Python
Advanced Heart Attack Risk Prediction Using Stacked Hybrid Machine Learning
Tree-Based Personalized Clustered Federated Learning A Driver Stress Monitoring Through Physiological Data Case Study
Aim:
The aim of this study is to develop a privacy-preserving and personalized driver stress monitoring system using a Tree-Based Personalized Clustered Federated Learning (TPCFL) approach, which effectively addresses the challenges of non-IID physiological data by grouping drivers based on similarities in their data characteristics, optimizing cluster selection, and enabling accurate stress detection for both existing and new unlabeled drivers without compromising sensitive information.