Malaysian Coins Recognition Using Machine Learning Methods
Adaptive boosting; Discriminant analysis; Machine learning; Nearest neighbor search; Neural networks; Classifieds; Coin recognition; Complex Processes; Daily lives; Images processing; Machine learning approaches; Machine learning methods; Malaysians; Object categories; Training model; Image processi...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-260002023-05-29T17:05:59Z Malaysian Coins Recognition Using Machine Learning Methods Zainuddin N.N. Azhari M.S.N.B.N. Hashim W. Alkahtani A.A. Mustafa A.S. Alkawsi G. Noman F. 57337300700 57337300800 11440260100 55646765500 57218103026 57191982354 55327881300 Adaptive boosting; Discriminant analysis; Machine learning; Nearest neighbor search; Neural networks; Classifieds; Coin recognition; Complex Processes; Daily lives; Images processing; Machine learning approaches; Machine learning methods; Malaysians; Object categories; Training model; Image processing Coins have become essential to our daily lives. As a legal tender, identifying, validating, classifying, and sorting coins is a complex process. In essence, automated coin detectors should identify deteriorated or older coins and distinguish between genuine and fake coins. Nonetheless, we identified limited literature examining different Machine Learning (ML) approaches for detecting Malaysian coins. This study investigates machine learning approaches and identifies the most efficient and accurate for Malaysian coin recognition. The model was trained on 311 images of coins and classified into four object categories: 5, 10, 20, and 50 cents. Six classifiers are used to test the training model. For the Grey-Level Co-occurrence Matrix (GLCM) feature extraction, AdaBoost classifiers were the most accurate, whereas K-Nearest Neighbors (KNN) classifiers were the least accurate. Moreover, the Artificial Neural Networks (ANN) classifier had the highest accuracy in the Histogram of Oriented Gradients (HOG) feature, while the Linear Discriminant Analysis (LDA) classifier had the lowest. The study findings and future directions are discussed. � 2021 IEEE. Final 2023-05-29T09:05:58Z 2023-05-29T09:05:58Z 2021 Conference Paper 10.1109/AiDAS53897.2021.9574175 2-s2.0-85118967730 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118967730&doi=10.1109%2fAiDAS53897.2021.9574175&partnerID=40&md5=c4bc42d0dbcf31137e8b8dacaef88792 https://irepository.uniten.edu.my/handle/123456789/26000 Institute of Electrical and Electronics Engineers Inc. Scopus |
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| description |
Adaptive boosting; Discriminant analysis; Machine learning; Nearest neighbor search; Neural networks; Classifieds; Coin recognition; Complex Processes; Daily lives; Images processing; Machine learning approaches; Machine learning methods; Malaysians; Object categories; Training model; Image processing |
| author2 |
57337300700 |
| author_facet |
57337300700 Zainuddin N.N. Azhari M.S.N.B.N. Hashim W. Alkahtani A.A. Mustafa A.S. Alkawsi G. Noman F. |
| format |
Conference Paper |
| author |
Zainuddin N.N. Azhari M.S.N.B.N. Hashim W. Alkahtani A.A. Mustafa A.S. Alkawsi G. Noman F. |
| spellingShingle |
Zainuddin N.N. Azhari M.S.N.B.N. Hashim W. Alkahtani A.A. Mustafa A.S. Alkawsi G. Noman F. Malaysian Coins Recognition Using Machine Learning Methods |
| author_sort |
Zainuddin N.N. |
| title |
Malaysian Coins Recognition Using Machine Learning Methods |
| title_short |
Malaysian Coins Recognition Using Machine Learning Methods |
| title_full |
Malaysian Coins Recognition Using Machine Learning Methods |
| title_fullStr |
Malaysian Coins Recognition Using Machine Learning Methods |
| title_full_unstemmed |
Malaysian Coins Recognition Using Machine Learning Methods |
| title_sort |
malaysian coins recognition using machine learning methods |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806424524232589312 |
| score |
13.214268 |