[HTML][HTML] Machine learning prediction of permeability distribution in the X field Malay Basin using elastic properties
Accurate estimation of porosity and permeability distribution is vital for reservoir
characterization, particularly challenging in areas with limited well data. This study …
characterization, particularly challenging in areas with limited well data. This study …
Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models
This study aims to address accuracy challenges in assessing air pollution health impacts
using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited …
using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited …
Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies
An efficient and intelligent forewarning model for financial risk is essential to assist company
managers, investors, and market regulators in risk management. This research aims to …
managers, investors, and market regulators in risk management. This research aims to …
[HTML][HTML] Improving predictive maintenance: Evaluating the impact of preprocessing and model complexity on the effectiveness of eXplainable Artificial Intelligence …
Due to their performance in this field, Long-Short-Term Memory Neural Network (LSTM)
approaches are often used to predict the remaining useful life (RUL). However, their …
approaches are often used to predict the remaining useful life (RUL). However, their …
Prediction of grease performance and optimal additive ratio based on the SSA-GDA-LSSVM model
Y Xia, H Zhao, X Feng - Tribology International, 2025 - Elsevier
In this paper, to address the issue of compounding three additives in PTFE grease, we
propose a machine learning model based on SSA-GDA-LSSVM to predict both the …
propose a machine learning model based on SSA-GDA-LSSVM to predict both the …
[HTML][HTML] Machine learning approach with a posteriori-based feature to predict service life of a thermal cracking furnace with coking deposition
C Panjapornpon, C Rochpuang, S Bardeeniz… - Results in …, 2024 - Elsevier
A thermal cracking furnace is an important equipment in the petrochemical industry that is
typically used for breaking long hydrocarbons into short chains and producing coke as a …
typically used for breaking long hydrocarbons into short chains and producing coke as a …
[HTML][HTML] Integrating Network Theory and SHAP Analysis for Enhanced RUL Prediction in Aeronautics
Abstract The prediction of Remaining Useful Life (RUL) in aerospace engines is a challenge
due to the complexity of these systems and the often-opaque nature of machine learning …
due to the complexity of these systems and the often-opaque nature of machine learning …
Identification of Precursors to Go-Around Events Using Machine Learning to Enhance Collision Risk Modelling in the Terminal Area
A Cardenas Melgar, Y Auguste… - AIAA SCITECH 2025 …, 2025 - arc.aiaa.org
With air travel demand surging—projected to reach 4.7 billion passengers in 2024 and air
traffic anticipated to double by the mid-2030s—addressing collision risks in increasingly …
traffic anticipated to double by the mid-2030s—addressing collision risks in increasingly …
Explainable machine learning for groundwater contamination by arsenic remobilization from a vadose zone
The vadose zone serves as a barrier retaining arsenic (As) from reaching groundwater.
However, previous studies revealed that retained As can be remobilized from vadose zone …
However, previous studies revealed that retained As can be remobilized from vadose zone …