Security Information and Event Management (SIEM) systems have become essential assets in the realm of cybersecurity. They fulfill a central role in the prevention, detection, and response to cyber threats. Over time, ...
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Recognizing the nuanced spectrum of dialectness in Arabic text poses a significant challenge for natural language processing (NLP) tasks. Traditional dialect identification (DI) methods treat the task as binary, overl...
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Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new ap...
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Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in *** model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection *** model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing *** results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.
Formal component-based modeling has been shown to be invaluable for verifying the compatibility of specified components, discovering flaws early in design stages, and enabling the reuse of components, across multiple ...
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Stance detection, an evolving task in natural language processing, involves understanding a writer’s perspective on certain topics by analyzing his written text and interactions online, especially on social media pla...
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Breast cancer is one of the most diagnosed cancers in women worldwide. Thermography serves as a potential screening modality for early breast abnormality detection. A breast thermogram is a pseudo-colored RGB image th...
Arabic banking intent detection represents a challenging problem across multiple dialects. It imposes generalization difficulties due to the scarcity of Arabic language and its dialects resources compared to English. ...
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Training neural networks by using conventional supervised backpropagation algorithms is a challenging task. This is due to significant limitations, such as the risk for local minimum stagnation in the loss landscape o...
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Training neural networks by using conventional supervised backpropagation algorithms is a challenging task. This is due to significant limitations, such as the risk for local minimum stagnation in the loss landscape of neural networks. That may prevent the network from finding the global minimum of its loss function and therefore slow its convergence speed. Another challenge is the vanishing and exploding gradients that may happen when the gradients of the loss function of the model become either infinitesimally small or unmanageably large during the training. That also hinders the convergence of the neural models. On the other hand, the traditional gradient-based algorithms necessitate the pre-selection of learning parameters such as the learning rates, activation function, batch size, stopping criteria, and others. Recent research has shown the potential of evolutionary optimization algorithms to address most of those challenges in optimizing the overall performance of neural networks. In this research, we introduce and validate an evolutionary optimization framework to train multilayer perceptrons, which are simple feedforward neural networks. The suggested framework uses the recently proposed evolutionary cooperative optimization algorithm, namely, the dynamic group-based cooperative optimizer. The ability of this optimizer to solve a wide range of real optimization problems motivated our research group to benchmark its performance in training multilayer perceptron models. We validated the proposed optimization framework on a set of five datasets for engineering applications, and we compared its performance against the conventional backpropagation algorithm and other commonly used evolutionary optimization algorithms. The simulations showed the competitive performance of the proposed framework for most examined datasets in terms of overall performance and convergence. For three benchmarking datasets, the proposed framework provided increases of 2.7%, 4.83%, and
The rise of memes as a tool for spreading propaganda presents a significant challenge in the current digital environment. In this paper, we outline our work for the ArAIEval Shared Task2 in ArabicNLP 2024. This study ...
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This study investigates the reliability of code generation by Large Language Models (LLMs), focusing on identifying and analyzing defects in the generated code. Despite the advanced capabilities of LLMs in automating ...
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