An overview on twin support vector regression

H Huang, X Wei, Y Zhou - Neurocomputing, 2022 - Elsevier
Twin support vector regression (TSVR) is a useful extension of traditional support vector
regression (SVR). As a new regression model, the basic idea of TSVR is generating a pair of …

Research on theory, simulation and measurement of stress behavior under regenerated roof condition

X Li, S Chen, Q Zhang, X Gao… - Geomechanics and …, 2021 - koreascience.kr
To determine ground stress behavior under the special condition of a regenerated roof, we
established a model of elastic rectangular cantilever thin plates. Moreover, the critical …

[HTML][HTML] Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based …

C Xie, H Nguyen, XN Bui, VT Nguyen, J Zhou - Journal of Rock Mechanics …, 2021 - Elsevier
Due to the rapid industrialization and the development of the economy in each country, the
demand for energy is increasing rapidly. The coal mines have to pace up the mining …

Application of SVR models built with AOA and Chaos mapping for predicting tunnel crown displacement induced by blasting excavation

C Li, X Mei - Applied Soft Computing, 2023 - Elsevier
This study utilizes the support vector regression (SVR) model to predict the tunnel crown
displacement (TCD) induced by the blasting excavation. 95 blasting operations considering …

Orthogonal numerical analysis of deformation and failure characteristics of deep roadway in coal mines: A case study

X Feng, Z Ding, Q Hu, X Zhao, M Ali, JT Banquando - Minerals, 2022 - mdpi.com
With the development of deep, underground coal mines in China, the failure mechanism of
the rocks surrounding roadways is becoming increasingly complicated and the …

A hybrid PSO-ANFIS model for predicting unstable zones in underground roadways

S Mahdevari, MB Khodabakhshi - Tunnelling and Underground Space …, 2021 - Elsevier
The problem of roof failure in underground coal mines is responsible for many fatalities,
injuries, downtimes, and delays in production planning. Currently, the support systems in …

Application of adaptive neuro-fuzzy inference system and differential evolutionary optimization for predicting rock displacement in tunnels and underground spaces

X Zhou, H Nguyen, VT Hung, CW Lee, VD Nguyen - Structures, 2023 - Elsevier
In this paper, the rock displacement phenomenon was investigated to evaluate and prevent
the failure and collapse of tunnels and underground spaces. The historical datasets were …

Predicting the stock market prices using a machine learning-based framework during crisis periods

Z Zouaghia, Z Kodia, L Ben Said - Multimedia Tools and Applications, 2024 - Springer
Stock markets are highly volatile, complex, non-linear, and stochastic. Therefore, predicting
stock market behavior is one of finance's most complex challenges. Recently, political …

Seamount age prediction machine learning model based on multiple geophysical observables: methods and applications in the Pacific Ocean

Y Bai, Y Rong, J Sun, L Chen, D Zhang… - Marine Geophysical …, 2021 - Springer
Seamount ages are important for understanding crust-mantle interactions and exploring sea
bottom ore resources. Rock sampling and laboratory measurements are time-consuming …

[PDF][PDF] Journal of Rock Mechanics and Geotechnical Engineering

C Xie, H Nguyen, XN Bui, VT Nguyen… - Journal of Rock …, 2021 - researchgate.net
abstract Due to the rapid industrialization and the development of the economy in each
country, the demand for energy is increasing rapidly. The coal mines have to pace up the …