Arşiv logosu
  • Türkçe
  • English
  • Giriş
    Yeni kullanıcı mısınız? Kayıt için tıklayın. Şifrenizi mi unuttunuz?
Arşiv logosu
  • Koleksiyonlar
  • Sistem İçeriği
  • Analiz
  • Talep/Soru
  • Türkçe
  • English
  • Giriş
    Yeni kullanıcı mısınız? Kayıt için tıklayın. Şifrenizi mi unuttunuz?
  1. Ana Sayfa
  2. Yazara Göre Listele

Yazar "Akgun, Devrim" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
Sayfa Başına Sonuç
Sıralama seçenekleri
  • Yükleniyor...
    Küçük Resim
    Öğe
    A Machine Learning Method for Detecting the Trace of Seam Carving
    (Kaunas Univ Technology, 2021) Senturk, Zehra Karapinar; Akgun, Devrim
    Image retargeting is a manipulation approach for resizing the images while aiming to keep the image distortion at a low level. Detecting image retargeting is of importance in image forensics or sometimes of importance in checking the originality. The aim of this paper is to introduce a new blind detection method for identifying retargeted images based on seam carving. For this purpose, a new method based on stripes at various numbers, Local Binary Pattern (LBP) transform, and energy map is introduced. The sub-images were obtained from square root of the energy map of LBP transform in the form of stripes for the feature extraction and these were evaluated in terms of several statistical features. The features extracted both from the natural and the seam carved images were used to train a Support Vector Machine (SVM) as a binary classifier. Experimental results were obtained using four-fold cross validation to improve the validity of the results during the evaluation process. According to the experiments, the proposed method produces improved accuracies when compared with the state-of-the-art solutions for the image retargeting detection based on seam carving.
  • Yükleniyor...
    Küçük Resim
    Öğe
    A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data
    (Tubitak Scientific & Technical Research Council Turkey, 2021) Akgun, Devrim; Kabakus, Abdullah Talha; Senturk, Zehra Karapinar; Senturk, Arafat; Kucukkulahli, Enver
    The COVID-19 pandemic has caused millions of deaths and changed daily life globally. Countries have declared a half or full lockdown to prevent the spread of COVID-19. According to medical doctors, as many people as possible should be tested to identify their status, and corresponding actions then should be taken for COVID-19 positive cases. Despite the clear necessity of these medical tests, many countries are still struggling to acquire them. This fact clearly indicates the necessity of a large-scale, cheap, fast, and accurate alternative prescreening tool that can be used for the diagnosis of COVID-19 while waiting for the medical tests. To this end, a novel end-to-end transfer learning-based deep learning approach that uses only a given cough sound for the diagnosis of COVID-19 was proposed in this study. The proposed models employed various pretrained deep neural networks, namely, VGG19, ResNet50V2, DenseNet121, and MobileNet, via the transfer learning technique. Then, these models were evaluated on a gold standard dataset, namely, Cambridge data. According to the experimental result, the proposed model, which employed the MobileNet via the transfer learning technique, provided the best accuracy, 86.42%, and outperformed the state-of-the-art. Thus, the proposed model has the potential to provide automated COVID-19 diagnosis in an easily applicable and fast yet accurate way.

| Düzce Üniversitesi | Kütüphane | Açık Erişim Politikası | Rehber | OAI-PMH |

Bu site Creative Commons Alıntı-Gayri Ticari-Türetilemez 4.0 Uluslararası Lisansı ile korunmaktadır.


Düzce Üniversitesi, Kütüphane ve Dokümantasyon Daire Başkanlığı, Düzce, TÜRKİYE
İçerikte herhangi bir hata görürseniz lütfen bize bildirin

DSpace 7.6.1, Powered by İdeal DSpace

DSpace yazılımı telif hakkı © 2002-2025 LYRASIS

  • Çerez Ayarları
  • Gizlilik Politikası
  • Son Kullanıcı Sözleşmesi
  • Geri Bildirim