Prediction of smartphone overdependence and analysis of its influencing factors among older adults based on machine learning.
Research
RF Safe Research Library
Jan 1, 2026
This study used panel data from South Korea's 2023 Smartphone Overdependence Survey to build and compare machine-learning models predicting smartphone overdependence among adults aged 60+. Among evaluated classifiers, XGBoost had the best reported predictive performance (accuracy 0.925). The most important predictors…
Evaluation of Personal Radiation Exposure from Wireless Signals in Indoor and Outdoor Environments
Research
RF Safe Research Library
Jan 1, 2025
This exposure assessment measured personal RF electric field strength in multiple indoor and outdoor micro-environments in Malaysia using an ExpoM-RF 4 meter and modeled exposure with machine learning (FCNN, XG Boost) and linear regression. Reported exposures were usually below the stated public limit (61.4 V/m), but…
Analyzing the Impact of Occupational Exposures on Male Fertility Indicators: A Machine Learning Approach
Research
RF Safe Research Library
Jan 1, 2025
This occupational epidemiology study used machine learning to evaluate whether workplace exposures (including magnetic and electric fields, vibration, noise, and heat stress) predict male reproductive indicators in 80 workers. The models and explainable AI outputs highlighted magnetic and electric field exposures and…