Results 141 to 150 of about 404,815 (264)

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Random forest Gini importance favors SNPs with large minor allele frequency [PDF]

, 2011
Bermejo, Justo Lorenzo, Lorenzo Bermejo, Justo, Boulesteix, Anne-Laure, Bender, Andreas, Strobl, Carolin   +4 more
core   +1 more source

A radiology-based differential diagnostic model for pelvic chondrosarcoma using random forest algorithms. [PDF]

Front Oncol
Ma X, Jiang Y, Lin N, Ye Z, Li H.
europepmc   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Uncover This Tech Term: Random Forest. [PDF]

Korean J Radiol
Yuruk YY.
europepmc   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Forecasting Renewable Energy Generation Using Random Forest Analysis [PDF]

Renewable energy forecasting is critical for sustainable energy development and grid stability. This study applies a Random Forest model to analyze the contribution of different renewable energy sources to total energy generation in Taiwan.
Wu, Chien Hsin;Tseng, Yao Ting;Lo, Wen Fang;Haung, Yu Hsiang
core  

mRNALocator-imb: an imbalance-tolerant ensemble framework integrating random forest and transformer for mRNA subcellular localization prediction. [PDF]

Front Genet
Hu J, Liu H, Wang L, Wu H.
europepmc   +1 more source

Streaming Random Forests [PDF]

11th International Database Engineering and Applications Symposium (IDEAS 2007), 2007
Hanady M. Abdulsalam, David B. Skillicorn, Patrick Martin 0001   +2 more
openaire   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source