Results 91 to 100 of about 31,305 (267)

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

Leveraging SHAP values for superior prediction and efficient Bayesian optimization in material chemistry

Discover Artificial Intelligence
In recent years, machine learning has played a crucial role in data-driven material development. This study presents a feature extraction method for enhancing the predictive accuracy of regression models.
Takuya Ehiro
doaj   +1 more source

A Day-Ahead Wind Power Dynamic Explainable Prediction Method Based on SHAP Analysis and Mixture of Experts

Energies
Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpretability of ...
Hao Zhang, Guoyuan Qin, Xiangyan Chen, Linhai Lu, Ziliang Zhang, Jiajiong Song   +5 more
doaj   +1 more source

Bayesian Exploration of Metal‐Organic Framework‐Derived Nanocomposites for High‐Performance Supercapacitors

Advanced Intelligent Discovery, EarlyView.
An AI‐assisted approach is introduced to decode synthesis–performance relationships in metal‐organic framework‐derived supercapacitor materials using Bayesian optimization and predictive modeling, streamlining the search for optimal energy storage properties.
David Gryc, Pritam Banerjee, Felipe Jacques Lima Duarte, Joerg R. Jinschek, Premkumar Murugesan, Babak Rezaei, Stephan Sylvest Keller, Roland A. Fischer, Mian Zahid Hussain   +8 more
wiley   +1 more source

Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study

Intensive Care Medicine Experimental
Background Current machine learning (ML) prediction models offer limited guidance for individualized actionable management. Large language models (LLMs) can transform ML model-predicted risk estimates with Shapley Additive Explanations (SHAP) into ...
Yu-Chang Yeh, Hsin-Yu Yang, Ching-Tang Chiu, Anne Chao, Yu-Chen Chuang, Wing-Sum Chan   +5 more
doaj   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Nonlinear environmental controls and threshold effects on dust events in Iran’s arid and semi-arid regions

Scientific Reports
Wind erosion and dust storms are among the main causes of air pollution and represent one of the most significant environmental threats in the arid and semi-arid regions of Iran.
Mohammad Khosroshahi, Zohre Ebrahimi-Khusfi, Zahra Saeedifar   +2 more
doaj   +1 more source

Rapid diagnosis of Helicobacter pylori infection status based on endoscopic features and deep learning algorithms

Frontiers in Public Health
Background and aimsEndoscopic visualization for the diagnosis of Helicobacter pylori (HP) infection status is highly important for helping endoscopists quickly understand the status of gastric background mucosa and assisting in subsequent diagnosis and ...
Xinying Yu, Lianyu Li, Qiang He
doaj   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Analysis of Key Features in PCOS Diagnosis Using Random Forest and XGBoost with SMOTE and SHAP

International Journal of Applied Sciences and Smart Technologies
Polycystic Ovary Syndrome (PCOS) is a hormonal disorder in women of  reproductive age characterized by irregular cycles, hyperandrogenism, and  polycystic ovarian morphology.
Aulia Firdatunnisa, Eka Wahyu Hidayat, Siti Yuliyanti   +2 more
doaj   +1 more source