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Article Citation - WoS: 2Citation - Scopus: 3Predictors and Mediators of Pressure/Tension in University Students' Distance Learning During the Covid-19 Pandemic: a Self-Determination Theory Perspective(Routledge Journals, Taylor & Francis Ltd, 2024) Manuoglu, Elif; Gungor, ElisDue to the global restrictions to decrease the risk of infection in classrooms, the transition from face-to-face education to distance learning was a necessity during the Covid-19 pandemic. Grounded in Self-Determination Theory, the present research sought to explore how the pandemic affects university students during distance learning. Specifically, the study examined the predictors of pressure/tension and attempted to identify the unique and mediator roles of correlates of pressure/tension of university students. This cross-sectional study was conducted with 432 university students from different departments of different universities in Turkey. The online survey was administered between the last week of October and the second week of December 2020. Our findings revealed that there is a positive association between pressure/tension and Covid-specific worry. Also, there is a negative association between learning climate and pressure/tension and between perceived competence and pressure/tension. Further, learning climate mediated the link between Covid-specific worry and pressure/tension. The data of the present study depends on students' academic (learning climate) and also non-academic (Covid worry) experiences during the pandemic. Methodological limitations concerning the research design are discussed.Article Citation - WoS: 9Citation - Scopus: 7Identifying the Cycles in Covid-19 Infection: the Case of Turkey(Taylor & Francis Ltd, 2023) Akdi, Yilmaz; Karamanoglu, Yunus Emre; Unlu, Kamil Demirberk; Bas, CemThe new coronavirus disease, called COVID-19, has spread extremely quickly to more than 200 countries since its detection in December 2019 in China. COVID-19 marks the return of a very old and familiar enemy. Throughout human history, disasters such as earthquakes, volcanic eruptions and even wars have not caused more human losses than lethal diseases, which are caused by viruses, bacteria and parasites. The first COVID-19 case was detected in Turkey on 12 March 2020 and researchers have since then attempted to examine periodicity in the number of daily new cases. One of the most curious questions in the pandemic process that affects the whole world is whether there will be a second wave. Such questions can be answered by examining any periodicities in the series of daily cases. Periodic series are frequently seen in many disciplines. An important method based on harmonic regression is the focus of the study. The main aim of this study is to identify the hidden periodic structure of the daily infected cases. Infected case of Turkey is analyzed by using periodogram-based methodology. Our results revealed that there are 4, 5 and 62 days cycles in the daily new cases of Turkey.Article Citation - WoS: 1The Socio-Economic Impact of the Covid-19 Pandemic on Syrian Refugees in Turkey(Uluslararasi Iliskiler Konseyi dernegi, 2024) Memisoglu, Fulya; Ozkil, Altan; Kilinc, TunaBuilding upon empirical research, this study examines the socio-economic impact of the Covid-19 pandemic on Syrian refugees in Turkey by analyzing its implications on employment, livelihood opportunities, and social cohesion. More specifically, it focuses on the experiences of Syrian refugees to examine the ways in which they exert their agency to cope with the structural constraints when faced with 'multiple crises' in host countries, intersecting with the dynamics of a 'normalized refugee crisis'. Our findings from fieldwork conducted in the top six refugee-hosting cities reveal that loss of jobs, limited access to decent work, increased dependency on external financial assistance, and social exclusion have been some of the most acute effects of the pandemic on refugees. Meanwhile, the perceived effects that refugees have on the host community's welfare trigger problems that impede social cohesion. All in all, the study intends to highlight the far-reaching effects of the pandemic beyond its direct health implications by addressing the structural vulnerability of refugees and the importance of providing an enabling environment for socio-economic self-reliance.Article Deep Learning Based Covid-19 Detection Using Computed Tomography Images(Prof.Dr. İskender AKKURT, 2024) Yılmaz, A.A.; Sevinç, Ö.The infectious coronavirus disease (COVID-19), seen in Wuhan city of China in December 2019, led to a global pandemic, resulting in countless deaths. The healthcare sector has become extensively use of deep learning (DL), a method that is currently quite popular. The aim of this study is to identify the best and most successful deep learning model and optimizer approach combination for COVID-19 diagnosis. For this reason, several DL methods and optimizer techniques are tested on two comprehensive public data set to select the best DL model with optimizer technique. A variety of performance evaluation metrics, including f-score, precision, specificity, and accuracy, were used to assess the models' effectiveness. The experimental results show that the most suitable and effective architecture is DenseNet-201 in the network comparison, which achieved a 98% accuracy rate using the AdaGrad optimizer and 200 iterations. © IJCESEN.Article A Prediction Study About the Pandemic Era Based on Machine Learning Methods(Auricle Global Society of Education and Research, 2021) Eryılmaz,M.; Eryılmaz, Meltem; Yalçınkaya,F.; Kara, Erdi; Ertan,Ö.; Kara,E.; Eryılmaz, Meltem; Kara, Erdi; Mathematics; Computer Engineering; Mathematics; Computer EngineeringCoronavirus pandemic has been going on since late 2019, millions of people died worldwide, vaccination has recently started in many countries and new strategies are sought by countries since they are still struggling to defeat the virus. So, this research is made to predict the possible ending time of the coronavirus pandemic in Turkey using data mining and statistical studies. Data mining is a computer science study that processes large amounts of data and produces new useful information. It is especially used to support decision making in companies today. So, this project could support the decision making of authorities in developing an effective strategy against the on-going pandemic. During the research we have practiced on Turkey’s coronavirus and vaccination data between 13 January 2021 and 28 May 2021. We used Rapidminer and the Random Forest method for data mining. After all the simulations we have applied and observed during our research, it was clearly seen that vaccination parameters were decreasing the new cases. Also, the stringency index did not affect the new cases. As a conclusion of our research and observations, we think that the government should vaccinate as many people as it can in order to relax restrictions for the last time. © 2021 Authors. All rights reserved.
