Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques.

Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best-Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best-Worst Method-local linear neuro-fuzzy-adaptive neuro-fuzzy inference system approach, the relationship between COVID-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.

A hybrid decision-making framework to manage occupational stress in project-based organizations.

According to recent studies in the field of human resource management (HRM), especially in project-based organizations (PBOs), stress is recognized as a factor that has a paramount significance on the performance of staff. Previous studies in organizational stress management have mainly focused on identifying job stressors and their effects on organizations. Contrary to the previous studies, this paper aims to propose a comprehensive decision-support system that includes identifying stressors, assessing organizational stress levels, and providing solutions to improve the performance of the organization. A questionnaire is designed and distributed among 170 senior managers of a major project-based organization in the field of the energy industry in Iran to determine organizational stressors. Based on the questionnaire results and considering the best worst method (BWM) as an approach to determine the weighting vector, the importance degree of each stressor is calculated. In the next stage, a decision-support model is developed to assess the stress level of a PBO through fuzzy inference systems (FIS). Some main advantages of the proposed hybrid decision-support model include (i) achieving high-reliable results by not-so-time-consuming computational volume and (ii) maintaining flexibility in adding new criteria to assess the occupational stress levels in PBOs. Based on the obtained results, six organizational stressors, including job incongruity, poor organizational structure, poor project environment, work overload, poor job promotion, and type A behavior, are identified. It is also found that the level of organizational stress is not ideal. Finally, some main recommendations are proposed to manage occupational stresses at the optimum level in the considered sector.