José Armando Santas Ciavatta, Juan Ramón Bermejo Higuera^*, Javier Bermejo Higuera, Juan Antonio Sicilia Montalvo, Tomás Sureda Riera, Jesús Pérez Melero

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074566 - 12 January 2026

Abstract As artificial Intelligence (AI) continues to expand exponentially, particularly with the emergence of generative pre-trained transformers (GPT) based on a transformer’s architecture, which has revolutionized data processing and enabled significant improvements in various applications. This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models (LLM). Our primary objective is to evaluate the effectiveness of Static Application Security Testing (SAST) by applying various techniques such as prompt persona, structure outputs and zero-shot. To the selection of the LLMs (CodeLlama 7B, DeepSeek coder 7B, Gemini 1.5 Flash,… More >

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 >

Faisal Mahmud¹, Fahim Mahmud², Rashedur M. Rahman^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073519 - 12 January 2026

Abstract Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of… More >

Si-Woo Kim, Yong Soo Kim^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073492 - 12 January 2026

Abstract Predictive maintenance (PdM) is vital for ensuring the reliability, safety, and cost efficiency of heavy-duty vehicle fleets. However, real-world sensor data are often highly imbalanced, noisy, and temporally irregular, posing significant challenges to model robustness and deployment. Using multivariate time-series data from Scania trucks, this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification. First, the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness, allowing LightGBM to leverage its inherent split rules without ad-hoc imputation. Then, a two-stage LightGBM framework is developed… More >

Fei Tang^1,*, Ping Wang¹, Jiang Yu¹, Huihui Zhu¹, Mengxue Qin¹, Ling Yang²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073423 - 12 January 2026

Abstract Attribute-Based Encryption (ABE) has emerged as a fundamental access control mechanism in data sharing, enabling data owners to define flexible access policies. A critical aspect of ABE is key revocation, which plays a pivotal role in maintaining security. However, existing key revocation mechanisms face two major challenges: (1) High overhead due to ciphertext and key updates, primarily stemming from the reliance on revocation lists during attribute revocation, which increases computation and communication costs. (2) Limited universality, as many attribute revocation mechanisms are tailored to specific ABE constructions, restricting their broader applicability. To address these challenges,… 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