Results 211 to 220 of about 338,729 (282)

Optimizing credit card fraud detection with random forests and SMOTE.

Sci Rep
Sundaravadivel P, Isaac RA, Elangovan D, KrishnaRaj D, Rahul VVL, Raja R.   +5 more
europepmc   +1 more source

Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors. [PDF]

Front Public Health
Romero E, González-González J, Álvarez-Voces M, Costa-Montenegro E, Díaz-Vázquez B, Busto-Castiñeira A, Villar P, López-Romero L.   +7 more
europepmc   +1 more source

Generative AI‐Driven Accelerated Discovery of Passivation Molecules for Perovskite Solar Cells

Advanced Science, EarlyView.
A generative artificial intelligence (AI) framework combining a discriminative machine learning model (SMILES‐X) and a generative language model (GPT‐2) autonomously discovers new molecular passivators for perovskite solar cells (PSCs). Through an iterative design loop, over 100 000 candidates are generated and screened, and randomly selected molecules
Adroit T. N. Fajar, Guillaume Lambard, Jessie Manopo, Ruili Guo, Kevin Septioga, Rizfi F. Pari, Toshinori Matsushima, Zhanglin Guo   +7 more
wiley   +1 more source

Image-based yield prediction for tall fescue using random forests and convolutional neural networks. [PDF]

Front Plant Sci
Ghysels S, De Baets B, Reheul D, Maenhout S.   +3 more
europepmc   +1 more source

NQO1‐Mediated Anoikis Resistance and Immune Evasion Define a High‐Risk Multi‐Omic Subtype for Precision Management of T1 High‐Grade Bladder Cancer

Advanced Science, EarlyView.
Multi‐omic profiling of T1 high‐grade bladder cancer identifies a high‐risk subtype (T1HG1) driven by NQO1, which couples anoikis resistance with immune evasion. NQO1 orchestrates macrophage–T cell crosstalk suppression via CXCL9 modulation. Pharmacological NQO1 inhibition with skullcapflavone II enhances cisplatin efficacy, representing a promising ...
Bin Guo, Chunru Xu, Shufan Fu, Qiang Cheng, Juan Li, Linkuo Shang, Gaojie Li, Yang Yang, Ying Wang, Yanqing Gong, Shengwei Xiong, Jian Fan, Changwei Yuan, Mei Zhang, Yifan Zuo, Elena Papaleo, Yue Shi, Yuan Liang, Xuesong Li, Hongzhao Li, Weimin Ci   +20 more
wiley   +1 more source

Real-Time Correction and Long-Term Drift Compensation in MOS Gas Sensor Arrays Using Iterative Random Forests and Incremental Domain-Adversarial Networks. [PDF]

Micromachines (Basel)
Dong X, Han S.
europepmc   +1 more source

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

Explaining Person-by-Item Responses using Person- and Item-Level Predictors via Random Forests and Interpretable Machine Learning in Explanatory Item Response Models. [PDF]

Psychometrika
Cho SJ, Amanda G, Salas J, Mueller S.
europepmc   +1 more source

Integrating Radiomics and Computational Pathology to Predict Early Recurrence of Pancreatic Ductal Adenocarcinoma and Uncover Its Biological Basis in Tumor Microenvironment

Advanced Science, EarlyView.
Accurate prediction of early recurrence in pancreatic ductal adenocarcinoma is vital for optimizing treatment. A novel, integrated radiomics‐pathology machine learning model successfully forecasts recurrence risks by analyzing preoperative CT images and computational pathology.
Sihang Cheng, Fuze Cong, Shenbo Zhang, Rui Lv, Wenjia Zhang, Xinyi Ke, Juncheng Wu, Zhonghe Zhao, Kui Zhao, Di Dong, Ruofan Zhang, Zhengyu Jin, Max Seidensticker, Zhiwei Wang, Huanwen Wu, Xianlin Han, Nan Hong, Huadan Xue   +17 more
wiley   +1 more source

pyRforest: a comprehensive R package for genomic data analysis featuring scikit-learn Random Forests in R. [PDF]

Brief Funct Genomics
Kolisnik T, Keshavarz-Rahaghi F, Purcell RV, Smith ANH, Silander OK.   +4 more
europepmc   +1 more source