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Showing 1 - 20 results of 93 for search '(( element means algorithm ) OR ((( text processing algorithm ) OR ( element data algorithm ))))', query time: 0.16s Refine Results
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    A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text by M. Ghoniem , Rania

    Published 2019
    “…Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. …”
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    Cyberbullying Detection Model for Arabic Text Using Deep Learning by Albayari, Reem

    Published 2023
    “…The application of DL to cyberbullying detection problems within Arabic text classification can be considered a novel approach due to the complexity of the problem and the tedious process involved, besides the scarcity of relevant research studies.…”
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    A systematic review of text classification research based on deep learning models in Arabic language by Wahdan, Ahlam

    Published 2020
    “…Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. …”
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    Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

    Published 2023
    “…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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    Segmenting Handwritten Arabic Text by Haraty, Ramzi A.

    Published 2002
    “…In this work a technique is presented that segments difficult handwritten Arabic text. A conventional algorithm is used for the initial segmentation of the text into connected blocks of characters. …”
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    article
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    An efficient approach for textual data classification using deep learning by Abdullah Alqahtani (7128143)

    Published 2022
    “…<p dir="ltr">Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. …”
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    Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars by Abathar Al-Hamrani (16494884)

    Published 2023
    “…Finally, a design equation that can predict the shear capacity of one-way BFRC-BFRP slabs was proposed based on genetic algorithm. The proposed model showed the best prediction accuracy compared to the available design codes and guidelines with a mean of predicted to experimental shear capacities (V<sub>pred</sub>/V<sub>exp</sub>) ratio of 0.97 and a coefficient of variation of 17.91%.…”
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    Cyberbullying Detection in Arabic Text using Deep Learning by ALBAYARI, REEM RAMADAN SA’ID

    Published 2023
    “…The application of DL to cyberbullying detection problems within Arabic text classification can be considered a novel approach due to the complexity of the problem and the tedious process involved, besides the scarcity of relevant research studies. …”
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    A Neuro-Heuristic Approach for Segmenting Handwritten Arabic Text by Haraty, Ramzi

    Published 2001
    “…A conventional algorithm is used for the initial segmentation of the text into connected blocks of characters. …”
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    conferenceObject
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    Supervised term-category feature weighting for improved text classification by Attieh, Joseph

    Published 2022
    “…Text classification is a central task in Natural Language Processing (NLP) that aims at categorizing text documents into predefined classes or categories. …”
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    article
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    Cyberbullying Detection Model for Arabic Text Using Deep Learning by Albayari, Reem

    Published 2023
    “…The application of DL to cyberbullying detection problems within Arabic text classification can be considered a novel approach due to the complexity of the problem and the tedious process involved, besides the scarcity of relevant research studies.…”
    Get full text
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