Qian Yu^1,2, Gui Zhang^2,*, Ying Wang¹, Xin Wu², Jiangshu Xiao², Wenbing Kuang¹, Juan Zhang²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072172 - 12 January 2026

Abstract Detecting small forest fire targets in unmanned aerial vehicle (UAV) images is difficult, as flames typically cover only a very limited portion of the visual scene. This study proposes Context-guided Compact Lightweight Network (CCLNet), an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources. CCLNet employs a three-stage network architecture. Its key components include three modules. C3F-Convolutional Gated Linear Unit (C3F-CGLU) performs selective local feature extraction while preserving fine-grained high-frequency flame details. Context-Guided Feature Fusion Module (CGFM) replaces plain concatenation with triplet-attention interactions to… More >

Junhui Xu¹, Qi Wang^1,*, Chichen Lin², Weijian Fan³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071661 - 12 January 2026

Abstract Social bots are automated programs designed to spread rumors and misinformation, posing significant threats to online security. Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots: a higher number of connected components weakens their collaborative capabilities, thereby reducing their proportion within the overall network. However, current social bot detection methods still make limited use of topological features. Furthermore, both graph neural network (GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations, and existing studies lack an effective… More >

Jalees Ur Rahman¹, Muhammad Hanif¹, Usman Haider^2,*, Saeed Mian Qaisar^3,*, Sarra Ayouni⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071251 - 12 January 2026

Abstract Deep neural networks have achieved excellent classification results on several computer vision benchmarks. This has led to the popularity of machine learning as a service, where trained algorithms are hosted on the cloud and inference can be obtained on real-world data. In most applications, it is important to compress the vision data due to the enormous bandwidth and memory requirements. Video codecs exploit spatial and temporal correlations to achieve high compression ratios, but they are computationally expensive. This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos. However, contrary… More >

Chee Chin Lim^1,2,*, Hui Wen Tiu¹, Qi Wei Oung^1,3, Chiew Chea Lau⁴, Xiao Jian Tan^2,5

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070468 - 12 January 2026

Abstract Breast cancer remains one of the leading causes of cancer mortality world-wide, with accurate molecular subtyping is critical for guiding treatment and improving patient outcomes. Traditional molecular subtyping via immuno-histochemistry (IHC) test is invasive, time-consuming, and may not fully represent tumor heterogeneity. This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes. Four pretrained models, including two Convolutional Neural Networks (MobileNet_V3_Large and VGG-16) and two Vision Transformers (ViT_B_16 and ViT_Base_Patch16_Clip_224) were fine-tuned to classify images into HER2-enriched, Luminal, Normal-like, and Triple Negative subtypes. Hyperparameter tuning,… More >

Tiankui Sun^*, Shuyi Zhuang, Yongling Lu, Wenqiang Xie, Ning Guo, Sudi Xu

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070213 - 27 December 2025

Abstract To ensure an uninterrupted power supply, mobile power sources (MPS) are widely deployed in power grids during emergencies. Comprising mobile emergency generators (MEGs) and mobile energy storage systems (MESS), MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies, offering advantages such as flexibility and high resilience through electricity delivery via transportation networks. This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator (DWIG) intended for MEG applications, employing an improved particle swarm optimization (PSO) algorithm based on a back-propagation neural network (BPNN). A… More >

Zeyu Chen¹, Jian Sun^2,*, Zhengda Huan¹, Ziyi Zhang¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.071182 - 09 December 2025

Abstract To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication (JRC) systems under dynamic environments, an intelligent optimization framework integrating Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) is proposed. This framework models resource allocation as a Partially Observable Markov Game (POMG), designs a weighted reward function to balance radar and communication efficiencies, adopts the Multi-Agent Proximal Policy Optimization (MAPPO) framework, and integrates Graph Convolutional Networks (GCN) and Graph Sample and Aggregate (GraphSAGE) to optimize information interaction. Simulations show that, compared with traditional methods 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 >

Yuang Chen^1,2, Yong Li^1,*, Fang Lin^1,2, Shuhan Lv^1,2, Jiaze Jiang^1,2

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.070990 - 09 December 2025

Abstract Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet, this paper proposes a novel lightweight neural network model called ResghostNet. This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks, which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations. Specifically, ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow, and designs a weight self-attention mechanism combined with SE blocks to enhance feature More >

Haoxin Sun¹, Xiao Yu^1,*, Jiale Li¹, Yitong Xu¹, Jie Yu¹, Huanhuan Li¹, Yuanzhang Li², Yu-An Tan²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070930 - 09 December 2025

Abstract Since the advent of smart contracts, security vulnerabilities have remained a persistent challenge, compromsing both the reliability of contract execution and the overall stability of the virtual currency market. Consequently, the academic community has devoted increasing attention to these security risks. However, conventional approaches to vulnerability detection frequently exhibit limited accuracy. To address this limitation, the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks (GNNs). The proposed method first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts. More >

Le Wang¹, Bing Xu^1,*, Peng Liu², En Yuan¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-14, 2026, DOI:10.32604/cmc.2025.070794 - 09 December 2025

Abstract Deep learning has been recognized as an effective method for indoor positioning. However, most existing real-valued neural networks (RVNNs) treat the two constituent components of complex-valued channel state information (CSI) as real-valued inputs, potentially discarding useful information embedded in the original CSI. In addition, existing positioning models generally face the contradiction between computational complexity and positioning accuracy. To address these issues, we combine graph neural network (GNN) with complex-valued neural network (CVNN) to construct a lightweight indoor positioning model named CGNet. CGNet employs complex-valued convolution operation to directly process the original CSI data, fully exploiting… More >