Results 201 to 210 of about 44,463 (307)

Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Deep Underground Science and Engineering, EarlyView.
How artificial intelligence (AI) and digital twin (DT) technologies are revolutionizing tunnel surveillance, offering proactive maintenance strategies and enhanced safety protocols. It explores AI's analytical power and DT's virtual replicas of infrastructure, emphasizing their role in optimizing maintenance and safety in tunnel management.
Mohammad Afrazi, Danial Jahed Armaghani, Hossein Afrazi, Hadi Fattahi, Pijush Samui   +4 more
wiley   +1 more source

Predicting Depression Risk in Physically Inactive Older Adults Using Dietary Antioxidants and Machine Learning: A SHAP-Interpretable Analysis of NHANES. [PDF]

CNS Neurosci Ther
ShangGuan Y, Wu K, Li D, Sim YJ, Zhang C, Lin Z, Yan L.   +6 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

Explainable machine learning identifies knee morphology thresholds for arthroscopic medial meniscus posterior root tear: a retrospective cohort study. [PDF]

Front Med (Lausanne)
Zhang M, Guo F, Li Y, Zheng J, Yu Y, Chen M, Li R, Liao Y, Chen Q, He C.   +9 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

Identifying risk factors for vasculogenic etiology in patients with erectile dysfunction based on clinical features and machine learning. [PDF]

Front Endocrinol (Lausanne)
Wang J, Wu Y, Zhang X, Lu Y, Piao Z, Zhao W, Zhang M.   +6 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

Heart failure risk prediction based on machine learning and interpretability analysis. [PDF]

Front Med (Lausanne)
Li H.
europepmc   +1 more source

A Review of Grain Boundaries: Formation Mechanism, Synthesis Strategy, and Application in Electrocatalysis

EcoEnergy, EarlyView.
An overview of grain boundary engineering in the field of electrocatalysis. ABSTRACT Key electrocatalytic reactions such as HER, OER, ORR, CO2RR, and NRR offer promising routes for storing renewable energy as chemical fuels. However, their widespread application is constrained due to the lack of highly active and stable catalysts. Grain boundaries (GBs)
Jingyu Gao, Fengming Zhou, Xinying Li, Ying Deng, Zexing Wu, Siyuan Feng, Yujing Zhu, Dan Zhang, Zhenyu Xiao   +8 more
wiley   +1 more source

An explainable AI framework for enhanced software defect prediction using transformer-assisted boosting. [PDF]

Sci Rep
Kun Q, Shaikh ZA, Yang J, Kumar G, Mohammed HA, Yee PL.   +5 more
europepmc   +1 more source