Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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search.filters.applied.f.language: search.filters.language.en_USSubject: search.filters.subject.deep learning

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    Deep Generative Models İn Medical İmaging : A Literature Review
    (Başkent Üniversitesi Mühendislik Fakültesi, 2024-06-16) Sener, Begum
    Deep learning has been used extensively in recent years in numerous studies across many disciplines, including medical imaging. GANs (Generative Adversarial Networks) have started to be widely used in the medical field due to their ability to generate realistic images. Recent research has concentrated on three different deep generative models for improving medical images, and a review of deep learning architectures for data augmentation has been done. In this article, other generative models are emphasized, given the dominance of GANs in the field. Studies have conducted a literature review comparing different deep generative models for medical image data augmentation, without focusing solely on GANs or traditional data augmentation methods. In contrast to variational autoencoders, generative adversarial networks (GANs) are the generative model that is most frequently employed for enhancing medical image data. Recent studies have shown that diffusion models have received more attention in recent years compared to variational autoencoders and GANs for medical image data augmentation. This trend is thought to be related to the fact that many GAN-related research directions have previously been investigated, making it more challenging to advance these architectures' current applications.
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    Sarcasm Detection in News Headlines with Deep Learning
    (32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024, 2024-12-05) Karkiner, Zeynep; Sert, Mustafa
    Sarcasm detection is one of the recent topics studied in the field of natural language processing. Although sarcasm detection is generally carried out through social media comments in the literature, it can also be applied to news headlines that are expected to be completely objective and reflect reality. In this study, sarcasm detection was carried out using various deep learning models in a dataset containing sarcastic and non-sarcastic news headlines. The accuracy of classification results of BERT, RNN, LSTM, and GRU models and their training time performance were compared. While the BERT model reached the highest accuracy (0.88), RNN was the most successful model in terms of training time performance.