Yihao Kuang^1,2, Hong Zhang^1,2, Jiaqi Wang^1,2, Lingyu Jin^1,2, Bo Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076652 - 09 April 2026

Abstract 3D single object tracking (SOT) based on point clouds is a fundamental task for environmental perception in autonomous driving and dynamic scene understanding in robotics. Recent technological advancements in this field have significantly bolstered the environmental interaction capabilities of intelligent systems. This field faces persistent challenges, including feature degradation induced by point cloud sparsity, representation drift caused by non-rigid deformation, and occlusion in complex scenarios. Traditional appearance matching methods, particularly those relying on Siamese networks, are severely constrained by point cloud characteristics, often failing under rapid motions or structural ambiguities among similar objects. In response,… More >

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 >

Adwaa Mohammed Abdulmajeed¹, Duaa Abdul Rida Musa², Ola Abdul Hussain², Emad Kadum Njim³, Royal Madan^4,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076622 - 09 April 2026

Abstract A data-driven optimization framework that integrates machine learning surrogate models, finite element analysis (FEA), and a multi-objective optimization algorithm is used in this study for developing thermoplastic elastomer (TPE) parts for aerospace applications. By using FEA simulations and experiments, a database of input design parameters (e.g., geometry and structural shape modifier) is generated. Afterwards, we train surrogate models (e.g., Gaussian Process Regression, neural networks) to approximate mappings from design space to performance space. Finally, we propose Pareto-optimal TPE designs using the surrogate embedded in a multi-objective optimization loop (such as NSGA-II or gradient-based methods). The… More >

Malik Al-Essa^1,*, Mohammad Qatawneh^2,1, Ahmad Sami Al-Shamayleh³, Orieb Abualghanam¹, Wesam Almobaideen^4,1

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076608 - 09 April 2026

Abstract Machine Learning (ML) intrusion detection systems (IDS) are vulnerable to manipulations: small, protocol-valid manipulations can push samples across brittle decision boundaries. We study two complementary remedies that reshape the learner in distinct ways. Adversarial Training (AT) exposes the model to worst-case, in-threat perturbations during learning to thicken local margins; Counterfactual Augmentation (CF-Aug) adds near-boundary exemplars that are explicitly constrained to be feasible, causally consistent, and operationally meaningful for defenders. The main goal of this work is to investigate and compare how AT and CF-Aug can reshape the decision surface of the IDS. eXplainable Artificial Intelligence More >

Qinglei Zhang, Zhenzhen Wang^*, Jianguo Duan, Jiyun Qin, Ying Zhou

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076562 - 09 April 2026

Abstract In recent years, increasing urban mobility and complex traffic dynamics have intensified the need for accurate traffic flow forecasting in intelligent transportation systems. However, existing models often struggle to jointly capture short-term fluctuations and long-term temporal dependencies under noisy and heterogeneous traffic conditions. To address this challenge, this paper proposes a hybrid traffic flow forecasting framework that integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the Mamba state-space model, and the Transformer architecture. The framework first applies DBSCAN to multidimensional traffic features to enhance traffic state representation and reduce noise. The prediction module alternates… More >

Jun Qi^1,*, Chao Yang², Xinliu Wang², Junyou Yang¹, Haixin Wang¹, Huaqin Chen^2,3, Zhenyan Li³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076511 - 09 April 2026

Abstract With the diversification of electricity trading forms driven by distributed energy technologies, the continuous growth of blockchain’s chained data structure poses dual challenges to traditional B+ tree indexes in terms of query efficiency and storage costs. This paper proposes a sliding window-based learned index construction method (SW-LI). The method consists of two key components. First, block timestamp–height samples are selected using a sliding window and used to train a linear regression model that captures the timestamp-to-height mapping. Second, an adaptive window adjustment mechanism is introduced: when the prediction error within a window exceeds a threshold,… More >

Hongtao Guo¹, Shuai Li², Shu Li^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076492 - 09 April 2026

Abstract This paper reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine learning methods are developing rapidly in polymer material research; although they have significantly accelerated material prediction and design, their complexity has also caused difficulties in understanding and application for researchers in traditional fields. In response to the above issues, this paper first analyzes the inherent challenges in the research and development of polymer materials, including structural complexity and the limitations of traditional trial-and-error methods. To address these problems, it focuses on introducing key basic… More >

Udit Mamodiya^1,*, Indra Kishor², P. Satish Reddy³, K. Lakshmi Kalpana³, Radha Seelaboyina⁴, Harish Reddy Gantla⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076464 - 09 April 2026

Abstract The direct conversion of solid-state heat to electricity using thermoelectric materials has attracted attention; however, their effective application is limited because of the challenge of ensuring a balance between the microstructural features at the quantum, mesoscale, and continuum scales. Current computational and machine-learning methods have a small design space, wherein few to no interactions between the electronic structure, phonon transport, and device-level are considered. This makes it difficult to discover stable high-figure of merit (ZT) settings that are manufacturable and strong in the actual working environment. This study presents a multiscale hybrid optimization framework that… More >

Mingfang Li, Damin Zhang^*, Qing He, Chenglong Zhou, Mingrong Li, Xiaobo Zhou

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076433 - 09 April 2026

Abstract Traffic sign detection is a critical task in autonomous driving environmental perception. However, models often suffer from degraded detection performance in complex real-world scenarios due to variable target scales, blurred fine-grained features, and complex background interference. This paper proposes an improved YOLOv8n detection model, MSD-YOLO, to address these challenges. First, a Multi-scale Detail Enhancement Module (MDEM) is designed, which achieves targeted enhancement of edge features through high-frequency residual modulation and multi-scale cooperative attention. Second, an enhanced feature pyramid network termed SG-FPN is constructed. It introduces soft nearest neighbor interpolation (SNI) for semantic-spatial aligned feature fusion… 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 >