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Book Part Citation - Scopus: 2Novel Covid-19 Recognition Framework Based on Conic Functions Classifier(Springer Science and Business Media Deutschland GmbH, 2022) Karim,A.M.; Mishra,A.The new coronavirus has been declared as a global emergency. The first case was officially declared in Wuhan, China, during the end of 2019. Since then, the virus has spread to nearly every continent, and case numbers continue to rise. The scientists and engineers immediately responded to the virus and presented techniques, devices and treatment approaches to fight back and eliminate the virus. Machine learning is a popular scientific tool and is applied to several medical image recognition problems, involving tumour recognition, cancer detection, organ transplantation and COVID-19 diagnosis. It is proved that machine learning presents robust, fast and accurate results in various medical image recognition problems. Generally, machine learning-based frameworks consist of two stages: feature extraction and classification. In the feature extraction, overwhelmingly unsupervised learning techniques are applied to reduce the input data’s size. This step extracts appropriate features by reducing the computational time and increasing the performance of the classifiers. A classifier is the second step that aims to categorise the input. Within the proposed step, the unsupervised part relies on the feature extraction by using local binary patterns (LBP), followed by feature selection relying on factor analysis technique. The LBP is a kind of visual descriptor, mainly applied for image recognition problem. The aim of using LBP is to analyse the input COVID-19 image and extract salient features. Furthermore, factor analysis is a statistical technique applied to define variability among observed variables in less unnoticed variables named factors. The factor analysis applied to the LBP wavelet aims to select sensitive features from input data (LBP output) and reduce the size input. In the last stage, conic functions classifier is applied to classify two sets of data, categorising the extracted features by using LBP and factor analysis as positive or negative COVID-19 cases. The proposed solution aims to diagnose COVID-19 by using LBP and factor analysis, based on conic functions classifier. The conic functions classifier presents remarkable results compared with these popular classifiers and state-of-the-art studies presented in the literature. © 2022, Springer Nature Switzerland AG.Article Citation - WoS: 40Citation - Scopus: 53Impacts of Covid-19 Pandemic Period on Depression, Anxiety and Stress Levels of the Healthcare Employees in Turkey(Elsevier Ireland Ltd, 2021) Tengilimoglu, Dilaver; Zekioglu, Aysu; Tosun, Nurperihan; Isik, Oguz; Tengilimoglu, OnurThe COVID-19 pandemic has turned into a public health issue since December 2019 and has risen in all countries in the world. The healthcare employees taking part in the pandemic will eventually be affected by the process. The aim of the study is to determine the levels of the anxiety, depression, and stress of the healthcare employees during the COVID-19 pandemic in Turkey. As the data collection tool, an e-survey was used. In the first section, Depression, Anxiety and Stress Scale (DASS-21) was used. In the second section of the survey, the problems experienced by the healthcare employees during the pandemic and their working media were aimed to be defined. In the last section, the socio-demographic features of the employees were investigated. 2076 healthcare employees participated in the study. The results showed that the major cause of the anxiety or stress among healthcare employees comes from the fear to contaminate the COVID-19 virus to their families (86.9%). It was observed that the levels of depression, anxiety and stress of female employees are higher than that of male employees (p < 0.003). The highest depression, anxiety and stress levels of healthcare employees come from the pandemic, emergency, and internal services (p < 0.001). Health managers and policymakers need to make a move immediately to find solutions for the physical and psychological needs of the health employees. On the other hand, in order to minimize the risk, preparation of the work power plans beforehand and inclusion of obligatory referral chain into health services can be suggested.Article Citation - WoS: 3Citation - Scopus: 3Shared 6mer Peptides of Human and Omicron (21k and 21l) at Sars-Cov Mutation Sites(Mdpi, 2022) Adiguzel, Yekbun; Shoenfeld, YehudaWe investigated the short sequences involving Omicron 21K and Omicron 21L variants to reveal any possible molecular mimicry-associated autoimmunity risks and changes in those. We first identified common 6mers of the viral and human protein sequences present for both the mutant (Omicron) and nonmutant (SARS-CoV-2) versions of the same viral sequence and then predicted the binding affinities of those sequences to the HLA supertype representatives. We evaluated change in the potential autoimmunity risk, through comparative assessment of the nonmutant and mutant viral sequences and their similar human peptides with common 6mers and affinities to the same HLA allele. This change is the lost and the new, or de novo, autoimmunity risk, associated with the mutations in the Omicron 21K and Omicron 21L variants. Accordingly, e.g., the affinity of virus-similar sequences of the Ig heavy chain junction regions shifted from the HLA-B*15:01 to the HLA-A*01:01 allele at the mutant sequences. Additionally, peptides of different human proteins sharing 6mers with SARS-CoV-2 proteins at the mutation sites of interest and with affinities to the HLA-B*07:02 allele, such as the respective SARS-CoV-2 sequences, were lost. Among all, any possible molecular mimicry-associated novel risk appeared to be prominent in HLA-A*24:02 and HLA-B*27:05 serotypes upon infection with Omicron 21L. Associated disease, pathway, and tissue expression data supported possible new risks for the HLA-B*27:05 and HLA-A*01:01 serotypes, while the risks for the HLA-B*07:02 serotypes could have been lost or diminished, and those for the HLA-A*03:01 serotypes could have been retained, for the individuals infected with Omicron variants under study. These are likely to affect the complications related to cross-reactions influencing the relevant HLA serotypes upon infection with Omicron 21K and Omicron 21L.Article Citation - WoS: 3Citation - Scopus: 3Online Learning Perceptions Amid Covid-19 Pandemic: the Engineering Undergraduates' Perspective(Tempus Publications, 2022) Eryilmaz, Meltem; Kalem, Guler; Kilic, Hurevren; Tirkes, Guzin; Topalli, Damla; Turhan, Cigdem; Yazici, Ali; Information Systems Engineering; Computer Engineering; Software EngineeringThe COVID-19 pandemic caused face-to-face education in just about all universities worldwide to shift to online education. For most students, this educational model was a compulsory first experience. In this study, the survey results are analyzed and discussed related to a group of students in the Engineering Faculty of a university in Turkey regarding their online education perceptions. Briefly summarized, the findings of the study indicate that: (a) most of the students still prefer face-to-face learning, which is also favored if accompanied by distance learning; (b) the concentration level of the students has dropped due to the concerns about the COVID-19 pandemic which affects their learning negatively; and (c) around half of the students participating in the study feel that the online exams conducted without a secure exam software, is considered unsafe. Additionally, the study's results were further extended to evaluate the questionnaire results and reported along with the suggestions of necessary actions in emergency online learning (EOL).
