Data-driven mineral prospectivity mapping (MPM) with random forests (RF) has been
documented in various brownfield zones hosting known mineral deposits of the type being …

The traditional metallogenic prediction based on the geological model has been extensively
used in prospecting, and the emerging machine learning method has also been successfully …

The gradient boosting decision tree is a well-known machine learning algorithm. Despite
numerous advancements in its application, its efficiency still needs to be improved for large …

Prediction of geochemical concentration values is essential in mineral exploration as it plays
a principal role in the economic section. In this paper, four regression machine learning (ML) …

Cluster analysis can be used to organize samples and generate ideas regarding the
multivariate geochemistry of given dataset. Traditional clustering techniques have the …

In response to escalating global wastewater issues, particularly from dye contaminants,
many studies have begun using hydrochar to adsorb dye from wastewater. However, the …

Three-dimensional mineral prospectivity mapping (3DMPM) aims to explore deep mineral
resources and many methods have been developed for this task in recent years. The …

Modern computational techniques, particularly Support Vector Machines (SVM) and
Random Forest (RF) models, are revolutionizing predictive mineral prospectivity mapping …

The exploration of buried mineral deposits is required to generate innovative approaches
and the integration of multi-source geoscientific datasets. Mining geochemistry methods …

An effective approach to promoting sustainability within the construction industry is the use
of recycled aggregate concrete (RAC) as a substitute for natural aggregates. Ensuring the …