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GEMTELLIGENCE: Accelerating gemstone classification with deep learning

Engineering and Technology

GEMTELLIGENCE: Accelerating gemstone classification with deep learning

T. Bendinelli, L. Biggio, et al.

Discover GEMTELLIGENCE, an innovative deep learning method for automated gemstone origin and treatment detection, developed by Tommaso Bendinelli, Luca Biggio, Daniel Nyfeler, Abhigyan Ghosh, Peter Tollan, Moritz Alexander Kirschmann, and Olga Fink. This cutting-edge approach combines inexpensive techniques with advanced neural networks, delivering predictive accuracy that rivals costly traditional methods, revolutionizing gemstone analysis.

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Playback language: English
Abstract
This paper introduces GEMTELLIGENCE, a novel deep learning approach for automated gemstone origin determination and treatment detection. It leverages multi-modal data from various instruments (FTIR, UV-Vis, XRF, ICP-MS) and combines convolutional and attention-based neural networks to achieve predictive performance comparable to expensive instrumentation and expert analysis, while utilizing inexpensive methods. GEMTELLIGENCE significantly enhances automation in gemstone analysis, revolutionizing industry processes.
Publisher
Communications Engineering
Published On
Aug 20, 2024
Authors
Tommaso Bendinelli, Luca Biggio, Daniel Nyfeler, Abhigyan Ghosh, Peter Tollan, Moritz Alexander Kirschmann, Olga Fink
Tags
GEMTELLIGENCE
deep learning
gemstone analysis
origin determination
treatment detection
neural networks
automation

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