases are frequently encountered in children and adults for various reasons. There are many factors that cause the onset of these diseases and different symptoms are generally seen in each age group. Artificial Neural Networks can provide expert level accuracy in the diagnosis of dermatological findings of patients with COVID-19 disease. Therefore, the use of neural network classification methods can give the best estimation method in dermatology. In this study, the prediction of cutaneous diseases caused by COVID-19 was analyzed by Scaled Conjugate Gradient, Levenberg Marquardt, Bayesian Regularization neural networks. At some points, Bayesian Regularization and Levenberg Marquardt were almost equally effective, but Bayesian Regularization performed better than Levenberg Marquard and called Conjugate Gradient in performance. It is seen that neural network model predictions achieve the highest ac-curacy. For this reason, Artificial Neural Networks are able to classify these diseases as accurately as human experts in an experimental setting.
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    Finansal krizlerin gelişmekte olan ülke ekonomilerine etkileri
    (İstanbul Ticaret Üniversitesi, 2020) Demir, İbrahim
    İÇİNDEKİLER TEZ ONAY SAYFASI. i İÇİNDEKİLER . ii TABLOLAR LİSTESİ. iv ŞEKİLLER LİSTESİ. v ÖZ. 1 ABSTRACT. 2 GİRİŞ. 3 1. FİNANSAL KRİZ KAVRAMI . 4 1. 1. Finansal Krizin Tanımı . 4 1. 2. Finansal Kriz Türleri. 6 1. 2. 1. Para Krizi . 6 1. 2. 2. Bankacılık Krizi. 7 1. 2. 3. Dış Borç Krizi. 8 1. 2. 4. Sistemik Kriz . 8 1. 3. Finansal Kriz Teorileri. 9 1. 3. 1. Birinci Kuşak Teorileri . 9 1. 3. 2. İkinci Kuşak Teorileri. 10 1. 3. 3. Üçüncü Kuşak Teorileri. 12 1. 4. Finansal Krizlerin Nedenleri. 18 1. 4. 1. Faiz Oranları . 18 1. 4. 2. Asimetrik Bilgi Yaklaşımı. 20 1. 4. 3. Mali Aksaklıklar . 20 1. 5. Finansal Krizin Etkileri. 22 1. 5. 1. Makroekonomik Etkileri. 22 1. 5. 2. Bankacılık Sektörüne Etkileri. 24 iii 1. 6. Finansal İstikrarsızlığı Önlemeye Yönelik Politikalar. 25 1. 6. 1. Döviz Piyasalarının İstikrarına Yönelik Politikalar. 26 1. 2. 2. Spekülatif Sermaye Hareketlerine Yönelik Politikalar. 26 2. FİNANSAL KRİZLERİN ETKİLERİ VE GENEL OLARAK KRİZLERİN TÜRKİYE’YE ETKİLERİ . 29 2. 1. Finansal Krizlerin Etkileri . 29 2. 1. 1. Olumlu Etkileri . 29 2. 1. 2. Olumsuz Etkileri. 31 2. 1. 3. Ekonomik Etkileri. 32 2. 2. Dünya’da Meydana Gelen Krizlerin Türkiye Üzerindeki Etkileri . 32 2. 2. 1. 1929 Krizi ve Türkiye. 32 2. 2. 2. 1970 Petrol Krizi ve Türkiye . 34 2. 2. 3. Asya Krizi ve Türkiye. 35 2. 2. 4. Rusya Krizi ve Türkiye. 35 2. 2. 5. 2008 Krizi ve Türkiye. 36 2. 3. Türkiye Ekonomisinde Yaşanan Krizler. 38 2. 3. 1. 1994 Krizi . 39 2. 3. 2. 2001 Krizi . 43 2. 3. 3. 2008 Krizi . 45 3. BÖLÜM: LİTERATÜR TARAMASI. 50 SONUÇ. 63 KAYNAKÇA. 66 iv TABLOLAR LİSTESİ Tablo 1: Literatür Taraması . 58 v ŞEKİLLER LİSTESİ Şekil 1: Finansal Krizlerin Sınıflandırılması . 6 1 ÖZ Küreselleşmenin hız kazandığı 1980’li yıllardan itibaren finansal krizler daha sık karşımıza çıkmaktadır. Bölgesel ya da sektörel olarak ortaya çıkan krizler, piyasaların derinleşmesi ve birbirine daha bağımlı hale gelmesi sonucunda çok geniş alanlara yayılabilmektedir. 2008 yılında Amerika’da konut piyasasında başlayan kriz, kısa sürede önce Amerikan ekonomisinin tamamına ardından da küresel bir boyut kazanarak dünya finansal sistemine sirayet etmiştir. Kriz, her ne kadar dünyanın en büyük ekonomisinde ortaya çıkmış olsa da, gelişmekte olan ülkelerde de önemli boyutlarda hissedilmiştir. Türkiye’de finansal sistemin Amerika’daki kadar derin olmaması ve 2001 krizi sonrasında yapılan reformlar nedeniyle küresel kriz daha farklı boyutlarda yaşanmıştır. Türk finans kesiminde güçlü denetim mekanizmalarının varlığı krizin bu alanda daha az hissedilmesine neden olurken, dünya genelinde ticaret hacminin düşmesi özellikle üretim ve ihracat alanında olumsuz etkiler yaratmıştır. Bu bağlamda çalışmada finansal krizler ve Türkiye’ye olan etkileri incelenmiştir. Anahtar Kelimeler: Kriz, Finansal Kriz, Türkiye 2 ABSTRACT Since 1980s, when globalization has accelerated significantly, financial crises have occurred much more frequently. As a result of financial deepening across markets and as markets become more dependent on each other, regional or sectoral crises tend to spread further. In 2008, financial crisis that commenced in American Housing Market, spread quickly first to the entire U. S. economy, then went global and infected the global financial system. Although the crisis originated in the U. S, the world’s largest economy, it had profound impacts on developing countries. Since Turkey’s financial system is not as deep as United States’ and due to the reforms adopted in 2001, Turkey experienced the global crisis in a different way. While presence of strong control mechanisms in the Turkish financial sector causes the crisis to be felt less in this sector, the shrinkage in the world trade volüme influenced especially manufacturing and export sectors negatively. In this context, the effects of the financial crisis and to Turkey in the study were examined. Keywords: Crisis, Financial Crisis, Turkey
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    The novel VIKOR methods for generalized Pythagorean fuzzy soft sets and its application to children of early childhood in COVID-19 quarantine
    (Springer, 2021) Kirişçi, Murat; Demir, İbrahim; Şimşek, Necip; Topaç, Nihat; Bardak, Musa
    In this work, the new VIKOR methods are established using the generalized Pythagorean fuzzy soft sets (GPFSSs). For GPFSSs, the distance measures such as Hamming, Euclidean, and generalized are given. Further, the basic characteristics of these distance measures are examined. Fuzzy and soft sets are strong instruments for uncertainty. This strongness has been demonstrated by the GPFSS combining Pythagorean fuzzy sets and soft sets and applied to imprecise and ambiguous information. In this context, new remoteness index-based methods have been proposed, which are dissimilar from available VIKOR methods. The displaced and fixed ideals positive and negative Pythagorean fuzzy values (PFV) were defined. Thus, based on this definition, displaced positive ideal remoteness indices, negative ideal remoteness indices, and fixed positive ideal, negative ideal remoteness indices were discussed. Two different weights are used here: weights based on OF preference information and precise weights calculated with the expectation score function. The VIKOR method given here provides a different way from canonical VIKOR methods: rank candidate alternatives and determining a compromise solution based on different preference structures. The processes principles of the newly defined GPFSSs VIKOR methods are given by four algorithms. An example of these algorithms is given with the behavioral development and cognitive development of the children of Early Childhood children in the COVID-19 quarantine.
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    Soft set based new decision-making method with cardiovascular disease application
    (Yildiz Technical Univ, 2021) Kirişci, Murat; Demir, İbrahim; Şimşek, Necip
    A Pythagorean fuzzy set is characterized by values satisfying the condition that the square sum of the degree of membership and degree of non-membership is less than or equal to 1. As a generalized set, Pythagorean fuzzy sets have a close relationship with intuitionistic fuzzy sets. In this study, an algorithm is given that can select patients at risk of developing heart disease based on cardiovascular data. This given algorithm is created with Pythagorean fuzzy soft sets. The new algorithm is offered a medical decision-making method to assist in medical diagnosis. A medical case was examined as a real-life application to see if the proposed method is applicable. The real dataset which is called the Cleveland heart disease dataset has been chosen. In the application, the dataset is arranged as PFSS. In addition, the parameter set was determined and calculations were made in accordance with PFSS. A comparison table was created with the values obtained from these calculations. By choosing the maximum of the values obtained with the score function, the patient with the highest risk of developing heart disease was determined.