新しいアプローチにより、偽造硬貨をより簡単に検出できるようになった(Counterfeit coins can be detected more easily thanks to a novel approach developed at Concordia)

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2024-05-14 カナダ・コンコーディア大学

新しいアプローチにより、偽造硬貨をより簡単に検出できるようになった(Counterfeit coins can be detected more easily thanks to a novel approach developed at Concordia)

金属コインの価値を保証するために、最新の技術が必要です。偽造コインは世界中の通貨にとって脅威であり、4月末にヨーロッパの警察がスペイン拠点の犯罪組織を摘発しました。コンコルディア大学のCENPARMIの研究者たちは、新しいフレームワークを発表し、画像マイニング技術と機械学習アルゴリズムを使用して偽造コインの欠陥を特定します。ファジーアソシエーションルールマイニングにより、コインの特徴を分析し、正規品と偽造品を区別します。この技術は、コイン以外の偽造品の検出にも応用可能です。

<関連情報>

刈り込みファジー連想分類器を用いた画像ベースの偽造硬貨検出フレームワーク A framework for image-based counterfeit coin detection using pruned fuzzy associative classifier

Maryam Sharifi Rad, Saeed Khazaee, Ching Y. Suen
Expert Systems with Applications  Available online:5 March 2024
DOI:https://doi.org/10.1016/j.eswa.2024.123577

Abstract

Counterfeit coins pose a significant challenge in various real-world applications, from vending machines to currency exchange systems, making their reliable detection a matter of utmost importance. This research presents a novel framework designed to tackle this issue by harnessing the power of image-mining techniques. Our proposed framework is developed in two modules. In the first module, a method to detect the region of interest (ROIs) is applied that focuses on blob detection. In the second module, image mining is applied to find image patterns present in coin images using fuzzy association rules mining. The enhancement lies in utilizing Particle Swarm Optimization (PSO) within the image mining module. PSO refines the threshold parameters, thereby improving the efficiency of the fuzzy association rules mining process. This integration allows for the automatic determination of optimal values, contributing to the overall robustness of the counterfeit coin detection system. Comprising two modules, this framework offers a unique advantage as a compress, serving as a knowledge attainment tool. By harnessing the full power of fuzzy association rule mining, this paper introduces pruning methods to reduce redundant and insignificant rules. Moreover, we propose a novel algorithm for feature selection and a pruned-based fuzzy associative classifier to establish a robust counterfeit coin detection system. Comparative analysis with other methods using the same dataset showcases the superiority of our framework, exhibiting lower feature dimensions, smoother boundaries, and maintaining satisfactory accuracy. The generality of this study’s problem formulation offers a common framework for addressing similar challenges across various domains.

1600情報工学一般
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