A Survey of Methods and Architectures for 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 techniques, enabling organizations to make data-driven decisions regarding workforce optimization, performance evaluation, and retention strategies. Among various neural architectures, the Enhanced Elman Spike Neural Network (EESNN) has emerged as a promising approach for modeling temporal and sequential workforce data due to its ability to capture dynamic behavioral patterns. This survey paper presents a comprehensive analysis of existing methods and architectures used in employee management, with a particular focus on EESNN-based solutions. The study reviews recent advancements in machine learning and deep learning models applied in human resource management, highlighting their strengths, limitations, and applicability in real-world scenarios. Furthermore, it explores how spiking neural mechanisms enhance temporal learning, enabling improved prediction accuracy in tasks such as employee attrition, productivity assessment, and workforce planning. The paper also discusses integration challenges, ethical considerations, and scalability issues associated with AI-driven HR systems. By synthesizing current research trends and identifying gaps, this survey aims to provide valuable insights for researchers and practitioners seeking to develop intelligent, adaptive, and efficient employee management systems. The findings emphasize the potential of EESNN architectures in transforming traditional HR practices into predictive and proactive decision-making frameworks.