Deep Learning and Optimization Approaches in Analysing Employee Management Using Enhanced Elman Spike Neural Network Techniques and Solutions in Human Resource Management: A Review

Abstract

The rapid evolution of artificial intelligence and deep learning has significantly transformed human resource management by enabling data-driven decision-making and predictive analytics. This study presents a comprehensive review of deep learning and optimization approaches applied to employee management, with a particular focus on enhanced Elman spike neural network techniques. Traditional HR systems often struggle with handling temporal workforce data, employee behavior prediction, and performance analysis due to their static and rule-based nature. In contrast, advanced neural architectures, especially recurrent and spiking neural networks, offer superior capability in modeling dynamic and sequential employee data. This paper explores how enhanced Elman spike neural networks improve temporal pattern recognition, enabling more accurate predictions in employee attrition, performance evaluation, recruitment analytics, and workforce optimization. Additionally, optimization strategies such as gradient-based tuning, evolutionary algorithms, and hybrid learning frameworks are analyzed for improving model efficiency and scalability. The review synthesizes recent advancements, identifies research gaps, and highlights practical implications for modern HR systems. The findings suggest that integrating deep learning with optimization techniques can significantly enhance the intelligence and adaptability of HR analytics systems, leading to improved organizational productivity and decision-making. This paper serves as a foundational reference for researchers and practitioners aiming to implement advanced AI-driven employee management solutions.