ABSTRACT
Purpose. The study aimed to measure the impact of manufacturing employees' high work pressure on the urge for their family-life balance, and the impact of high work-life pressure and the urge for family-life balance on manufacturing employees' concern for mental health during COVID-19 pandemic from an emerging economy perspective. Methodology. The study has picked 20 items under three different variables such as work-life, family-life, and mental health. The structured questionnaire has been developed based on the literature survey and divided into two parts. The initial part has contained demographic information and the second part has contained measure items of the model. The questionnaire has been designed through Google Docs and distributed via Facebook messenger, E-mail, WhatsApp, IMO, etc. 400 data was collected through the snowball sampling technique and 201 data (response rate 50.25%) was found usable for the research. The exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were run to test the proposed research framework with the help of MS Excel 2007, SPSS 22.0, and AMOS 23.0. Finding. The findings revealed that high work-life pressure had a positive significant impact on the urge for family-life balance and both the high work-life pressure and urge for family-life balance had a positive significant impact on manufacturing employees' concern for mental health during the COVID-19 pandemic. Originality. The novelty of this research is the manufacturing employees' context during the COVID-19 pandemic.
ABSTRACT
With increasing rise of COVID-19 infected patients in India and worldwide, examining and detecting COVID-19 among such large number of populations is becoming a humongous task for the medical practitioners and civic authorities. RT-PCR, real time reverse transcription-polymerase chain reaction technique is widely accepted and one of the reliable methods for detection of novel COVID-19.However, being a time consuming, laborious and expensive method for declaring results for the patients in over 6-8 hours to even 3 days in remote places, this technique is not being widely used. The high and very fast spread rate of COVID-19 and low availability of RT-PCR kit, is making the use of computer assisted technologies an inevitable and a potentially faster response mechanism catering to a large population with least human error and a cost-effective solution. Therefore, an intelligent system COVIZONE has been presented, in the proposed work, designed using state of the art pre-trained CNN model to analyze and detect COVID-19 presence in the lungs using Chest X-Ray and CT-Scan Images. In the proposed work, a multi-class classification (Normal, Pneumonic and COVID-19) of patients using ResNet and ResNext CNN model has been done. Both the models show similar performance with high accuracy of 96% and 97% respectively on public dataset of COVID-19, Pneumonia and Normal CXR and CT-Scans. To avoid skewness due to lesser number of COVID-19 CXR images, dataset used has limited Pneumonia and Normal CXR images to train the system and achieved noticeable high accuracy. The proposed COVID-19 detection model i.e. COVIZONE, even if not used as a primary Covid testing and detection tool, can still be a very helpful tool for screening potentially infected persons and help the physicians who are yet not trained for this pandemic diagnosis. © 2021 IEEE