Browsing by Author "Abdallah, Sherief"

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    Cyberbullying Detection Model for Arabic Text Using Deep Learning
    (World scientific connect, 2023) Albayari, Reem; Abdallah, Sherief; Shaalan, Khaled
    . In the new era of digital communications, cyberbullying is a significant concern for society. Cyberbullying can negatively impact stakeholders and can vary from psychological to pathological, such as self-isolation, depression and anxiety potentially leading to suicide. Hence, detecting any act of cyberbullying in an automated manner will be helpful for stakeholders to prevent any unfortunate results from the victim’s perspective. Data-driven approaches, such as machine learning (ML), par ticularly deep learning (DL), have shown promising results. However, the meta-analysis shows that ML approaches, particularly DL, have not been extensively studied for the Arabic text classification of cyberbullying. Therefore, in this study, we conduct a performance evaluation and comparison for various DL algorithms (LSTM, GRU, LSTM-ATT, CNN-BLSTM, CNN-LSTM and LSTM-TCN) on different datasets of Arabic cyberbullying to obtain more precise and dependable findings. As a result of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the baseline models CNN, BLSTM and GRU for identifying cyberbullying. The proposed hybrid model improves the accuracy of all the studied datasets and can be integrated into different social media sites to automatically detect cyberbullying from Arabic social datasets. It has the potential to significantly reduce cyberbullying. 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
    (2023) Albayari, Reem; Abdallah, Sherief; Shaalan, Khaled
    In the new era of digital communications, cyberbullying is a significant concern for society. Cyberbullying can negatively impact stakeholders and can vary from psychological to pathological, such as self-isolation, depression and anxiety potentially leading to suicide. Hence, detecting any act of cyberbullying in an automated manner will be helpful for stakeholders to prevent any unfortunate results from the victim’s perspective. Data-driven approaches, such as machine learning (ML), par ticularly deep learning (DL), have shown promising results. However, the meta-analysis shows that ML approaches, particularly DL, have not been extensively studied for the Arabic text classification of cyberbullying. Therefore, in this study, we conduct a performance evaluation and comparison for various DL algorithms (LSTM, GRU, LSTM-ATT, CNN-BLSTM, CNN-LSTM and LSTM-TCN) on different datasets of Arabic cyberbullying to obtain more precise and dependable findings. As a result of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the baseline models CNN, BLSTM and GRU for identifying cyberbullying. The proposed hybrid model improves the accuracy of all the studied datasets and can be integrated into different social media sites to automatically detect cyberbullying from Arabic social datasets. It has the potential to significantly reduce cyberbullying. 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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    The Future of the Internet of Vehicles (IoV)
    (SpringerLink, 2024) AlGhanem, Hani; Abdallah, Sherief
    This paper examines the Internet of Things (IoT) and its application in the Internet of Vehicles (IoV). IoV combines AI and IoT for real-time monitoring and operation of vehicles. We discuss how the integration of AI and IoT enhances vehicle functionalities through the deployment of sensors, infrared gadgets, cameras, and heat detectors, facilitating advancements in autonomous vehicles. We explore the evolutionary trajectory of the IoV concept and its diverse applications, provide a structured progression of ideas, and shed light on the challenges and potential solutions for IoT. The final section projects the future landscape of IoV implementation, demonstrating how it can revolutionize business operations and management.so this paper aims to bridge current knowledge gaps, stimulate further research, and spark innovative applications of IoV.