Safwan Al-sayed¹, Xi Wang¹, Yijiang Peng^1,*, Esraa Hyarat², Ahmad Ali AlZubi³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074068 - 12 January 2026

Abstract In modern construction, Lightweight Aggregate Concrete (LWAC) has been recognized as a vital material of concern because of its unique properties, such as reduced density and improved thermal insulation. Despite the extensive knowledge regarding its macroscopic properties, there is a wide knowledge gap in understanding the influence of microscale parameters like aggregate porosity and volume ratio on the mechanical response of LWAC. This study aims to bridge this knowledge gap, spurred by the need to enhance the predictability and applicability of LWAC in various construction environments. With the help of advanced numerical methods, including the… More >

Girish Hariharan¹, Vinyas¹, Gowrishankar Mandya Chennegowda¹, Nitesh Kumar¹, Shiva Kumar¹, Deepak Doreswamy², Subraya Krishna Bhat^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073772 - 12 January 2026

Abstract This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite–epoxy (G–E) composites reinforced with cast iron (CI) filler particles. Hybrid G–E composite with added cast iron (CI) filler particles enhances stiffness, strength, and vibration damping, offering enhanced performance for vibration-sensitive engineering applications. Unlike conventional approaches, this work simultaneously employs Artificial Neural Networks (ANN) for high-accuracy property prediction and Response Surface Methodology (RSM) for in-depth analysis of factor interactions and optimization. A total of 24 experimental test data sets of varying input… More >

Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

Tong Wu, Dawei Zhou, Qingyu Ou^*, Fang Luo

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073482 - 12 January 2026

Abstract The increasing interconnection of modern industrial control systems (ICSs) with the Internet has enhanced operational efficiency, but also made these systems more vulnerable to cyberattacks. This heightened exposure has driven a growing need for robust ICS security measures. Among the key defences, intrusion detection technology is critical in identifying threats to ICS networks. This paper provides an overview of the distinctive characteristics of ICS network security, highlighting standard attack methods. It then examines various intrusion detection methods, including those based on misuse detection, anomaly detection, machine learning, and specialised requirements. This paper concludes by exploring More >

Lingyun Xiang^1,*, Nian Li², Yuling Liu³, Jiayong Hu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073347 - 12 January 2026

Abstract The rapid advancement of large language models (LLMs) has driven the pervasive adoption of AI-generated content (AIGC), while also raising concerns about misinformation, academic misconduct, biased or harmful content, and other risks. Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information. This survey reviews recent progress in detection methods, categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals. Passive detection is further divided into surface linguistic feature-based and language model-based methods, whereas active detection encompasses watermarking-based and semantic retrieval-based More >

Ahmed Awad Mohamed¹, Eslam Abdelhakim Seyam^2,*, Ahmed R. Elsaeed³, Laith Abualigah⁴, Aseel Smerat^5,6, Ahmed M. AbdelMouty⁷, Hosam E. Refaat⁸

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073171 - 12 January 2026

Abstract In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across… More >

Nikita Golovkin^1,2, Olesya Nikulenkova³, Vsevolod Pobezhimov¹, Alexander Nesmelov¹, Sergei Chvalun¹, Fedor Sorokin³, Arthur Krupnin^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073161 - 12 January 2026

Abstract This study presents and verifies a hybrid methodology for reliable determination of parameters in structural rheological models (Zener, Burgers, and Maxwell) describing the viscoelastic behavior of polyurethane specimens manufactured using extrusion-based 3D printing. Through comprehensive testing, including cyclic compression at strain rates ranging from 0.12 to 120 mm/min (0%–15% strain) and creep/relaxation experiments (10%–30% strain), the lumped parameters were independently determined using both analytical and numerical solutions of the models’ differential equations, followed by cross-verification in additional experiments. Numerical solutions for creep and relaxation problems were obtained using finite element analysis, with the three-parameter Mooney-Rivlin… More > Graphic Abstract

Jixin Wu^1,2, Mingtao Zhou^2,3, Di Wu^2,3, Wenqi Ren⁴, Jiatian Mei^2,3, Shu Zhang^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072964 - 12 January 2026

Abstract End-to-end Temporal Action Detection (TAD) has achieved remarkable progress in recent years, driven by innovations in model architectures and the emergence of Video Foundation Models (VFMs). However, existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs, which become particularly pronounced when processing long video sequences. Moreover, the need for precise temporal boundary annotations makes data labeling extremely expensive. In low-resource settings where annotated samples are scarce, direct fine-tuning tends to cause overfitting. To address these challenges, we introduce Dynamic Low-Rank Adapter (DyLoRA), a lightweight fine-tuning framework tailored specifically… More >

Hang Wen^1,2, Kai Zeng^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072909 - 12 January 2026

Abstract Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity. This problem becomes more severe when edge participation rate is low, as the information collected from different edge devices varies significantly. As a result, communication overhead increases, which further slows down the convergence process. To address this challenge, we propose a simple yet effective federated learning framework that improves consistency among edge devices. The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates. In parallel, a global momentum… More > Graphic Abstract

Anis Zarrad¹, Thomas Armstrong², Jaber Jemai^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072768 - 12 January 2026

Abstract Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability. While prioritization selects the most relevant test cases for optimal coverage, ranking further refines their execution order to detect critical faults earlier. This study investigates machine learning techniques to enhance both prioritization and ranking, contributing to more effective and efficient testing processes. We first employ advanced feature engineering alongside ensemble models, including Gradient Boosted, Support Vector Machines, Random Forests, and Naive Bayes classifiers to optimize test case prioritization, achieving an accuracy score of 0.98847… More >