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3D Visual Discomfort Assessment With a Weakly Supervised graph convolution neural network Based on Inaccurately Labeled EEG

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IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 1164-1176页

作者： Lu, Na Zhao, Xiaojie Yao, Li Beijing Normal Univ Sch Artificial Intelligence Engn Res Ctr Intelligent Technol & Educ Applicat Beijing 100875 Peoples R China

Visual discomfort significantly limits the broader application of stereoscopic display technology. Hence, the accurate assessment of stereoscopic visual discomfort is a crucial topic in this field. Electroencephalography (EEG) data, which can reflect changes in brain activity, have received increasing attention in objective assessment research. However, inaccurately labeled data, resulting from the presence of individual differences, restrict the effectiveness of the widely used supervised learning methods in visual discomfort assessment tasks. Simultaneously, visual discomfort assessment methods should pay greater attention to the information provided by the visual cortical areas of the brain. To tackle these challenges, we need to consider two key aspects: maximizing the utilization of inaccurately labeled data for enhanced learning and integrating information from the brain's visual cortex for feature representation purposes. Therefore, we propose the weakly supervised graph convolution neural network for visual discomfort (WSGCN-VD). In the classification part, a center correction loss serves as a weakly supervised loss, employing a progressive selection strategy to identify accurately labeled data while constraining the involvement of inaccurately labeled data that are influenced by individual differences during the model learning process. In the feature extraction part, a feature graph module pays particular attention to the construction of spatial connections among the channels in the visual regions of the brain and combines them with high-dimensional temporal features to obtain visually dependent spatio-temporal representations. Through extensive experiments conducted in various scenarios, we demonstrate the effectiveness of our proposed model. Further analysis reveals that the proposed model mitigates the impact of inaccurately labeled data on the accuracy of assessment.

关键词： EEG weakly supervised learning graph convolution neural network visual discomfort

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graph convolution neural network-Based Data Association for Online Multi-Object Tracking

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IEEE ACCESS 2021年 9卷 114535-114546页

作者： Lee, Jimi Jeong, Mira Ko, Byoung Chul Keimyung Univ Dept Comp Engn Daegu 42601 South Korea

In this paper, a graph convolutional network (GCN)-based multi-object tracking (MOT) algorithm, consisting of a module for extracting the initial features and a module for updating the features, that estimates the affinity between nodes is proposed. The feature extraction module utilizes the pose feature of the object such that the tracking is correct even when the object is partially occluded. Unlike previous graph neural network (GNN)-based MOT methods, this study is based on a GCN and includes a new feature update mechanism, which is updated by combining the output of the neural network and the node similarity between the tracker and detection nodes for each layer. The node feature is updated by aggregating the updated edge feature and the connection strength between the tracker and detection. In each GCN layer, the three networks for the node, edge update, and edge classification were designed to minimize the network parameters to enable faster MOT compared to other GCN-based MOTs. The entire GCN network was designed to learn end-to-end through an affinity loss. The experimental results for the MOT16 and 17 challenge datasets show that the proposed method achieves a superior or similar performance in terms of tracking accuracy and speed compared to state-of-the-art methods, including GCN-based MOT.

关键词： Feature extraction Image edge detection Real-time systems Training Three-dimensional displays Object tracking Licenses Multiple object tracking graph neural network graph convolution neural network data association

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Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年 139卷 100-108页

作者： Djenouri, Youcef Belhadi, Asma Srivastava, Gautam Lin, Jerry Chun -Wei SINTEF Digital Math & Cybernet Oslo Norway Kristiania Univ Coll Dept Technol Oslo Norway Brandon Univ Dept Math & Comp Sci Brandon MB Canada Western Norway Univ Appl Sci Dept Comp Sci Elect Engn & Math Sci Bergen Norway China Med Univ Res Ctr Interneural Comp Taichung Taiwan Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon

In this study, we combine graph optimization and prediction in a single pipeline to investigate an innovative convolutional graph-based neural network for urban traffic flow prediction in an edge IoT environment. Pre-processing of the linked graph is first performed to remove noise from the set of original road networks of urban traffic data. Outlier detection strategy is used to efficiently explore the road network and remove irrelevant patterns and noise. The resulting graph is then implemented to train an extended graph convolutional neural network to estimate the traffic flow in the city. To accurately tune the hyperparameter values of the proposed framework, a new optimization technique is developed based on branch and bound. For comparison, an intensive evaluation is conducted with multiple datasets and baseline methods. The results show that the proposed framework outperforms the baseline solutions, especially when the number of nodes in the graph is large.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Deep learning graph convolution neural network Forecasting Smart roads

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PN-GCN: Positive-negative graph convolution neural network in information system to classification

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INFORMATION SCIENCES 2023年第1期632卷 411-423页

作者： Yu, Bin Xie, Hengjie Xu, Zeshui Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Hunan Peoples R China Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China

graph convolution neural network (GCN) shows strong performance in non-Euclidean structure data. In recent years, many researchers have applied GCN to Euclidean structure data, such as images and languages, and obtained excellent results, which expanded the application range of GCN. Despite a large number of related methods, few studies deal with information systems from the perspective of graphs. In fact, the information system is also a non-Euclidean data structure, and there is serious information loss when using classical measurement methods to construct its data structure. Thus, the promotion of GCN is hindered in information systems. This paper applies GCN to the classification of information systems. Firstly, an intuitionistic fuzzy relation, called positive-negative relation, is established in the information system, and the physical meaning of this relationship is explained. Secondly, based on the positive-negative relation, a positive-negative relation graph convolution neural network model is constructed called PN-GCN. Finally, the effectiveness and robustness of PN-GCN are validated through experiments.

关键词： Information system graph convolution neural network Positive-negative relation Positive-negative graph convolution neural network Classification

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Survey On Traffic Flow Prediction Methods Based On graph convolution neural network 7

Survey On Traffic Flow Prediction Methods Based On Graph Con...

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7th International Conference on Traffic Engineering and Transportation System (ICTETS)

作者： Gao, Yi Wang, Xiaomeng Xizang Minzu Univ Xianyang 712082 Peoples R China

ISBN: (纸本)9781510674479

graph convolution is a deep learning method for graph data, which can process unstructured traffic network data, which is very suitable for traffic flow prediction tasks. Researchers have introduced graph neural networks to model the graph structure and contextual information within transportation systems, achieving state-of-the-art performance in various traffic prediction problems. In this survey, we provide an overview of the rapidly growing body of research utilizing diverse graph neural network methods across different domains. The paper begins with a review of the fundamental principles of graph convolutional models, emphasizing their advantages in capturing node features and local neighborhood information. Subsequently, the application process of graph convolution in traffic flow prediction is discussed. By utilizing graphs as inputs, certain models based on GCNs have demonstrated improved performance in tasks such as road traffic flow and speed prediction compared to previous methods. Building upon this foundation, various variants of graph convolution are explored, including residual graph convolution, multi-graph convolution, etc., aimed at progressively enhancing the model's representational capabilities. Furthermore, this paper focuses on the experimental results and application effectiveness of the research. Numerous studies have validated the performance of graph convolutional models on real-world traffic datasets. The experimental outcomes demonstrate that, in comparison to conventional methods, graph convolutional models have achieved significant improvements in traffic flow prediction. Nevertheless, challenges such as data quality, real-time applicability, and model interpretability still need to be addressed. Future research can delve into these issues to further propel the application and development of graph convolutional methods.

关键词： traffic flow prediction deep learning convolutional neural network graph convolution neural network residual neural network

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Long short-distance topology modelling of 3D point cloud segmentation with a graph convolution neural network

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IET COMPUTER VISION 2023年第3期17卷 251-264页

作者： Zhang, Wen Jing Su, Song Zhi Hong, Qing Qi Wang, Bei Zhan Sun, Li Xiamen Univ Sch Informat Xiamen Peoples R China Univ Sheffield Dept Comp Sci Sheffield England Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

3D point cloud segmentation is a non-trivial problem due to its irregular, sparse, and unordered data structure. Existing methods only consider structural relationships of a 3D point and its spatial neighbours. However, the inner-point interactions and long-distance context of a 3D point cloud have been less investigated. In this study, we propose an effective plug-and-play module called the Long Short-Distance Topologically Modelled (LSDTM) graph convolutional neural network (GCNN) to learn the underlying structure of 3D point clouds. Specifically, we introduce the concept of subgraph to model the contextual-point relationships within a short distance. Then the proposed topology can be reconstructed by recursive aggregation of subgraphs, and importantly, to propagate the contextual scope to a long range. The proposed LSDTM can parse the point cloud data with maximisation of preserving the geometric structure and contextual structure, and the topological graph can be trained end-to-end through a seamlessly integrated GCNN. We provide a case study of triple-layer ternary topology and experimental results on ShapeNetPart, Stanford 3D Indoor Semantics and ScanNet datasets, indicating a significant improvement on the task of 3D point cloud segmentation and validating the effectiveness of our research.

关键词： image segmentation feature extraction solid modelling Combinatorial mathematics graph convolution neural network data structures learning (artificial intelligence) contextual-point relationships 3D point cloud segmentation contextual structure neural nets unordered data structure computational geometry Long Short-Distance Topologically Modelled graph convolutional neural network Computer vision and image processing techniques convolutional neural nets irregular data structure Computational geometry long-distance context graph theory point cloud data short-distance topology modelling sparse, data structure File organisation inner-point interactions graphics techniques geometric structure

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MVMS-GCN: A Multi-view Multi-source data fusion graph convolution neural network for predicting autism spectrum disorder with fMRI 4

MVMS-GCN: A Multi-view Multi-source data fusion graph convol...

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4th International Conference on Bioinformatics and Intelligent Computing (BIC)

作者： Zhai, Kai Liu, Quan Xiong, Yuyang Qu, Xiaohan Yang, Jun Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China

ISBN: (纸本)9798400716645

Autism Spectrum Disorder (ASD) is a neurological disorder diagnosed based on symptoms, patient descriptions, and assessment scales such as the Hamilton Scale. Although Functional Magnetic Resonance Imaging (fMRI) and brain functional network analysis are used for the diagnosis and classification of neurological disorders, challenges remain due to the heterogeneity of the brain and noise connections. To overcome this, graph neural networks (GNNs) have gained attention in analyzing unstructured fMRI data. Our research aims to propose a novel fMRI-based auxiliary diagnosis method for ASD. We have developed a new method, MVMS-GCN for fMRI analysis that includes graph structure learning, multiview methodology, brain atlas-based graph clustering, and multisource data fusion. Our approach facilitates the discovery of functional connectivity patterns in the brain network via an end-to-end methodology. Furthermore, by integrating non-imaging data, our model improves subject similarities, leading to a more precise classification performance. Our approach, MVMS-GCN, was evaluated on the ABIDE data-set for autism analysis and the REST-meta-MDD data-set for depression analysis. In comparison to other methods, we found that MVMS-GCN achieved higher accuracy (71.9% on ABIDE, an increase of approximately 3.79% compared to other methods) and exhibited superior classification performance on the REST-meta-MDD data-set. These findings underscore the potential of our approach to enhance auxiliary diagnosis and classification of neurological disorders.

关键词： fMRI graph convolution neural network Multi-view method Multi-source data fusion graph Community Detection Clustering

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RG-GCN: Improved graph convolution neural network Algorithm Based on Rough graph

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IEEE ACCESS 2022年 10卷 85582-85594页

作者： Ding, Weiping Pan, Bairu Ju, Hengrong Huang, Jiashuang Cheng, Chun Shen, Xinjie Geng, Yu Hou, Tao Nantong Univ Sch Informat Sci & Technol Nantong 226019 Jiangsu Peoples R China

The graph convolution neural network uses topological graph to portray inter-node relationships and update node features. However, the traditional topological graph can only describe the certain relationship between nodes (that is, the weight of the connecting edge is a fixed value), while ignoring the uncertainty widely existing in the real world. These uncertainties not only affect the relationship between nodes, but also affect the final classification performance of the model. In order to overcome this defect, a graph convolution neural network algorithm based on rough graph is proposed in this paper. Specifically, the algorithm first constructs a rough graph using a combination of the upper and lower approximation theory of the rough set and the edge theory of the topological graph, the paired maximum-minimum relationship values are used to characterize the uncertain relationship between nodes. Then, this paper designs an end-to-end training neural network architecture based on rough graph, the trained rough graph is fed to this neural network to update node features with these uncertain relationship. Finally, nodes are classified according to these learned node features. The experimental results on real data show that the proposed algorithm can significantly improve the accuracy of node classification compared with the traditional graph convolution neural network.

关键词： graph convolution neural network topological graph rough set rough graph uncertain relationship

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Dynamic Virtual network Embedding Algorithm Based on graph convolution neural network and Reinforcement Learning

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IEEE INTERNET OF THINGS JOURNAL 2022年第12期9卷 9389-9398页

作者： Zhang, Peiying Wang, Chao Kumar, Neeraj Zhang, Weishan Liu, Lei China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 Punjab India Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 Taiwan Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttarakhand India Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by the heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This article proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.

关键词： Heuristic algorithms Clustering algorithms Internet of Things Dynamic scheduling Search problems Reinforcement learning convolution Fitness matrix graph convolution neural network reinforcement learning (RL) virtual network embedding (VNE)

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Multi-domain fusion deep graph convolution neural network for EEG emotion recognition

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neural COMPUTING & APPLICATIONS 2022年第24期34卷 22241-22255页

作者： Bi, Jinying Wang, Fei Yan, Xin Ping, Jingyu Wen, Yongzhao Northeastern Univ Coll Informat Sci & Engn Shenyang 110169 Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang 110819 Peoples R China

Electroencephalogram (EEG)-based emotion recognition has become a hot research field, with the most attention given to decoding three basic types of emotional states (i.e., positive, negative, and neutral) by EEG. Traditional EEG emotion recognition is a single feature input mode that cannot cover multiple feature information. For brain function networks, the feature extraction process is very cumbersome. There will also be individual differences in the characteristics of different subjects. Therefore, the theory of graph convolution and brain function connection is introduced into this research, and the multi-domain fusion features input deep graph convolution neural network (MdGCNN) is proposed in this paper. Pearson correlation is used to determine the adjacency matrix. The Sortpooling layer is employed as a bridge between the graph convolution neural layer and the normal neural network layer and sorts the node features in a consistent order. Based on analyzing the characteristics of a single electrode, the brain topology structure features are automatically extracted. Taking MdGCNN as the basic model and considering the method of minimizing the feature distance between the source and the target domain, we propose a transfer learning (TL) emotion recognition model for cross-subject called MdGCNN-TL. Meanwhile, MdGCNN-TL is extended to traverse the target domain of a single subject in a two-to-one domain form. According to the idea of principal component analysis, the transfer model with a high recognition effect is determined with the degree of subject correlation (SC), and MdGCNN-TL is upgraded to MdGCNN-TL-SC. Experimental analysis on the SEED dataset is performed to evaluate the proposed models. Further validation of the model is implemented on the DEAP dataset. The results show that the proposed model has achieved better performance in EEG emotion recognition.

关键词： Emotion recognition Multi-domain fusion features graph convolution neural network Transfer learning

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