Results 211 to 220 of about 3,566,037 (267)

Spatial prediction and drivers of surface soil organic carbon density in subtropical montane forests. [PDF]

PeerJ
He Z, He Y, Chen X, Xiong Y, Wang S, Liu Y, Wu J, Chen X, Chen Y, Cai Y.   +9 more
europepmc   +1 more source

Predicting Corporate Social Ratings Using Machine Learning Models

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This study predicts Korean firms' environmental, social, and governance (ESG) social ratings by integrating firm‐performance indicators, human resource management indicators based on Global Reporting Initiative (GRI) Social Standards, and Job Planet‐based employee‐experience signals.
Eunjung Jung, Ahrom Kang, Joongsan Oh
wiley   +1 more source

Machine learning models enhance detection of arrhythmogenic right ventricular cardiomyopathy. [PDF]

Mach Learn Health
Quansah KK, Murphy SA, Kwon E, Anderson E, Tichnell C, Murray B, Gaines S, Gasperetti A, James CA, Calkins H, Carrick RT, Kwon C.   +11 more
europepmc   +1 more source

Uniaxial compressive strength prediction and ratio parameter optimization of titanium tailings composite backfill materials using intelligent hybrid models

Deep Underground Science and Engineering, EarlyView.
Based on the 90 datasets, ERT and four optimization algorithms were used to build four hybrid models to predict the UCS of the backfill body. The SMA‐ERT model was the most effective model, and it can reliably guide the design of the backfill ratio parameters. Abstract This study analyzed the feasibility of using titanium (Ti) tailings as a backfilling
Weijun Liu, Zida Liu, Zhixiang Liu
wiley   +1 more source

Modeling the drivers of trip satisfaction among Texas coastal anglers using creel survey data. [PDF]

PLoS One
Collins C, Paudel S, Harris E, Bauer H, Hawk R, Fisher M, Anderson J.   +6 more
europepmc   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Integrating Remote Sensing and Machine Learning to Project Global Habitat Suitability and Productivity of Chinese Fir Under Climate Change. [PDF]

Ecol Evol
Sun J, He X, Wang T, Wang Q, Liu B, Qian J, Luo D, Xia H, Xu X, Lei X, Zhang J, Wang W, Xu M.   +12 more
europepmc   +1 more source

Explainable hybrid stacking ensemble method for hard rock pillar stability prediction and engineering applications

Deep Underground Science and Engineering, EarlyView.
This research proposes an interpretable hybrid stacking ensemble framework, optimized by the Sparrow Search Algorithm, to enhance hard rock pillar stability prediction. By integrating six machine learning models—k‐nearest neighbors, support vector machines, random forests, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and Light Gradient ...
Ning Wang, Guoyan Zhao, Meng Wang, Taraka Pattela   +3 more
wiley   +1 more source

Interpretable machine learning models for bladder cancer overall survival prediction development and external validation via SEER database and Chinese cohort analysis. [PDF]

Discov Oncol
Abudoubari S, Tuersun A, Mutailipu S, Chen W, Li Q, Nijiati M, Zou X.   +6 more
europepmc   +1 more source

Assessing the accuracy and stability of variable importance in species distribution modelling algorithms through sample perturbations

Ecography, EarlyView.
Variable importance derived from species distribution modelling (SDM) is widely used to identify key environmental drivers of species distributions. However, the reliability of these inferences has rarely been systematically evaluated, particularly with respect to their sensitivity to individual occurrences.
He Wu, Zhaoning Wu, Yunzhi Liu, Wenfang Zhang, Jiechen Wang   +4 more
wiley   +1 more source