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Öğe Analysing the success of level determination exam according to the school type and lesson type that is represented by questions in the exam(Elsevier Science Bv, 2013) Cavusoglu, Abdullah; Sen, Baha; Ucar, Emine; Ucar, MuratThe purpose of this study is to determine if there is any relation between level determination exam (LDE) point and the type of school and the points that student got from those lessons which are represented by questions in the exam. Randomly selected data belongs to 8th degree primary school students among whole Turkey are used as the sample data. To investigate the significance level of relationship between two variables independent of each other, first of all the correlation values between two variables has been examined and then some statistical tests has been applied. As a result, it has been observed that there is a strong, meaningful and positive relation between the achievement of students on the lessons which are represented by questions in the exam and the LDE points. Furthermore, it has been observed that the type of school that students attend is also effective to the success and it has been seen that the students who attend private schools are more successful than the students in public schools. (C) 2013 The Authors. Published by Elsevier Ltd.Öğe A Novel Classification and Estimation Approach for Detecting Keratoconus Disease with Intelligent Systems(Ieee, 2013) Ucar, Murat; Sen, Baha; Cakmak, Hasan BasriKeratoconus is an eye disease characterized by progressive thinning of cornea which is the front based transparent layer of the eye. In other words, it is a progressive distortion of corneal layer and at least getting conical shape that should be like a dome camber. The vision reduces more and more while cornea gets shape of cone which should be like a sphere normally. The aim of this study is to define a new classification method for detecting keratoconus based on statistical analysis and to realize the prediction of these classified data with intelligent systems. 301 eyes of 159 patients and 394 eyes of 265 refractive surgery candidates as the control group have been used for this study. Factor analysis, one of the multivariate statistical techniques, has been mainly used to find more meaningful, easy to understand, and independent factors amongst the others. Later, a new classification method has been defined using clustering analysis techniques on these factors and finally estimated by using artificial neural networks and support vector machines.Öğe Plant leaf disease classification using EfficientNet deep learning model(Elsevier, 2021) Atila, Umit; Ucar, Murat; Akyol, Kemal; Ucar, EmineMost plant diseases show visible symptoms, and the technique which is accepted today is that an experienced plant pathologist diagnoses the disease through optical observation of infected plant leaves. The fact that the disease diagnosis process is slow to perform manually and another fact that the success of the diagnosis is proportional to the pathologist's capabilities makes this problem an excellent application area for computer aided diagnostic systems. Instead of classical machine learning methods, in which manual feature extraction should be flawless to achieve successful results, there is a need for a model that does not need pre-processing and can perform a successful classification. In this study, EfficientNet deep learning architecture was proposed in plant leaf disease classification and the performance of this model was compared with other state-of-the-art deep learning models. The PlantVillage dataset was used to train models. All the models were trained with original and augmented datasets having 55,448 and 61,486 images, respectively. EfficientNet architecture and other deep learning models were trained using transfer learning approach. In the transfer learning, all layers of the models were set to be trainable. The results obtained in the test dataset showed that B5 and B4 models of EfficientNet architecture achieved the highest values compared to other deep learning models in original and augmented datasets with 99.91% and 99.97% respectively for accuracy and 98.42% and 99.39% respectively for precision.Öğe A statistical approach to classification of keratoconus(Ijo Press, 2016) Ucar, Murat; Cakmak, Hasan Basri; Sen, Baha[No abstract available]
