Search Results - Temporal Graph Conventional Autoencoder

Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting.

Authors: Immordino, Gabriele Vaiuso, Andrea Da Ronch, Andrea et al.

Source: Aerospace Science & Technology. Oct2025, Vol. 165, pN.PAG-N.PAG. 1p.

Subject Terms: *AUTOENCODERS, *TRANSONIC aerodynamics, *NONLINEAR mechanics, *GRAPH neural networks, *SPATIOTEMPORAL processes, *AERODYNAMIC load, *FORECASTING

A Comprehensive Method for Anomaly Detection in Complex Dynamic IoT Systems

Authors: Andrii Liashenko Larysa Globa

Source: Proceedings of the International Conference on Applied Innovations in IT, Vol 13, Iss 1, Pp 101-107 (2025)

Subject Terms: anomaly detection, temporal graphs, temporal graph neural networks, autoencoder, graph neural networks, reconstruction error, dynamic systems, transportation network, Electronic computers. Computer science, QA75.5-76.95, Technology

File Description: electronic resource

Relation: https://icaiit.org/paper.php?paper=13th_ICAIIT_1/2_4; https://doaj.org/toc/2199-8876

Access URL: https://doaj.org/article/fd3af3924db04d33b178b0d3e4f8eedf

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays.

Authors: Arifeen, Murshedul Petrovski, Andrei Hasan, Md Junayed et al.

Source: Machines; Dec2024, Vol. 12 Issue 12, p894, 15p

Subject Terms: SOLAR cells, RENEWABLE energy sources, PHOTOVOLTAIC power systems, SOLAR energy, AUTOENCODERS

Bidirectional Spatial–Temporal Graph Convolutional Model: Traffic Flow Forecasting With Enhanced Extended Capabilities.

Authors: Tan, Xiaogang Qian, Guoping Pei, Boyu et al.

Source: Journal of Advanced Transportation; 8/23/2025, Vol. 2025, p1-18, 18p

Subject Terms: INTELLIGENT transportation systems, SPATIOTEMPORAL processes, BOX-Jenkins forecasting, TRAFFIC flow measurement, TRAFFIC estimation, GEOSPATIAL data

Apstgcn: adaptive parallel spatio-temporal graph convolution network for traffic forecasting.

Authors: Yu, Xiaohan Hu, Zebin Baek, Seung Jun et al.

Source: Cluster Computing; Oct2025, Vol. 28 Issue 7, p1-19, 19p

Subject Terms: INTELLIGENT transportation systems, FORECASTING, COMPUTER performance, SPATIOTEMPORAL processes, TRAFFIC estimation, GRAPH neural networks

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data.

Authors: Choi, Seung-Hwan An, Dawn Lee, Inho et al.

Source: Mathematics (2227-7390). Dec2024, Vol. 12 Issue 23, p3750. 16p.

Subject Terms: *INDUSTRIAL robots, *ANOMALY detection (Computer security), *AUTOENCODERS, *SYSTEM failures, *FEATURE extraction, *DEEP learning

Variable Structure Learning-Based Spatio-Temporal Graph Convolutional Networks for Chemical Process Quality Prediction with SHAP-Enhanced Interpretability.

Authors: Tang, Siyuan Zhu, Zheren Zhou, Yuanqiang et al.

Source: Processes; Nov2025, Vol. 13 Issue 11, p3751, 22p

Subject Terms: MACHINE learning, GRAPH neural networks, PREDICTIVE validity

Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions.

Authors: Chen, Shiyang Liu, Yang Zhang, Qun et al.

Source: Advanced Intelligent Systems (2640-4567); Aug2025, Vol. 7 Issue 8, p1-14, 14p

Subject Terms: BLOCKCHAINS, GRAPH neural networks, DIGITAL forensics, TEMPORAL databases, CRYPTOCURRENCIES, MACHINE learning, ANOMALY detection (Computer security)

GAT-BiGRU-TPA City Pair 4D Trajectory Prediction Model Based on Spatio-Temporal Graph Neural Network.

Authors: Cao, Haibo Li, Yinfeng Mi, Xueyu et al.

Source: Aerospace (MDPI Publishing); Nov2025, Vol. 12 Issue 11, p999, 26p

Subject Terms: AIR traffic control, TRAJECTORY optimization, GRAPH neural networks, AIRLINE safety, AIR traffic, FLIGHT planning (Aeronautics)

Transformer based models with hierarchical graph representations for enhanced climate forecasting.

Authors: Ramu, T. Bhargava Kocherla, Raviteja Sirisha, G. N. V. G. et al.

Source: Scientific Reports; 7/2/2025, Vol. 15 Issue 1, p1-22, 22p

Subject Terms: METAHEURISTIC algorithms, TRANSFORMER models, ARTIFICIAL intelligence, REPRESENTATIONS of graphs, FEATURE selection, DEEP learning

Enhanced multi objective graph learning approach for optimizing traffic speed prediction on spatial and temporal features.

Authors: Karthika, B. Uma Maheswari, N.

Source: Scientific Reports; 9/30/2025, Vol. 15 Issue 1, p1-18, 18p

Subject Terms: INTELLIGENT transportation systems, GRAPH neural networks, MULTI-objective optimization, TRAFFIC engineering, URBAN transportation, NEURAL computers, TRAFFIC estimation

Integrated Spatio-Temporal Air Quality and Healthcare Burden Forecasting: A Novel Information System for Public Health Management in Transitioning Urban Environments.

Authors: Bodele, Harna M. Asutkar, G. M. Asutkar, Kiran G.

Source: Library of Progress-Library Science, Information Technology & Computer; Jul-Dec2024, Vol. 44 Issue 2, p777-791, 15p

Subject Terms: GRAPH neural networks, PUBLIC health officers, AIR quality, TRANSFORMER models, DISEASE incidence

Design of an integrated model with temporal graph attention and transformer-augmented RNNs for enhanced anomaly detection.

Authors: Veesam, Sai Babu Satish, Aravapalli Rama Tupakula, Sreenivasulu et al.

Source: Scientific Reports; 1/21/2025, Vol. 15 Issue 1, p1-24, 24p

Subject Terms: ANOMALY detection (Computer security), DEEP learning, SPATIOTEMPORAL processes, MACHINE learning, TELEVISION in security systems

Anchor-aware Graph Neural Network with Temporal Attention for Accurate Traffic Flow Forecasting.

Authors: Parashuram, Narayanagari Vijayalakshmi, Kumba

Source: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 8, p283-297, 15p

Subject Terms: INTELLIGENT transportation systems, GRAPH neural networks, DATA transformations (Statistics), TRAFFIC congestion, SPATIAL analysis (Statistics), TRAFFIC estimation

AMGET: An Adaptive Multimodal Graph-Enhanced Transformer for Cybersecure Detection of Evolving Fake News on Social Media.

Authors: Mohammed, Bahaa Kareem Nuiaa Alogaili, Riyadh Rahef Hasan, Baraa Mohammed et al.

Source: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 10, p166-183, 18p

Subject Terms: SOCIAL media, MISINFORMATION, MULTIMODAL user interfaces, GRAPH neural networks, INTERNET security, DYNAMICAL systems, MACHINE learning

Rolling Bearing Fault Diagnosis via Temporal-Graph Convolutional Fusion.

Authors: Li, Fan Li, Yunfeng Wang, Dongfeng

Source: Sensors (14248220); Jul2025, Vol. 25 Issue 13, p3894, 19p

Subject Terms: FAULT diagnosis, FEATURE extraction, ROLLER bearings, GRAPH neural networks, CONVOLUTIONAL neural networks

A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks.

Authors: Francies, Mariam Labib Khalil, Abeer Twakol Amer, Hanan M. et al.

Source: Complex & Intelligent Systems; Oct2025, Vol. 11 Issue 10, p1-28, 28p

Designing an Improved Cyberattack Prediction Model Using Context-Aware Behavioral Modeling Analysis.

Authors: Patil, Geeta Sapkal, Ashwini Ingale, Vaishali Sachin

Source: Engineering, Technology & Applied Science Research; Oct2025, Vol. 15 Issue 5, p27464-27469, 6p

Subject Terms: INTRUSION detection systems (Computer security), ANOMALY detection (Computer security), COMPUTER network traffic, INTERNET security, DEEP learning, CYBERTERRORISM, HUMAN behavior models

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays

Authors: Murshedul Arifeen Andrei Petrovski Md Junayed Hasan et al.

Source: Machines, Vol 12, Iss 12, p 894 (2024)

Subject Terms: solar array, photovoltaic array, fault detection, fault diagnosis, graph convolutional network, variational autoencoder, Mechanical engineering and machinery, TJ1-1570

File Description: electronic resource

Relation: https://www.mdpi.com/2075-1702/12/12/894; https://doaj.org/toc/2075-1702

Access URL: https://doaj.org/article/a68e9bb8f0bd42d6b8c9842c6321237e

B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data

Authors: Nji, Francis Ndikum Janaja, Vandana Wang, Jianwu

Subject Terms: Machine Learning, FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Artificial Intelligence, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2509.13202