A Comprehensive Review of Analysing Employee Management Using Enhanced Elman Spike Neural Network Techniques and Solutions in Human Resource Management

Abstract

Employee management has evolved significantly with the integration of artificial intelligence and advanced computational models in Human Resource Management (HRM). Traditional analytical methods often struggle to capture temporal dependencies and dynamic behavioral patterns inherent in employee data. This paper presents a comprehensive review of employee management analysis using enhanced Elman spike neural network techniques, which combine recurrent neural architectures with biologically inspired spiking mechanisms. These models enable improved representation of sequential workforce data such as performance trends, attendance patterns, and employee engagement metrics. The study explores the application of such hybrid neural frameworks in optimizing recruitment, retention, productivity analysis, and decision-making processes in HRM systems. Additionally, it reviews recent advancements, challenges, and practical implementations of intelligent HR analytics powered by deep learning and neuromorphic computing. The integration of Elman networks with spike-based learning provides a robust approach for handling temporal complexity and real-time data processing in organizational environments. This review aims to bridge the gap between theoretical neural network advancements and their application in HR domains, offering insights into scalable, adaptive, and efficient employee management solutions. The findings suggest that enhanced Elman spike neural networks can significantly improve predictive accuracy, decision support, and strategic workforce planning, thereby transforming modern HR practices.