Abuzar Khan¹, Abid Iqbal^2,*, Ghassan Husnain^1,*, Fahad Masood¹, Mohammed Al-Naeem³, Sajid Iqbal⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077222 - 09 April 2026

Abstract Cloud computing now supports large-scale maritime analytics, yet offloading rich Automatic Identification System (AIS) data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border privacy regulations. This work addresses the gap between growing demand for AI-driven vessel intelligence and the limited availability of practical, privacy-preserving cloud solutions. We introduce a privacy-by-design edge-cloud framework in which ports and vessels serve as federated clients, training vessel-type classifiers on local AIS trajectories while transmitting only clipped, Gaussian-perturbed updates to a zero-trust cloud coordinator employing secure and robust aggregation. Using a public AIS corpus with realistic… More >

Zhidong Wang, Yanping Lian^*, Mingjian Li, Jiawei Chen, Ruxin Gao

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078312 - 30 March 2026

Abstract Metal additive manufacturing (AM) technology has promising applications across many fields due to its near-net-shape advantages. The quality of the as-built component is closely linked to the temperature evolution during the metal AM process, which exhibits strong nonlinearities, localized high gradients, and rapid cooling rates. Therefore, real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality, which poses surprising challenges for numerical methods, as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations. In this study, we proposed an isothermal surface imaging and transfer… More >

Adeel Iqbal^1,#, Tahir Khurshaid^2,#, Syed Abdul Mannan Kirmani³, Mohammad Arif^4,*, Muhammad Faisal Siddiqui^5,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075819 - 12 March 2026

Abstract Vehicular Internet of Things (V-IoT) networks need intelligent and adaptive spectrum access methods for ensuring ultra-reliable and low-latency communication (URLLC) in highly dynamic environments. Traditional reinforcement learning (RL)-based algorithms, such as Q-Learning and Double Q-Learning, are often characterized by unstable convergence and inefficient exploration in the presence of stochastic vehicular traffic and interference. This paper proposes Adaptive Reinforcement Q-learning with Upper Confidence Bound (ARQ-UCB), a lightweight and reliability-aware RL framework, which explicitly reduces interruption and blocking probabilities while improving throughput and delay across diverse vehicular traffic conditions. This proposed ARQ-UCB algorithm extends the basic Q-updates More >

Xi Cai, Xiaoqiang Wang, Huiying Zhao, Guang Han^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075756 - 12 March 2026

Abstract Low-light image enhancement (LLIE) remains challenging due to underexposure, color distortion, and amplified noise introduced during illumination correction. Existing deep learning–based methods typically apply uniform enhancement across the entire image, which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction. To overcome these limitations, we propose a Semantic-Guided Visual Mamba Network (SGVMNet) that unifies semantic reasoning, state-space modeling, and mixture-of-experts routing for adaptive illumination correction. SGVMNet comprises three key components: (1) a semantic modulation module (SMM) that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant (LLaVA) and… More >

Ahmad Salehiyan¹, Nuria Serrano², Francisco Hernando-Gallego³, Diego Martín^2,*, José Vicente Álvarez-Bravo²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075336 - 12 March 2026

Abstract The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection (FDI) attacks, posing critical threats to operational integrity, resource management, and public safety. Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated, stealthy threats. To address these challenges, we propose a novel evolutionary optimized transformer-based deep reinforcement learning framework (Evo-Transformer-DRL) designed for robust and adaptive FDI detection in smart water infrastructures. The proposed architecture integrates three powerful paradigms: a transformer encoder for modeling complex temporal dependencies in multivariate time series, a… More >

Yong Hu¹, Weifan Xu², Xiangtong Du^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074437 - 12 March 2026

Abstract Deep learning-based methods have shown great potential in intelligent bearing fault diagnosis. However, most existing approaches suffer from the scarcity of labeled data, which often results in insufficient robustness under complex working conditions and a general lack of interpretability. To address these challenges, we propose a physics-informed multimodal fault diagnosis framework based on few-shot learning, which integrates a 2D time-frequency image encoder and a 1D vibration signal encoder. Specifically, we embed prior knowledge of multi-resolution analysis from signal processing into the model by designing a Laplace Wavelet Convolution (LWC) module, which enhances interpretability since wavelet More >

Mubarak Alanazi^1,*, Junaid Rashid²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077956 - 26 February 2026

Abstract Wind turbine blade defect detection faces persistent challenges in separating small, low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints. Conventional image-processing pipelines struggle with scalability and robustness, and recent deep learning methods remain sensitive to class imbalance and acquisition variability. This paper introduces TurbineBladeDetNet, a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types. Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability. The model achieves 97.14% accuracy, 98.65% More >

Jarrar Amjad¹, Muhammad Zaheer Sajid², Mudassir Khalil³, Ayman Youssef⁴, Muhammad Fareed Hamid⁵, Imran Qureshi^6,*, Haya Aldossary⁷, Qaisar Abbas⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.073473 - 26 February 2026

Abstract Generative Adversarial Networks (GANs) have become valuable tools in medical imaging, enabling realistic image synthesis for enhancement, augmentation, and restoration. However, their integration into clinical workflows raises concerns, particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making. To address this challenge, we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images, thereby reinforcing the reliability of AI-driven diagnostics. The framework integrates low-level statistical descriptors, including high-frequency residuals and Gray-Level Co-occurrence Matrix (GLCM) texture features, with high-level semantic representations extracted from… More >

Muhammad Ishtiaq¹, Yeon-Ju Lee², Annabathini Geetha Bhavani³, Sung-Gyu Kang^1,*, Nagireddy Gari Subba Reddy^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.075957 - 10 February 2026

Abstract Lithium manganese silicate (Li-Mn-Si-O) cathodes are key components of lithium-ion batteries, and their physical and mechanical properties are strongly influenced by their underlying crystal structures. In this study, a range of machine learning (ML) algorithms were developed and compared to predict the crystal systems of Li-Mn-Si-O cathode materials using density functional theory (DFT) data obtained from the Materials Project database. The dataset comprised 211 compositions characterized by key descriptors, including formation energy, energy above the hull, bandgap, atomic site number, density, and unit cell volume. These features were utilized to classify the materials into monoclinic… More >

Ping Fang, Mengjun Tong^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073272 - 10 February 2026

Abstract Defect detection in printed circuit boards (PCB) remains challenging due to the difficulty of identifying small-scale defects, the inefficiency of conventional approaches, and the interference from complex backgrounds. To address these issues, this paper proposes SIM-Net, an enhanced detection framework derived from YOLOv11. The model integrates SPDConv to preserve fine-grained features for small object detection, introduces a novel convolutional partial attention module (C2PAM) to suppress redundant background information and highlight salient regions, and employs a multi-scale fusion network (MFN) with a multi-grain contextual module (MGCT) to strengthen contextual representation and accelerate inference. Experimental evaluations demonstrate More >