Show simple item record

dc.contributor.authorKalyanapriya, R
dc.contributor.authorDayarathne, MAPP
dc.contributor.authorKoggalage, RLW
dc.date.accessioned2026-03-11T04:50:23Z
dc.date.available2026-03-11T04:50:23Z
dc.date.issued2026-01
dc.identifier.urihttps://ir.kdu.ac.lk/handle/345/9043
dc.description.abstractThe introduction of Artificial Intelligence (AI) and Machine Learning (ML) to gemology has revolutionized conventional processes by which gemstone identification, grading, and valuation are done; automation, objectivity and precision are now a part of the process. Traditional approaches to gem evaluation are highly dependent on human experience and human eye, which tend to be biased and subjective. The latest progress in computer vision, deep learning, and spectroscopic data interpretation allowed developing the intelligent systems that could recognize the type of the gemstones, determine the quality of their clarity and color, and determine their market prices with greater accuracy. Moreover, the neural networks that are mixed with the fuzzy logic and probabilistic reasoning have enhanced the making of decisions in unclear situations. This review discusses the intelligent gemstone assessment system development, including intelligent authentication, visual feature mining, and price prediction using data with the aid of AI. New applications of the internet of things enabled sensing, Ra-man spectroscopy, and multispectral imaging are also introduced, and the shift to the reality of real-time, scalable, and transparent systems of gem evaluation is highlighted. The paper ends by summarizing the existing challenges including lack of data, model interpretability, and standardization and provides some future directions of the next generation of intelligent gemstone analysis technologies.en_US
dc.language.isoenen_US
dc.subjectartificial intelligence, machine learning, computer vision, gemstone analysis, gem gradingen_US
dc.titleA Review on Artificial Intelligence-based methods for Gemstone Analysis, Quality Grading, and Valuationen_US
dc.typeArticle Abstracten_US
dc.identifier.facultyFOCen_US
dc.identifier.journalFOCSSen_US
dc.identifier.issue6en_US
dc.identifier.pgnos12en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record