Second Version on A Centralized Approach to Reducing Burnouts in the IT industry Using Work Pattern Monitoring Using Artificial Intelligence Using MongoDB Atlas and Python
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Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 187–228.
Article DOI: 10.30574/wjaets.2024.13.1.0398
Publication history:
Received on 20 July 2024; revised on 09 September 2024; accepted on 11 September 2024
Abstract:
Industry burnout is interlinked with cultural, individual, physical, or emotional exhaustion, and social factors, the resolution of which requires the technology-driven trends in the workplace and the technologies such as work pattern monitoring and Artificial Intelligence that can deal with large amounts of data. Industries face a gigantic problem i.e., employee burnout which can charge a firm loss in numerous hours and thousands of dollars every year. The more advanced companies use work pattern monitoring using Artificial Intelligence to make their employees work more professionally. In this research my attempts to understand the development and leadership, on the effects of work pattern monitoring using Artificial Intelligence technology on information technology organizations (Sasibhushan Rao Chanthati, 2022).
In this updated second version, the data of the employees will be stored on a cloud server with governance & compliances. The study discussed the development of methods which are configured as two different system interfaces, which are of minimum valuable product (MVP) and the results obtained from the two approaches. The system will provide work pattern monitoring via the ‘Real-Time Database – MongoDB Atlas’ which will synchronize the employee burnout data to improve the employee experience. This research also illustrates the advantages and disadvantages of the proposed solutions. “Burnout Detection Mechanism” that will help Industry management and Human Resource Management to manage the emotional state of the employees, understanding their real state. The study conducted a self-survey, and the outputs of the surveys are explained in this paper. The sample data we are using is mainly focused on information technology employment perception.
Keywords:
Burnouts; Work Pattern Monitoring; Python; Large Language; Vector Search; Artificial Intelligence; MongoDB Atlas
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0