Results 51 to 60 of about 7,493 (200)

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

A Comparative Study of Hyperparameter Optimization for CatBoost: Random Search, Optuna, and Successive Halving

Journal of Applied Informatics and Computing
This study aims to evaluate the effectiveness of three hyperparameter optimization approaches Random Search, Successive Halving, and Optuna in the CatBoost algorithm for modeling individual income using the 2024 SAKERNAS data.
Claudian Tikulimbong Tangdilomban, Kusman Sadik, Aji Hamim Wigena   +2 more
doaj   +1 more source

Fault Diagnosis of Oil-Immersed Power Transformer Based on Difference-Mutation Brain Storm Optimized Catboost Model

IEEE Access, 2021
To address the problem of low accuracy of power transformer fault diagnosis, this study proposed a transformer fault diagnosis method based on DBSO-CatBoost model.
Mei Zhang, Wanli Chen, Yu Zhang, Fei Liu, Dongshun Yu, Chaoyin Zhang, Li Gao   +6 more
doaj   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Islanding detection method for microgrids based on CatBoost

Frontiers in Energy Research, 2023
The occurrence of unintentional islanding will seriously threaten the stable operation of a microgrid (MG). Therefore, detecting the islanding of an microgrid timely is an important premise to ensure the microgrid operates safely and stably. However, the problem of dead zone exists in the traditional islanding detection process because the threshold of
Ran Chen, Li Zhou, Chuanyu Xiong, Hanping Xu, Zhaoyang Zhang, Xuhui He, Xuhui He, Qingguo Dong, Qingguo Dong, Can Wang, Can Wang   +10 more
openaire   +2 more sources

Comparison of LightGBM and CatBoost Algorithms for Diabetes Prediction Based on Clinical Data

Journal of Applied Informatics and Computing
Diabetes Mellitus presents a global health challenge necessitating accurate early detection to prevent fatal complications. However, clinical data often exhibit imbalanced class distributions, hindering standard prediction models from effectively ...
Muhammad Sidik Latuconsina, Majid Rahardi   +1 more
doaj   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Analisis Performa Model Random Forest dan CatBoost dengan Teknik SMOTE dalam Prediksi Risiko Diabetes

Edumatic
Diabetes mellitus (DM) is increasing in prevalence globally and is becoming a serious health problem. Early detection reduces long-term complications.
Rony Irfannandhy, Lekso Budi Handoko, Noval Ariyanto   +2 more
doaj   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source