https://hdl.handle.net/20.500.12809/248 2026-04-12T19:41:24Z https://hdl.handle.net/20.500.12809/11081 Sahte Popülerliğin Sosyal Medyada Algı ve Etkileşim Üzerindeki Etkisi: Instagram Üzerinde Bir Vaka Çalışması Akın, Hasan Eren; İlkucar, Muhammer Bu çalışma, dijital medya ortamında sahte popülerlik içeriklerinin sosyal medya kullanıcılarının algı, güven ve etkileşim davranışları üzerindeki etkisini deneysel bir yaklaşımla incelenmiştir. Instagram platformunda özel olarak oluşturulan ve bot takipçilerle desteklenen bir hesap üzerinden yürütülen deneysel süreçte hem gerçek hem de sahte içeriklerin kullanıcılar üzerindeki etkileri analiz edilmiştir. Çalışma kapsamında bir ay boyunca paylaşılan 44 Reels videosu, toplamda üç milyonu aşkın görüntülenme veyüz binlerce etkileşim almış, kullanıcı beğeni, paylaşım, yorum, kayıt etme gibi veriler nicel ve nitel olarak değerlendirilmiştir. Yapılan analize göre; kullanıcıların büyük oranda sahte popüler içeriklere duyduğu güveni, eleştirel sorgulamanın zayıflığını, içeriklerin kaynağı ve doğruluğunun araştırılmadığı, kullanıcıların kolaylıkla manipüle edilebileceği ve sosyal medya algoritmalarının manipülasyona açık olduğu gibi sonuçlara varılabilir. 2026-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/10836 What makes survival of heart failure patients? Prediction by the iterative learning approach and detailed factor analysis with the SHAP algorithm İlkuçar, Muammer; Çifci A.; Kirbaş I. Cardiovascular disease is the leading cause of global death and disability. There are many types of cardiovascular diseases. The diagnosis of heart failure, one of the cardiovascular disease types, is a challenging task and plays a significant role in guiding the treatment of patients. However, machine learning approaches can be helpful for assisting medical institutions and practitioners in predicting heart failure in the early phase. This study is the first application that analyzes the dataset containing clinical records of 299 patients with heart failure using a feedforward backpropagation neural network (NN). The aim of this study is to predict the survival of heart failure patients based on the clinical data and to identify the strongest factors influencing heart failure disease development. We adopted the Shapley additive explanations (SHAP) values, which have been used to interpret model findings. From the study, it is observed that the best and highest accuracy of 91.11% is obtained compared to previous studies and it is found that feedforward backpropagation NN performed better than the previous approaches. Also, this study revealed that time, ejection fraction (EF), serum creatinine, creatinine phosphokinase (CPK), and age are the strongest risk factors for mortality among patients suffering from heart failure. 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/9368 USE OF FOURIER TRANSFORM INFRARED SPECTROSCOPY AND ARTIFICIAL NEURAL NETWORKS TO PREDICT THE WOOD DENSITY OF CEDRUS LIBANI A. RICH. Kaya, Ali İhsan; Çiftçi, Ahmet; İlkuçar, Muhammer The aim of this study was to measure the wood density of Cedrus libani A. Rich. samples from its Fourier Transform Infrared Spectroscopy spectrum. 40 density values were obtained by using 3600 properties belonging to C. libani tree in laboratory environment. Since 1045 properties between 832-1876 from 3600 properties were found to be sufficient to determine the density, 1045 data between 832 and 1876 were used for training and testing of the network. Data used as attribute were normalized between 0.1 and 0.9. 20% of the data were used as the test set and the remaining 80% of the data are used as the training set. This analysis indicated that Fourier Transform Infrared Spectroscopy combined with Artificial Neural Network can be used to measure the density of wood in less effort and in less time than other laboratory methods. 2021-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/6279 ITERATIVE METHOD APPLIED to the FRACTIONAL NONLINEAR SYSTEMS ARISING in THERMOELASTICITY with MITTAG-LEFFLER KERNEL Gao, W.; Veeresha, P.; Prakasha, D.G.; Şenel, Bilgin; Başkonuş, H.M. In this paper, we study on the numerical solution of fractional nonlinear system of equations representing the one-dimensional Cauchy problem arising in thermoelasticity. The proposed technique is graceful amalgamations of Laplace transform technique with q-homotopy analysis scheme and fractional derivative defined with Atangana-Baleanu (AB) operator. The fixed-point hypothesis is considered in order to demonstrate the existence and uniqueness of the obtained solution for the proposed fractional order model. In order to illustrate and validate the efficiency of the future technique, we consider three different cases and analyzed the projected model in terms of fractional order. Moreover, the physical behavior of the obtained solution has been captured in terms of plots for diverse fractional order, and the numerical simulation is demonstrated to ensure the exactness. The obtained results elucidate that the proposed scheme is easy to implement, highly methodical as well as accurate to analyze the behavior of coupled nonlinear differential equations of arbitrary order arisen in the connected areas of science and engineering. © 2020 Georg Thieme Verlag. All rights reserved. 2020-01-01T00:00:00Z