Recent Advances in Analysing Employee Management Using Enhanced Elman Spike Neural Network Techniques and Solutions in Human Resource Management: A Systematic Review

Abstract

The rapid evolution of artificial intelligence has significantly transformed human resource management by enabling data-driven decision-making and predictive analytics. Among emerging approaches, neural network-based models have shown remarkable potential in modeling complex employee behavior and organizational dynamics. This paper presents a systematic review of recent advances in employee management analysis using enhanced Elman spike neural network techniques. The study focuses on how temporal neural architectures combined with spiking mechanisms improve the understanding of workforce patterns, including employee performance, engagement, and attrition prediction. Enhanced Elman networks, characterized by their recurrent structure and memory capabilities, are particularly effective in capturing sequential dependencies in employee-related data. When integrated with spike-based learning paradigms, these models demonstrate improved efficiency, reduced computational complexity, and biologically inspired learning mechanisms. The review synthesizes findings from multiple studies, highlighting methodological trends, data utilization strategies, and performance improvements over traditional machine learning techniques. Furthermore, the paper discusses the practical implications of these models in HR analytics, including workforce optimization and decision support systems. The results indicate that enhanced Elman spike neural networks provide a robust framework for intelligent employee management systems. Future research directions emphasize hybrid architectures, explainability, and ethical considerations in AI-driven HR systems.