Coping mechanisms as a predictor of fear of COVID-19 and quality of life: A Malaysian nationwide survey

Objective: The COVID-19 is a major health crisis that has changed the life of millions globally. The purpose of this study was to assess the coping mechanism among the general population of Malaysia as well as its possible correlates such as Fear of COVID-19, quality of life (QOL) and associated sociodemographic background. Methods: This is an online cross-sectional study involving a total of 4904 adults across Malaysia from June to December 2021. Brief Coping Orientation to Problems Experienced was used to measure coping mechanisms, while the level of fear toward COVID-19 was assessed by fear of COVID-19 scale. QOL was measured by WHOQOL-BREF. Results: Sociodemographic data shows that 59.1% of Malaysians report an overall reduced QOL during the COVID-19 pandemic. 32% of respondents were unemployed during the pandemic and 18% of respondents were working from home. 71% of respondents had at least one or more encounter with COVID-19. We find that problem focused coping mechanism may be effective in face of COVID-19, both, in reducing overall fear toward COVID-19 and improving QOL, while emotional and avoidance coping mechanism has a negative correlation. Other socioeconomic factors such as age, gender, levels of education, income, and employment have a significant positive correlation with QOL and a negative correlation with Fear of COVID-19. Another factor which affects QOL is work from home which has a negative correlation. Conclusion: Based on study findings, problem based coping mechanism is beneficial to reduce Fear of COVID-19 and improve QOL.

Masked Face Recognition Using Histogram-Based Recurrent Neural Network.

Masked face recognition (MFR) is an interesting topic in which researchers have tried to find a better solution to improve and enhance performance. Recently, COVID-19 caused most of the recognition system fails to recognize facial images since the current face recognition cannot accurately capture or detect masked face images. This paper introduces the proposed method known as histogram-based recurrent neural network (HRNN) MFR to solve the undetected masked face problem. The proposed method includes the feature descriptor of histograms of oriented gradients (HOG) as the feature extraction process and recurrent neural network (RNN) as the deep learning process. We have proven that the combination of both approaches works well and achieves a high true acceptance rate (TAR) of 99 percent. In addition, the proposed method is designed to overcome the underfitting problem and reduce computational burdens with large-scale dataset training. The experiments were conducted on two benchmark datasets which are RMFD (Real-World Masked Face Dataset) and Labeled Face in the Wild Simulated Masked Face Dataset (LFW-SMFD) to vindicate the viability of the proposed HRNN method.