Results 11 to 20 of about 32,124 (206)
Indoor Home Scene Recognition Using Capsule Neural Networks [PDF]
Procedia Computer Science, 2020 This paper presents the use of a class of Deep Neural Networks for recognizing indoor home scenes so as to aid Intelligent Assistive Systems (IAS) in performing indoor services to assist elderly or infirm people. Identifying exact indoor location is important so that objects associated with particular tasks can be located speedily and efficiently ...
Basu, Amlan +3 more
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Attention enhanced capsule network for text classification by encoding syntactic dependency trees with graph convolutional neural network [PDF]
PeerJ Computer Science, 2022 Text classification is a fundamental task in many applications such as topic labeling, sentiment analysis, and spam detection. The text syntactic relationship and word sequence are important and useful for text classification.
Xudong Jia, Li Wang
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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, Chau Yuen
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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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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.
Lei, Yu, Zhang, Jing
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Structural-parametric Synthesis of Capsule Neural Networks
Electronics and Control Systems, 2023 This work is dedicated to the structural-parametric synthesis of capsule neural networks. A methodology for structural-parametric synthesis of capsule neural networks has been developed, which includes the following algorithms: determining the most influential parameters of the capsule neural network, a hybrid machine learning algorithm.
Victor Sineglazov, Denys Kudriev
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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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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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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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An Adaptive Capsule Network for Hyperspectral Remote Sensing Classification
Remote Sensing, 2021 The capsule network (Caps) is a novel type of neural network that has great potential for the classification of hyperspectral remote sensing. However, the Caps suffers from the issue of gradient vanishing.
Xiaohui Ding +6 more
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