• Jamshina V

In today’s competitive corporate environment, organizations face increasing challenges in managing employee turnover and stress. ZENZONE presents a proactive system that integrates facial emotion recognition and predictive analytics to identify and mitigate employee churn and stress levels. The framework harnesses data from employee interactions, emotional cues, and organizational inputs to support HR decision-making, promote mental well-being, and enhance workforce engagement. The study outlines the design, implementation, and evaluation of this system, demonstrating its potential to foster healthier, more resilient workplaces.

Employee churn, stress detection, emotion recognition, machine learning, predictive analytics, HR technology

"ZENZONE: A Predictive Framework for Employee Churn and Stress Management Using Emotion Recognition and Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b119-b123, May-2025, Available :http://www.jetir.org/papers/JETIR2505113.pdf

"ZENZONE: A Predictive Framework for Employee Churn and Stress Management Using Emotion Recognition and Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb119-b123, May-2025, Available at : http://www.jetir.org/papers/JETIR2505113.pdf