ABSTRACT
The primary objective of this study is to determine the effect of the COVID-19 pandemic on a representative sample of Bangladeshi uni- versity students. The study conducted a cross-sectional approach including HADS (Hospital Anxiety and Depression Scale) and CAS (Coronavirus Anxiety Scale), obtaining sufficient data to evaluate the correlation between COVID- 19 Lockdown lifestyle and psychological impact on the students. The CAS (Coronavirus Anxiety Scale), Anxiety and Depression models were constructed to predict individuals' psychotic state, and an indisputable interpretation process has been consummated to assemble sufficient results. The study conducted an unequivocal evaluation to observe the crucial socio and environmental factors associated with young age, low socioeconomic position, gender, scholastic lifestyle, immobility, solitary, academic and occupational impediments. © 2022 ACM.
ABSTRACT
The COVID-19 coronavirus pandemic is wreaking havoc on the world's health. The healthcare sector is in a state of disaster. Many precautionary steps have been taken to prevent the spread of this disease, including the usage of a mask, which is strongly recommended by the World Health Organization (WHO). In this paper, we used three deep learning methods for face mask detection, including Max pooling, Average pooling, and MobileNetV2 architecture, and showed the methods detection accuracy. A dataset containing 1845 images from various sources and 120 co-author pictures taken with a webcam and a mobile phone camera is used to train a deep learning architecture. The Max pooling achieved 96.49% training accuracy and validation accuracy is 98.67%. Besides, the Average pooling achieved 95.190/0 training accuracy and validation accuracy is 96.23%. MobileNetV2 architecture gained the highest accuracy 99.72% for training and 99.82 % for validation. © 2021 IEEE.