Artificial Intelligence Techniques for IoT based Human Resources Balanced Allocation Method Based on Recalling-Enhanced Salp Swarm Recurrent Neural Network: Trends and Challenges

Abstract

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has significantly transformed human resource management by enabling intelligent decision-making and real-time workforce monitoring. This paper presents a comprehensive study on AI-driven techniques for balanced human resource allocation using a recalling-enhanced Salp Swarm Recurrent Neural Network (SS-RNN) framework. The proposed approach leverages IoT-generated workforce data to capture temporal patterns in employee behavior, workload distribution, and performance metrics. The recalling-enhanced mechanism improves memory retention in recurrent neural networks, allowing more accurate modeling of dynamic work environments. Meanwhile, the Salp Swarm Optimization (SSO) algorithm enhances the allocation process by identifying optimal resource distribution strategies through adaptive exploration and exploitation. This study explores recent trends in AI-based HR allocation, highlighting advancements in deep learning, swarm intelligence, and hybrid optimization techniques. Furthermore, it identifies critical challenges such as data privacy, scalability, interpretability, and system integration. The paper emphasizes the importance of combining intelligent learning models with optimization algorithms to achieve balanced workload distribution, increased productivity, and organizational efficiency. The findings contribute to the development of smart HR systems capable of adapting to rapidly changing industrial environments and provide insights into future research directions for AI-driven workforce management.