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Chenglin Yang, Dongliang Xu and Xiao Ma
Due to the increasing severity of network security issues, training corresponding detection models requires large datasets. In this work, we propose a novel method based on generative adversarial networks to synthesize network data traffic. We introduced...
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Liang Liu, Tianbin Li and Chunchi Ma
Three-dimensional (3D) models provide the most intuitive representation of geological conditions. Traditional modeling methods heavily depend on technicians? expertise and lack ease of updating. In this study, we introduce a deep learning-based method fo...
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Kenneth David Strang
A critical worldwide problem is that ransomware cyberattacks can be costly to organizations. Moreover, accidental employee cybercrime risk can be challenging to prevent, even by leveraging advanced computer science techniques. This exploratory project us...
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Shancheng Tang, Ying Zhang, Zicheng Jin, Jianhui Lu, Heng Li and Jiqing Yang
The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same time, t...
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Kre?imir Nincevic, Thierry Guillet, Omar Al Mansouri and Roman Wan-Wendner
This contribution summarizes the largest available literature data collection on tensile and shear loaded anchor tests, obtained in two independent studies and performed by two different research groups. It was the objective of the two studies to investi...
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Woonghee Lee, Mingeon Ju, Yura Sim, Young Kul Jung, Tae Hyung Kim and Younghoon Kim
Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulner...
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Shui Jiang, Yanning Ge, Xu Yang, Wencheng Yang and Hui Cui
Reinforcement learning (RL) is pivotal in empowering Unmanned Aerial Vehicles (UAVs) to navigate and make decisions efficiently and intelligently within complex and dynamic surroundings. Despite its significance, RL is hampered by inherent limitations su...
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Lina Zhou, Leijinyu Zhou, Hongbo Wu, Tingting Jing, Tianhao Li, Jinsheng Li, Lijuan Kong and Limei Chen
In order to monitor cadmium contamination in lettuce quickly, non-invasively, and accurately, and to understand the growth status of lettuce under cadmium pollution, lettuce was used as the test material to detect and analyze the visible?near-infrared re...
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Javad Hassannataj Joloudari, Abdolreza Marefat, Mohammad Ali Nematollahi, Solomon Sunday Oyelere and Sadiq Hussain
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, ma...
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Xianchang Wang, Siyu Dong and Rui Zhang
In the prediction of time series, Empirical Mode Decomposition (EMD) generates subsequences and separates short-term tendencies from long-term ones. However, a single prediction model, including attention mechanism, has varying effects on each subsequenc...
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