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    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, Murat
    The 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.
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    Evaluating the achievements of computer engineering department of distance education students with data mining methods
    (Elsevier Science Bv, 2012) Sen, Baha; Ucar, Emine
    Recently, the internet technology has become an indispensable part of life, a very useful application that cannot be earlier have made it possible. One of these is distance learning technologies. Due to limitations of traditional learning-teaching methods in classroom activities and practitioners who intend to conduct training activities in the absence of the possibility of communication and interaction among learners with special education units are prepared and provided a wide range of media center through a certain method of teaching. According to a further recognition of Distance Education, although far away from each other with the student who teaches the same time (synchronous) or different time (asynchronous) communications with a tool as training system established. The aim of this study is to compare the achievements of Computer Engineering Department students in Karabuk University according to criteria such as age, gender, type of high school graduation and whether the students studying in distance education or regular education using data mining techniques. Also discussing the differences of the techniques according to the results and to make suggestions for which technique would be more effective. (C) 2011 Published by Elsevier Ltd.
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    Plant leaf disease classification using EfficientNet deep learning model
    (Elsevier, 2021) Atila, Umit; Ucar, Murat; Akyol, Kemal; Ucar, Emine
    Most 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.
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    Predicting and analyzing secondary education placement-test scores: A data mining approach
    (Pergamon-Elsevier Science Ltd, 2012) Sen, Baha; Ucar, Emine; Delen, Dursun
    Understanding the factors that lead to success (or failure) of students at placement tests is an interesting and challenging problem. Since the centralized placement tests and future academic achievements are considered to be related concepts, analysis of the success factors behind placement tests may help understand and potentially improve academic achievement. In this study using a large and feature rich dataset from Secondary Education Transition System in Turkey we developed models to predict secondary education placement test results, and using sensitivity analysis on those prediction models we identified the most important predictors. The results showed that CS decision tree algorithm is the best predictor with 95% accuracy on hold-out sample, followed by support vector machines (with an accuracy of 91%) and artificial neural networks (with an accuracy of 89%). Logistic regression models came out to be the least accurate of the four with and overall accuracy of 82%. The sensitivity analysis revealed that previous test experience, whether a student has a scholarship, student's number of siblings, previous years' grade point average are among the most important predictors of the placement test scores. (C) 2012 Elsevier Ltd. All rights reserved.