Trong-Thua Huynh^1,*, De-Thu Huynh², Du-Thang Phu¹, Hong-Son Nguyen¹, Quoc H. Nguyen³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076623 - 09 April 2026

Abstract This paper introduces MobiIris, a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources. The proposed model is based on the large version of MobileNetV3 and adds more spatial attention blocks and an embedding-based head that was trained using margin-based triplet learning, enabling fine-grained modeling of iris textures in a compact representation. To further improve discriminability, we design a training pipeline that combines dynamic-margin triplet loss, a staged hard/semi-hard negative mining strategy, and feature-level knowledge distillation from a ResNet-50 teacher.… More >

Zuoquan Zhu^*, Menghan Wang, Xinyu Li, Mengxin Zhao

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076418 - 09 April 2026

Abstract Investigating the deformation behavior of graphene-reinforced composite structures holds significant engineering implications, while the rapid advancement of machine learning has introduced new technical approaches to structural bending analysis. In this study, we investigate the mechanical bending behavior of graphene origami (GOri)-enabled auxetic metamaterial beams using a physics-informed neural network (PINN). GOri-enabled auxetic metamaterials represent an innovative composite system characterized by a negative Poisson’s ratio (NPR) and superior mechanical performance. Here, we propose a composite beam model incorporating the modified coupled stress theory (MCST) and employing the PINN method to solve higher-order bending governing equations. Compared More >

Sultan Kahla¹, Zuping Zhang^1,*, Majed Alsafyani², Ahmed Emara^3,*, Mohammod Abdullah Bin Hossain⁴, Abdulwahab Osman Sheikhdon¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076402 - 09 April 2026

Abstract The effective diagnosis and treatment planning require the correct classification of the cerebral neoplasia, such as glioma, meningioma, and pituitary tumors. The recent developments in the deep learning field have made a significant contribution to the field of image analysis in medicine; however, Vision Transformers (ViTs) have achieved good results but are computationally complex. This paper presents NeuroTriad-ViT, a proprietary large-scale Vision Transformer of 235 million parameters, which is represented as a high-performance teacher model to classify brain tumors. Knowledge distillation is applied in an attempt to transfer the representations that the teacher learned to… More >

Zahra Farhadpour^1,*, Tan Fong Ang^1,*, Chee Sun Liew²

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076163 - 09 April 2026

Abstract The heterogeneity and dynamic behavior of fog computing environments introduce major challenges to achieving optimal application placement. Limited fog resources and varying workloads often necessitate offloading applications beyond their local clusters, making it difficult to maintain the required level of service quality under varying conditions. In this context, placement methods must ensure a balanced trade-off between multiple objectives, such as time and cost, while maintaining reliable adherence to constraints like application deadlines and limited fog-node memory. Existing solutions, including heuristic, metaheuristic, learning-based, and hybrid optimization approaches, have been proposed to address these challenges. However, many… More >

Longquan Ma¹, Huarong Zhao^1,*, Liqin Zhou¹, Linbo Xie^1,*, Hongnian Yu²

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075679 - 09 April 2026

Abstract This paper studies a sampling-based dynamic event-triggered fixed-time bipartite formation algorithm for a class of continuous-time multi-agent systems with communication constraints. First, a periodic sampling mechanism is designed to reduce the system’s communication frequency. Then, a dynamic event-triggered control algorithm based on auxiliary variables is developed for sampled-data systems to further reduce the system’s triggering frequency. Next, to enhance the convergence speed of the dynamic event-triggered control method, a dynamic event-triggered fixed-time bipartite formation control scheme is investigated. Finally, using Lyapunov stability theory, signed graph theory, and relevant inequalities, a rigorous theoretical proof of the More >

Sarmad Dheyaa Azeez¹, Muhammad Ilyas^2,*, Saadaldeen Rashid Ahmed^3,4

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074930 - 09 April 2026

Abstract The rapid evolution of 5G-enabled Software Defined Networks (SDNs) has transformed modern communication systems by enabling ultra-low latency, massive connectivity, and high throughput. However, the increased complexity of traffic flows and the rise of sophisticated cyber-attacks such as Distributed Denial of Service (DDoS), Botnets, Fake Base Stations, and Zero-Day exploits have made intrusion detection a critical challenge. Traditional Intrusion Detection System (IDS) approaches often suffer from poor gen-eralization, high false positives, and lack of interpretability, making them unsuitable for dynamic 5G environments. This paper presents a novel Graph Neural Network (GNN) with Multi-Head Attention (MHA)… More >

Fangxiang Hu¹, Qi Fu^1,2,*, Shiwen Zhang¹, Jing Huang¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074154 - 09 April 2026

Abstract In the Internet of Vehicles (IoV) environment, the growing demand for computational resources from diverse vehicular applications often exceeds the capabilities of intelligent connected vehicles. Traditional approaches, which rely on one or more computational resources within the cloud-edge-device computing model, struggle to ensure overall service quality when handling high-density traffic flows and large-scale tasks. To address this issue, we propose a computational offloading scheme based on a cloud-edge-device collaborative 6G IoV edge computing model, namely, Multi-Agent Deep Reinforcement Learning-based and Server-weighted scoring Selection (MADRLSS), which aims to optimize dynamic offloading decisions and resource allocation. The… More >

T. Subash^1,*, M. Sekar², R. Selvabharathi³

Journal of Polymer Materials, Vol.43, No.1, 2026, DOI:10.32604/jpm.2026.075026 - 03 April 2026

Abstract The two distinct types of composite materials (5% to 10%) were developed using recycled polyvinyl alcohol fiber (RPA), silicon nitride fiber (SN), and reduced carbon nanoparticles (RCN). Enhanced microstructural properties and mechanical strength were attained through the application of the 3-glycidoxypropyltrimethoxysilane coupling method. The combination of the resin-like properties of RPA-SN fiber resulted in the formation of robust outer strength and a high bonding structure. RPA-RCN composite materials with a weight percentage of 10% exhibited a tensile strength of 42 MPa. In contrast, RPA-SN-RCN composite materials containing 5% to 10% demonstrated enhanced tensile, bending, and… More >

Zakaria S. M. Abdelhalim^*, Nahla Belal, Mohamed Seifeldin

Journal of Cyber Security, Vol.8, pp. 153-169, 2026, DOI:10.32604/jcs.2026.079097 - 06 April 2026

Abstract The rapid expansion of IoT and cloud services has increased the scale and complexity of modern networks, making intrusion detection challenging. Although deep learning-based Network Intrusion Detection Systems (NIDS) often report high accuracy, such metrics can be misleading on highly imbalanced datasets, where performance is dominated by majority classes and rare attacks remain poorly detected. This issue stems from global optimization strategies that encourage models to rely on dominant feature patterns, limiting their ability to capture the class-specific features required to identify infrequent attack types. To address this limitation, this work proposes a domain knowledge-guided… More >

Ehsan Momeni¹, Mohammad Dehghannezhad¹, Fereydoon Omidinasab¹, Danial Jahed Armaghani^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077300 - 30 March 2026

Abstract In the last decade, the importance of sustainable construction and artificial intelligence (AI) in civil engineering has been underlined in many studies. Numerous studies highlighted the superiority of AI techniques over simple and mathematical regression analyses, which suffer from relatively poor generalization and an inability to capture highly non-linear relationships among inputs and output(s) parameters. In this study, to evaluate the compressive strength of concrete with glass powder (GP) and recycled aggregates, 600 concrete samples were tested in the laboratory, and their results were evaluated. For intelligent assessment of concrete compressive strength (CCS), the study… More >