Search Results - Graph convolutional autoencoder*

Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder

Authors: Jiang, Junhao Zuo, Yi Li, Zhiyuan

Source: Reliability Engineering and System Safety. 267

Subject Terms: Graph convolutional network, Trajectory prediction, Cooperative intention constructor, Intelligent situational awareness systems, Conditional variational autoencoder, Multi-modal interaction extractor

Access URL: https://research.chalmers.se/publication/549377

Deep Graph Convolutional Autoencoder With Conditional Normalizing Flow for Power Distribution Systems Fault Classification and Location

Authors: Mohsen Saffari Mahdi Khodayar Mohammad E. Khodayar et al.

Source: IEEE Transactions on Artificial Intelligence. 6:2448-2463

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN

Authors: Ming Zeng Min Wang Fuqiang Xie et al.

Source: BMC Bioinformatics, Vol 26, Iss 1, Pp 1-29 (2025)

Subject Terms: Drug-target interaction, Dynamic weighting convolutional residual connection, Dual self-supervised joint training mechanism, Graph convolutional autoencoder, Generative adversarial network, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

File Description: electronic resource

Relation: https://doaj.org/toc/1471-2105

Access URL: https://doaj.org/article/c9b441325e6d409dbc981ebfca8af0c6

Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders

Authors: Mishra, Aditya Samin, Ahnaf Mozib Etemad, Ali et al.

Source: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1-5

Subject Terms: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2501.16626

Blockchain-Powered Secure and Scalable Threat Intelligence System With Graph Convolutional Autoencoder and Reinforcement Learning Feedback Loop

Authors: Mohamad Khayat Ezedin Barka Mohamed Adel Serhani et al.

Source: IEEE Access, Vol 13, Pp 24736-24748 (2025)

Subject Terms: blockchain, reinforcement learning, cybersecurity, hybrid optimization, Autoencoder, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Access URL: https://doaj.org/article/f8c8f1f864b04ea3b3ecfcbc0e75dde8

An Elliptic Kernel Unsupervised Autoencoder—Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing

Authors: Estefania Alfaro-Mejia Carlos J. Delgado Vidya Manian

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 14594-14614 (2025)

Subject Terms: Ocean engineering, graph convolutional network (GCN), hyperspectral images (HSIs), QC801-809, endmembers, ensemble, Geophysics. Cosmic physics, TC1501-1800, convolutional autoencoder, Abundance maps

Access URL: https://doaj.org/article/30bdd4d0412b4964adf8b67b0384ef64

Few-shot traffic classification based on autoencoder and deep graph convolutional networks

Authors: Shengwei Xu Jijie Han Yilong Liu et al.

Source: Sci Rep
Scientific Reports, Vol 15, Iss 1, Pp 1-16 (2025)

Subject Terms: Few-shot, Traffic classification, Science, Medicine, Autoencoder, Graph convolutional networks, Article

Access URL: https://pubmed.ncbi.nlm.nih.gov/40089628
https://doaj.org/article/464869ab495e488a847aeed6f0cbaa18

Epileptic Seizure Detection from EEG Signals using Autoencoder-based Graph Convolutional Neural Network

Authors: Ferdaus Anam Jibon Fazlul Hasan Siddiqui

Source: 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). :1-6

Design of an Improved Model for Blockchain Forensics Using Graph Convolutional Networks and Variational Autoencoders

Authors: Sweta A. Bokade V.K. Sharma Bhushan M. Manjre

Source: 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). :1-6

Integrating Autoencoder and Multi-View Graph Convolutional Networks for Spatial Domains Identification

Authors: Fanhao Meng Feng Li Yanru Gao et al.

Source: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). :558-564

Early Parkinson’s Disease Prediction Using rS-fMRI Functional Connectivity and Autoencoder Graph Convolutional Network

Authors: Lesbia Lopez Limas Vidya Manian

Source: IEEE Access, Vol 13, Pp 178862-178875 (2025)

Subject Terms: Deep learning, Euclidean distance, graph convolutional network, Parkinson’s disease, prodromal, radial basis function, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/11202904/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/d8cbf35713964300be20ec1354eae57f

CautionGCN: Cancer Subtype Classification by Developing Causal Multi-Head Autoencoder and Graph Convolutional Network

Authors: Na Li Fan Yang Qingke Zhang et al.

Source: 2024 IEEE International Conference on Medical Artificial Intelligence (MedAI). :626-634

GDGC-AE: A New Approach to Mechanical Anomaly Detection Based on Graph Convolutional Networks and Autoencoders

Authors: Mingzhe Zhang Zhengchang Su Pengyuan Hao et al.

Source: 2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). :1-7

Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition

Authors: Jiajun Guo Qingge Ji Guangwei Shan

Source: IET Image Processing, Vol 18, Iss 1, Pp 233-246 (2024)

Subject Terms: QA76.75-76.765, convolutional neural nets, signal denoising, graph theory, Photography, 0202 electrical engineering, electronic engineering, information engineering, Computer software, 02 engineering and technology, TR1-1050, computer vision

Access URL: https://doaj.org/article/00b9628a0619472cb07d01a0acb202f8

Entropy Stacked Autoencoder based Diffusion Model (ESADM) and Fuzzy Clustering with Semi Supervised Fuzzy Graph Convolutional Network (FC-SSFGCN) for Rail Surface Defect Detection

Authors: Samyuktha Sasi Sekaran Dr.M. Subaji

Source: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 15:17-35

Graph Convolutional Networks With Autoencoder-Based Compression And Multi-Layer Graph Learning

Authors: Giusti, L Battiloro, C Di Lorenzo, P et al.

Source: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :3593-3597

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, deep learning, graph convolutional networks, graph signal processing, graph learning, autoencoder

File Description: application/pdf

spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation

Authors: Tianjiao Zhang Hongfei Zhang Zhongqian Zhao et al.

Source: Genome Biol
Genome Biology, Vol 26, Iss 1, Pp 1-32 (2025)

Subject Terms: Multi-source feature fusion, Spatial multi-omics data, QH301-705.5, Discrete distribution spatial domain, Genetics, Methodology, Spatial domain identification, Biology (General), QH426-470

Access URL: https://doaj.org/article/b869d40174f1476ca8be9c4125e661e3

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis

Authors: Yueying Li Xiaotong Zhang Shihan Guan et al.

Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 33, Pp 1550-1561 (2025)

Subject Terms: Male, Adolescent, population diseases diagnosis, Autism Spectrum Disorder, functional connectivity, neurodevelopmental disorder diagnosis, Electroencephalography, RM1-950, Autoencoder, Brain network, Machine Learning, Neurodevelopmental Disorders, Attention Deficit Disorder with Hyperactivity, graph convolutional network, Medical technology, Humans, Female, Therapeutics. Pharmacology, Neural Networks, Computer, R855-855.5, Child, Algorithms

Access URL: https://pubmed.ncbi.nlm.nih.gov/40257873
https://doaj.org/article/e0d8758092894d71824fcd1792535af1

Batch Process Monitoring with Attention-Based Two-Dimensional Stacked Graph Convolutional Autoencoder

Authors: Xingke Gao Zheng Zhang Furong Gao et al.

Source: 2024 6th International Conference on Industrial Artificial Intelligence (IAI). :1-6

VAE-GCN: Deep Graph Convolutional Network via Variational Autoencoder for Multivariable Time Series Classification

Authors: Lu Li Gongqing Wu

Source: Lecture Notes in Computer Science ISBN: 9789819698806