A review of artificial intelligence technologies in mineral identification : classification and visualization

dc.contributor.authorLong, Teng
dc.contributor.authorZhou, Zhangbing
dc.contributor.authorHancke, Gerhard P.
dc.contributor.authorBai, Yang
dc.contributor.authorGao, Qi
dc.date.accessioned2022-12-15T10:47:19Z
dc.date.available2022-12-15T10:47:19Z
dc.date.issued2022-08-29
dc.description.abstractArtificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification.en_US
dc.description.departmentElectrical, Electronic and Computer Engineeringen_US
dc.description.sponsorshipThe National Natural Science Foundation of China.en_US
dc.description.urihttps://www.mdpi.com/journal/jsanen_US
dc.identifier.citationLong, T.; Zhou, Z.; Hancke, G.; Bai, Y.; Gao, Q. A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization. Journal of Sensor and Actuator Networks 2022, 11, 50. https://doi.org/10.3390/jsan11030050.en_US
dc.identifier.issn2224-2708 (online)
dc.identifier.other10.3390/jsan11030050
dc.identifier.urihttps://repository.up.ac.za/handle/2263/88833
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en_US
dc.subjectMineral identificationen_US
dc.subjectDeep learningen_US
dc.subjectVisualization analysisen_US
dc.subjectArtificial intelligence (AI)en_US
dc.titleA review of artificial intelligence technologies in mineral identification : classification and visualizationen_US
dc.typeArticleen_US

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