{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:45:53Z","timestamp":1780587953348,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004515","name":"Universiti Kebangsaan Malaysia (UKM)","doi-asserted-by":"publisher","award":["GUP-2020-065"],"award-info":[{"award-number":["GUP-2020-065"]}],"id":[{"id":"10.13039\/501100004515","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER).<\/jats:p>","DOI":"10.3390\/jimaging9040079","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T03:32:25Z","timestamp":1680060745000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Genetic Algorithm Based One Class Support Vector Machine Model for Arabic Skilled Forgery Signature Verification"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5580-2444","authenticated-orcid":false,"given":"Ansam A.","family":"Abdulhussien","sequence":"first","affiliation":[{"name":"Centre of Artificial Intelligence, Faculty of Information Sciences and Technology, University Kebangsaan Malaysia, Bangi 50300, Malaysia"},{"name":"Information Technology Center, Iraqi Commission for Computers and Informatics, Baghdad 10009, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2003-8853","authenticated-orcid":false,"given":"Mohammad F.","family":"Nasrudin","sequence":"additional","affiliation":[{"name":"Centre of Artificial Intelligence, Faculty of Information Sciences and Technology, University Kebangsaan Malaysia, Bangi 50300, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2723-1549","authenticated-orcid":false,"given":"Saad M.","family":"Darwish","sequence":"additional","affiliation":[{"name":"Institute of Graduate Studies and Research, University of Alexandria, Alexandria 21526, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4228-9298","authenticated-orcid":false,"given":"Zaid Abdi Alkareem","family":"Alyasseri","sequence":"additional","affiliation":[{"name":"Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf 540011, Iraq"},{"name":"College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"ref_1","unstructured":"Taherzadeh, G., Karimi, R., Ghobadi, A., and Beh, H.M. 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