ABSTRACT
Employees that are highly engaged and devoted to their companies provide important competitive advantages, leading in minimal employee turnover and great productivity. Surprisingly, firms of all sizes and kinds have begun spending significantly on policies and practises that encourage employee engagement and commitment. The pandemic COVID 19 has placed an exceptional burden on organisations to keep their staff completely engaged in their job during these times of widespread stress and concern due to the epidemic. The study begins by discussing the variables affecting the workplace engagement of work-at-home workers during the Corona epidemic, as well as the correlation between employee engagement and production throughout the pandemic.
ABSTRACT
The research paper discuss the Artificial Intelligence based Multiple Transfer Learning Mechanism in identification of lung diseases like pneumothorax, tension pneumothorax from a set of chest X-rays. Pneumothorax being a primary stage of many sorts of pulmonary diseases, it has now a days being noticed as an impact with COVID cases due to the insertion of the tubes into the lungs. The proper diagnosis of the various stages of Pneumothorax is thus essential in the current scenario. Identification of the patients with Pneumothrax with less diagnostic time is the highlight of this research work. The deep learning technology of AI has enlightened the research in the medical imaging field. The chest X-ray images are with the pre-processing analysis, normalised the images for a uniform image data processing. The advanced method of transfer learning is equipped with modifications in the various fully connected convolutional network layers. The modified transfer learning has been used with DenseNet and VGG 19. The convolutional neural networks with DenseNet201 and VGG19 utilized stochastic gradient decent optimization for parameter optimization. The data set with pneumothorax and tension pneumothorax along with the control set has been trained and validated. The training and validation of these network has proven results with 89% accuracy with VGG19 and 100% accuracy with Densenet. The evaluation of modified Multi-transfer learning algorithm is identified successfully with new random input chest X-ray with a less diagnostic time. © 2022 IEEE.
ABSTRACT
Physician burnout is a common problem among US physicians. Burnout has been associated with absenteeism, mood disorders, and medical errors. Over the last several decades, physician burnout has become more prevalent because of increasing workloads, increasing administrative burden, and time spent on electronic medical records, among several other reasons. The rate of suicidal ideation in physicians is almost twice as high as the general population. In addition, studies on mortality related to suicide show that the rates of suicides in physicians are consistently higher than in the general population. Firearms are the most common suicide method in both groups, while physicians are more likely to use poisoning and blunt force trauma, as physicians who committed suicide were more likely to have benzodiazepines, barbiturates, or antipsychotics detectable in their blood. Unfortunately, coronavirus disease 2019 (COVID-19) brought to the surface multiple prevailing issues in the US healthcare system, including physician burnout and the prevalence of suicidality among physicians in the recent past. With this editorial, we plan to discuss the current understanding of the impact on physician suicide in the context of COVID-19.