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    Citation - Scopus: 8
    On Comparative Classification of Relevant COVID-19 Tweets
    (Institute of Electrical and Electronics Engineers Inc., 2021) Bakal, Gokhan; Abar, Orhan; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
    Due to the impressive information dissemination power of social networks such as Twitter, people tend to check social networks and Web pages more than other traditional news sources, including newspapers, TV news programs, or radio channels. In that sense, the information carried by the content of the shared social media posts becomes much more considerable. However, most of the posts are commonly either irrelevant or inaccurate. Besides, the more critical case than the correctness of the information is the diffusion speed on Twitter through the reply or retweet actions. These activities make the initial situation even more complicated than itself due to the unregulated nature of the social networks and the lack of an immediate verification mechanism for the correctness of the posts. When we consider the current Covid-19 pandemic period (causing the coronavirus disease), one of the most utilized information resources is Twitter except the official health administration institutions. Thereupon, examining the correctness of the information related to the Covid-19 pandemic by computational techniques (e.g., Data Mining, Machine Learning, and Deep Learning) has been gaining popularity and remains a substantial task. Hence, we mainly focused on analyzing the correctness of the posts related to the current pandemic shared on the Twitter platform. Therefore, the overall goal of this work is to classify the relevant tweets using linear and non-linear machine learning models. We achieved the best F1 performance score (99%) with the neural network model using the unigram features & threshold value of 50 among all model configurations. © 2022 Elsevier B.V., All rights reserved.
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    Oscillator Phase Noise Impact on Monostatic/Bistatic Space-Borne Sub- THz ISAR
    (IEEE Computer Society, 2025) Bekar, Ali; Gashinova, Marina S.; Bekar, Muge; Martorella, Marco; Antoniou, Michail; 01. Abdullah Gül University; 02. Mühendislik Fakültesi; 02.05. Elektrik & Elektronik Mühendisliği
    This study develops an oscillator phase noise model and analyzes its effects on the performance of spaceborne monostatic and bistatic Inverse Synthetic Aperture Radar (B-ISAR) systems operating at the sub-THz band. The B-ISAR study is of current importance as it can provide a basis for distributed space-based ISAR to enable persistent co-operative space domain awareness (Co-SDA). © 2025 Elsevier B.V., All rights reserved.