Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms
M Hajihosseinlou, A Maghsoudi… - Earth Science Informatics, 2024 - Springer
The current research employed the least absolute shrinkage and selection operator (Lasso)
and Elastic-net algorithms to examine their potential utilization in MVT Pb-Zn prospectivity …
and Elastic-net algorithms to examine their potential utilization in MVT Pb-Zn prospectivity …
A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins
The process of data-driven clustering to uncover geochemical anomalies linked to sample
catchment basins (SCBs) includes a comprehensive framework to discern areas exhibiting …
catchment basins (SCBs) includes a comprehensive framework to discern areas exhibiting …
Spatiotemporal assessment of groundwater quality and quantity using geostatistical and ensemble artificial intelligence tools
The study investigated the spatiotemporal relationship between surface hydrological
variables and groundwater quality/quantity using geostatistical and AI tools. AI models were …
variables and groundwater quality/quantity using geostatistical and AI tools. AI models were …
Optimized AI-MPM: Application of PSO for tuning the hyperparameters of SVM and RF algorithms
Modern computational techniques, particularly Support Vector Machines (SVM) and
Random Forest (RF) models, are revolutionizing predictive mineral prospectivity mapping …
Random Forest (RF) models, are revolutionizing predictive mineral prospectivity mapping …
A hybrid framework for detection of multivariate porphyry Cu signatures and anomaly enhancement: Incorporation of SFA, GMPI, and Grey Wolf Optimization
Geochemical data from stream sediment are commonly employed for regional scale mineral
exploration, as they can be utilized to detect geochemical anomalies associated with …
exploration, as they can be utilized to detect geochemical anomalies associated with …
Geo-Hgan: Unsupervised anomaly detection in geochemical data via latent space learning
L Ding, B Chen, Y Zhu, H Dong, G Chan… - Computers & …, 2024 - Elsevier
Reconstructing geochemical data for anomaly detection using Generative Adversarial
Networks (GANs) has become a prevalent method in identifying geochemical anomalies …
Networks (GANs) has become a prevalent method in identifying geochemical anomalies …
Mineral Prospectivity Mapping Based on Spatial Feature Classification with Geological Map Knowledge Graph Embedding: Case Study of Gold Ore Prediction at …
Q Yan, J Zhao, L Xue, L Wei, M Ji, X Ran… - Natural Resources …, 2024 - Springer
Prospectivity mapping based on deep learning typically requires substantial amounts of
geological feature information from known mineral deposits. Due to the limited spatial …
geological feature information from known mineral deposits. Due to the limited spatial …
[HTML][HTML] Effective Machine Learning Solution for State Classification and Productivity Identification: Case of Pneumatic Pressing Machine
A Kolokas, P Mallioris, M Koutsiantzis, C Bialas… - Machines, 2024 - mdpi.com
The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing,
driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics …
driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics …
Exploration and Development of a Structured Multi-Level Fusion in an Ensemble-Based Large-Scale Meta-Decision Model
Despite significant advancements in Multi-Criteria Decision-Making (MCDM) over recent
decades, the absence of formal quality assessments raises concerns about the robustness …
decades, the absence of formal quality assessments raises concerns about the robustness …
Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils
Abstract Machine learning (ML) models for accurately predicting heavy metals with
inconsistent outputs have improved owing to dataset outliers, which influence model …
inconsistent outputs have improved owing to dataset outliers, which influence model …