Bilgisayar ve Bilişim Fakültesi Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/10834

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Browsing Bilgisayar ve Bilişim Fakültesi Koleksiyonu by Author "Arslan, Ahmet"

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    Conference Object
    Citation - Scopus: 1
    Gender Determination From Teeth Images Via Hybrid Feature Extraction Method
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Uzbaş, Betül; Arslan, Ahmet; Kök, Hatice; Acılar, Ayşe Merve
    Teeth are a significant resource for determining the features of an unknown person, and gender is one of the important pieces of demographic information. For this reason, gender analysis from teeth is a current topic of research. Previous literature on gender determination have generally used values obtained through manual measurements of the teeth, gingiva, and lip area. However, such methods require extra effort and time. Furthermore, since sexual dimorphism varies among populations, it is necessary to know the optimum values for each population. This study uses a hybrid feature extraction method and a Support Vector Machine (SVM) for gender determination from teeth images. The study group was composed of 60 Turkish individuals (30 female, 30 male) between the ages of 19 and 27. Features were automatically extracted from the intraoral images through a hybrid method that combines two-dimensional Discrete Wavelet Transformation (DWT) and Principle Component Analysis (PCA). Classification was performed from these features through SVM. The system can be easily used on any population and can perform fast and low-cost gender determination without requiring any extra effort.
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    Conference Object
    A New Variable Ordering Method for the K2 Algorithm
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Uzbaş, Betül; Arslan, Ahmet
    K2 is an algorithm used for learning the structure of a Bayesian networks (BN). The performance of the K2 algorithm depends on the order of the variables. If the given ordering is not sufficient, the score of the network structure is found to be low. We proposed a new variable ordering method in order to find the hierarchy of the variables. The proposed method was compared with other methods by using synthetic and real-world data sets. Experimental results show that the proposed method is efficient in terms of both time and score.
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    Doctoral Thesis
    Özellik Modelleri için Bulanık İntegral Operatörü
    (Konya Teknik Üniversitesi, 2019) Kılıç, Alper; Arslan, Ahmet
    Özellik modelleri son yıllarda yazılım ürün hatlarının modellenmesi ve ürün varyantlarının sistem üzerindeki etkilerinin gösterilmesi amacıyla kullanılan yöntemlerin başında gelmektedir. Bulanık integral de farklı seçeneklerin farklı öncelik ve kriterlere sahip varyantların değerlendirilmesi için kullanılabilecek etkin bir hesaplama yöntemi olarak değerlendirilmektedir. Bu tez çalışmasında duvar arkası canlı tespit, görüntüleme ve sınıflandırma amacı ile ultra geniş bant radar sistemin özellik modeli oluşturulmuş, farklı kriterlere ve önceliklere sahip sistem varyantları bulanık integral yöntemi ile değerlendirilerek oluşturulan radar sisteminin sınıflandırma amacı ile kullanılması ele alınmıştır. Sınıflandırma metodu olarak derin öğrenme yöntemlerinden olan evrişimsel sinir ağları kullanılmış ve başarılı sonuçlar elde edilmiştir.
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    Article
    Citation - WoS: 25
    Citation - Scopus: 36
    Through-Wall Radar Classification of Human Posture Using Convolutional Neural Networks
    (HINDAWI LTD, 2019) Kılıç, Alper; Babaoğlu, İsmail; Babalık, Ahmet; Arslan, Ahmet
    Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred due to their advantages. On the other hand, the success of classification with deep learning methods in different problems is remarkable. Since the radar signals contain valuable information about the objects behind the wall, the use of deep learning techniques for classification purposes will give a different direction to the research. This paper focuses on the classification of the human posture behind the wall using through-wall radar signals and a convolutional neural network (CNN). The SFCW radar is used to collect radar signals reflected from the human target behind the wall. These signals are employed to classify the presence of the human and the human posture whether he/she is standing or sitting by using CNN. The proposed approach achieves remarkable and successful results without the need for detailed preprocessing operations and long-term data used in the traditional approaches.