An Ensemble Classification and Regression Neural Network for Evaluating Role-based Tasks Associated with Organizational Unit

In this paper, we have looked at how easy it is for users in an organisation to be given different roles, as well as how important it is to make sure that the tasks are done well using predictive analytical tools. As a result, ensemble of classification and regression tree link Neural Network was adopted for evaluating the effectiveness of role-based tasks associated with organization unit. A Human Resource Manangement System was design and developed to obtain comprehensive information about their employees' performance levels, as well as to ascertain their capabilities, skills, and the tasks they perform and how they perform them. Datasets were drawn from evaluation of the system and used for machine learning evaluation. Linear regression models, decision trees, and Genetic Algorithm have proven to be good at prediction in all cases. In this way, the research findings highlight the need of ensuring that users tasks are done in a timely way, as well as enhancing an organization's ability to assign individual duties.

Automated summary

This paper examines the ease of assigning different roles to users in an organization and emphasizes the importance of utilizing predictive analytical tools for effective task completion. The study adopts an ensemble of classification and regression tree link neural network to evaluate role-based tasks within an organization unit. A Human Resource Management System is designed and developed to gather comprehensive information on employee performance levels, capabilities, skills, and task execution. The findings demonstrate that linear regression models, decision trees, and genetic algorithms are effective in predicting outcomes. The research highlights the significance of ensuring timely task completion and enhancing an organization's ability to assign individual duties.