Results 11 to 20 of about 51,131 (269)
Detecting fake news with capsule neural networks [PDF]
Applied Soft Computing, 2021 Fake news is dramatically increased in social media in recent years. This has prompted the need for effective fake news detection algorithms. Capsule neural networks have been successful in computer vision and are receiving attention for use in Natural Language Processing (NLP).
Mohammad Hadi Goldani +2 more
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Capsule Graph Neural Networks with EM Routing [PDF]
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021 To effectively classify graph instances, graph neural networks need to have the capability to capture the part-whole relationship existing in a graph. A capsule is a group of neurons representing complicated properties of entities, which has shown its advantages in traditional convolutional neural networks.
Yu Lei, Jing Zhang 0015
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Capsule neural network and its applications in drug discovery. [PDF]
iScienceDeep learning holds great promise in drug discovery, yet its application is hindered by high labeling costs and limited datasets. Developing algorithms that effectively learn from sparsely labeled data is crucial. Capsule networks (CapsNet), introduced in 2017, solve the spatial information loss in traditional neural networks and excel in handling ...
Wang Y +7 more
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ECG signal classification using capsule neural networks [PDF]
IET Networks, 2021 Abstract Cardiovascular diseases (CVD) are the dominant cause of deaths in the world, of which 90% are curable. The electrocardiogram (ECG) measures the electrical stimulus of the heart noninvasively. Convolutional neural networks (CNN) act as one of the powerful machine learning techniques to classify ECG arrhythmia classification ...
Neela Tejashwini, Namburu Swetha
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Remote Sensing, 2021 In order to overcome the disadvantages of convolution neural network (CNN) in the current hyperspectral image (HSI) classification/segmentation methods, such as the inability to recognize the rotation of spatial objects, the difficulty to capture the ...
Le Sun +4 more
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Robot Communication: Network Traffic Classification Based on Deep Neural Network
Frontiers in Neurorobotics, 2021 With the rapid popularization of robots, the risks brought by robot communication have also attracted the attention of researchers. Because current traffic classification methods based on plaintext cannot classify encrypted traffic, other methods based ...
Mengmeng Ge, Xiangzhan Yu, Likun Liu
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Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction
IEEE Transactions on Vehicular Technology, 2023 Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network.
Yan Qin, Yong Liang Guan 0001, Chau Yuen
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GE: Portuguese Journal of Gastroenterology, 2021 Introduction: Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its ...
Miguel Mascarenhas Saraiva +8 more
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Examining the Benefits of Capsule Neural Networks
CoRR, 2020 Capsule networks are a recently developed class of neural networks that potentially address some of the deficiencies with traditional convolutional neural networks. By replacing the standard scalar activations with vectors, and by connecting the artificial neurons in a new way, capsule networks aim to be the next great development for computer vision ...
Arjun Punjabi +2 more
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Scientific Reports, 2023 Fully convolutional neural network has shown advantages in the salient object detection by using the RGB or RGB-D images. However, there is an object-part dilemma since most fully convolutional neural network inevitably leads to an incomplete ...
Kun Xu, Jichang Guo
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