Machine learning estimates factory-worker stress from brain signals
An exploratory study of 20 participants linked EEG and fNIRS signals to self-reported anxiety-related stress, with the best model estimating ...
An exploratory study of 20 participants linked EEG and fNIRS signals to self-reported anxiety-related stress, with the best model estimating ...
A parallel weakly supervised framework combined activation maps, native-resolution sliding windows and model voting to achieve 96.33% accuracy on a ...
A hybrid CatBoost pipeline using synthetic sampling and optimized class weights achieved the strongest reported classification results in a 258-record ...
Researchers at VIT University propose AFI-ID, an intrusion detection framework that unifies adaptive feature selection with attention-based ensemble fusion, achieving ...
Researchers at the University of Gondar report that a voting ensemble of tree-based machine learning models combined with permutation-based feature ...
A new architecture-diverse TabNet ensemble classifies seven crop types from harmonized multi-source soil data with 90.5 percent accuracy, outperforming XGBoost ...
Researchers in China have developed an interpretable machine learning model that analyzes all liver metastases on contrast-enhanced CT scans to ...
The new study arrives at a moment when the remote sensing community is increasingly confronting a paradox that has quietly ...
Researchers in India have built a unified deep learning framework that classifies brain tumors in MRI scans with 98.52 percent ...
Researchers in Tamil Nadu have combined IoT sensing, stacked ensemble machine learning, and fuzzy goal programming to turn textile wastewater ...
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© 2025 Scienmag - Science Magazine