Artificial Intelligence Techniques for Optimized Graph Transformer with Alpine Skiing Optimization: Improving Initiative IoT in Human Resource Management by Predicting Workers’ Stress: Trends and Challenges

Abstract

The integration of Artificial Intelligence (AI) with Internet of Things (IoT) technologies has significantly transformed human resource management by enabling intelligent monitoring and prediction of employee well-being. Among emerging approaches, Graph Transformers combined with metaheuristic optimization techniques such as Alpine Skiing Optimization (ASO) present a promising paradigm for modeling complex relational data and improving predictive performance. This study explores the application of optimized Graph Transformer architectures enhanced with ASO to predict workers’ stress levels using IoT-generated physiological and behavioral data. The proposed framework leverages graph-based attention mechanisms to capture dependencies among heterogeneous data sources, while ASO is employed to optimize model parameters, thereby enhancing convergence and accuracy. The paper examines recent trends in AI-driven HR analytics, focusing on stress prediction as a critical factor influencing productivity and organizational sustainability. Furthermore, it discusses challenges related to data privacy, model interpretability, scalability, and real-time deployment in IoT environments. The findings highlight that combining Graph Transformers with evolutionary optimization can significantly improve prediction accuracy and robustness compared to traditional machine learning and deep learning approaches. This research provides a comprehensive overview of advancements, identifies research gaps, and outlines future directions for intelligent HR systems driven by AI and IoT integration.