Results 221 to 230 of about 212,260 (313)

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

Machine learning model for predicting hypotension following continuous renal replacement therapy initiation in end-stage kidney disease patients: a SHAP-interpretable approach. [PDF]

Front Med (Lausanne)
Qiu S, Qiu D, Tao Y, Fu P, Bai J, Cao N.
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

Fault Detection Approach of Cyclotron Ion Sources Based on KPCA-ISSA-SVM. [PDF]

Sensors (Basel)
Li Y, Liu Y, Guan F, Zhang H, Hou S, Huang P, Nong Z.   +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

Intelligent system for portfolio optimization for novel volatility forecasting using machine learning. [PDF]

Sci Rep
Biswas T, Dey A, Mandal G, Ghosh N.
europepmc   +1 more source

Machine Learning‐Assisted Design of BaTiO3‐Based Superparaelectric High‐Entropy Ceramics with Superior Energy Storage

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This study employed an adaptive iterative strategy combining machine learning algorithms, domain knowledge, experimental design, and experimental feedback to aim to precisely and quickly discover high‐entropy ceramics with excellent energy storage performance.
Haowen Liu, Xiaoyan Zhang, Zhiyuan Ma, Kailong Ma, Xiwei Qi   +4 more
wiley   +1 more source

Biomass-Derived Activated Carbon for Congo Red Dye Adsorption: Machine-Learning-Based Prediction and Comparative Evaluation. [PDF]

ACS Omega
Sudarsan S, Vinayagam R, Selvaraj R.
europepmc   +1 more source

Nanophotonic Biosensors and the Paradigm of Trans‐Scale Autonomous Bio‐Integrated Diagnostics for Addressing Health Complexities

Electron, EarlyView.
Physics‐driven advances in optical nanobiosensors for rapid, miniaturized, and point‐of‐care diagnostics for next‐generation decentralized and personalized healthcare based on sensor intelligence. ABSTRACT Public health emergencies and the escalating burden of chronic diseases necessitate a paradigm shift from centralized laboratory testing to rapid ...
Vishal Chaudhary, Harsh Sable, Sonu Sonu, Lakshita Phor, Reddicherla Umapathi, Pradeep Bhadola   +5 more
wiley   +1 more source

Prediction of Bond Strength in Corroded Reinforced Concrete Using SVM and XGB Methods. [PDF]

Materials (Basel)
Chen ZQ, Chen Z, Zhong YZ.
europepmc   +1 more source