Search Results - Temporal Graph Conventional Autoencoder

Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting by Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea, Righi, Marcello

“…This study presents a framework for predicting unsteady transonic wing pressure distributions due to pitch and plunge movement, integrating an autoencoder architecture with graph convolutional…”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data by Choi, Seung-Hwan, An, Dawn, Lee, Inho, Lee, Suwoong

“… To address this issue, we employ a semi-supervised learning approach that relies solely on normal data to effectively detect abnormal patterns, overcoming the limitations of conventional methods…”
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Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays by Arifeen, Murshedul, Petrovski, Andrei, Hasan, Md Junayed, Noman, Khandaker, Navid, Wasib Ul, Haruna, Auwal

“… However, traditional autoencoder models often struggle to capture the spatial and temporal relationships present in photovoltaic sensor data…”
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Temporal network embedding using graph attention network by Mohan, Anuraj, Pramod, K V

“… In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network…”
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A survey on anomaly detection for technical systems using LSTM networks by Lindemann, Benjamin, Maschler, Benjamin, Sahlab, Nada, Weyrich, Michael

“…•Graph-based approaches enable unified representation of heterogeneous data.•Transfer learning addresses frequent lack of sufficiently large and diverse datasets…”
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Multi-Task Graph Attention Net for Electricity Consumption Prediction and Anomaly Detection by Bai, Na, Zhang, Jian, Wu, Zhaoli

“… To overcome these challenges, we propose a novel Multi-Task Graph Attention Network (MGAT) framework leveraging an adaptive entropy analysis…”
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Epileptic Seizure Detection from EEG Signals using Autoencoder-based Graph Convolutional Neural Network by Jibon, Ferdaus Anam, Hasan Siddiqui, Fazlul

“… In this study, a novel hybrid framework is proposed for detecting epileptic seizures using a graph convolutional neural network (GCNN…”
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Abnormal structural and functional network topological properties associated with left prefrontal, parietal, and occipital cortices significantly predict childhood TBI-related attention deficits: A semi-supervised deep learning study by Cao, Meng, Wu, Kai, Halperin, Jeffery M., Li, Xiaobo

“… Most of these existing studies have utilized conventional parametric models for group comparisons, which have limited capacity in dealing with large-scale and high dimensional neuroimaging measures…”
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Open-world structured sequence learning via dense target encoding by Zhang, Qin, Liu, Ziqi, Li, Qincai, Xiang, Haolong, Yu, Zhizhi, Chen, Junyang, Zhang, Peng, Chen, Xiaojun

“…) to learn graph streams in the open-world learning setting. To capture both structural and temporal information, DOSSL uses a GNN-based stochastic recurrent neural…”
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A Comprehensive Method for Anomaly Detection in Complex Dynamic IoT Systems by Andrii Liashenko, Larysa Globa

“… In this paper, we propose a novel anomaly detection approach that combines Temporal Graph Neural Networks (TGNN) with Autoencoders…”
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Sleep EEG Signal Analysis using Graph Embedding Simplicial Convolutional Recurrent Attention Network with Duck Swarm Algorithm by Khrais, Ibrahim Mohammad, S, Sheela, Sethi, Gaurav, Salomi Victoria, D. Rosy, Sashmi, S.Nooray, Vijay, G.

“… Temporal and spatial EEG patterns cannot be processed easily by conventional models, and they decrease classification accuracy and generalization…”
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Real-Time Anomalous Activity Detection in Surveillance Videos by Susitra, D, Reddy, Sathi Abhinay, Prasanth, Sajjala, Dhanalakshmi, K, J, Sylvia Grace, Shamreen Ahamed, B

“… In this paper, a robust spatio temporal auto encoder framework that takes advantage of spatial structure as well as temporal dynamics in video sequences and dynamic thresholding for anomaly detection is developed…”
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Spatio-temporal unsupervised individual clustering for operating room videos by Yokoyama, Koji, Yamamoto, Goshiro, Liu, Chang, Mitarai, Sho, Kishimoto, Kazumasa, Mori, Yukiko, Kuroda, Tomohiro

“… However, conventional models are trained using supervised or semi-supervised learning, which makes their direct application to real-world videos challenging…”
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Graph embedding and clustering collaborative optimization model for fraudster group detection by Peng, Haoran, Ma, Ru, Chao, Jinbo, Li, Xuchao, Zhang, Fuzhi

“… However, existing methods face two major limitations: conventional graph construction overlooks implicit user relationships, while the separation of graph embedding…”
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Graph Transformer Network Incorporating Sparse Representation for Multivariate Time Series Anomaly Detection by Yang, Qian, Zhang, Jiaming, Zhang, Junjie, Sun, Cailing, Xie, Shanyi, Liu, Shangdong, Ji, Yimu

“…Cyber–physical systems (CPSs) serve as the pivotal core of Internet of Things (IoT) infrastructures, such as smart grids and intelligent transportation,…”
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Variational Graph Convolutional Networks for Dynamic Graph Representation Learning by Mir, Aabid A., Zuhairi, Megat F., Musa, Shahrulniza, Alanazi, Meshari H., Namoun, Abdallah

“…) with the structural learning capabilities of Graph Convolutional Networks (GCNs). The proposed model is designed to capture both temporal dependencies and uncertainties inherent…”
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Hybrid attention network-based students behavior data analytics framework with enhanced capuchin search algorithm using multimodal data by Sridharan, Thulasi Bharathi, Akilashri, P. S. S

“…Adaptive learning activities have a major impact on the achievement of a student, and it is helpful to identify how much effort they have to put forth. In…”
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Collaborative optimization of dynamic early warning and control in desulphurization process via integration of causal inference and temporal features by Li, He, Yang, Bozhi, Gu, Xinyu

“… This study presents a novel collaborative optimization framework integrating causal inference with temporal feature engineering to achieve dynamic early warning and intelligent control…”
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A Novel Data Enhancement Method for Photovoltaic Power Forecasting Considering Spatio-Temporal Features Extraction by Li, Cong, Zhang, Zhen, Qin, Zibei, Wu, Yan, Qin, Yunyi

“… Initially, temporal and spatial features of PV power station data are extracted using improved autoencoder combined with graph convolutional network…”
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Explainable Machine Learning for Activity Modeling in GeoAi by Zhang, Liming

“…GeoAI is a recent cutting-edge discipline that combines advancements in Big Geospatial Data Management (Geo) and Artificial Intelligence (AI). This thesis…”
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