Sabina-Cristiana Necula^*, Napoleon-Alexandru Sireteanu

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070861 - 09 December 2025

Abstract Modern business information systems face significant challenges in managing heterogeneous data sources, integrating disparate systems, and providing real-time decision support in complex enterprise environments. Contemporary enterprises typically operate 200+ interconnected systems, with research indicating that 52% of organizations manage three or more enterprise content management systems, creating information silos that reduce operational efficiency by up to 35%. While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision, their systematic application to business information systems remains largely unexplored. This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System (HABIS)… More >

Xu Tao¹, Qiang Xiao², Zhaoqi Jin², Hao Li^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.070790 - 09 December 2025

Abstract Image fusion technology aims to generate a more informative single image by integrating complementary information from multi-modal images. Despite the significant progress of deep learning-based fusion methods, existing algorithms are often limited to single or dual-dimensional feature interactions, thus struggling to fully exploit the profound complementarity between multi-modal images. To address this, this paper proposes a parallel multi-dimensional complementary fusion network, termed PMCFusion, for the task of infrared and visible image fusion. The core of this method is its unique parallel three-branch fusion module, PTFM, which pioneers the parallel synergistic perception and efficient integration of… 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 >

Song Xu^1,2,*, Liang Xuan^1,2, Yifeng Li^1,2, Qiang Zhang^1,2

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070472 - 09 December 2025

Abstract The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts. In the industrial robot production scenarios, the 6D pose estimation of industrial parts mainly faces two challenges: one is the loss of information and interference caused by occlusion and stacking in the sorting scenario, the other is the difficulty of feature extraction due to the weak texture of industrial parts. To address the above problems, this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts, namely CB-PVNet. On the… More >

Yuqiang Wu^1,2, Zhao Ji¹, Guanqi You¹, Zihan Zhang¹, Chaoping Lu³, Huanliang Xu¹, Zhaoyu Zhai^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-22, 2026, DOI:10.32604/cmc.2025.070414 - 09 December 2025

Abstract Understanding fish movement trajectories in aquaculture is essential for practical applications, such as disease warning, feeding optimization, and breeding management. These trajectories reveal key information about the fish’s behavior, health, and environmental adaptability. However, when multi-object tracking (MOT) algorithms are applied to the high-density aquaculture environment, occlusion and overlapping among fish may result in missed detections, false detections, and identity switching problems, which limit the tracking accuracy. To address these issues, this paper proposes FishTracker, a MOT algorithm, by utilizing a Tracking-by-Detection framework. First, the neck part of the YOLOv8 model is enhanced by introducing… More >

Yifan Zhang¹, Yong Gan^2,*, Mengke Tang¹, Xinxin Gan³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068880 - 09 December 2025

Abstract High-resolution remote sensing imagery is essential for critical applications such as precision agriculture, urban management planning, and military reconnaissance. Although significant progress has been made in single-image super-resolution (SISR) using generative adversarial networks (GANs), existing approaches still face challenges in recovering high-frequency details, effectively utilizing features, maintaining structural integrity, and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery. To address these limitations, this paper proposes the Improved Residual Module and Attention Mechanism Network (IRMANet), a novel architecture specifically designed for remote sensing image reconstruction. IRMANet builds upon the Super-Resolution… More >

Seyong Jin¹, Muhammad Fayaz², L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667 - 10 November 2025

Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… More >

Zitong Zhao¹, Zixuan Zhang², Zhenxing Niu^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069752 - 10 November 2025

Abstract Reliable traffic flow prediction is crucial for mitigating urban congestion. This paper proposes Attention-based spatiotemporal Interactive Dynamic Graph Convolutional Network (AIDGCN), a novel architecture integrating Interactive Dynamic Graph Convolution Network (IDGCN) with Temporal Multi-Head Trend-Aware Attention. Its core innovation lies in IDGCN, which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs, and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data. For 15- and 60-min forecasting on METR-LA, AIDGCN achieves MAEs of 0.75% and 0.39%, and RMSEs More >

Haihao He^1,2, Xianzhou Dong^1,*, Shuangshuang Wang¹, Chengzhang Zhu¹, Xiaotong Zhao^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069513 - 10 November 2025

Abstract Clock synchronization has important applications in multi-agent collaboration (such as drone light shows, intelligent transportation systems, and game AI), group decision-making, and emergency rescue operations. Synchronization method based on pulse-coupled oscillators (PCOs) provides an effective solution for clock synchronization in wireless networks. However, the existing clock synchronization algorithms in multi-agent ad hoc networks are difficult to meet the requirements of high precision and high stability of synchronization clock in group cooperation. Hence, this paper constructs a network model, named DAUNet (unsupervised neural network based on dual attention), to enhance clock synchronization accuracy in multi-agent wireless ad hoc… More >

Yongbo Jiang, Shengnan Huang, Tao Feng, Baofeng Duan^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.069423 - 10 November 2025

Abstract In the context of modern software development characterized by increasing complexity and compressed development cycles, traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates. This paper proposes a Syntax-Aware Hierarchical Attention Network (SAHAN) model, which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms. The SAHAN model first generates Syntax Independent Units (SIUs), which slices the code based on Abstract Syntax Tree (AST) and predefined grammar rules, retaining vulnerability-sensitive contexts. Following this, through More >