Results 151 to 160 of about 31,305 (267)

Application of artificial neural network and general machine learning modelling on CO2 adsorption in moisture equilibrated South African high and medium rank coals

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract This study investigates the effect of moisture on CO2 adsorption in South African coals using both experimental and machine learning approaches. Three coal samples (SL, TN, and EM) with varying ranks (RoVmr: 3.49%, 1.26%, and 0.64%, respectively) were collected from different regions of South Africa.
Kasturie Premlall, Lawrence Koech, Douw Faurie, Khanyisile Lepota   +3 more
wiley   +1 more source

Exploring the Impact of Physical Activity Metrics on Calorie Consumption: A Machine Learning Approach Combined with SHAP Analysis. [PDF]

Sci Rep
Lin S, Zhang Y.
europepmc   +1 more source

Efficiency and Perceptions in Public CSR: An Integrated Efficiency–Perception Analysis of Spanish Defence Delegations

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Public organisations often experience a discrepancy between improvements in technical efficiency and stakeholders' perceptions of integrity and performance. This study analyses the mechanisms that may underlie this efficiency–perception discrepancy in Spanish Defence Delegations during 2020–2023.
José Solana‐Ibáñez, Manuel Caravaca‐Garratón   +1 more
wiley   +1 more source

Interpretable Taxi-Out Time Prediction of Departure Flights Using Stacking Ensemble Learning and SHAP Analysis. [PDF]

Sci Rep
Wu T, Mao Y, Huang J, Zeng X, Ma J, Jia X.   +5 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

Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming

Reviews on Advanced Materials Science
Ashraf Muhammad Waqas, Khan Adnan, Tu Yongming, Wang Chao, Ben Kahla Nabil, Javed Muhammad Faisal, Ullah Safi, Tariq Jawad   +7 more
doaj   +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

Network toxicology reveals key genes of amiodarone induced pulmonary fibrosis: based on machine learning and SHAP analysis. [PDF]

Front Pharmacol
Yan X, Liu Y, Fan R.
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

Research on the predicted height of water-conducting fracture zones based on the BO-RFR model and SHAP analysis. [PDF]

Sci Rep
Qiu M, Wen Y, Teng C, Shi M.
europepmc   +1 more source