Machine Learning Models Predict Lithology with Over 90% Accuracy
A comparative analysis in Natural Resources Research shows that five machine learning algorithms achieve over 90% accuracy in lithology prediction ...
A comparative analysis in Natural Resources Research shows that five machine learning algorithms achieve over 90% accuracy in lithology prediction ...
A retrospective phenotyping study of 254 patients identified an exploratory two-cluster structure containing a candidate neutrophil-enriched inflammatory-nutritional phenotype in osteoarticular ...
A new analysis published in EPJ Data Science reveals that graph neural networks, specifically the subgraph-based model known as SEAL, ...
A data-driven cluster analysis of 799 ischemic stroke patients identified four distinct phenotypes associated with significantly different rates of long-term ...
A new study combines qualitative analysis and neural networks to predict chaotic dynamics in the nonlinear Benney-Luke equation.
A case study of the Damiao–Hongshila belt shows geographically weighted models improve mineral prospectivity mapping under spatially non-stationary conditions.
A study analyzing 348,404 charging sessions in Jiaxing, China, demonstrates that machine learning forecasts and an offline decision twin reduce ...
An outcome-blind clustering analysis of 6,667 adults with type 2 diabetes and diabetic kidney disease identified three clinical phenotypes with ...
A machine-learning framework trained on published experimental data predicts the compressive strength of rice husk ash concrete, with a support ...
A new study projects that India's greenhouse gas emissions will continue rising through 2035 under current trends.
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© 2026 Scienmag - Science Magazine