Face Mask Detection System using Convolutional Neural Network

According to the World Health Organization, the coronavirus also known as the COVID-19 pandemic, is causing a global health crisis, WHO and Other health organizations recommended wearing face masks as a protection measurement especially in work, school, university, malls, and any public places. The proposed system consists of a Convolutional Neural Network-based system that can recognize faces with masks and without a mask, The dataset used for this project consists of 3835 images of two types of images, people with masks and people without masks. By utilizing the TensorFlow framework we're able to get a 98.80% accuracy rate in training time while training the model with 200 epochs. © 2022 IEEE.

Turkish Stock Market from Pandemic to Russian Invasion, Evidence from Developed Machine Learning Algorithm.

In recent time, the two significant events; Coronavirus epidemic and Russian invasion are effecting all over the world in various aspects; healthily, economically, environmentally, and socially, etc. The first event has brought uncertainties to the economic situation in most countries based on the epidemic transmission. In addition to that, on 24th February 2022 the Russian invasion of Ukraine affected negatively almost all stock markets all over the world, but the effects are heterogeneous across countries according to their economic-political relationship or neighbourhood, etc. Due to that, the stock market price in Turkey has been affected dramatically over that period. This empirical study is the first attempts to explore the impact of Coronavirus epidemic and Russian invasion on the stock market index XU100 in Turkey by applying the developed statistical method namely elastic-net regression based on empirical mode decomposition which can precisely tackle the nonstationary and nonlinearity data. Then we performed the robustness check by applying a nonlinear techniques Markov switching regression. The data are collected from the beginning of the epidemic in Turkey from March 11, 2020 until May 31, 2022. The finding reveals that there is significant effect of the Coronavirus spreading on the Turkish stock market index, particularly during the first wave. Then after the Russian Invasion the XU100 index is effected more negatively. As the credit default swap and TL reference interest rate have a negative impact but the foreigner exchange rate has a positive significant impact on the XU100 index, and it varies according to the period of short term and long term. Moreover, the results obtained by using the robustness check shows a robust and consistent finding. In conclusion, understanding the impact of Coronavirus pandemic and Russian invasion on the Turkish stock market can provide important implications for investors, financial sectors, and policymakers.

Psychological impact of COVID-19 pandemic on University Students: A cross-sectional study

Objectives: This study is an attempt to detect the psychological impact of (COVID-19) among Taif University students. Methods: A cross-sectional survey was conducted among Taif University students using Google Forms questionnaire in the period from May to December 2020. The questionnaire was designed to assess psychological impact of the COVID-19 pandemic at the time of the lockdown and curfew. 619 participants age ranged from 18-30 years were eligible to participate in this study. This study was carried out using well-structured electronic questionnaire contain socio-demographic characteristics and psychological health parameters (Depression, Anxiety and Stress Scale-21 (DASS-21)). Results: This study revealed a significant correlation between age and depression;age 18-21 years old showed higher frequency of mild to moderate and severe to very severe depression than age 22-30 years old (p>0.003). Similarly, there was a significant correlation between age and stress among participants;age 18-21 years old, showed higher frequency of severe to very severe stress and anxiety compared to aged 22-30 years (p > 0.05). On the other hand, there was no significant correlation between gender, nationality and marital status. Regarding the type of education, there was a significant correlation between medical students and depression (p > 0.06). Conclusion: According to the obtained results, it can be concluded that there was a significant association between age, depression and stress and non- significant relation between gender, nationality and marital status, while there was a significant correlation between medical students and depression.