[HTML][HTML] Spatial Mapping and Prediction of Groundwater Quality Using Ensemble Learning Models and SHapley Additive exPlanations with Spatial Uncertainty …
S Yang, D Luo, J Tan, S Li, X Song, R Xiong, J Wang… - Water, 2024 - mdpi.com
The spatial mapping and prediction of groundwater quality (GWQ) is important for
sustainable groundwater management, but several research gaps remain unexplored …
sustainable groundwater management, but several research gaps remain unexplored …
Computer-aided mobility solutions: Machine learning innovations to secure smart urban transportation
J Wu, RF Yang, P Zhao, LX Yang - Sustainable Cities and Society, 2024 - Elsevier
This study introduces a groundbreaking hybrid deep learning Intrusion Detection System
(IDS) tailored to improve the security of Electric Vehicle (EV) charging processes in the …
(IDS) tailored to improve the security of Electric Vehicle (EV) charging processes in the …
Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane
Machine learning (ML), which has been increasingly applied to complex problems such as
catalyst development, encounters challenges in data collection and structuring. Quantum …
catalyst development, encounters challenges in data collection and structuring. Quantum …
Predicting the properties of metamaterials consisting of curved-wall triangles using ensemble neural networks with interpretability
Abstract Machine learning has emerged as a promising tool for predicting the properties of
metamaterials, owing to its substantially faster prediction speed compared to conventional …
metamaterials, owing to its substantially faster prediction speed compared to conventional …
Advanced Integrated Fast-Charging Protocol for Lithium-Ion Batteries by Considering Degradation
In the modern electric vehicle industry, the fast charging of lithium-ion batteries is essential.
Charging at a high C-rate minimizes the charging time; however, this results in degradation …
Charging at a high C-rate minimizes the charging time; however, this results in degradation …
Novel natural gradient boosting-based probabilistic prediction of physical properties for polypropylene-based composite data
Accurately predicting the physical properties of polypropylene composites is challenging
because they are highly complex due to the numerous combinations of materials used in …
because they are highly complex due to the numerous combinations of materials used in …
Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning
Introduction The aboveground carbon storage (AGC) in forests serves as a crucial metric for
evaluating both the composition of the forest ecosystem and the quality of the forest. It also …
evaluating both the composition of the forest ecosystem and the quality of the forest. It also …
Accelerated intelligent prediction and analysis of mechanical properties of magnesium alloys based on scaled Super learner machine-learning algorithms
The use of machine learning algorithms in magnesium (Mg) alloys has evolved a scientific
innovation for lightweight. The dataset was compiled by collecting data from the experiment …
innovation for lightweight. The dataset was compiled by collecting data from the experiment …
[HTML][HTML] Thickness regression for backfill grouting of shield tunnels based on GPR data and CatBoost & BO-TPE: A full-scale model test study
Ground penetrating radar (GPR) is a vital non-destructive testing (NDT) technology that can
be employed for detecting the backfill grouting of shield tunnels. To achieve intelligent …
be employed for detecting the backfill grouting of shield tunnels. To achieve intelligent …
Prediction of force chains for dense granular flows using machine learning approach
CH Cheng, CC Lin - Physics of Fluids, 2024 - pubs.aip.org
Force chain networks among particles play a crucial role in understanding and modeling
dense granular flows, with widespread applications ranging from civil engineering structures …
dense granular flows, with widespread applications ranging from civil engineering structures …