The effect of layoffs on the performance of survivors at healthcare organizations

For years, workers of an organization have strived to strengthen it and expand it with fresh concepts and strategies to accomplish new objectives. The layoff is, by definition, a spontaneous release from an institution, i.e. a compulsory resignation for certain purposes of employee categories, of permanent or temporary personnel (economic reasons, downsizing personal management). Outsourcing is a way of reducing costs and changing fixed costs to varying expenses for companies. It transfers work or researches to outside households, which lead to job losses. Finishing is a major business challenge, forced disconnected jobs and survivors. This sudden dismantling was triggered by the economic depression, which increased with corruption by governments. The COVID-19 spread around the world is further overcome day by day. When the layoff is mass, companies may notify the workers of the reasons for the reduction. Some hospitals issued departed personnel a warning for clarifying things and preparing the workers even though they could do serious harm. This form of warning illustrates a pandemic COVID-19 by delivering unemployment until layoffs explain the financial downturn for workers. Certain hospitals and organizations offer warning without consideration about the discharged workers on the same day. Moreover, this existed in tiny clinics, where there were not very significant layoffs. Substantial government institutions, including A.U.B.M.C., B.M.G. and other hospitals, prefer cuts as a remedy. This research aims to determine the effect of forced termination health care institutions on survivors' effectiveness, performance, quality of service, and relational results.

HCUGAN: Hybrid Cyclic UNET GAN for Generating Augmented Synthetic Images of Chest X-Ray Images for Multi Classification of Lung Diseases

In 2019, when COVID-19 started spreading as a virally infection across the globe, people started researching using the available chest x-ray images of lungs. But during the detection process, the research article found that the available images are less and most of the symptoms are similar to pneumonia disease. So in this article, before performing the multi-classification process, tries to enhance the size of the dataset by combining the UNET with cyclic GAN’s. Most of the real-world data exist in an imbalanced form, which affects the overall architecture and performance of the design. Many of the researchers have implemented manipulation techniques like translation, rotation, and others for enhancement of dataset size, but due to the high dimensionality of the medical images, these basic and simple approaches doesn’t have any impact on the model. So, the improved cyclic GAN’s mechanism helps the research article to create a balanced dataset with more augmented or reconstructed CXR images by performing segmentation using the UNET operation. © 2022 Seventh Sense Research Group®

IoT based COVID De-Escalation System using Bluetooth Low Level Energy

An Internet-of-Things (IoT) can be an effective solution to de-escalate the spread of a pandemic/epidemic using open source technology-Bluetooth Low level Energy. The major objective of this contribution is to monitor real time cases and prevent the contagious spread of this viral disease. The entire paper focuses on cross checking the data fed by the user with the IoT database which consists of the data from the COVID RADAR application. The experimental model uses MCU and a Bluetooth module, which are easy to realize and cost effective as well. The realization and prediction of the pandemic hotspots is also facilitated using this data. © 2021 IEEE.