Title

A neural-fuzzy network approach to employee performance evaluation

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Article

Source Title

International Journal of Advanced Trends in Computer Science and Engineering

Volume

8

Issue

3

First Page

573

Last Page

581

Publication Date

5-1-2019

Abstract

This neuro -fuzzy system enables the algorithm to identify performing and non-performing employees as organizations currently use several traditional employee evaluation performance methods that utilizes different approaches that are inaccurate and subjective by nature and usually deficient in approximating the accurate capability and nature of employee performance. Results revealed that this artificial intelligence technique utilizing the neuro-fuzzy profiling system, optimizes the objective function in the employee quality evaluation and determines the most distinctive employees deserving career advancement or those who further need appropriate training and development in the achievement, leadership and behavior categories. Since the coefficients of Neural Network can be tuned to the manager's evaluation results, the logic of the overall judgment can be adjusted to the characteristics of the department. The evaluation of this system is also performed with the same evaluation logic of the objective input values thus, the objectivity and transparency of the evaluation are extremely high. This enables HR and decision makers in the organization to truly understand employee strengths and weaknesses that is also an essential part in promoting a positive company culture unlike the traditional employee performance evaluation methods still being adopted by many organizations at present that is impaired with unreliability and rating errors. © 2019, World Academy of Research in Science and Engineering. All rights reserved.

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Digitial Object Identifier (DOI)

10.30534/ijatcse/2019/37832019

Disciplines

Electrical and Computer Engineering | Human Resources Management

Keywords

Employees—Rating of--Automation; Employees—Training of; Artificial intelligence

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