Deep Learning and Optimization Approaches in Optimized Graph Transformer with Alpine Skiing Optimization: Improving Initiative IoT in Human Resource Management by Predicting Workers’ Stress: A Review

Abstract

The integration of deep learning and optimization techniques has significantly transformed intelligent systems in human resource management, particularly within the domain of Internet of Things-driven environments. This review presents a comprehensive analysis of optimized Graph Transformer architectures enhanced with Alpine skiing optimization techniques for predicting workers’ stress levels in initiative IoT frameworks. The study explores how graph-based deep learning models capture complex relational dependencies among physiological, behavioral, and environmental data, enabling more accurate stress prediction. Furthermore, Alpine skiing optimization, a nature-inspired metaheuristic approach, is examined for its ability to improve model convergence, parameter tuning, and generalization performance. The review systematically evaluates recent advancements in hybrid deep learning models, including Graph Neural Networks, Transformers, and IoT-based sensing systems, highlighting their applications in workforce monitoring and adaptive decision-making. Key challenges such as data privacy, scalability, real-time processing, and model interpretability are also discussed. The findings emphasize that the synergy between optimized Graph Transformers and advanced optimization strategies significantly enhances predictive accuracy and operational efficiency in smart HR ecosystems. This paper contributes by consolidating existing research, identifying research gaps, and proposing future directions for developing robust, scalable, and ethical stress prediction systems in IoT-enabled workplaces.