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Article

RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers

by
Keyur Patel
1,
Karan Sheth
1,
Dev Mehta
1,
Sudeep Tanwar
1,*,
Bogdan Cristian Florea
2,
Dragos Daniel Taralunga
2,*,
Ahmed Altameem
3,
Torki Altameem
3 and
Ravi Sharma
4
1
Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
2
Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
3
Computer Science Department, Community College, King Saud University, Riyadh 11451, Saudi Arabia
4
Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248007, India
*
Authors to whom correspondence should be addressed.
Mathematics 2022, 10(19), 3714; https://doi.org/10.3390/math10193714
Submission received: 8 September 2022 / Revised: 6 October 2022 / Accepted: 7 October 2022 / Published: 10 October 2022
(This article belongs to the Special Issue Application of Advanced Computing and Artificial Intelligence in Engineering and Science)
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Abstract

An organization’s success depends on its employees, and an employee’s performance decides whether the organization is successful. Employee performance enhances the productivity and output of organizations, i.e., the performance of an employee paves the way for the organization’s success. Hence, analyzing employee performance and giving performance ratings to employees is essential for companies nowadays. It is evident that different people have different skill sets and behavior, so data should be gathered from all parts of an employee’s life. This paper aims to provide the performance rating of an employee based on various factors. First, we compare various AI-based algorithms, such as random forest, artificial neural network, decision tree, and XGBoost. Then, we propose an ensemble approach, RanKer, combining all the above approaches. The empirical results illustrate that the efficacy of the proposed model compared to traditional models such as random forest, artificial neural network, decision tree, and XGBoost is high in terms of precision, recall, F1-score, and accuracy.
Keywords: employee performance; machine learning; ensemble learning; low performer employee performance; machine learning; ensemble learning; low performer

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MDPI and ACS Style

Patel, K.; Sheth, K.; Mehta, D.; Tanwar, S.; Florea, B.C.; Taralunga, D.D.; Altameem, A.; Altameem, T.; Sharma, R. RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers. Mathematics 2022, 10, 3714. https://doi.org/10.3390/math10193714

AMA Style

Patel K, Sheth K, Mehta D, Tanwar S, Florea BC, Taralunga DD, Altameem A, Altameem T, Sharma R. RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers. Mathematics. 2022; 10(19):3714. https://doi.org/10.3390/math10193714

Chicago/Turabian Style

Patel, Keyur, Karan Sheth, Dev Mehta, Sudeep Tanwar, Bogdan Cristian Florea, Dragos Daniel Taralunga, Ahmed Altameem, Torki Altameem, and Ravi Sharma. 2022. "RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers" Mathematics 10, no. 19: 3714. https://doi.org/10.3390/math10193714

APA Style

Patel, K., Sheth, K., Mehta, D., Tanwar, S., Florea, B. C., Taralunga, D. D., Altameem, A., Altameem, T., & Sharma, R. (2022). RanKer: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers. Mathematics, 10(19), 3714. https://doi.org/10.3390/math10193714

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