Yazar "Yiner, Zuleyha" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    A new approach to COVID-19 detection from x-ray images using angle transformation with GoogleNet and LSTM
    (Iop Publishing Ltd, 2022) Kaya, Yilmaz; Yiner, Zuleyha; Kaya, Mahmut; Kuncan, Fatma
    Declared a pandemic disease, COVID-19 has affected the lives of millions of people and had significant effects on public health. Despite the development of effective vaccines against COVID-19, cases continue to increase worldwide. According to studies in the literature, artificial intelligence methods are used effectively for the detection of COVID-19. In particular, deep-learning-based approaches have achieved very good results in clinical diagnostic studies and other fields. In this study, a new approach using x-ray images is proposed to detect COVID-19. In the proposed method, the angle transform (AT) method is first applied to the x-ray images. The AT method proposed in this study is an important novelty in the literature, as there is no such approach in previous studies. This transformation uses the angle information created by each pixel on the image with the surrounding pixels. Using the AT approach, eight different images are obtained for each image in the dataset. These images are trained with a hybrid deep learning model, which combines GoogleNet and long short-term memory (LSTM) models, and COVID-19 disease detection is carried out. A dataset from the Mendeley database is used to test the proposed approach. A high classification accuracy of 98.97% is achieved with the AT + GoogleNet + LSTM approach. The results obtained were also compared with other studies in the literature. The presented results reveal that the proposed method is successful for COVID-19 detection using chest x-ray images. Direct transfer methods were also applied to the data set used in the study. However, worse results were observed according to the proposed approach. The proposed approach has the flexibility to be applied effectively to different medical images.
  • [ X ]
    Öğe
    Attack Detection in Cloud Networks Based on Artificial Intelligence Approaches
    (IGI Global, 2020) Yiner, Zuleyha; Sertbas, Nurefsan; Durukan-Odabasi, Safak; Yiltas-Kaplan, Derya
    Cloud computing that aims to provide convenient, on-demand, network access to shared software and hardware resources has security as the greatest challenge. Data security is the main security concern followed by intrusion detection and prevention in cloud infrastructure. In this chapter, general information about cloud computing and its security issues are discussed. In order to prevent or avoid many attacks, a number of machine learning algorithms approaches are proposed. However, these approaches do not provide efficient results for identifying unknown types of attacks. Deep learning enables to learning features that are more complex, and thanks to the collection of big data as a training data, deep learning achieves more successful results. Many deep learning algorithms are proposed for attack detection. Deep networks architecture is divided into two categories, and descriptions for each architecture and its related attack detection studies are discussed in the following section of chapter. © 2020 by IGI Global. All rights reserved.