Results 121 to 130 of about 61,045 (270)

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 Unveils Isolated‐Surrounded Pt Motifs in High‐Entropy Alloys for Superior Low‐Temperature Ammonia Oxidation

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A physics‐informed machine learning approach successfully decodes the complex catalytic activity of high‐entropy alloys for ammonia oxidation. By revealing a synergistic mechanism involving lattice and electronic couplings, the study identifies a superior “isolated‐surrounded” platinum motif.
Shangfeng Jiang, Ting Tao, Kexiang Guo, Xinyu Fan, Xu Zhang, Zhen Zhou   +5 more
wiley   +1 more source

Discussion of the article “Prediction of creep of recycled aggregate concrete using back‐propagation neural network and support vector machine”

, 2023
Structural Concrete, EarlyView.
Nikola Tošić, Albert de la Fuente, Snežana Marinković   +2 more
wiley   +1 more source

A New Hybrid Method for Parameter Optimization of SVR

, 2018
The performance of Support Vector Regression (SVR) depends heavily on its parameters, but some optimization methods based on Grid Search (GS) or evolutionary algorithms still have several issues that must be addressed.
null null, Wu Zhang, Jieyu Zhang, Taifeng Sun, Jiang Xie   +4 more
core   +1 more source

Machine Learning Modeling of Drill Cuttings Transport Based on Computational Fluid Dynamics Simulation Data

Energy Science &Engineering, EarlyView.
Machine learning surrogates accurately predict drill cuttings transport from computational fluid dynamics data, with Reynolds number dominating system behavior. While tree‐based models excel in accuracy, Gaussian process regression provides physically consistent, smooth predictions essential for reliable drilling simulations.
Kathrin Skinder, Mostafa M. Abdelhafiz, Khizar Shahid, Gunther Brenner   +3 more
wiley   +1 more source

ROC curves of MLR and RBF SVR.

, 2013
ROC curves showing true-positive rates (sensitivity) plotted against the false-positive rate for different cut off points of the quantified components of MLR (gray diamonds) and RBF SVR (black circles).
Robyn M. Lucas (490021), Anne-Louise Ponsonby (340858), Shuyu Guo (490020)   +2 more
core   +1 more source

Trendelenburg positioning with or without norepinephrine infusion for haemodynamic support in anaesthetised hypovolemic horses

Equine Veterinary Journal, EarlyView.
Abstract Background Intraoperative haemorrhage during equine anaesthesia is a rare but potentially fatal complication requiring prompt haemodynamic support. Postural manoeuvres such as Trendelenburg (TL) positioning and vasopressor support augment venous return and improve cardiac output (CO), yet their efficacy in anaesthetised hypovolemic horses ...
Vaidehi V. Paranjape, Klaus Hopster, Ismar Lutvikadic, Bernd Driessen   +3 more
wiley   +1 more source

Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data sparsity undermine the ...
Hanbyeol Park, Sunghyun Sim, Kikun Park, Yongjae Lee, Eunhee Park, Hyerim Bae   +5 more
wiley   +1 more source

Comparison of the evaluation results of FCCS-SVR model and CS-SVR model (Termination of iteration = 200, Number of nest Nest = 20).

, 2019
Comparison of the evaluation results of FCCS-SVR model and CS-SVR model (Termination of iteration = 200, Number of nest Nest = 20).
Maohui Zhang (6475487), Yifei Yang (585215), Yuewei Dai (3737326)   +2 more
core   +1 more source

SVR-2024

Prompt Used to Evaluate Empathy Levels for Submission to SVR ...
Heng Yao
core   +2 more sources