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Hafiz Suliman Munawar, Fahim Ullah, Amirhossein Heravi, Muhammad Jamaluddin Thaheem and Ahsen Maqsoom
Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of da...
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Christos Bormpotsis, Mohamed Sedky and Asma Patel
In the realm of foreign exchange (Forex) market predictions, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been commonly employed. However, these models often exhibit instability due to vulnerability to data perturbations...
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Fekhr Eddine Keddous and Amir Nakib
Convolutional neural networks (CNNs) have powerful representation learning capabilities by automatically learning and extracting features directly from inputs. In classification applications, CNN models are typically composed of: convolutional layers, po...
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Markus S. Mueller and Boris Jutzi
The navigation of Unmanned Aerial Vehicles (UAVs) nowadays is mostly based on Global Navigation Satellite Systems (GNSSs). Drawbacks of satellite-based navigation are failures caused by occlusions or multi-path interferences. Therefore, alternative metho...
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Markus S. Mueller and Boris Jutzi
The navigation of Unmanned Aerial Vehicles (UAVs) nowadays is mostly based on Global Navigation Satellite Systems (GNSSs). Drawbacks of satellite-based navigation are failures caused by occlusions or multi-path interferences. Therefore, alternative metho...
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Sergei Strijhak, Daniil Ryazanov, Konstantin Koshelev and Aleksandr Ivanov
In this article the procedure and method for the ice accretion prediction for different airfoils using artificial neural networks (ANNs) are discussed. A dataset for the neural network is based on the numerical experiment results?obtained through iceFoam...
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Sergey Golubev, Evgenia Novikova and Elena Fedorchenko
Recently, approaches based on the transformation of tabular data into images have gained a lot of scientific attention. This is explained by the fact that convolutional neural networks (CNNs) have shown good results in computer vision and other image-bas...
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Kerang Cao, Jingyu Gao, Kwang-nam Choi and Lini Duan
To classify the image material on the internet, the deep learning methodology, especially deep neural network, is the most optimal and costliest method of all computer vision methods. Convolutional neural networks (CNNs) learn a comprehensive feature rep...
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Tianhao Gao, Meng Zhang, Yifan Zhu, Youjian Zhang, Xiangsheng Pang, Jing Ying and Wenming Liu
Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convoluti...
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Yiheng Zhou, Kainan Ma, Qian Sun, Zhaoyuxuan Wang and Ming Liu
Over the past several decades, deep neural networks have been extensively applied to medical image segmentation tasks, achieving significant success. However, the effectiveness of traditional deep segmentation networks is substantially limited by the sma...
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Jiazhu Dai and Siwei Xiong
Capsule networks are a type of neural network that use the spatial relationship between features to classify images. By capturing the poses and relative positions between features, this network is better able to recognize affine transformation and surpas...
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Zizhao Guo and Sancong Ying
Incorporating multi-modality data is an effective way to improve action recognition performance. Based on this idea, we investigate a new data modality in which Whole-Body Keypoint and Skeleton (WKS) labels are used to capture refined body information. U...
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Jiakai Tian, Gang Li, Mingle Zhou, Min Li and Delong Han
Relation extraction is an important task in natural language processing. It plays an integral role in intelligent question-and-answer systems, semantic search, and knowledge graph work. For this task, previous studies have demonstrated the effectiveness ...
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César G. Pachón, Dora M. Ballesteros and Diego Renza
Recently, some state-of-the-art works have used deep learning-based architectures, specifically convolutional neural networks (CNNs), for banknote recognition and counterfeit detection with promising results. However, it is not clear which design strateg...
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Hailun Xia and Tianyang Zhang
Estimating the positions of human joints from monocular single RGB images has been a challenging task in recent years. Despite great progress in human pose estimation with convolutional neural networks (CNNs), a central problem still exists: the relation...
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Konstantina Fotiadou, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Dimitrios Skias, Sofia Tsekeridou and Theodore Zahariadis
Network intrusion detection is a key pillar towards the sustainability and normal operation of information systems. Complex threat patterns and malicious actors are able to cause severe damages to cyber-systems. In this work, we propose novel Deep Learni...
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Wenting Liu, Li Zhou and Jie Chen
Face recognition algorithms based on deep learning methods have become increasingly popular. Most of these are based on highly precise but complex convolutional neural networks (CNNs), which require significant computing resources and storage, and are di...
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Roberto G. Pacheco, Kaylani Bochie, Mateus S. Gilbert, Rodrigo S. Couto and Miguel Elias M. Campista
In computer vision applications, mobile devices can transfer the inference of Convolutional Neural Networks (CNNs) to the cloud due to their computational restrictions. Nevertheless, besides introducing more network load concerning the cloud, this approa...
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Sen Zhang, Shaobo Li, Xiang Li and Yong Yao
In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting...
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Fernando Moya Rueda, René Grzeszick, Gernot A. Fink, Sascha Feldhorst and Michael Ten Hompel
Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive tec...
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