Bo Yang^1,*, Chao Liu²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.083726 - 27 May 2026

Abstract This article has no abstract. More >

Chuanjiang Chen¹, Junyong Zhou^1,*, Binbin Du¹, Miaosi Dong^2,*, Liwen Zhang¹, Bitang Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081546 - 27 May 2026

Abstract Computer vision has been widely adopted in intelligent construction monitoring; however, existing studies primarily focus on identifying individual construction elements or isolated activities, with limited capability for integrated monitoring of complete construction workflows. Such workflow-level automation is a prerequisite for intelligent construction and unmanned job sites. To address the challenge of reliable visual recognition in drill-and-blast tunnel environments characterized by uneven illumination, localized glare, and dust interference, this study proposes a methodological framework for construction workflow recognition at the tunnel face using computer vision and context reasoning. The framework consists of three components: (1) a… 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 >

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 >

Luis Sánchez Calderón^*, David Valverde Burneo, Walter Hurtares Orrala

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.077137 - 18 May 2026

Abstract Accurate characterization of live load histories remains critical for structural safety and efficient design; however, traditional codes often overestimate in-service loads. This study introduced an AI-driven framework integrating YOLOv8 object detection and DeepFace gender classification with continuous video surveillance to monitor live loads in academic buildings. Gender classification used local anthropometric data (77 kg males, 61 kg females) for precise load estimation, with privacy ensured via local processing and anonymized metadata only. Observed peaks were substantially below Eurocode and IBC provisions, confirming code conservatism. Uncertainty propagation from detector errors (recall 0.57, ±0.02 Kn/m²) minimally impacted projections. More >

Jigar Sarda¹, Rohan Vaghela¹, Akash Kumar Bhoi², Chang-Won Yoon^3,*, Mangal Sain^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.080112 - 08 May 2026

Abstract Printed circuit boards (PCBs) are essential components that strongly influence the performance and reliability of modern electronic systems. However, minor and visually subtle manufacturing defects can degrade product quality and pose serious challenges for automated inspection systems. Existing deep learning–based methods often struggle to simultaneously achieve high detection accuracy, real-time processing speed, and compact model size. This study proposes an enhanced approach for real-time PCB defect detection using advanced object detection models. A dedicated dataset of bare PCBs was developed and carefully annotated with six defect categories: open circuits, missing holes, spurs, mouse bites, short… More >

Giulio Caporro¹, Paolo Russo^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079331 - 08 May 2026

Abstract Monocular depth estimation (MDE) has become a practical alternative to active range sensing in many indoor scenarios, enabled by supervised deep learning models that predict dense depth maps from a single RGB image. However, most modern MDE systems assume mid-to-high resolution inputs and non-trivial compute budgets, limiting their direct applicability in embedded and bandwidth-constrained settings. This paper studies low resolution MDE, focusing on 96×96 inputs, where geometric cues are strongly degraded and naively downsizing high-resolution architectures often leads to unstable training and poor accuracy. We propose DeepEchoNet, a lightweight hybrid CNN-transformer model tailored to operate natively More > Graphic Abstract

Jinjiang Lin, Yuan Lu, Han Li, Xiaolong Cai, Enyi Chen, Jiansheng Guan^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077916 - 08 May 2026

Abstract Manipulating objects based on verbal commands in cluttered environments remains a critical challenge in robotic arm research. Verbal commands possess high semantic abstraction, while precise grasping and placement actions rely on fine-grained geometric perception. The disparity between these two domains is the primary cause of operational errors. Particularly in certain cluttered scenarios, visual-spatial noise and background redundancy further disrupt attention distribution, significantly degrading the generalization capabilities of existing methods in unseen environments. To address these issues, this paper proposes the Secondary Realignment (SR) framework. It decouples vision-language alignment and vision-action alignment into two stages, mitigating More >

Mustafa Qaid Khamisi¹, Zuping Zhang^1,*, Mohammed Al-Habib¹, Muhammad Asim², Sajid Shah²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077556 - 08 May 2026

Abstract Large Vision-Language models (VLMs) such as Contrastive Language-Image Pretraining (CLIP) have transformed open world image recognition. Nevertheless, few-shot classification, particularly in the extremely low-shot regime, requires not only high accuracy but also reliably calibrated uncertainty for decisions with high confidence. Existing training-free CLIP adapters are primarily designed to increase accuracy and efficiency; integrate the zero-shot text logits with the few-shot feature caches, but not definitely model predictive uncertainty and therefore often exhibit considerable miscalibration and weak selective performance. Bayesian adapters move in the direction of probabilistic modeling by placing priors over adapter parameters and employing… More >

Savindi Thathsarani¹, Ashan Lakshitha², Pasindu Pramodya², Praveen Perera², Rasanjali Samarakoon^1,*, Shagufta Henna³, Upaka Rathnayake^4,*

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.078657 - 28 April 2026

Abstract Accurately determining the optimal post-harvest storage period is still a major challenge in mango processing, especially for the Tom EJC (TEJC) variety, due to reliance on subjective visual evaluations, leading to inconsistent product quality and increased post-harvest losses. This study presents an artificial intelligence-based framework combining computer vision and physicochemical analysis to objectively predict the optimal post-harvest storage period of TEJC mango before processing. TEJC mangoes of grade one were stored for eight days at 24–28°C temperature and 66.4–80% relative humidity. Daily measurements of pH, Total Soluble Solids (TSS), firmness, and peel color parameters (L*,… More > Graphic Abstract