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.
The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are ins...
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The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED *** most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.
Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal **...
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Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal *** at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is ***,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample *** the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original *** algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.
The attention mechanism is the primary component of the transformer architecture;it has led to significant advancements in deep learning spanning many domains and covering multiple tasks. In computer vision, the atten...
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Deep learning models have demonstrated remarkable success in addressing a wide range of real-world problems. Specifically, "convolutional neural networks (CNNs)" have proven to be effective in various comput...
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In this paper, we propose a robust defense framework combining Gaussian Mixture Variational Autoencoders (GMVAE) with Reinforcement Learning (RL) to counter adversarial attacks in Maritime Autonomous Systems, specific...
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The capability of a system to fulfill its mission promptly in the presence of attacks,failures,or accidents is one of the qualitative definitions of *** this paper,we propose a model for survivability quantification,w...
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The capability of a system to fulfill its mission promptly in the presence of attacks,failures,or accidents is one of the qualitative definitions of *** this paper,we propose a model for survivability quantification,which is acceptable for networks carrying complex traffic *** network traffic is considered as general multi-rate,heterogeneous traffic,where the individual bandwidth demands may aggregate in complex,nonlinear *** probability is the chosen measure for survivability *** study an arbitrary topology and some other known topologies for the *** and dependent failure scenarios as well as deterministic and random traffic models are ***,we provide survivability evaluation results for different network *** results show that by using about 50%of the link capacity in networks with a relatively high number of links,the blocking probability remains near zero in the case of a limited number of failures.
Automatic licence plate detection and recognition (ALPDR) systems are widely used in various sectors such as traffic control, toll payment, parking systems, border control, and law enforcement. However, these systems ...
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Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-...
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Buildings that are constructed without the necessary permits and building inspections affect many areas, including safety, health, the environment, social order, and the economy. For this reason, it is essential to de...
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Buildings that are constructed without the necessary permits and building inspections affect many areas, including safety, health, the environment, social order, and the economy. For this reason, it is essential to determine the number of buildings and their boundaries. Determining the boundaries of a building based solely on its location in the world is a challenging task. In the context of this research, a new approach, BBD, is proposed to detect architectural objects from large-scale satellite imagery, which is an application of remote sensing, together with the geolocations of buildings and their boundaries on the Earth. In the proposed BBD method, open-source GeoServer and TileCache software process huge volumes of satellite imagery that cannot be analyzed with classical data processing techniques using deep learning models. In the proposed BBD method, YOLOv5, DETR, and YOLO-NAS models were used for building detection. SAM was used for the segmentation process in the BBD technique. In addition, the performance of the RefineNet model was investigated, as it performs direct building segmentation, unlike the aforementioned methods. The YOLOV5, DETR and YOLO-NAS models in BBD for building detection obtained an f1 score of 0.744, 0.615, and 0.869 respectively on the images generated by the classic TileCache. However, the RefineNet model, which uses the data generated by the classic TileCache, achieved an f1 score of 0.826 in the building segmentation process. Since the images produced by the classic TileCache are divided into too many parts, the buildings cannot be found as a whole in the images. To overcome these problems, a fine-tuning based optimization was performed. Thanks to the proposed fine-tuning, the modified YOLOv5, DETR, YOLO-NAS, and RefineNet models achieved F1 scores of 0.883, 0.772, 0.975 and 0.932, respectively. In the proposed BBD approach, the modified YOLO-NAS approach was the approach that detected the highest number of objects with an F1 score
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