Xiaoyu Liu, Yong Hu^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4413-4432, 2024, DOI:10.32604/cmc.2024.053938 - 12 September 2024

Abstract Multi-label image classification is recognized as an important task within the field of computer vision, a discipline that has experienced a significant escalation in research endeavors in recent years. The widespread adoption of convolutional neural networks (CNNs) has catalyzed the remarkable success of architectures such as ResNet-101 within the domain of image classification. However, in multi-label image classification tasks, it is crucial to consider the correlation between labels. In order to improve the accuracy and performance of multi-label classification and fully combine visual and semantic features, many existing studies use graph convolutional networks (GCN) for… More >

Jianfeng Lu^1,2, Caijin Li¹, Xiangye Huang¹, Chen Cui³, Mahmoud Emam^1,2,4,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3047-3065, 2024, DOI:10.32604/cmc.2024.053680 - 15 August 2024

Abstract The widespread availability of digital multimedia data has led to a new challenge in digital forensics. Traditional source camera identification algorithms usually rely on various traces in the capturing process. However, these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms. Convolutional Neural Networks (CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices. However, their performances is not ideal in case of distinguishing between individual devices of the same model, because cameras of the same model typically use the same… More >

Kexin Wang^*, Yingdong Gou, Dingrui Xue^*, Jiancheng Liu, Wanlong Qi, Gang Hou, Bo Li

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2941-2962, 2024, DOI:10.32604/cmc.2024.052893 - 15 August 2024

Abstract The collective Unmanned Weapon System-of-Systems (UWSOS) network represents a fundamental element in modern warfare, characterized by a diverse array of unmanned combat platforms interconnected through heterogeneous network architectures. Despite its strategic importance, the UWSOS network is highly susceptible to hostile infiltrations, which significantly impede its battlefield recovery capabilities. Existing methods to enhance network resilience predominantly focus on basic graph relationships, neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS. To address these limitations, we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network (E-MAGCN), designed to augment the adaptability of More >

Suyi Liu^1,*, Jianning Chi¹, Chengdong Wu¹, Fang Xu^2,3,4, Xiaosheng Yu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4471-4489, 2024, DOI:10.32604/cmc.2024.049450 - 20 June 2024

Abstract In recent years, semantic segmentation on 3D point cloud data has attracted much attention. Unlike 2D images where pixels distribute regularly in the image domain, 3D point clouds in non-Euclidean space are irregular and inherently sparse. Therefore, it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space. Most current methods either focus on local feature aggregation or long-range context dependency, but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks. In this paper, we propose a Transformer-based… More >

Chuyuan Wei^*, Jinzhe Li, Zhiyuan Wang, Shanshan Wan, Maozu Guo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3299-3314, 2024, DOI:10.32604/cmc.2024.047811 - 15 May 2024

Abstract Deep neural network-based relational extraction research has made significant progress in recent years, and it provides data support for many natural language processing downstream tasks such as building knowledge graph, sentiment analysis and question-answering systems. However, previous studies ignored much unused structural information in sentences that could enhance the performance of the relation extraction task. Moreover, most existing dependency-based models utilize self-attention to distinguish the importance of context, which hardly deals with multiple-structure information. To efficiently leverage multiple structure information, this paper proposes a dynamic structure attention mechanism model based on textual structure information, which deeply… More >

Yunhe Wang¹, Yuxin Xia², Shuai Liu^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4489-4507, 2024, DOI:10.32604/cmc.2024.048813 - 26 March 2024

Abstract In recent years, skeleton-based action recognition has made great achievements in Computer Vision. A graph convolutional network (GCN) is effective for action recognition, modelling the human skeleton as a spatio-temporal graph. Most GCNs define the graph topology by physical relations of the human joints. However, this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs, resulting in a low recognition rate for specific actions with implicit correlation between joint pairs. In addition, existing methods ignore the trend correlation between adjacent frames within an action… More >

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 - 26 March 2024

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 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 - 31 October 2023

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… More >

Chengfei Hua^1,2,3, Wenzhong Yang^2,3,*, Liejun Wang^2,3, Fuyuan Wei^2,3, KeZiErBieKe HaiLaTi^2,3, Yuanyuan Liao^2,3

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 893-909, 2023, DOI:10.32604/cmc.2023.040862 - 31 October 2023

Abstract With the development of social media and the prevalence of mobile devices, an increasing number of people tend to use social media platforms to express their opinions and attitudes, leading to many online controversies. These online controversies can severely threaten social stability, making automatic detection of controversies particularly necessary. Most controversy detection methods currently focus on mining features from text semantics and propagation structures. However, these methods have two drawbacks: 1) limited ability to capture structural features and failure to learn deeper structural features, and 2) neglecting the influence of topic information and ineffective utilization… More >

Qiankun Zuo^1,4, Junhua Hu², Yudong Zhang^3,*, Junren Pan⁴, Changhong Jing⁴, Xuhang Chen⁵, Xiaobo Meng⁶, Jin Hong^7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2129-2147, 2023, DOI:10.32604/cmes.2023.028732 - 03 August 2023

Abstract The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders. The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders. However, it is challenging to access considerable amounts of brain functional network data, which hinders the widespread application of data-driven models in dementia diagnosis. In this study, a novel distribution-regularized adversarial graph auto-Encoder (DAGAE) with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset, improving the dementia diagnosis accuracy of data-driven… More > Graphic Abstract