Hafsa Sidaq¹, Lei Wang¹, Sghaier Guizani^2,*, Hussain Haider³, Ateeq Ur Rehman^4,*, Habib Hamam^5,6,7

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071254 - 12 January 2026

Abstract Numerous sectors, such as education, the IT sector, and corporate organizations, transitioned to virtual meetings after the COVID-19 crisis. Organizations now seek to assess participants’ fatigue levels in online meetings to remain competitive. Instructors cannot effectively monitor every individual in a virtual environment, which raises significant concerns about participant fatigue. Our proposed system monitors fatigue, identifying attentive and drowsy individuals throughout the online session. We leverage Dlib’s pre-trained facial landmark detector and focus on the eye landmarks only, offering a more detailed analysis for predicting eye opening and closing of the eyes, rather than focusing… More >

Dao Phuc Minh Huy¹, Gia Nhu Nguyen¹, Dac-Nhuong Le^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070948 - 12 January 2026

Abstract Online examinations have become a dominant assessment mode, increasing concerns over academic integrity. To address the critical challenge of detecting cheating behaviours, this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification. The methodology utilises object detection models—You Only Look Once (YOLOv12), Faster Region-based Convolutional Neural Network (RCNN), and Single Shot Detector (SSD) MobileNet—integrated with classification models such as Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Unit (Bi-GRU), and CNN-LSTM (Long Short-Term Memory). Two distinct datasets were used: the Online Exam Proctoring (EOP) dataset from Michigan State University and… More >

Muhammad Asim^1,*, Abdelhamied A. Ateya¹, Mudasir Ahmad Wani^1,2, Gauhar Ali¹, Mohammed ElAffendi¹, Ahmed A. Abd El-Latif¹, Reshma Siyal³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070684 - 12 January 2026

Abstract The growing developments in 5G and 6G wireless communications have revolutionized communications technologies, providing faster speeds with reduced latency and improved connectivity to users. However, it raises significant security challenges, including impersonation threats, data manipulation, distributed denial of service (DDoS) attacks, and privacy breaches. Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks. This survey provides a comprehensive review of how Federated Learning (FL), Blockchain, and Digital Twin (DT) technologies can collectively enhance the security of 5G and 6G systems. Blockchain offers decentralized, immutable, and transparent mechanisms for securing More >

Nghia Dinh¹, Vinh Truong Hoang^1,*, Viet-Tuan Le¹, Kiet Tran-Trung¹, Ha Duong Thi Hong¹, Bay Nguyen Van¹, Hau Nguyen Trung¹, Thien Ho Huong¹, Kittikhun Meethongjan^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069587 - 12 January 2026

Abstract Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators, which serve both mechanical and electrical functions. Even a single defective insulator can lead to equipment breakdown, costly service interruptions, and increased maintenance demands. While unmanned aerial vehicles (UAVs) enable rapid and cost-effective collection of high-resolution imagery, accurate defect identification remains challenging due to cluttered backgrounds, variable lighting, and the diverse appearance of faults. To address these issues, we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced More >

Sarah M. Alhammad¹, Diaa Salama AbdElminaam^2,3,*, Asmaa Rizk Ibrahim⁴, Ahmed Taha²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069263 - 12 January 2026

Abstract Accurate parameter extraction of photovoltaic (PV) models plays a critical role in enabling precise performance prediction, optimal system sizing, and effective operational control under diverse environmental conditions. While a wide range of metaheuristic optimisation techniques have been applied to this problem, many existing methods are hindered by slow convergence rates, susceptibility to premature stagnation, and reduced accuracy when applied to complex multi-diode PV configurations. These limitations can lead to suboptimal modelling, reducing the efficiency of PV system design and operation. In this work, we propose an enhanced hybrid optimisation approach, the modified Spider Wasp Optimization… More >

Longqi Liao¹, Jing Li², Yuhua Li³, Yuemin Wang³, Jinhua Li^1,*, Liyuan Cao^4,*, Chunxiang Li^4,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.072237 - 08 January 2026

Abstract Quantitative detection of sleeve grouting compactness is a technical challenge in civil engineering testing. This study explores a novel quantitative detection method based on ultrasonic time-frequency dual-domain analysis. It establishes a mapping relationship between sleeve grouting compactness and characteristic parameters. First, this study made samples with gradient defects for two types of grouting sleeves, G18 and G20. These included four cases: 2D, 4D, 6D defects (where D is the diameter of the grouting sleeve), and no-defect. Then, an ultrasonic input/output data acquisition system was established. Three-dimensional sound field distribution data were obtained through an orthogonal… More >

Zhuangqiang Wen¹, Min Zhang², Zhekun Shou^3,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071300 - 08 January 2026

Abstract Roadbed disease detection is essential for maintaining road functionality. Ground penetrating radar (GPR) enables non-destructive detection without drilling. However, current identification often relies on manual inspection, which requires extensive experience, suffers from low efficiency, and is highly subjective. As the results are presented as radar images, image processing methods can be applied for fast and objective identification. Deep learning-based approaches now offer a robust solution for automated roadbed disease detection. This study proposes an enhanced Faster Region-based Convolutional Neural Networks (R-CNN) framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation (2D-DFT) for… More >

Hua-Qin Wu^1,2, Hao Yan^1,2, Hong Zhang^1,2,*, Shun-Wu Xu^1,2, Feng-Yu Gao^1,2, Zhao-Wen Chen^1,2

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.070589 - 08 January 2026

Abstract In industrial manufacturing, efficient surface defect detection is crucial for ensuring product quality and production safety. Traditional inspection methods are often slow, subjective, and prone to errors, while classical machine vision techniques struggle with complex backgrounds and small defects. To address these challenges, this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset. Three key improvements are introduced in the proposed model. First, a lightweight Guided Attention Feature Module (GAFM) is incorporated to enhance multi-scale feature fusion, allowing the model to better capture and integrate semantic and… More >

Thi Kim Chi Duong¹, Bich-Ngoc. Mach², Hoa-Cuc. Nguyen², Thi Nhu Quynh Trinh², Thanh Q. Nguyen^3,4,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.070185 - 08 January 2026

Abstract This study explores theoretical insights and experimental results on monitoring load-carrying capacity degradation in bridge spans through frequency analysis. Experiments were conducted on real bridge structures, including the Binh Thuan Bridge, focusing on analyzing the power spectral density (PSD) of vibration signals under random traffic loads. Detailed digital models of various bridge spans with different structural designs and construction periods were developed to ensure diversity. The study utilized PSD to analyze the vibration signals from the bridge spans under various loading conditions, identifying the vibration frequencies and the corresponding response regions. The research correlated the… More >

Hongcheng Zhao¹, Tong Yang ², Yihui Hu², Fengxiang Guo^2,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.068987 - 08 January 2026

Abstract With the rapid development of transportation infrastructure, ensuring road safety through timely and accurate highway inspection has become increasingly critical. Traditional manual inspection methods are not only time-consuming and labor-intensive, but they also struggle to provide consistent, high-precision detection and real-time monitoring of pavement surface defects. To overcome these limitations, we propose an Automatic Recognition of Pavement Defect (ARPD) algorithm, which leverages unmanned aerial vehicle (UAV)-based aerial imagery to automate the inspection process. The ARPD framework incorporates a backbone network based on the Selective State Space Model (S³M), which is designed to capture long-range temporal dependencies.… More >