Yunchang Liu^1,*, Fei Wan¹, Chengwu Liang²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4343-4361, 2024, DOI:10.32604/cmc.2024.047211

Abstract Traffic flow prediction plays a key role in the construction of intelligent transportation system. However, due to its complex spatio-temporal dependence and its uncertainty, the research becomes very challenging. Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes. However, due to the time-varying spatial correlation of the traffic network, there is no fixed node relationship, and these methods cannot effectively integrate the temporal and spatial features. This paper proposes a novel temporal-spatial dynamic graph convolutional network (TSADGCN). The dynamic… More >

Yajing Ma^1,2,3, Gulila Altenbek^1,2,3,*, Yingxia Yu¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2023.045486

Abstract Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events, we propose an Independent Recurrent Temporal Graph Convolution Networks (IndRT-GCNets) framework to efficiently and accurately capture event attribute information. The framework models the knowledge graph sequences to learn the evolutionary representations of entities and relations within each period. Firstly, by utilizing the temporal graph convolution module in the evolutionary representation unit, the framework captures the structural dependency relationships within the knowledge graph in each period. Meanwhile, to achieve better event representation and establish effective correlations,… More >

Xiaorui Zhang^1,2,3,*, Qijian Xie¹, Wei Sun^3,4, Yongjun Ren^1,2,3, Mithun Mukherjee⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 47-61, 2023, DOI:10.32604/cmc.2023.042561

Abstract Fall behavior is closely related to high mortality in the elderly, so fall detection becomes an important and urgent research area. However, the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy. To solve the above problems, this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose. Lightweight OpenPose uses MobileNet as a feature extraction network, and the prediction layer uses bottleneck-asymmetric structure, thus reducing the amount of the network. The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by… More >

Motasem S. Alsawadi^1,*, El-Sayed M. El-kenawy^2,3, 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 >

Motasem S. Alsawadi^*, Miguel Rio

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4643-4658, 2022, DOI:10.32604/cmc.2022.022783

Abstract Action recognition has been recognized as an activity in which individuals’ behaviour can be observed. Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events. A skeleton representation of the human body has been proven to be effective for this task. The skeletons are presented in graphs form-like. However, the topology of a graph is not structured like Euclidean-based data. Therefore, a new set of methods to perform the convolution operation upon the skeleton graph is proposed. Our proposal is based on the Spatial Temporal-Graph Convolutional Network (ST-GCN) framework.… More >