Weiqi Mao^1,2,3, Enbo Yu^1,*, Guoji Xu³, Xiaozhen Li³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.069733 - 29 January 2026

Abstract Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration. This study presents a novel machine learning model that integrates clustering, deep learning, and transfer learning to mitigate accuracy degradation in 24-h forecasting. Initially, an optimized DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters wind fields based on wind direction, probability density, and spectral features, enhancing physical interpretability and reducing training complexity. Subsequently, a ResNet (Residual Network) extracts multi-scale patterns from decomposed wind signals, while transfer learning adapts the backbone network across clusters, cutting training time by over… More >

Bo Li, Jie Jia^*, Peiwen Tan, Xinyan Chen, Dongjin Li

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071186 - 12 January 2026

Abstract Medical image segmentation is of critical importance in the domain of contemporary medical imaging. However, U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information. Although the subsequent U-KAN model enhances nonlinear representation capabilities, it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling, resulting in insufficient segmentation accuracy for edge structures and minute lesions. To address these challenges, this paper proposes the RE-UKAN model, which innovatively improves upon U-KAN. Firstly, a residual network is introduced into the encoder to effectively… More >

Tahira Khalil¹, Sadeeq Jan^2,*, Rania M. Ghoniem³, Muhammad Imran Khan Khalil¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.072655 - 09 December 2025

Abstract The contemporary era is characterized by rapid technological advancements, particularly in the fields of communication and multimedia. Digital media has significantly influenced the daily lives of individuals of all ages. One of the emerging domains in digital media is the creation of cartoons and animated videos. The accessibility of the internet has led to a surge in the consumption of cartoons among young children, presenting challenges in monitoring and controlling the content they view. The prevalence of cartoon videos containing potentially violent scenes has raised concerns regarding their impact, especially on young and impressionable minds.… 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 >

Abrar M. Alajlan^1,*, Abdul Razaque²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 919-946, 2025, DOI:10.32604/cmes.2025.071996 - 30 October 2025

Abstract Biometric authentication provides a reliable, user-specific approach for identity verification, significantly enhancing access control and security against unauthorized intrusions in cybersecurity. Unimodal biometric systems that rely on either face or voice recognition encounter several challenges, including inconsistent data quality, environmental noise, and susceptibility to spoofing attacks. To address these limitations, this research introduces a robust multi-modal biometric recognition framework, namely Quantum-Enhanced Biometric Fusion Network. The proposed model strengthens security and boosts recognition accuracy through the fusion of facial and voice features. Furthermore, the model employs advanced pre-processing techniques to generate high-quality facial images and voice… More >

Abrar M. Alajlan^1,*, Marwah M. Almasri²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5237-5262, 2025, DOI:10.32604/cmc.2025.066736 - 23 October 2025

Abstract Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats. Attackers can non-invasively manipulate sensors and spoof controllers, which in turn increases the autonomy of the system. Even though the focus on protecting against sensor attacks increases, there is still uncertainty about the optimal timing for attack detection. Existing systems often struggle to manage the trade-off between latency and false alarm rate, leading to inefficiencies in real-time anomaly detection. This paper presents a framework designed to monitor, predict, and control dynamic systems with a particular emphasis on detecting and adapting to… More >

Afaq Khattak^1,*, Pak-wai Chan², Feng Chen³, Abdulrazak H. Almaliki⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1565-1584, 2025, DOI:10.32604/cmes.2025.059914 - 27 January 2025

Abstract Intense wind shear (I-WS) near airport runways presents a critical challenge to aviation safety, necessitating accurate and timely classification to mitigate risks during takeoff and landing. This study proposes the application of advanced Residual Network (ResNet) architectures including ResNet34 and ResNet50 for classifying I-WS and Non-Intense Wind Shear (NI-WS) events using Doppler Light Detection and Ranging (LiDAR) data from Hong Kong International Airport (HKIA). Unlike conventional models such as feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), ResNet provides a distinct advantage in addressing key challenges such as capturing intricate… More >

Deema Alsekait¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2395-2436, 2024, DOI:10.32604/cmc.2024.055469 - 18 November 2024

Abstract The widespread adoption of Internet of Things (IoT) devices has resulted in notable progress in different fields, improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks. Further, the study suggests using an advanced approach that utilizes machine learning, specifically the Wide Residual Network (WRN), to identify hidden malware in IoT systems. The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices, using the MalMemAnalysis dataset. Moreover, thorough experimentation provides evidence for the effectiveness of the WRN-based strategy, resulting in… More >

Bolin Guo^1,2, Shi Qiu^1,*, Pengchang Zhang¹, Xingjia Tang³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1809-1833, 2024, DOI:10.32604/cmc.2024.056706 - 15 October 2024

Abstract Mural paintings hold significant historical information and possess substantial artistic and cultural value. However, murals are inevitably damaged by natural environmental factors such as wind and sunlight, as well as by human activities. For this reason, the study of damaged areas is crucial for mural restoration. These damaged regions differ significantly from undamaged areas and can be considered abnormal targets. Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false detections. Hyperspectral imaging can reflect the material properties more effectively than visual characterization methods. Thus, this study employs hyperspectral imaging… More >

Zhihong Lin¹, Zeng Zeng², Yituan Yu², Yinlin Ren¹, Xuesong Qiu^1,*, Jinqian Chen¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1641-1665, 2024, DOI:10.32604/cmc.2024.055802 - 15 October 2024

Abstract For permanent faults (PF) in the power communication network (PCN), such as link interruptions, the time-sensitive networking (TSN) relied on by PCN, typically employs spatial redundancy fault-tolerance methods to keep service stability and reliability, which often limits TSN scheduling performance in fault-free ideal states. So this paper proposes a graph attention residual network-based routing and fault-tolerant scheduling mechanism (GRFS) for data flow in PCN, which specifically includes a communication system architecture for integrated terminals based on a cyclic queuing and forwarding (CQF) model and fault recovery method, which reduces the impact of faults by simplified… More >