We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by ...
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With the continuous development of computer virtual reality technology, in recent years, Chinese foreign students can learn more knowledge and multi-dimensional and multi-stage online open courses in the process of cl...
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The Internet of Things (loT) facilitates the interconnection of a vast array of devices, services, and individuals for data exchange in multi-domain loT ecosystems, the social, physical, and cyber domains. That emerge...
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In recent years,aquaculture has developed rapidly,especially in coastal and open ocean *** practice,water quality prediction is of critical ***,traditional water quality prediction models face limitations in handling ...
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In recent years,aquaculture has developed rapidly,especially in coastal and open ocean *** practice,water quality prediction is of critical ***,traditional water quality prediction models face limitations in handling complex spatiotemporal *** address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality *** aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the *** results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,*** indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.
Deep learning based multi-view stereo algorithms have shown great reconstruction performance. We propose a method called DCMVSNet for depth inference from multi-view images. We choose to build a cost volume pyramid in...
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The Change Detection (CD) in high-resolution images is significant for understanding the surface of land. There have developed numerous Deep Learning (DL) approaches to CD, many of these algorithms failed to predict e...
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In response to the inherent characteristics of electroencephalogram (EEG) signals and the requirements of EEG acquisition systems, this paper investigates a portable multichannel EEG signal acquisition device. The acq...
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The realm of intelligent systems empowers the development of applications that play a vital role in our real-world scenarios. Fuzzy logic, a valuable asset in contemporary times, finds application across diverse indus...
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With the rapid development of artificial intelligence, multimodal large models have shown great potential and application value in the fusion understanding of image and text. Aiming at this problem, this paper discuss...
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The increasing computational demand for real-time mobile applications has led to the development of mobile edge computing (MEC), with support from unmanned aerial vehicles (UAVs), as a promising paradigm for construct...
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