Graph-Based AI Finds Hidden Copper and Nickel Deposits with Almost No Training Data
A new label-scarce graph AI framework reliably detects deep, subtle mineralization anomalies at the Kalatongke copper-nickel deposit using only 906 ...
A new label-scarce graph AI framework reliably detects deep, subtle mineralization anomalies at the Kalatongke copper-nickel deposit using only 906 ...
A new statistical framework extracts standardized gain scores from reference training data to quantify and compare the social performance of ...
Researchers have created a flexible capacitive pressure sensor from a carbonyl iron particle and multilayer graphene composite that lets a ...
A new study shows that an AI brain age model trained on mortality risk from MRI scans mediates how health ...
Researchers have combined near-infrared hyperspectral imaging with a self-organizing map neural network to identify and semi-quantitatively map microplastic contamination on ...
Researchers have developed a scalable machine learning method that uses time-series clustering to automatically identify visual search strategies from raw ...
A new dual-layer knowledge graph model detects corporate financial fraud with up to 94.2 percent accuracy while providing auditors with ...
A 25-year monitoring record and machine learning forecasts reveal shifting heavy metal contamination in Moroccan Mediterranean river sediments, with lead ...
A machine-learning proof-of-concept study shows that abdominal electrohysterography combined with maternal data can moderately estimate cervical dilation during model development, ...
Researchers at Yantai University have developed SSAO, an adaptive oversampling method that uses Gaussian mixture model clustering and minimum enclosing ...
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© 2025 Scienmag - Science Magazine