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 >

Trong-Thua Huynh^1,*, De-Thu Huynh², Cong-Sang Duong¹, Hong-Son Nguyen¹, Quoc H. Nguyen³, Lam-Thanh Tu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080616 - 27 May 2026

Abstract Deep iris recognition models are often trained on Cartesian grids, whereas iris texture follows a concentric structure with angular periodicity. This representational mismatch can weaken rotation robustness and limit pupil-to-limbus context modeling, while many pipelines still rely on accurate segmentation masks. We propose RadialFormer, an efficient mask-free iris recognition framework that performs representation learning directly in the polar domain. The pipeline first estimates pupil/iris parameters (cx,cy,rin,rout) using a percentile radial-gradient operator with anatomical constraints, and then applies a crop-based polar transform to obtain a compact 64×512 unwrapped iris map. To better match polar… More >

Shuvodeep De^1,*, Agnivo Gosai^2,#, Karun Thankachan^3,#, Ramadan A. ZeinEldin⁴, Abdulaziz T. Almaktoom⁵, Mustafa Bayram⁶, Ali Wagdy Mohamed^7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080601 - 27 May 2026

Abstract Sentiment analysis (SA) has evolved from a niche text-classification task into a central problem in natural language processing, spanning multiple domains, modalities, and languages. This survey provides a comprehensive review of sentiment analysis methods from their origins in lexicon-based approaches through classical machine learning, deep learning architectures, pre-trained transformers, and the current era of large language models (LLMs). We formalize the SA problem across multiple granularity levels (document, sentence, and aspect) and present a taxonomy that encompasses classification, regression, aspect-based sentiment analysis (ABSA), emotion detection, and stance detection tasks across diverse domains including movie reviews,… More >

Amel Ksibi^1,*, Manel Ayadi¹, Hela Elmannai², Monia Hamdi², Ala Saleh Alluhaidan¹, Imen Ksibi³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078347 - 27 May 2026

Abstract Cerebral palsy is a prevalent neurodevelopmental syndrome that disrupts motor development in children, making early detection vital for effective intervention. Traditional clinical assessments rely on subjective observations, often missing minor motor abnormalities until they become severe, typically after 12 months of age. This article presents a novel deep learning model, TransCP-Net (Transformer-based Cerebral Palsy Network), designed for early detection of infant cerebral palsy through spatiotemporal pose representation learning. The architecture employs hierarchical spatial and temporal attention to analyze complex motion patterns in video sequences, integrating multi-modal data for improved accuracy. TransCP-Net incorporates specialized preprocessing, including More >

Xinyu Yue¹, Zhenhua Li^1,2,*, Zhenxing Li¹, Tao Zhang¹, Yanchun Xu¹, Xiaozhen Zhao³

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.077395 - 27 May 2026

Abstract Winding deformation is a predominant cause of transformer failures and critically compromises the safe, reliable, and economic operation of power systems. To overcome the inadequacy of the conventional three-dimensional (3D) Lissajous curve method in discriminating among various types of winding faults, this paper proposes an online monitoring method for transformer winding deformation based on 3D Lissajous curves. In the proposed method, the primary current di1(t)/dt, the derivative of the primary current di1(t)/dt, and the voltage difference between the primary and secondary sides Δu(t) are adopted as the coordinate axes to construct 3D Lissajous… More >

Ziliang Yang¹, Mykola Sysyn², Jin Li¹, Jizhe Zhang¹, Jian Liu¹, Lei Kou^1,3,*

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.078140 - 18 May 2026

Abstract Accurate assessment of the failure stage of rail rolling contact fatigue (RCF) is critical for guiding timely maintenance by track personnel, ensuring safe rail operations, and reducing maintenance costs. Although various methods have been developed to detect rail damage and classify surface defects, the rolling contact fatigue failure state of rails has not yet been comprehensively and objectively evaluated. This paper introduces the application of image processing and improved deep-learning network algorithms in rail failure evaluation and judgment. Based on Swin Transformer, a deep learning network is developed. By dividing the rail rolling contact fatigue More >

Yingying Yu^1,2,#,*, Jun Yuan^3,#, Tong Liu^1,2

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080767 - 08 May 2026

Abstract Accurate grasp detection is fundamental to successful robotic manipulation. Existing methods achieve reliable performance under good light conditions. However, their performance in low-light environments suffers from severe degradation due to the diminishing discriminative ability of visual features. In this paper, a novel low-light aware hybrid network LAH-Net is proposed. It comprises an alternating transformer-CNN module (ATCM) between the encoder and decoder, and a knowledge distillation-guided low-light enhancement module (KDLEM) before the encoder, which is activated by an illumination gate under low-light conditions. To generate highly robust and synergistic features, the ATCM module facilitates the iterative… More >

Lingyu Jin^1,2, Rui Wang^1,2, Bo Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080050 - 08 May 2026

Abstract Unmanned Aerial Vehicle (UAV) target tracking is one of the key technologies in aerial intelligent perception systems, playing a vital role in applications such as traffic monitoring, border patrol, disaster response, search and rescue, environmental monitoring, and military reconnaissance. Compared with generic object tracking tasks, UAV platforms exhibit significant differences in imaging perspectives, target scales, motion patterns, and onboard computing capabilities, which pose unique challenges for UAV target tracking, including small targets and drastic scale variations, platform motion and motion blur, complex backgrounds and frequent occlusions, low-light conditions at night, as well as real-time and… More >

Raza Hasan^*, Shakeel Ahmad, Ismet Gocer, Zakirul Bhuiyan

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079708 - 08 May 2026

Abstract Maritime Domain Awareness (MDA) is critical for global security and economic stability, yet it is increasingly challenged by sophisticated adversarial tactics such as signal spoofing and “dark vessel” activities. Traditional surveillance systems, often reliant on single-sensor modalities, are ill-equipped to handle these deceptive behaviors. To address this, we propose the Multimodal Attention-based Fusion Transformer (MAFT), a novel deep learning architecture that integrates four distinct data modalities—Aerial imagery, Synthetic Aperture Radar (SAR), acoustic signatures, and Automatic Identification System (AIS) data—to achieve robust and interpretable maritime anomaly detection. A key contribution of our work is a principled… More > Graphic Abstract

Lijun Cui¹, Yongjie Niu², Yuxiang Sun¹, Xiaokang Gu¹, Jing Guo¹, Pengfei Xu^1,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079691 - 08 May 2026

Abstract Indoor intrusion detection is essential for various applications, including security systems and smart homes. Recently, WiFi-based detection has gained popularity due to its low cost and non-invasive nature. Current Channel State Information (CSI) based frameworks primarily use deep learning to extract gait signatures; however, their performance depends heavily on extensive labeled datasets. These methods struggle to differentiate between unlabeled and labeled data that exhibit similar features. To address this challenge, we propose a novel Two-level Feature Fusion model for Indoor Intrusion Detection (TFF-IID) utilizing commercial WiFi CSI. The model adopts a two-level structure to learn… More >