Artificial Intelligence Techniques for Analysing Employee Management Using Enhanced Elman Spike Neural Network Techniques and Solutions in Human Resource Management: Trends and Challenges

Abstract

Artificial Intelligence (AI) has significantly transformed Human Resource Management (HRM) by enabling data-driven decision-making, predictive analytics, and automation of complex workforce processes. This research explores advanced AI techniques for employee management analysis, focusing on the integration of Enhanced Elman Spike Neural Networks (EESNN) to capture temporal workforce patterns and behavioral dynamics. Traditional HR systems often fail to model sequential dependencies and evolving employee performance trends, whereas EESNN provides improved temporal learning through recurrent connections and spike-based processing. The proposed framework leverages employee datasets, including performance metrics, attendance records, and engagement indicators, to develop predictive models for attrition, performance evaluation, and workforce optimization. Additionally, this study examines emerging trends such as explainable AI, ethical considerations, and hybrid intelligent systems in HRM. Key challenges including data privacy, model interpretability, scalability, and bias mitigation are also discussed. The findings highlight that EESNN-based approaches outperform conventional machine learning models in handling sequential HR data, offering higher accuracy and adaptability. This research contributes to the growing domain of intelligent HR analytics by proposing a robust and scalable solution while addressing critical challenges that influence real-world implementation.