Abstract Machine learning and artificial intelligence techniques have an ever-increasing
presence and impact on a wide-variety of research and commercial fields. Disappointed by …

Machine learning is a subcategory of artificial intelligence, which aims to make computers
capable of solving complex problems without being explicitly programmed. Availability of …

Recent developments in smart mining technology have enabled the production, collection,
and sharing of a large amount of data in real time. Therefore, research employing machine …

Deep learning is a sub-field of artificial intelligence that combines feature engineering and
classification in one method. It is a data-driven technique that optimises a predictive model …

Automated mineralogy methods and tools, such as the Mineral Liberation Analyser (MLA)
and the QEMSCAN, are now widely used for ore characterization, process design and …

Drilling problems such as stick slip vibration/hole cleaning, pipe failures, loss of circulation,
BHA whirl, stuck pipe incidents, excessive torque and drag, low ROP, bit wear, formation …

Dry laboratories (dry labs) are laboratories dedicated to using and creating data (they are
data-centric). Several aspects of the minerals industry (eg, exploration, extraction and …

Machine learning methods for data processing are gaining momentum in many geoscience
industries. This includes the mining industry, where machine learning is primarily being …

The massive accumulation of available multi-modal mineral exploration data for most
metallogenic belts worldwide provides abundant information for the discovery of mineral …

This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …