Abstract: This article explores the design and implementation of a Generative AI-based data encoding system aimed at enhancing human resource management processes. Addressing the complexity of HR data and the need for informed decision-making, the study introduces a novel approach that leverages Generative AI for data encoding. This approach is applied to an HR database to develop a machine learning model designed to create a salary simulator, capable of generating accurate and personalized salary estimates based on factors such as work experience, skills, geographical location, and market trends. The aim of this approach is to improve the performance of the machine learning model. Experimental results indicate that this encoding approach improves the accuracy and fairness of salary determinations. Overall, the article demonstrates how AI can revolutionize HR management by delivering innovative solutions for more equitable and strategic compensation practices.

Abdelkrim SAOUABE, Hicham OUALLA and Imad MOURTAJI, “Data Encoding with Generative AI: Towards Improved Machine Learning Performance” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151007

@article{SAOUABE2024,
title = {Data Encoding with Generative AI: Towards Improved Machine Learning Performance},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151007},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151007},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Abdelkrim SAOUABE and Hicham OUALLA and Imad MOURTAJI}
}