Mrugendrasinh Rahevar¹, Martin Parmar¹, Hemant Yadav¹, Chun-Ta Li^2,*, Agbotiname Lucky Imoize³, Hiren Mewada⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085393 - 28 August 2026

Abstract Quantum kernel techniques aim to leverage quantum computational capabilities on social data. However, their application to natural language processing tasks faces formidable obstacles, such as extreme dimensionality reduction ($D=384\to k=8$), concentration of measure in quantum feature spaces, and the lack of theoretical understanding of when quantum advantages occur in kernel-based text classification. Filling this gap, we provide a comprehensive study of quantum kernels for text classification that addresses three major challenges in existing studies: general data compression approaches that ignore class structure, the lack of a predictive diagnostic toolkit, and overlooked approaches for handling concentration… More >

Noha Alnazzawi¹, Nazik Alturki^2,*, Umar Mujahid³, Fahad Masood⁴, Jawad Ahmad⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085378 - 28 August 2026

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 >

Mengyao Wang¹, Ailian Zhu², Longji Zhu^3,*, Chen Lan^4,*, Yang Li⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085279 - 28 August 2026

Abstract Accurate prediction of asphalt pavement distress is essential for proactive maintenance and life-cycle infrastructure management. However, existing data-driven methods often struggle to jointly represent multi-source inspection data, distress evolution mechanisms, and spatial propagation relationships among pavement sections. To address these limitations, this study proposes a Dual-Layer Progressive Physics-Constrained Graph Neural Network (DLPC-GNN) for asphalt pavement distress prediction and maintenance strategy classification. The proposed model represents pavement deterioration using a dual-layer graph structure. At the microscopic level, cracks, surface deterioration, and structural moisture-induced damage are modeled as physically associated distress nodes. At the macroscopic level, pavement-section… More >

Min Huang¹, Cuichen Zhou¹, Yuming Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085152 - 28 August 2026

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 >

Asim Niaz^1,#, Muhammad Umraiz^2,#, Syed Farhan Alam Zaidi³, Kwang Nam Choi^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085102 - 28 August 2026

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 >

Wei Wei^1,2, Shujuan Li¹, Legend Zhang³, Junmin Lyu³, Qi Hu⁴, Wenfeng Zheng^3,4,*, Bo Yang^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085095 - 28 August 2026

Abstract Active motion compensation is essential for improving the precision and safety of robot-assisted surgery in the presence of physiological motion such as heartbeat and respiration. Conventional direct error feedback controllers often show limited performance when sensing delay and measurement noise are present. To address this issue, this study proposes an active motion compensation framework based on deep reinforcement learning enhanced with a Long Short-Term Memory (LSTM) network, where the target position is assumed to be known. The motion compensation task is formulated as a Markov decision process, and the controller is trained to generate continuous… More >

Jeng-Shyang Pan^1,2,3, Wenda Li², Shu-Chuan Chu^4,*, Zhi-Gang Du⁵, Hongmei Yang², Lingping Kong⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084683 - 28 August 2026

Abstract Formulating efficient updating techniques is essential for the efficacy of metaheuristic algorithms. Traditional approaches, however, depend significantly on manually developed formulas and empirical intuition, which frequently constrain their adaptability and scalability across various optimization tasks. This research introduces a Genetic Programming-based Metaheuristic framework, referred to as GP-MAs, designed to autonomously develop and enhance symbolic update rules for metaheuristic algorithms. Within the suggested GP-MAs architecture, genetic programming (GP) is integrated into the learning phase of the Growth Optimizer (GO) to dynamically formulate symbolic update equations, hence enhancing the algorithm’s adaptability to diverse optimization landscapes. A hybrid… More >

Guangyu Xu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084644 - 28 August 2026

Abstract The fusion of GNNs and SSMs is creating a new era in the realm of dynamical learning with structures. Graph-Mamba is one of the most promising works in this fast-moving field. With its unique capability to incorporate graph topology and selective dynamics in a stable manner, Graph-Mamba has demonstrated its ability to model intricate relationships. However, the existing research on Graph-Mamba has not been compiled into a coherent form; the theoretical basis and practical implementation of the framework have not been synthesized systematically across different domains. First, we demonstrate the theoretical connection between graph propagation… More >

Heng Wu^1,2, Junjie Wang^1,2, Benzhuo Lu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084608 - 28 August 2026

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 >

Jui-Sheng Chou^*, Dani Nugraha Limantono, Asmare Molla

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084591 - 28 August 2026

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 >