The use of social media has increased during the COVID-19 pandemic because people are isolated and working from home. The use of social media enhances information exchange in society and may influence public protective behavior against the COVID-19 pandemic. The purpose of this study is to identify the factors affecting public protective behavior when relying on COVID-19 pandemic-related content shared on social media. A model based on Protection Motivation Theory (PMT) was proposed and validated using a quantitative survey approach. A questionnaire was distributed to random respondents, and 488 responses were received and analyzed using Smart-PLS software. The findings showed that perceived risk, e-health literacy, public awareness, and health experts' participation influence public protective behavior when using social media to share COVID-19-relevant content. The outcomes of this study can enhance government agencies' and public health care authorities' understanding of how to use social media to raise awareness and reduce panic among the public.
Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality.