Kiran Deep Singh¹, Prabh Deep Singh², Narinder Kaur³, Jawad Khan^4,*, Dildar Hussain⁵, Yeong Hyeon Gu^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080915 - 27 May 2026

Abstract In smart healthcare systems, Image data of critical patients is essential in controlling and diagnosing the disease development. To acquire the medical images, traditional methods encountered the difficulty of generating cost-effective data. This research work introduces a novel and innovative approach to collect high-quality image data from individuals with atypical clinical presentations. Initially, a new Internet of Medical Things (IoMT) image collection architecture is introduced. This design uses edge intelligence and motion-static synergy to make it easier to record both coarse-grained and fine-grained patient images. This study introduces an image acquisition technique that leverages edge… More >

Qilong Wang¹, Ning Wang¹, Shuhan Luo¹, Xiang Gao², Yuqian Lu³, Min He^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080372 - 27 May 2026

Abstract Tunnel environments often suffer from GPS denial, uneven illumination, and structural uniformity, which lead to feature degradation, loop closure failure, and long-distance drift in SLAM systems. To solve these problems, this study aims to propose a high-precision SLAM method suitable for tunnel structural health monitoring. Firstly, an ABA-CLAHE image enhancement algorithm is proposed, which adopts cascaded processing of nonlinear brightness adjustment in HSV space and CLAHE local contrast optimization to improve low-light image quality and enhance feature stability. Then, SURF feature matching combined with the RANSAC algorithm is used to ensure feature matching accuracy. Finally, More >

Shunpeng Yang¹, Yunpeng Wu¹, Wenwen Qin¹, Cheng Yang^2,*, Yu Qian³

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.077878 - 18 May 2026

Abstract To enhance traffic infrastructure health monitoring via computer vision (CV) in adverse weather conditions, image dehazing has emerged as a critical processing step. However, current supervised dehazing models, typically trained on synthetic hazy-clean image pairs, often demonstrate limited generalization ability when deployed in real-world haze scenarios. This study proposes a novel unsupervised dehazing framework named the unpaired dual-domain dehazing network (UD³Net). Initially, a novel dual-domain convolutional mixer (DCM) is developed, which can extract local features in the spatial domain and global features in the frequency domain to achieve thorough information fusion, aiming to facilitate accurate estimation… More >

Muhammad Shaheer¹, Feng Zeng^1,*, Aqsa Yasmeen², Mudasir Ahmad Wani^3,*, Kashish Ara Shakil⁴, Muhammad Asim⁵

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079701 - 08 May 2026

Abstract Malware remains a persistent and evolving threat to digital security, highlighting the need for advanced and resilient detection frameworks capable of mitigating increasingly sophisticated and evasive cyberattacks. Although deep learning ensembles have been explored, many existing approaches fail to balance computational efficiency with the diverse feature extraction capabilities needed for complex variants. To address this gap, this study proposes a novel stacking ensemble framework, MalDetect-IoT, which specifically eliminates the requirement for manual feature engineering and domain specific preprocessing traditionally required in malware classification. By fine-tuning two pre-trained models MobileNetV3 for its lightweight efficiency and Xception… More >

Bing-Zhou Chen^1,2, Hai-Ying Zheng^1,2, Ao-Wen Wang^1,3, Ke-Lei Xia^1,2, Li-Feng Fan^1,3, Zhong-Yi Wang^1,3, Lan Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078894 - 08 May 2026

Abstract Many sensing and imaging modalities naturally yield complex-valued signals, where magnitude and phase jointly convey information. Complex-valued neural networks (CVNNs) possess unique advantages in processing phase-sensitive data (e.g., synthetic aperture radar (SAR) and magnetic resonance imaging (MRI)), yet their widespread adoption is hindered by significant computational overhead and training instability. To address these challenges, this paper presents the Wirtinger Derivative Complete Complex Network (WCCN), a unified and efficient framework for complex-valued deep learning. The proposed framework systematically addresses three key challenges in CVNNs: computational efficiency, parameter redundancy, and training stability. WCCN integrates three core components.… More >

Wenfeng Wang^1,*, Wenjie Fan¹, Fang Dong¹, Bin Zeng¹, Wenxin Yu¹, Xiangping Deng²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078416 - 08 May 2026

Abstract Unmanned Aerial Vehicle (UAV) image object detection has been widely applied in many fields. However, compared with ordinary natural images, UAV images often exhibit complex backgrounds, a predominance of small objects, and significant variations in target scales, which cause traditional detection algorithms to easily suffer from missed or false detections with insufficient accuracy. To address these issues, this paper proposes a novel UAV image object detection algorithm named DMA-YOLO based on the YOLOv8s model, incorporating a deep diverse branch block and multi-scale auxiliary feature. First, a DF-C2f module integrating a deep diverse branch block and… More >

Walaa Alayed¹, Asad Ur Rehman², M.Awais Ehsan³, Waqar Ul Hassan⁴, Ahmed Zeeshan^5,6,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078078 - 08 May 2026

Abstract The rapid growth in the field of data and cloud computing has made it essential to ensure information security. Encryption consists of multiple layers, among which a critical component is the Substitution box (S-box). The S-box provides nonlinearity and confusion between the original and cipher forms, and its performance directly determines the security of the cipher against cryptanalysis. Chaotic systems have been widely used for image encryption, however, they suffer from well known limitations such as deterministic periodicity and reduced unpredictability in finite field digital environments. To address these issues, we propose a new S-box… More >

Yuanjing Luo^1,2,#, Xichen Tan^1,#, Yinuo Jiang¹, Zhiping Cai^1,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077803 - 08 May 2026

Abstract With increasing artwork plagiarism incidents, the necessity of using digital watermarking technology for high-quality artwork copyright protection is evident. Current digital watermarking methods are limited in imperceptibility and robustness. To address this, based on comprehensive copyright protection research, we develop a novel watermark framework named ArtFlow, using Invertible Neural Networks (INN). Our framework treats watermark embedding and recovery as inverse image transformations, implemented through forward and reverse processes of INN. To ensure high-quality watermark embedding, we utilize frequency domain transformations and attention mechanisms to guide the watermark into high-frequency areas of the image that have More >

Chan-Min Hsu¹, Shang-Ru Yang¹, Yi-Ju Lee¹, An-Chi Wei^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076098 - 08 May 2026

Abstract U-Net, a fully convolutional neural network (FCNN) with U-shaped features, has demonstrated significant success in biomedical image segmentation. However, the locality of convolution operations in the U-Net limits its ability to learn long-range dependencies. Transformers, originally developed for natural language processing, have recently been adapted for image segmentation because of their global self-attention mechanisms. Inspired by the long-range feature learning capability of transformers, we propose Dense-Transformer (DenT), an architecture designed for volumetric microscopy image segmentation. DenT incorporates transformers as encoders within each convolutional layer to capture global contextual information. Additionally, dense skip connections at multiple More >

Hafeez Rahman¹, Naveed Butt¹, Naila Sammar Naz¹, Fahad Ahmed¹, Muhammad Saleem¹, Adnan Khan^2,3,4, Khan Muhammad Adnan^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.081316 - 27 April 2026

Abstract Gastrointestinal polyps are well-known precursors to colorectal cancer (CRC), making their accurate detection and segmentation during colonoscopy essential for early diagnosis and cancer prevention. Deep learning–based segmentation models trained on publicly available datasets such as Kvasir-SEG have demonstrated promising performance; however, two key challenges remain: limited robustness across diverse polyp morphologies and endoscopic imaging conditions, and the lack of interpretable decision-making mechanisms that support clinical trust and validation. Many existing centralized segmentation approaches are primarily optimized using overlap-based metrics such as the Dice coefficient and intersection over union (IoU), without adequately analyzing challenging cases such… More >