Yu-Feng Yu^1,*, Xiaoying Tan¹, Qirong Wu¹, Guoxia Xu²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085413 - 28 August 2026

Abstract To tackle critical visual tracking difficulties arising in UAV tracking tasks, including frequent target occlusion and abrupt fast motion during high-altitude inspection, we propose an adaptive correlation filter tracking algorithm incorporating a motion smoothing module and adaptive residual regularization, named MACF. The tracker is constructed via multi-strategy fusion of two elaborately designed components at the algorithmic modeling level. First, we design a Motion Smoothing Module (MSM) that conducts weighted fusion of historical motion trends in the modeling pipeline. It suppresses search window jitter arising from instantaneous positioning errors and lowers target drift risk by providing More >

Sathishkumar Veerappampalayam Easwaramoorthy^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.087043 - 27 July 2026

Abstract This article has no abstract. More >

Mahmood Ashraf^1,2, Nuha Zamzami³, Shtwai Alsubai⁴, Raed Alharthi⁵, Muhammad Umer^6,*, Yunyoung Nam⁷, Yongwon Cho^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.078738 - 30 June 2026

Abstract Hyperspectral image (HSI) denoising is a crucial preprocessing step that significantly enhances the performance of downstream applications, such as object detection and classification. Whereas deep neural networks have achieved remarkable performance in HSI denoising, many existing models rely mostly on vanilla convolutions, which often fail to capture fine-grained noise patterns and structural details in real-time HSIs. To address these limitations, we propose a novel Center-Difference Convolutional Network (CDCN) designed to effectively suppress various noise types while preserving the inherent structure of HSIs. By leveraging center-difference convolution (CDC), our model captures both gradient and intensity information… More >

Mohammed Shamar Yadkar¹, Sefer Kurnaz¹, Saadaldeen Rashid Ahmed^2,3,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079408 - 09 April 2026

Abstract This advanced research describes CycleGAN-RRW, a new reversible watermarking system for secure image ownership authentication. It uses Cycle-Consistent Generative Adversarial Networks with adaptive feature encoding. In areas such as law, forensics, and telemedicine, digital images usually contain private info that may be changed or used without authorization. Existing watermarking methods may decrease image quality, may not be reversible, or need outside keys. To address these problems, our model embeds metadata into intermediate feature maps with Adaptive Instance Normalization (AdaIN), based on adversarial and perceptual loss. The dual-generator design permits two-way translation between original and watermarked… More >

Abdul Jabbar Siddiqui^1,2,*, Saheed Ademola Bello², Muhammad Liman Gambo², Abdul Khader Jilani Saudagar^3,*, Mohamad A. Alawad⁴, Amir Hussain⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.074283 - 26 February 2026

Abstract Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities, variations in skin texture, the presence of hair, and inconsistent illumination. Deep learning models have shown promise in assisting early detection, yet their performance is often limited by the severe class imbalance present in dermoscopic datasets. This paper proposes CANNSkin, a skin cancer classification framework that integrates a convolutional autoencoder with latent-space oversampling to address this imbalance. The autoencoder is trained to reconstruct lesion images, and its latent embeddings are used as features for classification. To enhance minority-class representation, the Synthetic Minority Oversampling… More >

Waseem Akhtar^1,2, Mahwish Ilyas³, Romana Aziz^4,*, Ghadah Aldehim⁴, Tassawar Iqbal⁵, Muhammad Ramzan⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.071669 - 26 February 2026

Abstract Human Activity Recognition (HAR) is a novel area for computer vision. It has a great impact on healthcare, smart environments, and surveillance while is able to automatically detect human behavior. It plays a vital role in many applications, such as smart home, healthcare, human computer interaction, sports analysis, and especially, intelligent surveillance. In this paper, we propose a robust and efficient HAR system by leveraging deep learning paradigms, including pre-trained models, CNN architectures, and their average-weighted fusion. However, due to the diversity of human actions and various environmental influences, as well as a lack of… More >

Williams Kyei^*, Chunyong Yin, Kelvin Amos Nicodemas, Khagendra Darlami

Journal on Artificial Intelligence, Vol.8, pp. 19-38, 2026, DOI:10.32604/jai.2026.074988 - 20 January 2026

Abstract The COVID-19 pandemic has underscored the need for rapid and accurate diagnostic tools to differentiate respiratory infections from normal cases using chest X-rays (CXRs). Manual interpretation of CXRs is time-consuming and prone to errors, particularly in distinguishing COVID-19 from viral pneumonia. This research addresses these challenges by proposing a customized EfficientNet-B0 model for ternary classification (COVID-19, Viral Pneumonia, Normal) on the COVID-19 Radiography Database. Employing transfer learning with architectural modifications, including a tailored classification head and regularization techniques, the model achieves superior performance. Evaluated via accuracy, F1-score (macro-averaged), AUROC (macro-averaged), precision (macro-averaged), recall (macro-averaged), inference… More >

Hongcheng Zhao¹, Tong Yang ², Yihui Hu², Fengxiang Guo^2,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.068987 - 08 January 2026

Abstract With the rapid development of transportation infrastructure, ensuring road safety through timely and accurate highway inspection has become increasingly critical. Traditional manual inspection methods are not only time-consuming and labor-intensive, but they also struggle to provide consistent, high-precision detection and real-time monitoring of pavement surface defects. To overcome these limitations, we propose an Automatic Recognition of Pavement Defect (ARPD) algorithm, which leverages unmanned aerial vehicle (UAV)-based aerial imagery to automate the inspection process. The ARPD framework incorporates a backbone network based on the Selective State Space Model (S³M), which is designed to capture long-range temporal dependencies.… More >

Cyreneo Dofitas¹, Yong-Woon Kim², Yung-Cheol Byun^3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069374 - 09 December 2025

Abstract Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery. However, conventional convolutional neural networks (CNNs) often struggle in complex flood scenarios involving reflections, occlusions, or indistinct boundaries due to limited contextual modeling. To address these challenges, we propose a hybrid flood segmentation framework that integrates a Vision Transformer (ViT) encoder with a U-Net decoder, enhanced by a novel Flood-Aware Refinement Block (FARB). The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms. We evaluate our model on a UAV-acquired flood More >

Tiantian Wang^1,2, Zhihua Hu^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2357-2381, 2025, DOI:10.32604/cmes.2025.073640 - 26 November 2025

Abstract Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks. We present a novel image deraining framework built on a three-stage dual-residual architecture that progressively restores rain-degraded content while preserving fine structural details. Each stage begins with a multi-scale feature extractor and a channel attention module that adaptively emphasizes informative representations for rain removal. The core restoration is achieved via enhanced dual-residual blocks, which stabilize training and mitigate feature degradation across layers. To further refine representations, we integrate cross-dimensional spatial attention supervised by ground-truth guidance, ensuring that More >