Hyunjun Park¹, Hee-Gook Jun², Seongyong Kim³, Dong-Hyuk Im^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082506 - 30 June 2026

Abstract Time-series forecasting can significantly aid decision-making in fields in which immediate action is required, such as power demand forecasting, financial market analysis, and traffic flow management. Transformer-based models achieve high forecasting accuracy by learning complex temporal patterns; however, their extensive parameters and substantial computational costs make practical deployment difficult in latency-sensitive environments. Therefore, lightweight models based on linear layers have recently been studied for improved efficiency. However, existing linear-based models have difficulty capturing local patterns and fail to reflect sudden volatility or fine-grained local trends, limiting their overall representational capacity. In this paper, SegTSF is… More >

Jaffar Hussain¹, Tahira Nazir¹, Junaid Rashid^2,*, Jungeun Kim^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081617 - 30 June 2026

Abstract Early detection of diabetic retinopathy (DR), media haze (MH), optic disc cupping (ODC), and glaucoma is crucial for preventing vision loss. However, timely diagnosis is often constrained by limited specialist availability and high diagnostic costs. This study proposes a You Only Look Once (YOLO)-based deep learning (DL) framework for the automated classification of fundus images into disease-specific categories. We unified diverse annotations from the Retinal Fundus Multi-Disease image Dataset (RFMiD), RFMiD2.0, and the DR Fundus Image Dataset (DR-FID) by standardizing annotation files and class labels. A custom filtering module was used to isolate single-pathology cases,… More >

Lin Zang¹, Muhammad Fadhil Marsani^2,*, Caiyan Long^1,2,*, Yongping Yu³

Structural Durability & Health Monitoring, Vol.20, No.4, 2026, DOI:10.32604/sdhm.2026.080465 - 30 June 2026

Abstract To bridge the gap between isolated structural and connection analyses in lightweight rail vehicle design, this study establishes an integrated modelling-simulation-verification framework. This integrated framework, developed via parametric FE modelling, multi-axial load simulation (per DIN EN 12663 & BS 7608), and standards-based verification, was applied to a specific housing design. Key results: (1) Under vertical ±3 g static loads, the tray experiences the maximum equivalent stress of 186.6 MPa, yielding a safety factor of 1.1 against the yield strength, which satisfies the minimum requirement (s₁ ≥ 1.15) of the DIN EN 12663 standard. (2) Under fatigue More >

Jianfeng Liu¹, Yongjiao Yang¹, Kangyi Yang¹, Changhua Hu¹, Zijia Xu¹, Qingguo Shi², Yi Su^2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082804 - 15 June 2026

Abstract Power enterprise inspection and supervision require greater intelligence, efficiency, and standardization; however, existing approaches are limited by inefficient knowledge retrieval, inaccurate issue identification, and insufficient support for standardized reporting and rectification tracking. This study proposes a lightweight, domain-adaptive large language model (LLM) framework based on Low-Rank Adaptation (LoRA), integrating Retrieval-Augmented Generation (RAG) and structured prompt engineering to enable evidence-grounded inspection tasks. The framework achieves parameter-efficient adaptation through low-rank decomposition and constructs a domain-specific multimodal knowledge base, enhancing output traceability, consistency, and task generalization. A key contribution is the introduction of a Sensitive Information Control Gate, More >

Shuaiyu Zhu¹, Sergey Ablameyko^1,2, Ji Li^3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082486 - 15 June 2026

Abstract Satellite remote sensing images pose significant challenges for object detection due to their high resolution, complex scenes, and large variations in target scales. To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery, this paper proposes two improvement strategies. Method 1: (a) a Large Separable Kernel Attention (LSKA) mechanism is introduced into the backbone network to enhance feature extraction for small objects; (b) a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion, thereby improving the detection performance of objects at different scales. Method 2: (a) the More >

Kemeng Zhu¹, Dingju Zhu^1,2,*, Shihua Mao¹, Jinchen Wu³, Depeng Kong⁴, Kaileung Yung⁵, Andrew W. H. Ip⁶

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082431 - 15 June 2026

Abstract Unmanned aerial vehicles (UAVs) have become an increasingly important platform for agricultural remote sensing, yet the accurate recognition of pests and diseases is frequently compromised by drastic scale variability and complex environmental backgrounds. To address these challenges, this study introduces a novel attention-driven approach centered on a Multi-Scale Grouped Channel–Spatial Dual Attention (MS-GCDA) mechanism. The MS-GCDA module achieves robust feature calibration by decoupling and jointly modeling multi-scale spatial contexts and grouped channel dependencies, which significantly enhances the model’s sensitivity to fine-grained disease symptoms while suppressing background clutter. This core mechanism is integrated into Augmented EfficientNet… More >

Zheng Xu^1,2, Zihao Pan¹, Ning Yang¹, Daoxing Guo^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080559 - 15 June 2026

Abstract Unmanned Aerial Vehicle (UAV) communications in complex electromagnetic environments face challenges such as strong interference, high dynamic Doppler shifts, and limited onboard computing power. In these scenarios, traditional blind beamforming algorithms suffer from slow convergence and difficulty in handling Gaussian-like signals (e.g., Orthogonal Frequency Division Multiplexing (OFDM)). To address these issues, this paper proposes a Lightweight Robust Transfer learning-based Blind Beam Forming method (LRT-BF). This method constructs a self-supervised optimization framework centered on a pre-trained signal classifier and innovatively introduces a joint loss function combining classification confidence guidance with output power minimization, achieving fully blind… More >

Weiguang Wang^1,2, Jincai Li¹, Mengqi Liu¹, Mengke Liu¹, Yuan Zhang¹, Jingyan Wu^1,*, Yang Liu^3,*, Junbin Lou⁴, Yixin He⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080341 - 15 June 2026

Abstract To improve the accuracy of small object feature detection in complex backgrounds for Unmanned Aerial Vehicle (UAV) aerial photography and reduce computational complexity, we propose the lightweight UAV aerial photography small object detection method based on multi-scale feature fusion and contextual information. Firstly, by introducing the grouped content-aware reassembly (GCA) operator and designing lightweight pinwheel context convolution (LPConv), we extend the feature fusion path to the P2 layer, constructing a lightweight multi-scale feature fusion network (SG-PANet). Through the decoupling of fine-grained small object features and background interference features by the GCA operator, combined with the… More >

Ying Tang¹, Chuanyi Ma², Feng Guo^1,*, Wenhao Sun¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079851 - 15 June 2026

Abstract Road Abandoned Objects (RAOs) pose significant threats to traffic safety, particularly due to their small size, irregular shapes, and unpredictable distribution in complex road environments. The primary objective of this study is to develop an accurate and real-time detection framework for RAOs while maintaining low computational cost for practical deployment. To achieve this, we propose RAO-YOLO, a lightweight vision-based detection framework built upon an enhanced YOLO architecture. Specifically, a Mixed Aggregation Network (MANet) is introduced to improve multi-scale feature representation, and a Lightweight Shared Detail-Enhanced Detection (LSDD) head is designed to enhance localization accuracy for More >

Munthir Qasaimeh¹, Mostafa Ali¹, Qasem Abu Al-Haija^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080864 - 27 May 2026

Abstract Vision Transformers (ViTs) have recently achieved high performance in retinal Optical Coherence Tomography (OCT) classification studies. However, ViT models continue to face significant challenges, including high computational cost, vulnerability to adversarial attacks, and pronounced sensitivity to preprocessing techniques. This study introduces GreenShield, a unified framework designed to produce an efficient and robust ViT model, referred to as GreenShield-ViT, which outperforms existing lightweight ViT variants in terms of adversarial robustness for retinal OCT classification. The framework integrates a gradient-based block-importance pruning strategy to compress the ViT/B-16 architecture, and adversarial training with proper ImageNet normalization and anti-saturation… More >