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  • Open Access


    Research on PM2.5 Concentration Prediction Algorithm Based on Temporal and Spatial Features

    Song Yu*, Chen Wang

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5555-5571, 2023, DOI:10.32604/cmc.2023.038162

    Abstract PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM2.5 flow between cities when analyzing the impact of inter-city PM2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such as altitude and… More >

  • Open Access


    Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting

    Wonyong Chung, Jaeuk Moon, Dongjun Kim, Eenjun Hwang*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5817-5836, 2023, DOI:10.32604/cmc.2023.036830

    Abstract Multivariate time-series forecasting (MTSF) plays an important role in diverse real-world applications. To achieve better accuracy in MTSF, time-series patterns in each variable and interrelationship patterns between variables should be considered together. Recently, graph neural networks (GNNs) has gained much attention as they can learn both patterns using a graph. For accurate forecasting through GNN, a well-defined graph is required. However, existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes, and consider all nodes with the same weight when constructing graph. In this paper, we propose a novel graph construction method that solves aforementioned limitations.… More >

  • Open Access


    An Intelligent Identification Approach of Assembly Interface for CAD Models

    Yigang Wang1, Hong Li1, Wanbin Pan1,*, Weijuan Cao1, Jie Miao1, Xiaofei Ai1, Enya Shen2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 859-878, 2023, DOI:10.32604/cmes.2023.027320

    Abstract Kinematic semantics is often an important content of a CAD model (it refers to a single part/solid model in this work) in many applications, but it is usually not the belonging of the model, especially for the one retrieved from a common database. Especially, the effective and automatic method to reconstruct the above information for a CAD model is still rare. To address this issue, this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics. First, as the geometry of an… More >

  • Open Access


    Heterogeneous Network Embedding: A Survey

    Sufen Zhao1,2, Rong Peng1,*, Po Hu2, Liansheng Tan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 83-130, 2023, DOI:10.32604/cmes.2023.024781

    Abstract Real-world complex networks are inherently heterogeneous; they have different types of nodes, attributes, and relationships. In recent years, various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks (HINs) into low-dimensional embeddings; this task is called heterogeneous network embedding (HNE). Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification, recommender systems, and information retrieval. Here, we provide a comprehensive survey of key advancements in the area of HNE. First, we define an encoder-decoder-based HNE model taxonomy. Then, we systematically overview, compare, and summarize various… More > Graphic Abstract

    Heterogeneous Network Embedding: A Survey

  • Open Access


    Identification of Anomaly Scenes in Videos Using Graph Neural Networks

    Khalid Masood1, Mahmoud M. Al-Sakhnini2,3, Waqas Nawaz4,*, Tauqeer Faiz5,6, Abdul Salam Mohammad7, Hamza Kashif8

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5417-5430, 2023, DOI:10.32604/cmc.2023.033590

    Abstract Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and… More >

  • Open Access


    Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

    Yong Fang, Zhiying Zhao, Yijia Xu*, Zhonglin Liu

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4099-4118, 2023, DOI:10.32604/cmc.2023.033124

    Abstract System logs are essential for detecting anomalies, querying faults, and tracing attacks. Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection, it cannot meet the actual needs. The implementation of automated log anomaly detection is a topic that demands urgent research. However, the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data. Meanwhile, there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection. This paper proposes a novel and practical detection model… More >

  • Open Access


    Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

    Motasem S. Alsawadi1,*, El-Sayed M. El-kenawy2,3, Miguel Rio1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 19-36, 2023, DOI:10.32604/cmc.2023.032499

    Abstract The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community's attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to… More >

  • Open Access


    Interpreting Randomly Wired Graph Models for Chinese NER

    Jie Chen1, Jiabao Xu1, Xuefeng Xi1,*, Zhiming Cui1, Victor S. Sheng2

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 747-761, 2023, DOI:10.32604/cmes.2022.020771

    Abstract Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose a Randomly Wired Graph Neural Network (RWGNN) by using graph to model the structure of Neural Network, which could solve two major problems (word-boundary ambiguity and polysemy) of Chinese NER. Besides, we develop a pipeline to explain the RWGNN by using Saliency Map and Adversarial Attacks. Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for… More >

  • Open Access


    KGSR-GG: A Noval Scheme for Dynamic Recommendation

    Jun-Ping Yao1, Kai-Yuan Cheng1,*, Meng-Meng Ge2, Xiao-Jun Li1, Yi-Jing Wang1

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5509-5524, 2022, DOI:10.32604/cmc.2022.030150

    Abstract Recommendation algorithms regard user-item interaction as a sequence to capture the user’s short-term preferences, but conventional algorithms cannot capture information of constantly-changing user interest in complex contexts. In these years, combining the knowledge graph with sequential recommendation has gained momentum. The advantages of knowledge graph-based recommendation systems are that more semantic associations can improve the accuracy of recommendations, rich association facts can increase the diversity of recommendations, and complex relational paths can hence the interpretability of recommendations. But the information in the knowledge graph, such as entities and relations, often fails to be fully utilized and high-order connectivity is unattainable… More >

  • Open Access


    Research on Cross-domain Representation Learning Based on Multi-network Space Fusion

    Ye Yang1, Dongjie Zhu2,*, Xiaofang Li3, Haiwen Du4, Yundong Sun4, Zhixin Huo2, Mingrui Wu2, Ning Cao1, Russell Higgs5

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1379-1391, 2022, DOI:10.32604/iasc.2022.025181

    Abstract In recent years, graph representation learning has played a huge role in the fields and research of node clustering, node classification, link prediction, etc., among which many excellent models and methods have emerged. These methods can achieve better results for model training and verification of data in a single space domain. However, in real scenarios, the solution of cross-domain problems of multiple information networks is very practical and important, and the existing methods cannot be applied to cross-domain scenarios, so we research on cross-domain representation is based on multi-network space integration. This paper conducts representation learning research for cross-domain scenarios.… More >

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