Guangyu Huo, Chang Su, Xiaoyu Zhang^*, Xiaohui Cui, Lizhong Zhang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.072147 - 09 December 2025

Abstract Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks, requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize traffic management and enhance urban mobility and sustainability. However, traditional predictive models struggle to capture long-term temporal dependencies and are computationally intensive, limiting their practicality in real-time. Moreover, many approaches overlook the periodic characteristics inherent in traffic data, further impacting performance. To address these challenges, we introduce ST-MambaGCN, a State-Space-Based Spatio-Temporal Graph Convolution Network. Unlike conventional models, ST-MambaGCN replaces the temporal attention layer with Mamba, a state-space More >

Eman Alsalmi, Abeer Alhuzali^*, Areej Alhothali

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071012 - 09 December 2025

Abstract Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems. Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems. In this study, we propose a hybrid model, BertGCN, that integrates BERT-based contextual embedding with Graph Convolutional Networks (GCNs) to identify anomalies in raw system logs, thereby eliminating the need for log parsing. The BERT module captures semantic representations of log messages, while the GCN models the structural relationships among log entries through a text-based graph. This combination More >

Qiuru Fu¹, Shumao Zhang¹, Shuang Zhou¹, Jie Xu^1,*, Changming Zhao², Shanchao Li³, Du Xu^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070493 - 09 December 2025

Abstract Knowledge graphs often suffer from sparsity and incompleteness. Knowledge graph reasoning is an effective way to address these issues. Unlike static knowledge graph reasoning, which is invariant over time, dynamic knowledge graph reasoning is more challenging due to its temporal nature. In essence, within each time step in a dynamic knowledge graph, there exists structural dependencies among entities and relations, whereas between adjacent time steps, there exists temporal continuity. Based on these structural and temporal characteristics, we propose a model named “DKGR-DR” to learn distributed representations of entities and relations by combining recurrent neural networks More >

Thi Phuong Thao Nguyen¹, Tho Thong Nguyen¹, Huu Quynh Nguyen², Tien Duc Nguyen³, Chu Kien Nguyen¹, Nguyen Giap Cu^4,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4899-4920, 2025, DOI:10.32604/cmc.2025.067707 - 23 October 2025

Abstract Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches, leveraging the relational structure between image regions to improve accuracy. This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network (CNN) features with graph convolutional network (GCN) learning, leveraging superpixel-based image representations. The proposed framework initiates the process by segmenting input images into significant superpixels, reducing computational complexity while preserving essential spatial structures. A pre-trained CNN backbone extracts both global and local features from these superpixels, capturing critical texture and shape information. These features are structured into a… More >

Jiayi Geng¹, Yuxuan Wu¹, Wenbo Lu², Pengxiang Su^1,*, Amel Ksibi³, Wei Li¹, Zaffar Ahmed Shaikh^4,5, Di Gai⁶

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3689-3707, 2025, DOI:10.32604/cmc.2025.066944 - 23 September 2025

Abstract Predicting human motion based on historical motion sequences is a fundamental problem in computer vision, which is at the core of many applications. Existing approaches primarily focus on encoding spatial dependencies among human joints while ignoring the temporal cues and the complex relationships across non-consecutive frames. These limitations hinder the model’s ability to generate accurate predictions over longer time horizons and in scenarios with complex motion patterns. To address the above problems, we proposed a novel multi-level spatial and temporal learning model, which consists of a Cross Spatial Dependencies Encoding Module (CSM) and a Dynamic… More >

Erlin Tian¹, Shuai Zhao^2,*, Min Huang², Yushan Pan^3,4, Yihong Wang^3,4, Zuhe Li¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1427-1456, 2025, DOI:10.32604/cmc.2025.066476 - 29 August 2025

Abstract With the increasing importance of multimodal data in emotional expression on social media, mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches. However, the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis. To address these challenges, this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception (TGICP). Specifically, we utilize a Inter-sample Commonality Perception (ICP) module to extract common features from similar samples within the same modality, and use these common features to enhance the original features of… More >

Xuelin Qin¹, Huinan Sang², Shihua Wu², Shishu Chen², Zhiwei Chen², Yongjun Ren^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1959-1980, 2025, DOI:10.32604/cmc.2025.066266 - 29 August 2025

Abstract As an essential tool for quantitative analysis of lower limb coordination, optical motion capture systems with marker-based encoding still suffer from inefficiency, high costs, spatial constraints, and the requirement for multiple markers. While 3D pose estimation algorithms combined with ordinary cameras offer an alternative, their accuracy often deteriorates under significant body occlusion. To address the challenge of insufficient 3D pose estimation precision in occluded scenarios—which hinders the quantitative analysis of athletes’ lower-limb coordination—this paper proposes a multimodal training framework integrating spatiotemporal dependency networks with text-semantic guidance. Compared to traditional optical motion capture systems, this work… More >

Ya-Jie Sun¹, Li-Wei Qiao¹, Sai Ji^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1769-1785, 2025, DOI:10.32604/cmc.2025.062950 - 09 June 2025

Abstract Vehicle re-identification involves matching images of vehicles across varying camera views. The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images, which increases the complexity of re-identification tasks. To tackle these challenges, this study proposes AG-GCN (Attention-Guided Graph Convolutional Network), a novel framework integrating several pivotal components. Initially, AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically, thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones. Moreover, AG-GCN adopts More >

Sony Peng¹, Sophort Siet¹, Ilkhomjon Sadriddinov¹, Dae-Young Kim^2,*, Kyuwon Park^3,*, Doo-Soon Park²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2041-2057, 2025, DOI:10.32604/cmc.2025.061166 - 16 April 2025

Abstract Recommendation systems (RSs) are crucial in personalizing user experiences in digital environments by suggesting relevant content or items. Collaborative filtering (CF) is a widely used personalization technique that leverages user-item interactions to generate recommendations. However, it struggles with challenges like the cold-start problem, scalability issues, and data sparsity. To address these limitations, we develop a Graph Convolutional Networks (GCNs) model that captures the complex network of interactions between users and items, identifying subtle patterns that traditional methods may overlook. We integrate this GCNs model into a federated learning (FL) framework, enabling the model to learn… More >

Yuxin Gao¹, Xiaodong Duan^2,3, Qiguo Dai^2,3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 701-718, 2025, DOI:10.32604/cmc.2025.060137 - 26 March 2025

Abstract Graph convolutional network (GCN) as an essential tool in human action recognition tasks have achieved excellent performance in previous studies. However, most current skeleton-based action recognition using GCN methods use a shared topology, which cannot flexibly adapt to the diverse correlations between joints under different motion features. The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms. In this work, we propose a novel graph convolutional learning framework, called PCCTR-GCN, which integrates pose correction and channel topology refinement for skeleton-based human action… More >