Published Paper Details:

Employee Promotion Criteria analysis using Adaptive Linear Neuron Methodology Promoting employees to next category is to be done carefully, otherwise, it can lead to repercussions in the organization. For instance, if an employee who is not good, is promoted to the next level, he may not be able to perform well in the next level. Also, good employees, who are not promoted may leave the organization with dissatisfaction. This project aims at developing a tool for identifying the employees to be promoted, using measurable criteria like hardwork index, Delivery Index. Hard work index is calculated by using positive factors like: Additional hours spent during last year, Additional week end days (either sat/sun), Additional total week ends, Compensatory Leaves not Utilized. Negative factor like LOPs are also considered during the calculation. The delivery index is calculated by considering the positive factors: Number of on-time deliveries, Role played during delivery (Crucial/Important/Medium/Low), and Number of delayed deliveries as a negative factor. PyQt tool is used to create the Graphical User interfaces. All the Front end code is generated automatically by PyUIC.