Anandkumar Balasubramaniam, Seung Yeob Nam^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077582 - 09 April 2026

Abstract The increasing connectivity of modern vehicles exposes the in-vehicle controller area network (CAN) bus to various cyberattacks, including denial-of-service, fuzzy injection, and spoofing attacks. Existing machine learning and deep learning intrusion detection systems (IDS) often rely on labeled data, struggle with class imbalance, lack interpretability, and fail to generalize well across different datasets. This paper proposes a lightweight and interpretable IDS framework based on non-negative matrix factorization (NMF) to address these limitations. Our contributions include: (i) evaluating NMF as both a standalone unsupervised detector and an interpretable feature extractor (NMF-W) for classical, unsupervised, and deep… More >

Yang Lei^1,2, Kangshuo Zhu^3,4,*, Bo Jiang¹, Yaodong Wang^3,4, Feiyu Jia¹, Zhaoning Wang¹, Falin Qi¹, Qiming Qu¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077314 - 09 April 2026

Abstract The safe operation of railway systems necessitates efficient and automated inspection of tunnel defects. While deep learning offers solutions, a clear pathway for selecting and optimizing the latest object detectors for distinct defects under strict speed constraints is lacking. This paper presents a two-stage, task-specific framework for high-speed tunnel defect detection. First, this study conducts a comprehensive comparative analysis of state-of-the-art YOLO models (YOLOv5s, YOLOv8s, YOLOv10s, YOLOv11s) on self-constructed datasets. This systematic comparison identifies YOLOv5s as the optimal model for crack detection, achieving an mAP@0.5 of 0.939 at 77.5 FPS, sufficient for inspection at 50… More >

Sang-Hyun Lee^*, Qingtao Meng

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077207 - 09 April 2026

Abstract Accurate and timely detection of rice leaf diseases is critical for ensuring global food security and maximizing agricultural yields. However, existing deep learning methods often struggle to balance the high accuracy required for detecting multi-scale lesions in complex field environments with the computational efficiency necessary for edge device deployment. This paper proposes You Only Look Once for Lightweight Detection (YOLOv11-LD), a lightweight object detection model for multi-scale rice leaf disease detection in real paddy field environments. The model is built on YOLOv11n and integrates a Re-parameterized Vision Transformer (RepViT) backbone, a Bidirectional Feature Pyramid Network… More >

Hao Li^1,2, Zhoujingzi Qiu^1,2, Jianxiao Zou^1,2, Haojie Wu¹, Shicai Fan^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076784 - 09 April 2026

Abstract Self-supervised monocular depth estimation has attracted considerable attention due to its ability to learn without ground-truth depth annotations and its strong scalability. However, existing approaches still suffer from inaccurate object boundaries and limited inference efficiency. To address these issues, we present a Lightweight Conditional Diffusion Model for Monocular Depth Estimation (LCDM-Mono). The proposed framework integrates an efficient diffusion inference strategy with a knowledge distillation scheme, enabling the model to generate high-quality depth maps with only two sampling steps during inference. This design substantially reduces computational overhead and ensures real-time performance on resource-constrained platforms. In addition, More >

Chung-Wei Kuo^1,2,*, Cheng-Xuan Wu¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076767 - 09 April 2026

Abstract The rapid proliferation of the Internet of Things (IoT) has not only reshaped the digital ecosystem but also significantly widened the attack surface, leading to a surge in network traffic and diverse security threats. Deploying effective defense mechanisms in such environments is challenging, as conventional Intrusion Detection Systems (IDS) often struggle to balance computational efficiency with the reliable detection of low-frequency, high-impact threats, particularly within the tight resource constraints of edge devices. To address these limitations, we propose a lightweight, high-efficiency IDS framework specifically optimized for edge-based IoT applications, incorporating Mutual Information (MI)-based feature selection… 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 >

Chin-Wen Liao^1,2,3, En-Shiuh Lin¹, Wei-Lun Huang^4,5,6, I-Chi Wang⁷, Bo-Siang Chen^8,*, Wei-Sho Ho^1,2,9,*

Journal of Polymer Materials, Vol.43, No.1, 2026, DOI:10.32604/jpm.2026.076807 - 03 April 2026

Abstract The demand for extended electric vehicle (EV) range necessitates advanced lightweighting strategies. This study introduces a materials genome approach, augmented by machine learning (ML), for optimizing lightweight composite designs for EVs. A comprehensive materials genome database was developed, encompassing composites based on carbon, glass, and natural fibers. This database systematically records critical parameters such as mechanical properties, density, cost, and environmental impact. Machine learning models, including Random Forest, Support Vector Machines, and Artificial Neural Networks, were employed to construct a predictive system for material performance. Subsequent material composition optimization was performed using a multi-objective genetic More >

Yiyan Chen^1,2, Yihu Chen^1,2,*, Min Zhang³, Xiaogang Ye⁴, Jindan Zhang^1,2

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.073581 - 31 March 2026

Abstract The large thickness of the common composite precast base slab leads to difficulties in construction through reinforcement installation and pipeline laying. To solve this problem, this paper proposes a lightweight ribbed base slab, reducing the base slab thickness to 30 mm compared to the ordinary precast base slab, adding concrete ribs to improve the mechanical properties of the base slab, and analyzing its damage pattern, stiffness change, and deflection deformation through static loading experiments. Based on the experimental conditions, the effect of concrete rib height, rib width, and top chord reinforcement diameter on the short-term… More >

Chengang Dong^1,2, Yongkang Ding², Jianwei Hu^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079062 - 30 March 2026

Abstract Real-time multi-person pose estimation (MPE) built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes. However, due to factors such as high model complexity and limited expression of keypoint information, both the efficiency and accuracy of real-time MPE remain to be improved. To mitigate the adverse impacts caused by the aforementioned issues, this work develops FSEM-Pose, a real-time MPE model rooted in the YOLOv10 framework. In detail, first, FSEM-Pose upgrades the backbone module of the baseline network by introducing the Feature Shuffling-Convolution (FS-Conv), which effectively reduces More >

Chia-Hui Liu^*, Hao-Feng Liu

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077837 - 30 March 2026

Abstract Artificial Intelligence (AI)-native sixth-generation (6G) wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments. In such networks, massive device heterogeneity and time-varying channel conditions pose significant challenges, as reliable authentication must be achieved with limited labeled data and constrained edge resources. To address this challenge, this paper proposes an Artificial Intelligence (AI)-assisted few-shot physical-layer modeling framework for channel robust device identification, formulated within the paradigm of Specific Emitter Identification (SEI) based on radio… More >