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    AFWDroid- Deep feature extraction and weighting for android malware detection
    (2021) Er, Orhan; Arslan, Recep Sinan; Ölmez, Emre
    Android malware detection is a critical and important problem that must be solved for a widely used operating system. Conventional machine learning techniques first extract some features from applications, then create classifiers to distinguish between malicious and benign applications. Most of the studies available today ignore the weighting of the obtained features. To overcome this problem, this study proposes a new software detection method based on weighting the data in feature vectors to be used in classification. To this end, firstly, the manifest file was read from the Android application package. Different features such as activities, services, permissions were extracted from the file, and for classification, a selection was made among these features. The parameters obtained as a result of selection were optimized by the deep neural network model. Studies revealed that through feature selection and weighting, better performance values could be achieved and more competitive results could be obtained in weight-sensitive classification.
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    Bölgesel Tabanlı Evrişimli Sinir Ağı ile Araç Plaka Tanıma
    (Düzce Üniversitesi, 2023) Çay, Talip; Ölmez, Emre; Er, Orhan
    Bu çalışmada, Bölgesel Tabanlı Evrişimli Sinir Ağları (R-CNN) ile araç plaka lokasyonu belirleme ve belirlenen lokasyon içerisinden plaka okuma işlemi gerçekleştirilmiştir. İki aşamadan oluşan çalışmanın ilk aşamasında giriş görüntüleri üzerinden plaka lokasyonları R-CNN ile belirlenirken ikinci aşamada geleneksel görüntü işleme teknikleri ile belirlenen lokasyonlardan plaka okuma işlemi gerçekleştirilmektedir. Çalışmada tasarlanan R-CNN eğitiminde veri setinde bulunan 550 adet görüntüden 450 adedi eğitimde ve 100 adedi test işleminde kullanılmıştır. R-CNN ile plaka lokasyonu bulma işleminde test seti üzerinde %95 başarı oranına ulaşılırken doğru olarak belirlenen lokasyonlardan plaka okuma işleminde %97 başarı oranına ulaşılmıştır.
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    Classification of Bovine Cumulus-Oocyte Complexes with Convolutional Neural Networks
    (2023) Çavuşoğlu, Türker; Gökhan, Aylin; Şirin, Cansın; Tomruk, Canberk; Kılıç, Kubilay Doğan; Ölmez, Emre; Er, Orhan
    Aim: Determining oocyte quality is crucial for successful fertilization and embryonic development, and there is a serious correlation between live birth rates and oocyte quality. Parameters such as the regular/irregular formation of the cumulus cell layer around the oocyte, the number of cumulus cell layers and the homogeneity of the appearance of the ooplasm are used to determine the quality of the oocytes to be used in in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) methods. Material and Methods: In this study, classification processes have been carried out using convolutional neural networks (CNN), a deep learning method, on the images of the cumulus-oocyte complex selected based on the theoretical knowledge and professional experience of embryologists. A convolutional neural network with a depth of 4 is used. In each depth level, one convolution, one ReLU and one max-pooling layer are included. The designed network architecture is trained using the Adam optimization algorithm. The cumulus-oocyte complexes (n=400) used in the study were obtained by using the oocyte aspiration method from the ovaries of the bovine slaughtered at the slaughterhouse. Results: The CNN-based classification model developed in this study showed promising results in classifying three-class image data in terms of cumulus-oocyte complex classification. The classification model achieved high accuracy, precision, and sensitivity values on the test dataset. Conclusion: Continuous research and optimization of the model can further improve its performance and benefit the field of cumulus-oocyte complexes classification and oocyte quality assessment.
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    Classification of Breast Cancer using Artificial Neural Network Algorithms
    (2021) Ölmez, Emre; Areta, Ourania; Er, Orhan
    Breast cancer is a malignant tumor that has developed from cells of the breast. Breast cancer is one of the most fatal diseases in the world and a relatively common cancer in Turkey. Breast cancer diagnosis has been approached by various machine learning techniques for many years. In this study, two different probabilistic neural network (PNN) structures were used for breast cancer’s diagnosis. The PNN results were compared with the results of the multilayer, learning vector quantization neural networks and the results of the previous reported studies focusing on breast cancer’s diagnosis and using the same dataset. It was observed that the PNN is the best classification accuracy with 98.10% accuracy obtained via 3-fold cross validation. The present paper describes how this technique can be applied to the breast tissue classification and the breast cancer detection for medical devices. The purpose of this study is the classification of the variability of impedivity observed in normal and pathological breast tissue.