A Study On Impact Of Work From Home On Work Life Balance During Covid-19

In ancient times, work and personal life are considered as separate. But the present scenario, it has been interrelated with each other and considered to be the important criteria which decides the family well-being and work effectiveness. Now the pandemic situation COVID-19 leads the life of the employee with no differentiation between work and personal life. Usually, employees face difficult to balance their work-life. In India, following the working hours is typically rare and it is just in a notice board. Even after office time many employees are work with a laptop and office files. Their mind is completely baggage with a work-related task allotted to them. So, they cannot able to turn sight on their families. Even if they turn sight on them, it was not with affection and care instead of that they expose their frustration. This problem was continuing day by day even many technologies grown up, many policies were reframed, etc. Especially during COVID-19, companies decided to get the work from home. This paper tries to find the impact of work from home on work life balance of the employees during Covid 19.

Pervasive computing in the context of COVID-19 prediction with AI-based algorithms

Purpose: The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters. Design/methodology/approach: For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease. Findings: Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history. Originality/value: The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model. © 2020, Emerald Publishing Limited.