Huo Ye¹, Jiantong Li², Lingna Xu^3,*

Chalcogenide Letters, Vol.23, No.8, 2026, DOI:10.32604/cl.2026.087654 - 18 September 2026

Abstract SF₆ is the primary insulating and arc extinction medium in gas-insulated switchgear (GIS). Sulfur hexafluoride (SF₆) decomposes to create diagnostic markers, such as sulfur dioxide (SO₂), thionyl fluoride (SOF₂), and hydrogen sulfide (H₂S) when electrical problems occur, such as partial discharge and local overheating. Accurate quantification of these fault-marker gases is important for the early identification of insulation defects and the condition assessment of SF₆-insulated equipment. Molybdenum disulfide (MoS₂) is a well-known and atomically thin van der Waals semiconductor that has attracted considerable attention as a platform for gas-sensing applications. This is due to its large accessible surface area,… More >

Chun-Hsiu Yeh^1,*, Xian-Zhong Lin^1,*, Yi-Teng Lin¹, Yung-Chen Chou², Wei-Cheng Shen¹

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087068 - 15 September 2026

Abstract Printed circuit board (PCB) defect detection is critical for industrial quality control, where detection models must identify small and irregular defects while satisfying real-time inspection requirements. However, conventional deep learning-based detectors often require substantial computational resources, making deployment on edge devices difficult. To address this issue, FGE-YOLO is proposed as a deployment-oriented lightweight object detection model based on YOLOv8. The proposed model integrates a FasterNet-based backbone, a GhostConv-Based Neck, and an Efficient Channel Attention (ECA) mechanism. In the backbone, standard convolutions are retained in the shallow P1 and P2 stages to preserve low-level spatial details,… More >

Hui Xu, Shuang Qu^*, Pan Hu

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087054 - 15 September 2026

Abstract With the rapid growth in the number of end devices in the Internet of Things (IoT), network traffic has become increasingly complex and redundant, while multiple attack types often coexist, posing major challenges to traffic anomaly detection. Traditional machine learning-based methods for IoT traffic anomaly detection often suffer from severe feature redundancy, high computational complexity, and low detection efficiency, making it difficult to simultaneously achieve high detection accuracy and computational efficiency. To address this issue, metaheuristic algorithms are often introduced in the feature selection stage to reduce feature redundancy and improve detection efficiency. However, the… More >

Maryam Almarwani^*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086887 - 15 September 2026

Abstract OAuth authorization ecosystems contain a large and diverse collection of capabilities distributed across multiple cloud platforms. Although previous studies have investigated OAuth security, privacy, and authorization management, the structural organization of authorization capabilities across providers has received limited attention. This study presents a cross-provider structural analysis of OAuth capabilities from seven major authorization platforms. A unified capability dictionary is constructed by normalizing publicly documented OAuth scopes into a common semantic representation. The normalized capabilities are transformed into an undirected semantic topology in which nodes represent capabilities and edges represent deterministic semantic relationships. Standard network analysis… More >

Baofeng Duan¹, Xinghai Yu¹, Peng Wang², Tao Feng¹, Yongbo Jiang^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086743 - 15 September 2026

Abstract With the sustained expansion of complex Internet of Things (IoT) ecosystems, malicious traffic detection has become critical for maintaining both cyber security and operational continuity. Modern IoT deployments contain heterogeneous devices, ubiquitous sensing layers, edge services, and autonomous assets, so abnormal communication may affect not only data confidentiality but also physical operations. To address the limitations of independent flow-level detection and heavy graph propagation, this paper proposes a Lightweight Graph-Attentive Network for Traffic Detection (LGNT). LGNT constructs a directed traffic-interaction graph from NetFlow records, where communication entities are represented as nodes and traffic sessions are… More >

Rongfa Chen^1,*, Liping Chen², Xiuzhe Meng¹, Daniel Zeng^1,2,3

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086702 - 15 September 2026

Abstract Existing fake news detection methods largely rely on single-source datasets, leading models to overfit platform-specific features and perform poorly on heterogeneous multi-source data. Even with the emergence of Large Language Models (LLMs), our benchmarks show that general-purpose LLMs still struggle to identify deceptive intent when source-specific context is unavailable. To address unseen-source-group generalization, we propose SHIELD (Social Heterogeneous Interaction Embedding for Latent Deception). SHIELD models interaction patterns shared across sources rather than relying only on isolated text features or semantic inference. Specifically, we construct a Social Reaction-Aware Heterogeneous Interaction Graph to capture consistencies and discrepancies… More >

Mohammad Alshinwan^1,*, Walaa Alayed^2,*, Fatma A. Hashim³, Arar Al Tawil⁴

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086257 - 15 September 2026

Abstract The increasing trend of phishing sites is among the important threats against the Internet security, associated with monetary loss, data leakage, and identity swindle. In response to this urgent problem, this paper proposes a new phishing website detection framework based on the Modified Differential Evolution (mDE) algorithm in conjunction with state-of-the-art machine learning classifiers. The proposed mDE integrates with dynamic mutation and crossover strategies to improve the global search capability and the convergence speed, which is superior to traditional single optimization methods. We conduct experiments on two benchmark datasets: the UCI Phishing Websites dataset and… More >

Peng Shen¹, Tenglong Li^1,2, Yongpeng Sun^1,2, Hao Cui^1,2, Guoqing Zhang^3,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086207 - 15 September 2026

Abstract Agriculture plays an important role in food security and social development. With the rapid development of the Fourth Agricultural Revolution, also known as Agriculture 4.0, traditional weed control methods that rely on manual experience and uniform herbicide application can no longer meet the demands for efficient, precise, and environmentally friendly production. Farmland weeds compete with crops for light, water, and nutrients, thereby seriously affecting crop yield and quality. Therefore, the development of efficient weed detection and recognition technologies is of great significance. In recent years, the rapid progress of deep learning in computer vision has… More >

Xinglong Cao, Cong Wang^*, Jie Yan, Songcan Yu, Mingze He

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086163 - 15 September 2026

Abstract There is currently a lack of systematic research on the fine-grained detection of multi-language and multi-type exploit scripts. To address this gap, this study proposes a model named ASTBertX (AST + BERT + XGBoost) for identifying the specific exploit types of malicious scripts; the model organically integrates code sequence semantics with structural semantics. First, the model utilizes the pre-trained model GraphCodeBERT to extract contextual semantic representations of the scripts; simultaneously, it introduces semantic enhancement nodes into the Abstract Syntax Tree (AST) and employs GATv2 to learn the AST’s structural representation. These two representations are mapped… More >

Weijia Liu¹, Junwen Deng², Hao Li³, Zhenyong Zhang^3,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086022 - 15 September 2026

Abstract With the rapid development of artificial intelligence technologies in machine learning-as-a-service (MLaaS), deep learning-based intelligent models have demonstrated high value in applications such as trend prediction and risk assessment. However, MLaaS data are typically highly sensitive and contain private information. In cross-institutional collaborative modeling scenarios, different departments and local centers often hold partial, heterogeneous data resources on MLaaS platforms. Given data security, privacy, and compliance requirements, raw data are difficult to share directly, which makes it challenging to apply centralized model training methods. This paper proposes FedE, a multi-precision, multi-source, heterogeneous privacy-preserving federated learning training More >