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PrFA: a framework for image-based counterfeit coin detection using Pruned Fuzzy Associative Classifier


PrFA: a framework for image-based counterfeit coin detection using Pruned Fuzzy Associative Classifier

Sharifi Rad, Seyedeh Maryam (2020) PrFA: a framework for image-based counterfeit coin detection using Pruned Fuzzy Associative Classifier. PhD thesis, Concordia University.

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The numismatic industry has been dealing with the problem of counterfeit coins for centuries. With the growth of technology in counterfeiting, untrained users cannot distinguish fake from genuine coins, especially for rare and precious coins. Using coin experts is also very expensive, and even with them, in some cases, detecting counterfeit coin is not guaranteed. Consequently, the demand for a computer-aided system that can detect counterfeit coins and be robust and reliable has increased. This thesis proposes a new fake coin detection method that focuses on image mining approaches instead of extracting only statistical features from the coin image, as suggested by other researchers.
In this research, a novel framework called PrFA is proposed for counterfeit coin detection that shows the effectiveness of image mining techniques. We develop a new image mining system on top of the fuzzy concept that helps us to discover the implicit information from the images in the way closer to the human’s viewpoint. The advantage of the fuzzy set concept is that it can deal with uncertain objects, and we take this advantage for the decision-making problem by implementing an associative classifier model.
Our proposed framework is developed in two modules, and the principle of least privilege of it is a compressed system that can be considered as a knowledge attainment tool. In the first module, a method to detect the region of interests (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.
Image data are generally high dimensional due to a wide range of resolution levels. According to state of the art, the rule-based association methods demonstrated their efficiency by generating defensible solutions at an acceptable level of accuracy when dealing with small and medium-size samples. Regrettably, to cope with a large amount of data such as the image database, these methods were not robust enough. To tackle the above
challenge, we propose a new algorithm for feature selection to reduce the dimensions of features via analyzing the relationships among different features.
Image classification is imperative to search for more available and appropriate information. In recent years, various methods based on image mining approaches for classification tasks have been explored. Apart from their usefulness, the available classifiers are often vulnerable to low accuracy. Accordingly, we present a pruned based fuzzy associative classifier algorithm to create a robust counterfeit coin detector system. This classifier is a mixture of the association rules method and the fuzzy set concept.
In this research, we preserve the full power of fuzzy association rule mining to reduce the amount of redundant and insignificant rules by focusing on pruning methods. By comparing the achieved results with some other methods obtained from the same dataset, we demonstrate that our framework surpasses in terms of lower feature dimensions, and smoother boundaries while maintaining satisfactory accuracy. Besides that, we show that our proposed classifier is more accurate compared to other associative classifiers.
In this research, the problem with a general form will be described to provide a common framework for issues appearing in other domains.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (PhD)
Authors:Sharifi Rad, Seyedeh Maryam
Institution:Concordia University
Degree Name:Ph. D.
Program:Computer Science
Date:16 August 2020
Thesis Supervisor(s):Suen, Ching Yee
ID Code:987905
Deposited By: Seyedeh Maryam Sharifi Rad
Deposited On:29 Jun 2021 21:06
Last Modified:29 Jun 2021 21:06
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