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Comparison with several state-of-the-art meta-heuristic optimization techniques indicates the efficacy of the developed method.<\/jats:p>","DOI":"10.1142\/s0219691323500182","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T06:29:31Z","timestamp":1682058571000},"source":"Crossref","is-referenced-by-count":0,"title":["A novel multi-level image segmentation algorithm via random opposition learning-based Aquila optimizer"],"prefix":"10.1142","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8942-8668","authenticated-orcid":false,"given":"Jia","family":"Cai","sequence":"first","affiliation":[{"name":"School of Digital Economics, Guangdong University of Finance and Economics, Guangzhou 510320, P. R. China"}]},{"given":"Tianhua","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou 510320, P. R. 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