2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 326-340, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Fahad Ghabban 1 *
Doi: https://doi.org/10.54216/JISIoT.180125
Artificial immune systems (AIS) represent an emerging facet of artificial intelligence, offering innovative solutions to a spectrum of problems. It draws inspiration from the biological immune system's fascinating properties, mechanisms, and principles, resulting in mathematical and computer-based implementations. In this paper, we aim to assess the accuracy of artificial immune systems as classification tools in the realm of Arabic handwriting recognition. Among the repertoire of immune-computing models, we focus on the Artificial Immune Recognition System (AIRS), Immunos, Clonal Selection Algorithm (CLONALG), and Clonal Selection Classification Algorithm (CSCA), which have garnered significant attention for their prowess in pattern recognition applications. To conduct this investigation, we leverage the comprehensive IFN-INIT Arabic handwriting database, which comprises contributions from 411 distinct writers. Feature selection plays a pivotal role in enhancing classification performance, and for this purpose, we harness the grey level co-occurrence matrix. In pursuit of a thorough comparative analysis, we also employ well-established classifiers such as Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Naive Bayes. The obtained results exhibit the promising potential of AIS-based classifiers in the context of Arabic handwriting recognition, offering insights into the evolving landscape of AI solutions in this domain.
Handwritten analysis , Arabic Writer identification , Artificial Immune Systems , Image processing , Pattern Recognition
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