Machine learning on the largest curated alkali-activated slag dataset shows that average metal oxide dissociation energy serves as a compact, physically interpretable reactivity descriptor enabling strength prediction and low-emission design space exploration.
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A multiresolution complex Gabor dictionary with orthogonal matching pursuit produces consistent time-frequency features from multiple heart-sound segments that a vision transformer classifies into four systolic murmur types at 95.96% accuracy on the CirCor DigiScope dataset.
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Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag
Machine learning on the largest curated alkali-activated slag dataset shows that average metal oxide dissociation energy serves as a compact, physically interpretable reactivity descriptor enabling strength prediction and low-emission design space exploration.
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Classification of systolic murmurs in heart sounds using multiresolution complex Gabor dictionary and vision transformer
A multiresolution complex Gabor dictionary with orthogonal matching pursuit produces consistent time-frequency features from multiple heart-sound segments that a vision transformer classifies into four systolic murmur types at 95.96% accuracy on the CirCor DigiScope dataset.