A STFT-based ML framework defines Arc Stability Index, spectral entropy, and harmonic distortion features to classify welding arc stability, with SVM achieving 85-94% accuracy depending on validation method.
Interpretable deep learning for EEG-based cognitive state assessment
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Nested cross-validation reveals optimistic bias in standard validation for EEG alcoholism classification, with AdaBoost reaching 78.3% accuracy and most model differences not statistically significant per McNemar's test.
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A Hybrid STFT-Based Machine Learning Framework for Physically Interpretable Arc Stability Classification in Electric Arc Welding Systems
A STFT-based ML framework defines Arc Stability Index, spectral entropy, and harmonic distortion features to classify welding arc stability, with SVM achieving 85-94% accuracy depending on validation method.
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Impact of Validation Strategy on Machine Learning Performance in EEG-Based Alcoholism Classification
Nested cross-validation reveals optimistic bias in standard validation for EEG alcoholism classification, with AdaBoost reaching 78.3% accuracy and most model differences not statistically significant per McNemar's test.