{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T14:42:28Z","timestamp":1784126548280,"version":"3.55.0"},"reference-count":79,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62002332"],"award-info":[{"award-number":["62002332"]}]},{"name":"National Natural Science Foundation of China","award":["62072443"],"award-info":[{"award-number":["62072443"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Artificial 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.<\/jats:p>","DOI":"10.3390\/jsan11030050","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T21:01:31Z","timestamp":1661806891000},"page":"50","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9773-418X","authenticated-orcid":false,"given":"Teng","family":"Long","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangbing","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Telecom SudParis, 91011 Evry, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4026-687X","authenticated-orcid":false,"given":"Gerhard","family":"Hancke","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South Africa"},{"name":"College of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8648-3466","authenticated-orcid":false,"given":"Qi","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.measurement.2017.12.006","article-title":"A computer vision system for identification of granite-forming minerals based on RGB data and artificial neural networks","volume":"117","author":"Ramil","year":"2018","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/s10921-017-0431-7","article-title":"Automatic Identification of Rock-Forming Minerals in Granite Using Laboratory Scale Hyperspectral Reflectance Imaging and Artificial Neural Networks","volume":"36","author":"Ramil","year":"2017","journal-title":"J. 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