Using Transfer Learning To Determine The Type Of Mathematical Fractals Image Of Islamic Geometric Patterns | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Using Transfer Learning To Determine The Type Of Mathematical Fractals Image Of Islamic Geometric Patterns Seham Ebrahim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6760751/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Dec, 2025 Read the published version in The Visual Computer → Version 1 posted 8 You are reading this latest preprint version Abstract Islamic geometric Patterns (IGP) are one of the most elegant forms of art that reflect the Islamic cultural and religious heritage. These Patterns are characterized by their complexity and precision. Which makes them a source of inspiration for the design of decorations in light of technological progress. Geometric patterns have never been distinguished before using Deep Learning. This data is the first to be used and classified. The research includes classifying IGP into 8 classes Arabesque, Tessellation, Euclidean tiling by convex regular polygons, Koch snowflake, logarithmic spiral, Mandelbrot set, Pythagorean Fractal Tree ,Sierpinski Triangles using Transfer Learning. six Deep Learning modes are used VGG19, Mobilenetv2, inception_resnet_v2, xception, NASNetLarge, and Rasnet v2. Adjusting the initialized performance parameters for all model to be able to compare between them. The evaluated performance parameters are training accuracy and loss, evaluation accuracy and loss, Precision, recall, confusion matrix and F1 score. Rasnetv2 gives the higher accuracy, F1 score at small size data. Image Processing Computer Vision Deep Learning Auto Augment Transfer Learning and Fine Tuning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Dec, 2025 Read the published version in The Visual Computer → Version 1 posted Editorial decision: Revision requested 01 Oct, 2025 Reviews received at journal 28 Sep, 2025 Reviewers agreed at journal 28 Sep, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 28 May, 2025 Submission checks completed at journal 28 May, 2025 First submitted to journal 27 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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