Results 231 to 240 of about 86,959 (305)
Identification of Laminar Structure in the Yingxiongling Shale Oil Sediment in China with Random Forests and SHAP Analysis. [PDF]
Zhang F, Liu X, Aldrich C, Deng S, Li G.
europepmc +1 more source
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei +7 more
wiley +1 more source
Optimizing credit card fraud detection with random forests and SMOTE.
Sundaravadivel P +5 more
europepmc +1 more source
Cuttlebone‐inspired architected metamaterials introduce controlled structural disorder to simultaneously enhance strength, energy absorption, and compressive stability. Tunable disorder suppresses shear anisotropy, outperforms periodic lattices, and establishes discrete randomness as a robust route to high‐performance lightweight mechanical ...
Zengqin Shi +7 more
wiley +1 more source
Correction: EFFNet: A skin cancer classification model based on feature fusion and random forests. [PDF]
Ma X, Shan J, Ning F, Li W, Li H.
europepmc +1 more source
Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking
Aysan AF, Ciftler BS, Unal IM.
europepmc +1 more source
Mechanism‐Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy
MISPOP integrates ensemble learning with membrane‐active physicochemical priors to identify Dermaseptin‐S9, a natural oncolytic peptide that disrupts tumor membranes, triggers immunogenic cell death, and shows strong antitumor activity. The study illustrates a mechanism‐informed route from peptide sequence data to cancer immunotherapy leads.
Wen Zhang +11 more
wiley +1 more source
Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors. [PDF]
Romero E +7 more
europepmc +1 more source
SMarT‐Diff introduces a multi‐objective generative paradigm that integrates scaffold hopping with structure‐aware scoring to enable controlled exploration beyond the training distribution. The framework consistently balances drug‐likeness, synthesizes accessibility and bioactivity, yielding chemically diverse candidates with enhanced properties.
Yuwei Yang +8 more
wiley +1 more source
Image-based yield prediction for tall fescue using random forests and convolutional neural networks. [PDF]
Ghysels S +3 more
europepmc +1 more source

