Suchergebnisse - graph convolutional autoencoder (GCAE)

Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes

Autoren: Minkyu Kim Junho Song

Quelle: Earthquake Engineering & Structural Dynamics. 53:815-837

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0201 civil engineering

Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps.

Autoren: Yan, Xiongfeng Ai, Tinghua Yang, Min et al.

Quelle: International Journal of Geographical Information Science. Mar2021, Vol. 35 Issue 3, p490-512. 23p.

Schlagwörter: *Maps, Cognition, Learning strategies, Vector data

Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes.

Autoren: Kim, Minkyu Song, Junho

Quelle: Earthquake Engineering & Structural Dynamics; Feb2024, Vol. 53 Issue 2, p815-837, 23p

Schlagwörter: GROUND motion, SEISMIC response, SPACE frame structures, STRUCTURAL frames, SYSTEM failures, STEEL framing

Blockchain-based decentralized smart healthcare using improved wild horse optimizer with Graph Convolutional Autoencoder in IoT environment

Autoren: José Escorcia-Gutierrez Melitsa Torres-Torres Roosvel Soto-Diaz et al.

Quelle: International Journal of Cognitive Computing in Engineering, Vol 7, Iss , Pp 199-212 (2026)

Schlagwörter: Internet of Things, Blockchain, Healthcare, Deep learning, Decentralized system, Security, Electronic computers. Computer science, QA75.5-76.95, Science

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666307425000464; https://doaj.org/toc/2666-3074

Zugangs-URL: https://doaj.org/article/bbd4f6186227406a816b9792e75cb190

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.

Autoren: Lei Li Yu-Tian Wang Cun-Mei Ji et al.

Quelle: PLoS Computational Biology, Vol 17, Iss 12, p e1009655 (2021)

Schlagwörter: Biology (General), QH301-705.5

Dateibeschreibung: electronic resource

Relation: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009655&type=printable; https://doaj.org/toc/1553-734X; https://doaj.org/toc/1553-7358

Zugangs-URL: https://doaj.org/article/d95a5fa5f5134ea2b0b8874223fe6312

Graph Anomaly Detection With Disentangled Prototypical Autoencoder for Phishing Scam Detection in Cryptocurrency Transactions

Autoren: Junha Kang Seok-Jun Buu

Quelle: IEEE Access, Vol 12, Pp 91075-91088 (2024)

Schlagwörter: Graph autoencoder, anomaly detection, triplet network, graph neural network, representation learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/10571963/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/c0be14a0cb0040f68a53bafb7aad9dca

Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder.

Autoren: He, Chaobo Zheng, Yulong Cheng, Junwei et al.

Quelle: Information Sciences. Aug2022, Vol. 608, p1464-1479. 16p.

Schlagwörter: *ARTIFICIAL neural networks

A novel intelligent multicross domain fault diagnosis of servo motor-bearing system based on Domain Generalized Graph Convolution Autoencoder.

Autoren: Zhao, Xiaoli Hu, Yuanhao Liu, Jiahui et al.

Quelle: Structural Health Monitoring; May2025, Vol. 24 Issue 3, p1485-1499, 15p

Schlagwörter: AUTOENCODERS, FAULT diagnosis, DEEP learning, REPRESENTATIONS of graphs, ROLLER bearings

Gradient-guided Convolutional AutoEncoder for predicting CO2 storage in saline aquifers with multiple geological scenarios and well placements.

Autoren: Hu, Zongwen Wang, Jian Yan, Xia et al.

Quelle: Physics of Fluids; Nov2024, Vol. 36 Issue 11, p1-20, 20p

Schlagwörter: CARBON sequestration, NONLINEAR differential equations, PARTIAL differential equations, AUTOENCODERS, DIFFERENTIAL operators

GCN-Transformer Autoencoder with Knowledge Distillation for Unsupervised Video Anomaly Detection.

Autoren: Yan, Mingchao Xiong, Yonghua She, Jinhua

Quelle: Journal of Advanced Computational Intelligence & Intelligent Informatics; May2025, Vol. 29 Issue 3, p659-667, 9p

Schlagwörter: AUTOENCODERS, TRANSFORMER models, SCARCITY, VIDEOS, CLASSIFICATION

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.

Autoren: Li, Lei Wang, Yu-Tian Ji, Cun-Mei et al.

Quelle: PLoS Computational Biology; 12/10/2021, Vol. 17 Issue 12, p1-19, 19p, 3 Diagrams, 2 Charts, 6 Graphs

Schlagwörter: MICRORNA, NON-coding RNA, BREAST tumors, PREVENTIVE medicine, LUNGS, BREAST, THERAPEUTICS

Bearing Fault Diagnosis via Graph Autoencoder Networks with Multi-kernel Subdomain Adversarial Domain Adaptation.

Autoren: Guo, Junfeng Hu, Zeming Wang, Zhiming et al.

Quelle: Journal of Failure Analysis & Prevention; Dec2024, Vol. 24 Issue 6, p2831-2846, 16p

Schlagwörter: FAULT diagnosis, ARTIFICIAL intelligence, AUTOENCODERS, INFORMATION architecture, IMAGE processing

Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network.

Autoren: Li, Nanjun Chang, Faliang Liu, Chunsheng

Quelle: Neurocomputing. Jun2022, Vol. 490, p482-494. 13p.

Schlagwörter: *VIDEO surveillance, *HUMAN skeleton, *SKELETON

METHODS, APPARATUS AND SYSTEMS FOR GRAPH-CONDITIONED AUTOENCODER (GCAE) USING TOPOLOGY-FRIENDLY REPRESENTATIONS

Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges.

Autoren: REN, JING XIA, FENG HOSHYAR, AZADEH NOORI et al.

Quelle: ACM Transactions on Intelligent Systems & Technology; Apr2023, Vol. 14 Issue 2, p1-29, 29p

Schlagwörter: DEEP learning, ALGORITHMS

Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

Autoren: Chen, Hongruixuan Yokoya, Naoto Wu, Chen et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing, text

URL: http://arxiv.org/abs/2210.00941

Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposes.

Autoren: Knura, Martin

Quelle: Cartography & Geographic Information Science; Jan2024, Vol. 51 Issue 1, p146-167, 22p

Schlagwörter: VECTOR data, RECURRENT neural networks, CONVOLUTIONAL neural networks, GENERALIZATION, DEEP learning

基于时间-通道拓扑解耦图卷积的异常行为检测. (Chinese)

Alternate Title: Abnormal Behavior Detection Based on Time-Channel Topology Decoupling Graph Convolution. (English)

Autoren: 张家波 唐上松 何阿娟

Quelle: Journal of Signal Processing; Dec2024, Vol. 40 Issue 12, p2193-2205, 13p

A domain knowledge infused gated network using integrated sentiment prediction framework for aspect-based sentiment analysis.

Autoren: Dubey, Gaurav Kaur, Kamaljit Chadha, Anupama et al.

Quelle: Evolving Systems; Feb2025, Vol. 16 Issue 1, p1-26, 26p

A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder.

Autoren: Ni, Yanchun Jin, Qiyuan Hu, Rui

Quelle: Sensors (14248220); Nov2025, Vol. 25 Issue 21, p6724, 27p

Schlagwörter: AUTOENCODERS, STRUCTURAL health monitoring, ONLINE monitoring systems, VIBRATION (Mechanics), SPATIOTEMPORAL processes, GRAPH neural networks, MACHINE learning, DEEP learning