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  • The 9th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA’25), Volume 1

    The 9th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA’25), Volume 1 by Hassanien, Aboul Ella; El-Sayed, Eman Karam; Darwish, Ashraf; Snasel, Vaclav;

    Series: Lecture Notes on Data Engineering and Communications Technologies;

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      • Publisher's listprice EUR 235.39
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        97 628 Ft (92 979 Ft + 5% VAT)
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      • Discounted price 78 102 Ft (74 383 Ft + 5% VAT)

    97 628 Ft

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    Product details:

    • Publisher Springer Nature Switzerland
    • Date of Publication 14 October 2025

    • ISBN 9783032073358
    • Binding Paperback
    • No. of pages244 pages
    • Size 235x155 mm
    • Language English
    • Illustrations XIX, 244 p. 122 illus., 97 illus. in color.
    • 695

    Categories

    Long description:

    This volume showcases transformative AI research addressing real-world challenges across healthcare, education, law, and digital security. Highlights include explainable deep learning for berry classification, AI-driven communication skill assessment in virtual interviews, and Arabic legal judgment prediction systems. From Quran recitation tools for children and retinal disease diagnosis to DeepFake detection, post-quantum cryptography, and socio-economic impact modeling, these works reflect AI’s growing role in building a smarter, safer, and more inclusive future.

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    Table of Contents:

    Explainable Deep Learning in Berry Classification through Attention Mechanisms and Grad-CAM.- AI-Driven Multimodal Evaluation of Communication Skills in Virtual Interviews.- Monsif: Automated Arabic Legal Judgment Prediction System for Egyptian Criminal Law.- A Novel Dimensionality Reduction Technique Based on Levels Analysis of Partially Ordered Sets.

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