يعرض 1 - 20 نتائج من 65 نتيجة بحث عن '(((( text detection algorithm ) OR ( element data algorithm ))) OR ( level coding algorithm ))', وقت الاستعلام: 0.15s تنقيح النتائج
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    Cyberbullying Detection in Arabic Text using Deep Learning حسب ALBAYARI, REEM RAMADAN SA’ID

    منشور في 2023
    "…Therefore, this study aims to evaluate several versions of Recurrent Neural Networks (RNNs) and Feedforward Neural Networks (FNNs) for detecting cyberbullying in the Arabic language. Although these algorithms are widely used in text classification and outperform the performance of classical classifiers, many have been extensively investigated in other domains such as sentiment analysis and dialect identification, as well as cyberbullying detection in English text. …"
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  2. 2

    Cyberbullying Detection Model for Arabic Text Using Deep Learning حسب Albayari, Reem

    منشور في 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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    Cyberbullying Detection Model for Arabic Text Using Deep Learning حسب Albayari, Reem

    منشور في 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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    Bird’s Eye View feature selection for high-dimensional data حسب Samir Brahim Belhaouari (16855434)

    منشور في 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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    Large language models for code completion: A systematic literature review حسب Rasha Ahmad Husein (19744756)

    منشور في 2024
    "…Different techniques can achieve code completion, and recent research has focused on Deep Learning methods, particularly Large Language Models (LLMs) utilizing Transformer algorithms. …"
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    Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis حسب Hassan, Ali

    منشور في 2023
    "…Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. …"
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    article
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    Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis حسب Hassan Ali (3348749)

    منشور في 2023
    "…<p>Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. …"
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    Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach حسب Sarif, Bambang

    منشور في 2006
    "…Multiple-Valued Logic (MVL) has been used in the design of a number of logic systems, including memory, multi-level data communication coding, and a number of special purpose digital processors. …"
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    article
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    Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning حسب ALFALASI, FARIS Jr

    منشور في 2023
    "…Due to the difficulty of manually detecting and categorizing vast volumes of electronic text data, conventional methods for recognizing and combating cyberbullying have not proven successful. …"
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    XBeGene: Scalable XML Documents Generator by Example Based on Real Data حسب Harazaki, Manami

    منشور في 2012
    "…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …"
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    conferenceObject
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