A Scalable Machine Learning Framework for Predictive Analytics and Employee Performance Enhancement in Large Enterprises

Abstract: Employee performance prediction and workforce optimization are critical for sustainable growth in large enterprises, yet traditional performance forecasting techniques often rely on regression analysis and conventional machine learning models that fail to capture the dynamic, nonlinear nature of human resource data. The approaches are not flexible, explainable, and actionable in terms of appropriate optimizations, which makes them less effective intelligent decision support systems. To overcome these limitations, this study presents a novel Hybrid Deep Dense Attention Network (HD-DAN) model combined with reinforcement learning (RL) to predict employee performance and optimally manage the workforce. The HD-DAN optimally combines self-attention in dense layers to dynamically emphasize performance-critical aspects, such as engagement, skills, and behavioral attributes. The RL agent learns to map the predictions into optimized interventions, such that continuous performance improvement is achieved. The HD-DAN achieves a Mean Absolute Error (MAE) of 0.076, Root Mean Square Error (RMSE) of 0.129, and an R² of 0.421—corresponding to an 11.5% RMSE reduction and a 15.6% R² increase over the best available baselines. In addition to higher predictive accuracy, the framework delivers interpretability through attention weight visualization and decision reliability through RL-driven optimization, providing a scalable, adaptive, and explainable platform for intelligent decision support in employee performance forecasting and workforce management.

Jyoti Singh Kanwar, Ranju S Kartha, Chamandeep Kaur, Behara Venkata Nandakishore, Elangovan Muniyandy, Vuda Sreenivasa Rao and Yousef A.Baker El-Ebiary. “A Scalable Machine Learning Framework for Predictive Analytics and Employee Performance Enhancement in Large Enterprises”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.8 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160867

@article{Kanwar2025,
title = {A Scalable Machine Learning Framework for Predictive Analytics and Employee Performance Enhancement in Large Enterprises},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160867},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160867},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {8},
author = {Jyoti Singh Kanwar and Ranju S Kartha and Chamandeep Kaur and Behara Venkata Nandakishore and Elangovan Muniyandy and Vuda Sreenivasa Rao and Yousef A.Baker El-Ebiary}
}