Deep insight of HR management on work from home scenario during Covid pandemic situation using intelligent: analysis on IT sectors in Tamil Nadu

The unexpected Coronavirus Disease 2019 (COVID-19) pandemic has heavily hit on global business, causing major disruptions in Human Resource Management (HRM) across a wide range of industries. Since work and family are the major essential aspects of everyone's lives, work-life balance is a major factor impacting happiness. Changes brought on by the novel coronavirus COVID-19 have had far-reaching consequences for businesses all around the world, and had a significant impact on human resource management. HRM had to cope with the layoffs and staff reductions brought on by the pandemic lockdown. Work-life balance is a constant challenge for most of the employees. The work-from-home scenario shifted the balance between job and personal life. Most of the prior studies have looked at the nature of work-life balance, but only a few have covered the effects of a pandemic on the workplace. The goal of this research is to conduct a thorough examination of HRM in IT firms in Tamil Nadu under a work-from-home scenario in the event of a COVID pandemic. Here, the analysis is conducted on the basis of nine categories such as “employee wellbeing, flexible workplace, remote work, job loss, human capital, human resource development, leadership, performance, and communication”. A questionnaire is prepared to address the challenges facing in the above mentioned drives. Next, the responses are collected from different IT HR professionals in Tamil Nadu. These responses are given to the prediction phase that is performed using the proposed Deep Neural Network (DNN). DNN is used to obtain the result more quickly. Features are automatically deduced and optimally tuned for desired outcomes. The same neural network based approach can be applied to many different applications and data types. Here, the weight of DNN is optimized by the Oppositional Random Searched Tunicate Swarm Algorithm (ORS-TSA) for enhancing the performance through RMSE minimization. ORS-TSA has strong global optimization ability, robustness and fast convergence speed. It gives the optimal results when comparing the other traditional algorithms. Finally, the outcomes are compared with different methods using error measures to describe the superiority of the proposed method.