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  • Open Access

    ARTICLE

    A Novel Approach Based on Graph Attention Networks for Fruit Recognition

    Dat Tran-Anh1, Hoai Nam Vu2,3,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2703-2722, 2025, DOI:10.32604/cmc.2025.061086 - 17 February 2025

    Abstract Counterfeit agricultural products pose a significant challenge to global food security and economic stability, necessitating advanced detection mechanisms to ensure authenticity and quality. To address this pressing issue, we introduce iGFruit, an innovative model designed to enhance the detection of counterfeit agricultural products by integrating multimodal data processing. Our approach utilizes both image and text data for comprehensive feature extraction, employing advanced backbone models such as Vision Transformer (ViT), Normalizer-Free Network (NFNet), and Bidirectional Encoder Representations from Transformers (BERT). These extracted features are fused and processed using a Graph Attention Network (GAT) to capture intricate More >

  • Open Access

    ARTICLE

    Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems

    Yahia Said1,2,*, Yahya Alassaf3, Refka Ghodhbani4, Taoufik Saidani4, Olfa Ben Rhaiem5

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3005-3018, 2025, DOI:10.32604/cmc.2025.060928 - 17 February 2025

    Abstract Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic… More >

  • Open Access

    ARTICLE

    KD-SegNet: Efficient Semantic Segmentation Network with Knowledge Distillation Based on Monocular Camera

    Thai-Viet Dang1,*, Nhu-Nghia Bui1, Phan Xuan Tan2,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmc.2025.060605 - 17 February 2025

    Abstract Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid… More >

  • Open Access

    ARTICLE

    Decentralized Federated Graph Learning via Surrogate Model

    Bolin Zhang, Ruichun Gu*, Haiying Liu

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2521-2535, 2025, DOI:10.32604/cmc.2024.060331 - 17 February 2025

    Abstract Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation; however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized… More >

  • Open Access

    REVIEW

    An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security

    Uddagiri Sirisha1,*, Chanumolu Kiran Kumar2, Sujatha Canavoy Narahari3, Parvathaneni Naga Srinivasu4,5,6

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1757-1810, 2025, DOI:10.32604/cmc.2024.059715 - 17 February 2025

    Abstract The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work,… More >

  • Open Access

    ARTICLE

    MATD3-Based End-Edge Collaborative Resource Optimization for NOMA-Assisted Industrial Wireless Networks

    Ru Hao1,2,3, Chi Xu2,3,*, Jing Liu1

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3203-3222, 2025, DOI:10.32604/cmc.2024.059689 - 17 February 2025

    Abstract Non-orthogonal multiple access (NOMA) technology has recently been widely integrated into multi-access edge computing (MEC) to support task offloading in industrial wireless networks (IWNs) with limited radio resources. This paper minimizes the system overhead regarding task processing delay and energy consumption for the IWN with hybrid NOMA and orthogonal multiple access (OMA) schemes. Specifically, we formulate the system overhead minimization (SOM) problem by considering the limited computation and communication resources and NOMA efficiency. To solve the complex mixed-integer nonconvex problem, we combine the multi-agent twin delayed deep deterministic policy gradient (MATD3) and convex optimization, namely More >

  • Open Access

    ARTICLE

    TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional Networks

    Baoquan Liu1,3, Xi Chen2,3, Qingjun Yuan2,3, Degang Li2,3, Chunxiang Gu2,3,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3179-3201, 2025, DOI:10.32604/cmc.2024.059688 - 17 February 2025

    Abstract With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not… More >

  • Open Access

    ARTICLE

    TB-Graph: Enhancing Encrypted Malicious Traffic Classification through Relational Graph Attention Networks

    Ming Liu, Qichao Yang, Wenqing Wang, Shengli Liu*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2985-3004, 2025, DOI:10.32604/cmc.2024.059417 - 17 February 2025

    Abstract The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable challenge to cybersecurity. Traditional machine learning and deep learning techniques often fall short in identifying encrypted malicious traffic due to their inability to fully extract and utilize the implicit relational and positional information embedded within data packets. This limitation has led to an unresolved challenge in the cybersecurity community: how to effectively extract valuable insights from the complex patterns of traffic packet transmission. Consequently, this paper introduces the… More >

  • Open Access

    ARTICLE

    An Arrhythmia Intelligent Recognition Method Based on a Multimodal Information and Spatio-Temporal Hybrid Neural Network Model

    Xinchao Han1,2, Aojun Zhang1,2, Runchuan Li1,2,*, Shengya Shen3, Di Zhang1,2, Bo Jin1,2, Longfei Mao1,2, Linqi Yang1,2, Shuqin Zhang1,2

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3443-3465, 2025, DOI:10.32604/cmc.2024.059403 - 17 February 2025

    Abstract Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, More >

  • Open Access

    ARTICLE

    A Dynamic Social Network Graph Anonymity Scheme with Community Structure Protection

    Yuanjing Hao, Xuemin Wang, Liang Chang*, Long Li, Mingmeng Zhang

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3131-3159, 2025, DOI:10.32604/cmc.2024.059201 - 17 February 2025

    Abstract Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific… More >

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