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With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

Researchers in the social sciences are increasingly turning to online data collection panels for research purposes. While there is evidence that crowdsourcing platforms such as Amazon's Mechanical Turk can produce data as reliable as more traditional survey collection methods, little is known about Amazon's Mechanical Turk's most experienced respondents, their perceptions of crowdsourced data, and the degree to which these affect data quality. The current study utilises both quantitative and qualitative data to investigate Amazon's Mechanical Turk Masters' perceptions and attitudes related to the data quality (e.g. inattention). Recommendations for researchers using crowdsourcing data are provided.

The coronavirus disease 2019 (COVID-19) has taken the world by surprise and has impacted the lives of many, including the business sector and its stakeholders. Although studies investigating the impact of COVID-19 on the organizational structure, job design, and employee well-being have been on the rise, fewer studies examined the role of leadership and what it takes to be an effective leader during such times. This study integrates social cognitive theory and conservation of resources theory to argue for the importance of adaptive personality in the emergence of effective leaders during crisis times, utilizing the crisis of COVID-19 as the context for the study. We argue that managers with an adaptive personality tend to have increased self-efficacy levels to lead during a crisis, resulting in increased motivation to lead during the COVID-19 crisis. Furthermore, managers with increased motivation to lead during the COVID-19 crisis are argued to have enhanced adaptive performance, thereby suggesting a serial mediation model where crisis leader self-efficacy and motivation to lead during the COVID-19 crisis act as explanatory mechanisms of the relationship between the adaptive personality and performance of the manager. In order to test our hypotheses, we collected data from 116 full-time managers in Saudi Arabia during the COVID-19 crisis and used hierarchical linear regression as the method of analysis. The findings support all of the hypotheses. A discussion of the results, contributions, limitations, and future directions is included.

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