Výsledky vyhledávání - "Graph convolutional autoencoder"

Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder Autor He, Chaobo, Zheng, Yulong, Cheng, Junwei, Tang, Yong, Chen, Guohua, Liu, Hai

“…[Display omitted] The architecture of our proposed method SSGCAE for semi-supervised overlapping community detection in attributed graph. •An end-to-end method…”
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Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning Autor Chen, Hongruixuan, Yokoya, Naoto, Wu, Chen, Du, Bo

“…Unsupervised multimodal change detection is a practical and challenging topic that can play an important role in time-sensitive emergency applications. To…”
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Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps Autor Yan, Xiongfeng, Ai, Tinghua, Yang, Min, Tong, Xiaohua

“…The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for…”
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Federated learning enabled graph convolutional autoencoder and factorization machine for potential friendship prediction in social networks Autor Hu, He-xuan, Cao, Chengcheng, Hu, Qiang, Zhang, Ye

“…Friendships are the keystone of social networks. Predicting potential friendships of users in social networks has become a critical task in the real world…”
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Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network Autor Li, Nanjun, Chang, Faliang, Liu, Chunsheng

“…Automatic detection of human-related anomalous events in surveillance videos is challenging, owing to unclear definition of anomalies and insufficiency of…”
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Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series Autor Miele, Eric Stefan, Bonacina, Fabrizio, Corsini, Alessandro

“…Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonization process. However, wind turbines are subjected to…”
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Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations Autor Xuan, Ping, Gao, Ling, Sheng, Nan, Zhang, Tiangang, Nakaguchi, Toshiya

“…Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods…”
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Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions Autor Sun, Chang, Xuan, Ping, Zhang, Tiangang, Ye, Yilin

“…The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most…”
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Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes Autor Xuan, Ping, Cui, Hui, Zhang, Hongda, Zhang, Tiangang, Wang, Linlin, Nakaguchi, Toshiya, Duh, Henry B.L.

“…Extraction and integration of semantic connections, spatial relations and dependencies are critical in volumetric image segmentation. This is a challenging…”
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Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects Autor Ping Xuan, Kai Xu, Hui Cui, Toshiya Nakaguchi, Tiangang Zhang

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MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder Autor Wang, Ying, Gao, Ying-Lian, Wang, Juan, Li, Feng, Liu, Jin-Xing

“…Identifying drug-disease associations (DDAs) is critical to the development of drugs. Traditional methods to determine DDAs are expensive and inefficient…”
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Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder Autor Chen, Peng, Zheng, Haoran

“…Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods…”
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Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN Autor Zeng, Ming, Wang, Min, Xie, Fuqiang, Ji, Zhiwei

“…Background The exploration of drug-target interactions (DTIs) is a critical step in drug discovery and drug repurposing. Recently, network-based methods have…”
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Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects Autor Xuan, Ping, Xu, Kai, Cui, Hui, Nakaguchi, Toshiya, Zhang, Tiangang

“…Background: Inferring drug-related side effects is beneficial for reducing drug development cost and time. Current computational prediction methods have…”
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Health indicator construction based on normal states through FFT‐graph embedding Autor Kim, GwanPil, Jung, Jason J., Camacho, David

“…Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of…”
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Semantic meta-path enhanced global and local topology learning for lncRNA-disease association prediction Autor Xuan, Ping, Zhao, Yue, Cui, Hui, Zhan, Linyun, Jin, Qiangguo, Zhang, Tiangang, Nakaguchi, Toshiya

“…Since abnormal expression of long non-coding RNAs (lncRNAs) is associated with various human diseases, identifying disease-related lncRNAs helps reveal the…”
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建筑物形状特征分析表达与自适应化简方法 Autor 晏雄锋, 袁拓, 杨敏, 孔博, 刘鹏程

“…建筑物化简是地图制图领域关注的热点问题之一。集成不同算法构建形状特征自适应的化简模型是应对建筑物多样化形态的有效策略，但当前相关研究主要从局部结构模式或化简结果…”
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CVAM: CNA Profile Inference of the Spatial Transcriptome Based on the VGAE and HMM Autor Ma, Jian, Guo, Jingjing, Fan, Zhiwei, Zhao, Weiling, Zhou, Xiaobo

“…Tumors are often polyclonal due to copy number alteration (CNA) events. Through the CNA profile, we can understand the tumor heterogeneity and consistency. CNA…”
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GDGC-AE: A New Approach to Mechanical Anomaly Detection Based on Graph Convolutional Networks and Autoencoders Autor Zhang, Mingzhe, Su, Zhengchang, Hao, Pengyuan, Lin, Zesheng, Wang, Huaqing, Song, Liuyang

“…For the mechanical anomaly detection task, the neural network model trained only on normal data has limitations in multi-working condition anomaly detection…”
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Temporal Graph Convolutional Autoencoder based Fault Detection for Renewable Energy Applications Autor Arifeen, Murshedul, Petrovski, Andrei

“…Detecting faults in energy generation systems is a challenging task due to the complex nature of the system, measurement noise, and outliers. Recently,…”
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