A Survey of Methods and Architectures for Optimized Graph Transformer with Alpine Skiing Optimization: Improving Initiative IoT in Human Resource Management by Predicting Workers’ Stress

Abstract

The integration of Artificial Intelligence and Internet of Things technologies in Human Resource Management has opened new avenues for proactive workforce monitoring and stress prediction. This study presents a comprehensive survey of methods and architectures centered on optimized Graph Transformer models combined with Alpine Skiing Optimization to enhance predictive accuracy and system efficiency. Graph Transformers provide the capability to model complex relational dependencies among employee behavioral, physiological, and environmental data, while the Alpine Skiing Optimization algorithm contributes to parameter tuning and global optimization of learning processes. The survey explores how IoT-enabled systems collect real-time data such as wearable sensor outputs, work patterns, and contextual signals, which are then processed through advanced deep learning frameworks to detect early signs of stress. Emphasis is placed on architectural improvements, optimization strategies, and hybrid learning techniques that improve scalability, robustness, and interpretability. The paper also highlights challenges including data privacy, computational overhead, and model generalization across diverse workplace environments. Furthermore, emerging trends such as edge computing integration and federated learning are discussed to address these limitations. This work aims to provide a consolidated understanding of current advancements and future research directions for developing intelligent HRM systems capable of improving employee well-being and organizational productivity through predictive analytics.