Open Access
ARTICLE
Yu-Feng Yu1,*, Xiaoying Tan1, Qirong Wu1, Guoxia Xu2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085413
（This article belongs to the Special Issue: Advances in Deep Learning and Computer Vision for Intelligent Systems: Methods, Applications, and Future Directions)
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 >
Open Access
ARTICLE
Wen-Chin Hsu, Yi-Cheng Chen*, Yi-Hsuan Kuo
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085408
（This article belongs to the Special Issue: Advances in Natural Language Processing and Large-scale AI Models)
Abstract Due to the structural complexity of Chinese characters, the occurrence of homophones and visual similarity among glyphs directly increases the difficulties presented in Chinese spell checking (CSC). These factors also indicate the importance of the connection between CSC and context-dependency. In this study, a novel framework, the Summarization-Enhanced Chinese Spell Checking (abbreviated as SE-CSC) model, is proposed, which integrates phonetic and glyph embeddings to further enhance context awareness in error detection and correction. We utilize sentence-level summarization features to augment and generate an error-guided mask that can effectively detect errors and derive more precise corrections. More >
Open Access
ARTICLE
Ahmad Nawaz1,*, Hilal Khan2, Salamat Ullah3, Hamad Almujibah4,5, Ali E. A. Elshekh5, Maaz Osman Bashir5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086781
（This article belongs to the Special Issue: Machine Learning, Data-Driven and Novel Approaches in Computational Mechanics)
Abstract Calcium sulfoaluminate (CSA) cement is considered a promising low-carbon alternative to ordinary Portland cement owing to its lower clinkerization temperature and reduced CO2 emissions. The incorporation of blast furnace slag can further enhance the sustainability of CSA-based binders by lowering clinker content, reducing cost and embodied carbon emissions, while maintaining satisfactory mechanical performance. However, optimizing CSA-slag systems remains challenging due to the complex interactions among binder composition, clinker mineralogy, and slag replacement levels. This study therefore aims to predict the compressive strength of CSA-slag binders, identify the mixture parameters governing it, and optimize mixture proportions for… More >
Open Access
ARTICLE
Ziheng Lu1, Yingfeng Cai1,*, Wei Dong1, Hai Wang2,*, Long Chen1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086003
（This article belongs to the Special Issue: Multimodal Vision with Large Language Models)
Abstract Vision-language-action models have recently gained attention in autonomous driving, as language can express high-level behavioral intent beyond geometric waypoints. However, existing planners often treat language as an external command or auxiliary explanatory signal, while the hidden states of generated control instructions are rarely aligned explicitly with the latent variables that produce executable trajectories. This paper presents MALT-Drive, a moment-aligned language-to-trajectory end-to-end planning framework for autonomous driving. Given front-camera visual input, ego-state history, route priors, and a driving-oriented VQA prompt, MALT-Drive first autoregressively generates a concise control instruction from a controlled instruction space and then decodes… More >
Open Access
REVIEW
Shuaiqi Cheng1,2, Yuxi Chen2, Bo Yang1,*, Legend Zhang3, Junmin Lyu3, Guangyu Xu4, Chao Liu5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085787
（This article belongs to the Special Issue: The Collection of the Latest Reviews on Advances and Challenges in AI)
Abstract Computed tomography (CT) is an essential medical imaging technique that produces high-resolution cross-sectional images, but delivers substantial radiation dose to patients. Sparse-view CT (SVCT) reduces radiation dose by decreasing projection views, but causes streak artifacts that degrade image quality. Deep learning has emerged as a powerful tool to address this challenge. Although prospective paired acquisitions for low-current/voltage CT may be constrained by radiation-dose management and clinical workflow considerations, SVCT provides a practical way to construct paired training data through retrospective angular downsampling of full-view projections. This survey provides a systematic review of deep learning–based SVCT, More >
Graphic Abstract
Open Access
ARTICLE
Ling Wang1,2, Mohammad Faizuddin Md Noor2,*, Yanan Zhang3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084489
（This article belongs to the Special Issue: Emerging Artificial Intelligence & Data-Driven Modeling in Civil Engineering)
Abstract Ultra-high-performance concrete (UHPC) relies on multivariable mix design and curing regimes, which makes empirical estimation of compressive strength increasingly unreliable when material systems vary. In this context, unlike previous studies that only applied standard eXtreme Gradient Boosting (XGBoost), this study introduces an advanced hybrid optimization strategy, Bayesian Optimization and Hyperband (BOHB), which combines the sample efficiency of Bayesian optimization with the resource allocation mechanism of Hyperband, and incorporates SHapley Additive exPlanations (SHAP) for influencing factor analysis, thereby proposing a BOHB-XGBoost framework integrated with SHAP analysis. The proposed model demonstrates excellent predictive accuracy and stability, achieving… More >
Open Access
REVIEW
Hamza Rafik1, Oussama Khouili2, Mohamed Louzazni1, Petru Adrian Cotfas3, Daniel Tudor Cotfas3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084256
（This article belongs to the Special Issue: Advanced Computational Methods and AI algorithms for Renewable Energy)
Abstract The rapid expansion of photovoltaic (PV) technologies has necessitated the enhancement of energy conversion efficiency by developing more and more sophisticated control and optimization techniques. In particular, novel MPPT methods combined with solar tracking systems and AI approaches emerge as a promising solution to surmount the barriers of the conventional PV systems. This research presents a critical assessment of the recent developments in the research area of PV systems with MPPT algorithms, solar tracking mechanisms, and AI-based techniques. Therefore, papers with publication years from 2021 to 2025 were selected using Web of Science Core Collection. More >
Open Access
ARTICLE
Jaeseung Lee1, Jehyeok Rew2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084165
（This article belongs to the Special Issue: Intelligent Dynamics Modeling, Predictive Operations & Maintenance, and Control Optimization for Complex Systems)
Abstract Accurate and interpretable fault diagnosis of electric vehicle (EV) powertrains is essential for ensuring operational safety, reliability, and efficient maintenance. Undetected faults in key components such as motors, inverters, and batteries can lead to performance degradation and critical system failures. While machine learning (ML)-based fault diagnosis models have demonstrated strong predictive capability using multivariate sensor data, their black-box nature limits practical trust and adoption in real-world EV applications. In particular, understanding how individual sensor variables contribute to diagnostic decisions remains a major challenge. To address this issue, this study proposes a novel interpretability method for… More >
Open Access
ARTICLE
Yih-Tzoo Chen1, Thi-Hien Dao2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084193
（This article belongs to the Special Issue: Intelligent Scheduling and Optimization in Engineering and Management)
Abstract Construction strategy selection is a context-dependent decision problem in which multiple conflicting criteria must be considered under changing project conditions. Conventional multi-criteria decision-making (MCDM) methods generally produce rankings for predefined decision matrices but do not learn transferable preference structures across project scenarios. This study proposes a hybrid framework integrating T-spherical fuzzy sets, the Criteria Importance Through Intercriteria Correlation (CRITIC) method, the Combined Compromise Solution (CoCoSo) method, and RankNet-based pairwise learning. Fifty construction scenarios were designed using six contextual variables, and eight construction strategies were evaluated against eight criteria. Linguistic evaluations were transformed using a seven-level… More >
Open Access
ARTICLE
Feng Gao1, Guotao Meng2, Yuepeng Sun3,*, Xianglin Huang1, Heyi Yang1, Nuwen Xu3,4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.082323
Abstract During flood seasons in hydropower expansion projects, reservoir level rise may hydraulically connect the excavation pit and shaft to the reservoir. The resulting high-head boundary can impose a strong hydraulic gradient across the unexcavated blocking section, while staged bench excavation further redistributes stresses. This study proposes a three-dimensional hydro-mechanical coupled numerical framework based on Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC3D), which explicitly accounts for pore water pressure evolution, asymmetric hydraulic boundary conditions, and staged bench excavation disturbance. The framework enables systematic evaluation of rock-plug stability under different retained lengths using plastic-zone connectivity,… More >
Open Access
ARTICLE
Min Huang1, Cuichen Zhou1, Yuming Wang2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085152
Abstract With the complexity of the aviation equipment mission environment, aircraft are inevitably exposed to a high-intensity and stable complex electromagnetic radiation environment for a long time in some mission areas. It is difficult to effectively reduce electromagnetic risk by relying solely on compatible technologies such as electromagnetic shielding and flight path avoidance. Aiming at the problem that aircraft are vulnerable to electromagnetic interference in a harsh electromagnetic environment, this paper proposes an aircraft attitude angle optimization method based on an intelligent optimization algorithm under the framework of digital twin modeling, according to the characteristics that… More >
Open Access
REVIEW
Jui-Sheng Chou*, Dani Nugraha Limantono, Asmare Molla
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084591
（This article belongs to the Special Issue: Machine Learning Applications in Earthquake Engineering: Advances, Challenges, and Future Directions)
Abstract Artificial intelligence (AI) is increasingly transforming earthquake engineering by supporting prediction, assessment, monitoring, and decision support. However, existing studies remain fragmented because machine learning (ML), deep learning (DL), physics-informed AI, hybrid models, large language models (LLMs), multimodal AI, digital twins, and agentic AI are often examined separately. This study presents a systematic literature review of AI applications in earthquake engineering, synthesizing 130 studies published between 2016 and 2026. The reviewed applications include seismic hazard assessment, earthquake prediction, earthquake early warning, ground-motion modeling, structural response prediction, damage detection, bridge and building assessment, structural health monitoring, geotechnical… More >
Open Access
REVIEW
Jingfu Yan, Huachun Zhou*, Xiaojing Fan, Aoran Huang
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084537
（This article belongs to the Special Issue: The Evolution of Cybersecurity and AI: Surveys and Tutorials)
Abstract Artificial Intelligence (AI) has been widely used to detect complex attacks and abnormal behaviors in intelligent communication networks. However, existing studies are often limited to a single scenario or technical route, making it difficult to systematically explain the roles, boundaries, and collaboration mechanisms of different AI-integrated detection technologies. To address this gap, this paper reviews AI-integrated detection technologies in intelligent communication networks from both scenario and technical perspectives. From the perspective of application scenarios, the paper summarizes the requirements and characteristics of AI detection in cloud networks, edge computing, satellite and space networks, and Internet… More >
Open Access
REVIEW
Maryam Omar Abdullah Sawad1, Said Jadid Abdulkadir1,2,*, Hitham Seddig Alhussian1,2, Majdy Mohamed Eltayeb Eltahir3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085472
Abstract The increasing complexity and heterogeneity of cyberattacks targeting Internet of Things (IoT) environments, driven by the diversity of interconnected nodes and communication channels, necessitate the development of more advanced and intelligent cyber defence techniques. However, the most effective methods are Machine Learning (ML)-based and Deep Learning (DL)-based intrusion detection systems (IDS), which perform well but still face significant limitations and challenges. To address these issues, Deep Reinforcement Learning (DRL) has been proposed in recent years to automatically resolve the issues by detecting attacks in IoT environments. Therefore, this Systematic Literature Review (SLR) presents an up-to-date… More >
Open Access
ARTICLE
Asim Niaz1,#, Muhammad Umraiz2,#, Syed Farhan Alam Zaidi3, Kwang Nam Choi2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085102
Abstract Automated visual inspection in industrial settings often struggles with limited defect data and poor generalization to unseen anomalies. To overcome this challenge, we propose a hybrid anomaly detection pipeline, which integrates embedding-based, reconstruction-based, and self-supervised learning approaches. The framework also proposes a new Realistic Industrial Defect Synthesis (RIDS) module that synthesizes structured and textured synthetic anomalies based on the target masks, composite maps, and blending techniques. This helps to learn from pseudo-labeled data without the need for large annotated datasets. The pipeline further includes ViLoc-Net, a Vision Transformer-based localization network that obtains global features and More >
Open Access
ARTICLE
Hikmat Yar1,2, Imran Ullah Khan3, Naqqash Dilshad4, Weiwei Jiang5, Heung Soo Kim1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084202
Abstract Transportation has become an essential component of modern daily life, with continuous advancements aimed at reducing travel time and improving mobility. However, this increased convenience has also contributed to a rise in road accidents, often caused by driver distraction, fatigue, and age-related cognitive decline. These concerns have driven growing interest in Artificial Intelligence (AI)-based real-time driver monitoring systems designed to enhance road safety. Despite recent progress, several challenges remain, including limitations in detection accuracy, inadequate temporal reasoning, and high computational complexity. To address these challenges, we utilize the EfficientNetV2-S model for backbone feature extraction due… More >
Open Access
ARTICLE
Juncheng Zhang1,2, Xuhao Wei1,2, Xiaolei Gu3, Haixing Zhao4,*, Zhonglin Ye1,2,5,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083131
Abstract Graph structure learning depends heavily on the integrity and reliability of graph data. However, real-world graphs often contain noise, missing information, and bias, thereby limiting the expressive capacity of existing models. Single-layer structure learning methods fail to simultaneously capture local interactions and the global structure. Furthermore, they rely excessively on high-quality labeled data, leading to label scarcity issues and high annotation costs. To address these challenges, we propose a self-supervised dual-layer structure-driven graph structure learning method, termed D2GSL. Specifically, D2GSL constructs a semantic similarity channel and a spectral feature channel to model node relationships from More >
Open Access
ARTICLE
Kun-Lin Tsai1, Shih-Ting Chiu2, Chihhsiong Shih2, Fang-Yie Leu2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083958
Abstract With the quick development of 5G networks, network slicing and Open Radio Access Network (O-RAN) have become key technologies for improving network resource-allocation efficiency and flexibility. However, network slicing also faces intrusion-detection challenges, particularly for detecting DDoS attacks, which are difficult to detect due to traffic being silently transmitted across multiple sub-slices. To address this problem, this paper proposes a 5G network slicing intrusion detection mechanism, called the DDoS Defense Model on 5G Network Slices (2D5NS) which integrates machine learning and real-time traffic monitoring techniques to detect and mitigate DDoS attacks within an O-RAN. This… More >
Open Access
ARTICLE
Shamsh Parveen1, Mehtab Alam2,*, Ashraf Ali3, Abdullah Alourani4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083687
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract Accurate prediction of complex failure modes in anisotropic composite structures—specifically matrix cracking, fiber rupture, and delamination (stratification)—remains a central challenge in computational fracture mechanics. The primary goal of this work is to bridge the gap between high-fidelity physical modeling and computational efficiency. While the extended finite element method (XFEM) enables mesh-independent crack modeling, its computational cost limits scalability. This work proposes a deep learning–accelerated extended finite element framework (DL-XFEM) that couples physically admissible XFEM fields with a neural network surrogate to predict incremental crack growth. XFEM is employed to generate stress-intensity factors and fracture-consistent state… More >
Open Access
ARTICLE
Haitao Liu1,*, Xuyang Wang1, Shengcheng Quan1, Qiaosheng Guo2, Aichun Wang3, Lie Yang1, Tingfang Zhang1, Xiaojian Wu1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085747
Abstract Rapid advances in information and automation technologies have accelerated the development of smart manufacturing, thereby heightening the importance of reliable fault diagnosis for mechanical equipment. Although neural network-based algorithms are widely adopted in industrial applications due to their strong feature extraction and classification capabilities, their deployment in safety-critical fields such as aerospace remains limited. This limitation mainly arises from poor model interpretability and a heavy reliance on large-scale labeled training data. To address these challenges, this paper proposes an interpretable neural network framework that integrates discrete wavelet transform (DWT) with neural networks. Specifically, discrete wavelet… More >
Open Access
ARTICLE
Masood Karamoozian1, Zarrin Mahdavipour2, Mohammed Ameen3, Faisal Binzagr4, Abdolraheem Khader2,*, Ahmed Hamza Osman3, Ali Ahmed4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085421
Abstract Achieving net-zero carbon emissions in the building sector requires computational tools that are simultaneously efficient, physically rigorous, and scalable to complex multi-domain design spaces. Traditional physics-based simulators such as EnergyPlus and finite-element analysis (FEM) packages deliver high fidelity but are computationally prohibitive for large-scale parametric exploration and real-time multi-objective optimization. This study introduces a hybrid Physics-Informed Graph Neural Network (PI-GNN) surrogate that directly leverages semantic Building Information Modeling (BIM) data to enable rapid, accurate, and physically consistent multi-physics prediction and optimization of prefabricated modular buildings. Building components and their physical and functional relationships are encoded… More >
Open Access
ARTICLE
Noha Alnazzawi1, Nazik Alturki2,*, Umar Mujahid3, Fahad Masood4, Jawad Ahmad5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085378
Abstract The rapid deployment of Internet of Medical Things (IoMT) devices in current healthcare systems has made it much easier to maintain patient monitoring, make diagnoses, and provide long-distance medical treatment. The interconnection of these devices also creates significant cybersecurity problems, including distributed denial-of-service attacks, data breaches, and network intrusions. High detection accuracy and interpretability are essential for a trustworthy intrusion detection system. This study presents ExGAME, an Explainable Game-Theoretic Artificial Intelligence framework intended for intrusion detection in human-centric IoT networks. The proposed framework combines machine-learning-based anomaly detection with explainable AI and a game-theoretic defense strategy… More >
Open Access
ARTICLE
Heng Wu1,2, Junjie Wang1,2, Benzhuo Lu1,2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084608
Abstract Neural operator learning directly constructs the mapping relationship from the equation parameter space to the solution space, enabling efficient direct inference in practical applications without the need for repeated solution of partial differential equations (PDEs)—an advantage that is difficult to achieve with traditional numerical methods. In this work, we investigate a two-path formulation that combines affine and nonlinear computational components within such operator mappings to improve learning efficiency. This yields a novel network structure, namely the Linear–Nonlinear Fusion Neural Operator (LNF-NO), which models operator mappings via the multiplicative fusion of a linear component and a… More >
Open Access
ARTICLE
Kangjia Fan1, Biao He2,*, Shahab Hosseini3, Seyed Yaser Mousavi Siamakani4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084187
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-III)
Abstract Mining activities are associated with environmental side effects, which can be successfully predicted and strategies proposed for mitigating their adverse impacts. The cleaner production policies of green blasting focus on ecological issues related to mining operations and reduction plans. As a prediction part of this policy, this research proposed a mathematical model named Gene Expression Programming (GEP) to accurately predict the factors that generated pollutions, i.e., total suspended particles (TSP), particles dust with an analogous aerodynamic diameter of less than 10 μm (PM10), and dust emission distance due to mine blasting (DEMB), simultaneously. As the reduction… More >
Graphic Abstract
Open Access
ARTICLE
Chia-Hui Liu*, Chen-Chuan Cheng
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083813
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Industrial air quality forecasting remains challenging due to nonlinear pollutant formation, localized emissions, meteorological variability, and nonstationary spatiotemporal dependencies among monitoring stations. This study proposes FFTGNet, a frequency-aware spatiotemporal graph neural network for multi-pollutant forecasting in industrial air quality systems. It integrates an FFT-guided dominant-period estimation and period-folding module with a temporal-to-spatial graph backbone composed of TemporalGLU and Chebyshev graph convolution. The frequency-guided module reorganizes input sequences into intra-period and inter-period representations, TemporalGLU adaptively filters nonlinear temporal fluctuations and short-term spikes, and ChebGCN propagates information across inter-station spatial dependencies. Experiments were conducted using five years… More >
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