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graph convolutional neural network for Pressure Prediction in Water Distribution network Sites

WATER RESOURCES MANAGEMENT 2024年第7期38卷 2581-2599页

作者： Liu, Dan Ma, Pei Li, Shixuan Lv, Wei Fang, Danhui Wuhan Univ Technol Sch Safety Sci & Emergency Management Wuhan 430070 Peoples R China

The safe operation of water distribution networks (WDNs) is crucial for ensuring the city dwellers' living standards. Accurate and multi-step predictions of pressure at key sites in WDNs can prevent the occurrence of pipe bursts in the future. Therefore, this study proposes an EMD-graph-Wavenet-HGSRS model to predict the pressure at several monitoring sites in the WDNs. The LSTC-Tubal method is proposed to repair the abnormal pressure values of the WDNs. Then, the pressure features are enriched by EMD. The predefined adjacent matrix of monitoring points is obtained through the topology of WNDs. And, the enriched pressure features and the predefined adjacent matrix of the monitoring sites are input into the graph-Wavenet model to predict the pressure values for the next 12 h. In addition, the graph-Wavenet model is optimized by HGSRS in this study. The results of this study show that the MAE of EMD-graph-Wavenet decreased by 24.36%, KGE increased by 6.73% compared to graph-Wavenet. EMD-graph-Wavenet-HGSRS (optimized by HGSRS) prediction outperforms EMD-graph-Wavenet model. The MAE of graph-Wavenet decreased by 40.91% and KGE increased by 11.91% compared to Bi-LSTM. The Bi-LSTM exhibited the best performance among these temporal models, whereas the baseline LSTM had the worst performance. The method proposed in this study can better predict the pressure extremes at each stage of the monitoring sites and provide guidance for the pressure management of actual WDNs.

关键词： Empirical modal decomposition graph convolutional neural network Hyperparameter search Spatial and temporal correlation WDNs pressure

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graph convolutional neural network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos

Graph Convolutional Neural Network For Weakly Supervised Abn...

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： Adewole, Sodiq Fernandes, Philip Jablonski, James Copland, Andrew Porter, Michael Syed, Sana Brown, Donald Univ Virginia Syst & Info Engn Charlottesville VA 22901 USA Univ Virginia Sch Med Dept Pediat Charlottesville VA 22908 USA Univ Virginia Sch Data Sci Charlottesville VA USA

ISBN: (纸本)9781665439022

Temporal abnormality localization in long Wireless Capsule Endoscopy (WCE) videos is an important problem. The cost of obtaining frame level label for long WCE videos is prohibitive. In this paper, we propose an end-to-end temporal abnormality localization for long WCE videos using only weak video level labels. Physicians use Capsule Endoscopy (CE) as a non-surgical and non-invasive method to examine the entire digestive tract in order to diagnose diseases or abnormalities. While CE has revolutionized traditional endoscopy procedures, a single CE examination could last up to 8 hours generating as much as 100,000 frames. Physicians must review the entire video, frame-by-frame, in order to identify the frames capturing relevant lesion or abnormality. This, sometimes could be as few as just a single frame. Given this very high level of redundancy, analysing long CE videos can be very tedious, time consuming and also error prone. This paper presents a novel multi-step method for an end-to-end localization of target frames capturing abnormalities of interest in the long video using only weak video labels. First we developed an automatic temporal segmentation using change point detection technique to temporally segment the video into uniform, homogeneous and identifiable segments. Then we employed graph convolutional neural network (GCNN) to learn a representation of each video segment. Using weak video segment labels, we trained our GCNN model to recognize each video segment as abnormal if it contains at least a single abnormal frame. Finally, leveraging the parameters of the trained GCNN model, we replaced the final layer of the network with a temporal pool layer to localize the relevant abnormal frames within each abnormal video segment. We experimented with multiple real patients' endoscopy videos and achieved an accuracy of 89.9% on the graph classification task and a specificity of 97.5% on the abnormal frames localization task.

关键词： graph convolutional neural network Wireless Capsule Endoscopy Weakly Supervised Localization Video Temporal Segmentation graph Classification

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graph convolutional neural network for Multimodal Movie Recommendation 23

Graph Convolutional Neural Network for Multimodal Movie Reco...

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38th Annual ACM Symposium on Applied Computing (ACM SAC)

作者： Mondal, Prabir Chakder, Daipayan Raj, Subham Saha, Sriparna Onoe, Naoyuki Indian Inst Technol Patna Patna Bihar India Sony Res India Bengaluru Karnataka India

ISBN: (纸本)9781450395175

The Recommendation System (RS) development and recommending customers' preferred products to the customer are highly desirable motives in today's digital market. Most of the RSs are mainly based on textual information of the engaged entities in the platform and the ratings provided by the users to the products. This paper develops a movie recommendation system where the cold-start problem relating to rating information dependency has been dealt with and the multi-modality approach is introduced. The proposed method differs from existing approaches in three main aspects: (a) implementation of knowledge graph for text embedding, (b) besides textual information, other modalities of movies like video, and audio are employed rather than rating information for generating movie/user representation and this approach deals with the cold-start problem effectively, (c) utilization of graph convolutional network (GCN) for generating some further hidden features and also for developing regression system.

关键词： Multi-modal movie recommendation system graph convolutional neural network Knowledge graph

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graph convolutional neural network based on the Combination of Multiple Heterogeneous graphs 22

Graph Convolutional Neural Network based on the Combination ...

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22nd IEEE International Conference on Data Mining (ICDM)

作者： Mu, Caihong Huang, Heyuan Liu, Yi Luo, Jiashen Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9798350346091

Collaborative filtering algorithms based on graph neural networks have achieved good performance in many scenarios of recommender systems (RSs), most of which utilize the raw user-item interaction information for recommendation. However, user-item interaction information is usually sparse, limiting the recommendation performance of the algorithms. Therefore, how to fully mine the sparse user-item interaction information and improve the accuracy of the recommendation algorithms is an important issue worth studying. To address this issue, this paper proposes a graph convolutional neural network algorithm based on the combination of multiple heterogeneous graphs (GCN-MHG), which constructs a heterogeneous fully adjacent graph by connecting user-user and item-item nodes to each other (i.e., homogeneous nodes are connected to each other), and design a heterogeneous full adjacency auxiliary (HFAA) layer to aggregate information on this graph. The HFAA layer not only expands the influence of distant nodes on the target node, making the target node can receive global information, but also alleviates the information bottleneck phenomenon. In addition, we find that retaining the node features in the process of aggregating information at some layers (i.e., adopting self-connection of nodes at partial layers) will not only avoid the damage to the algorithms' performance caused by the features without semantic information such as ID of nodes in the scenarios of RSs, but also strengthen the influence of non-one-hop neighbor nodes. Experiments on three real datasets demonstrate that the proposed GCN-MHG performs better than the state-of-the-art algorithms. At the same time, several ablation experiments are also designed, and the rationality of GCN-MHG is analyzed theoretically and experimentally. Codes are available at https://***/huangheyuanbj/GCN-MHG.

关键词： recommendation graph convolutional neural network multiple heterogeneous graphs

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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural network

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Frontiers of Structural and Civil Engineering 2022年第11期16卷 1378-1396页

作者： Wenxuan CAO Junjie LI Faculty of Infrastructure Engineering Dalian University of TechnologyDalian 116024China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China

It is of great significance to quickly detect underwater cracks as they can seriously threaten the safety of underwater *** to date has mainly focused on the detection of above-water-level cracks and hasn’t considered the large scale *** this paper,a large-scale underwater crack examination method is proposed based on image stitching and *** addition,a purpose of this paper is to design a new convolution method to segment underwater *** improved As-Projective-As-Possible(APAP)algorithm was designed to extract and stitch keyframes from *** graph convolutional neural network(GCN)was used to segment the stitched *** GCN’s m-IOU is 24.02%higher than Fully convolutional networks(FCN),proving that GCN has great potential of application in image segmentation and underwater image *** result shows that the improved APAP algorithm and GCN can adapt to complex underwater environments and perform well in different study areas.

关键词： underwater cracks remote operated vehicle image stitching image segmentation graph convolutional neural network

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Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network

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EGYPTIAN INFORMATICS JOURNAL 2022年第1期23卷 1-12页

作者： Liu, Xiaoyang Tang, Ting Ding, Nan Chongqing Univ Technol Sch Comp Sci & Engn Chongqing 400054 Peoples R China

In view of most current studies on text sentiment classification focus on the deep learning model to obtain the sentimental characteristics of English text. Chinese text sentiment analysis is rarely involved, and only the context information of the statement is considered, but the syntax information of the statement is rarely considered. In this paper, a novel sentiment classification model is proposed (Dependency Tree graph convolutional network, DTGCN) combined Chinese syntactically dependent tree with graph convolution. Firstly, the Bi-GRU (Bi-directional Gated Recurrent Unit) model is used to learn the contextual feature representation of a given text. Secondly, the syntax-dependent tree structure of a given text is constructed, then obtain its adjacency matrix according to the syntax-dependent tree, with the initial features extracted from the bidirectional gate control network, input into the graph convolutional neural network (GCN) to extract the sentimental features of the text;the obtained sentimental characteristics are then input into the classifier SoftMax for text sentimental polarity classification. Finally, the data set is compared with the mainstream neural network model. The experimental results show that the accuracy of the proposed DTGCN model proposed on the data set is 90.51% and the recall rate is 90.34%. Compared with the benchmark models (LSTM, CNN, TextCNN and Bi-GRU), the proposed DTGCN model shows a 4.45% advantage in accuracy. It shows that the proposed DTGCN model can effectively use the grammatical information of Chinese text to mine the hidden relationship in statements, it can improve the accuracy of Chinese text sentiment classification. In addition, the proposed DTGCN model not only improves the performance of sentiment classification in the essay, it also provides a new research method for social network public opinion identification. (c) 2022 THE AUTHORS. Published by Elsevier B.V. on behalf of Faculty of Computers and Information

关键词： Sentiment classification graph convolutional neural network Text syntax Word embedding

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Spatial-Temporal Dynamic graph convolutional neural network for Traffic Prediction

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IEEE ACCESS 2023年 11卷 97920-97929页

作者： Xiao, Wenjuan Wang, Xiaoming Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Northwest Minzu Univ Coll Elect Engn Lanzhou 730030 Peoples R China

Due to the complexity and dynamics of transportation systems, traffic prediction has become a challenging task. The accuracy of prediction is influenced by the spatial-temporal correlation within the traffic system. Previous approaches mainly relied on a pre-defined static adjacency matrix combined with graph convolutional neural networks to capture spatial correlation, neglecting the dynamic relationships between nodes over time. In this study, we propose a novel prediction model called the spatial-temporal dynamic graph convolutional neural network (STDGCN). By fusing node embeddings and input features, we obtain a new node representation that incorporates both static and dynamic features. To capture the dynamic relationships, we introduce a similarity calculation to construct a dynamic adjacency matrix. This matrix contains rich spatial relationships that serve as a reference for subsequent prediction tasks. We further employ graph convolutional networks (GCN) and Gated Recurrent Units (GRU) to capture the spatial-temporal correlation. By combining these components, we establish a comprehensive traffic volume prediction model. To evaluate the performance of our proposed method, we conduct experiments on two real datasets. The experimental results demonstrate that our model achieves state-of-the-art performance in accurately predicting traffic volumes.

关键词： Correlation Predictive models convolutional neural networks Roads Detectors Data models Analytical models Traffic control graph neural networks Traffic prediction spatial-temporal dynamic graph dynamic adjacency matrix graph convolutional neural network

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HRGCNLDA: Forecasting of lncRNA-disease association based on hierarchical refinement graph convolutional neural network

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2024年第4期21卷 4814-4835页

作者： Peng, Li Yang, Yujie Yang, Cheng Li, Zejun Cheong, Ngai Hunan Univ Sci & Technol Coll Comp Sci & Engn Xiangtan 411201 Peoples R China Hunan Univ Sci & Technol Hunan Key Lab Serv Comp & Novel Software Technol Xiangtan 411201 Peoples R China Hunan Inst Technol Sch Comp Sci & Engn Hengyang 421002 Peoples R China Macao Polytech Univ Fac Appl Sci Macau 999078 Peoples R China

Long non -coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and therapeutic target. Nevertheless, conventional biological experimental techniques are both resource -intensive and laborious, making it essential to develop an accurate and efficient computational method to facilitate the discovery of potential links between lncRNAs and diseases. In this study, we proposed HRGCNLDA, a computational approach utilizing hierarchical refinement of graph convolutional neural networks for forecasting lncRNA-disease potential associations. This approach effectively addresses the over -smoothing problem that arises from stacking multiple layers of graph convolutional neural networks. Specifically, HRGCNLDA enhances the layer representation during message propagation and node updates, thereby amplifying the contribution of hidden layers that resemble the ego layer while reducing discrepancies. The results of the experiments showed that HRGCNLDA achieved the highest AUC-ROC (area under the receiver operating characteristic curve, AUC for short) and AUC-PR (area under the precision versus recall curve, AUPR for short) values compared to other methods. Finally, to further demonstrate the reliability and efficacy of our approach, we performed case studies on the case of three prevalent human diseases, namely, breast cancer, lung cancer and gastric cancer.

关键词： lncRNA-disease associations prediction graph convolutional neural network hierarchical refinement

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A Tennis Training Action Analysis Model Based on graph convolutional neural network

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IEEE ACCESS 2023年 11卷 113264-113271页

作者： Zhang, Xinyu Chen, Jihua Leshan Normal Univ Phys Culture Inst Leshan 614000 Sichuan Peoples R China

With the development of artificial intelligence technology, an increasing number of human action recognition (HAR) methods are being applied to tennis training action analysis. The HAR methods based on skeletal points have been extensively researched and applied due to their superior action expression capabilities. In order to enhance the HAR ability of tennis players and effectively capture the detailed features in training actions, this paper proposes a tennis training action analysis model based on graph convolutional neural networks. Firstly, this paper establishes the limb vectors of humans in three-dimensional spatial coordinates and extracts the features of tennis error techniques based on the distances between the skeletal joints of five parts of the human body. Secondly, the data's time frames are segmented to extract attention and improve the model's ability to capture detailed features. Additionally, the attention mechanism is introduced to embed the position information into the attention map, enhancing the model's generalization ability. Experiments conducted on several action datasets demonstrate that the proposed model in this paper achieves higher HAR accuracy and better recognition results compared to most current methods.

关键词： Human activity recognition convolutional neural networks Human action recognition tennis attention mechanism graph convolutional neural network

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graph convolutional neural network for multi-scale feature learning

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COMPUTER VISION AND IMAGE UNDERSTANDING 2020年 194卷 102881-000页

作者： Edwards, Michael Xie, Xianghua Palmer, Robert, I Tam, Gary K. L. Alcock, Rob Roobottom, Carl Swansea Univ Dept Comp Sci Singleton Pk Swansea SA2 8PP W Glam Wales Plymouth Hosp NHS Trust Peninsula Radiol Acad Plymouth PL6 5WR Devon England Plymouth Univ Plymouth Hosp NHS Trust Sch Med Plymouth PL6 8BT Devon England Plymouth Univ Plymouth Hosp NHS Trust Sch Dent Plymouth PL6 8BT Devon England

Automatic deformable 3D modeling is computationally expensive, especially when considering complex position, orientation and scale variations. We present a volume segmentation framework to utilize local and global regularizations in a data-driven approach. We introduce automated correspondence search to avoid manually labeling landmarks and improve scalability. We propose a novel marginal space learning technique, utilizing multi-resolution pooling to obtain local and contextual features without training numerous detectors or excessively dense patches. Unlike conventional convolutional neural network operators, graph-based operators allow spatially related features to be learned on the irregular domain of the multi-resolution space, and a graph-based convolutional neural network is proposed to learn representations for position and orientation classification. The graph-CNN classifiers are used within a marginal space learning framework to provide efficient and accurate shape pose parameter hypothesis prediction. During segmentation, a global constraint is initially non-iteratively applied, with local and geometric constraints applied iteratively for refinement. Comparison is provided against both classical deformable models and state-of-the-art techniques in the complex problem domain of segmenting aortic root structure from computerized tomography scans. The proposed method shows improvement in both pose parameter estimation and segmentation performance.

关键词： Deep learning graph convolutional neural network Medical image segmentation Marginal space learning Aortic root Computerized tomography

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