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ARTICLE

Muhammad Shamrooz Aslam^1,#, Wen-Jer Chang^2,*, Hazrat Bilal^3,*, Vyacheslav Gulvanskii⁴, Dmitrii Perevertaylo⁴, Dmitrii Kaplun^1,4,5, Muhammad Hashim Bukhari⁶, Muhammad Aamir Aman^7,#,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.084958 - 28 September 2026
Abstract In a multi-agent system, platoon vehicles receive a huge collection regarding autonomous models coordinating and their actions to improve traffic flow, lower fuel consumption, and boost safety. This paper examines the distributed consensus control problem for heterogeneous multi–agent systems (MASs) containing both first-order and second-order agents, under constrained network communication resources. Secondly, a novel event–triggered approach is proposed to tackle the problems of information transmission restrictions and bandwidth contention. Unlike conventional state-independent triggering methods, the proposed trigger condition depends on both the agent’s own state update error and the information mismatches between neighboring agents, enabling… More >

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Xianjian Jin^1,2,*, Yinchen Tao¹, Yuhuai Zhang¹, Haoze Wu¹, Jianning Lu¹, Nonsly Valerienne Opinat Ikiela¹
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.082644 - 28 September 2026
Abstract Autonomous parking is a key bottleneck to achieving fully autonomous driving, especially in the final parking problem of automated valet parking (AVP). Unlike traditional structured highway driving, parking scenarios impose stringent requirements on trajectory feasibility, and it requires the simultaneous resolution of nonholonomic motion constraints, narrow passage navigation, and collision avoidance in unstructured environments. This paper provides a comprehensive overview of parking trajectory planning and modeling techniques. Different aspects of parking trajectory planning strategies and modeling methodologies in recent literature are categorized into six major classes: graph-search-based methods, sampling-based methods, artificial potential field (APF)-based methods, More >

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Jiawei Tian¹, Kyungtae Kang^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087740 - 28 September 2026
（This article belongs to the Special Issue: The Collection of the Latest Reviews on Advances and Challenges in AI)
Abstract Deep learning has become increasingly important in magnetic resonance imaging (MRI)-based computer vision, but its application to three-dimensional MRI is still shaped by a basic methodological decision: how volumetric data are represented before model training. A 3D MRI scan may be processed as independent 2D slices, adjacent-slice 2.5D inputs, full 3D volumes, local patches, regions of interest, or multi-view representations. These choices influence spatial-context modeling, computational cost, annotation requirements, architectural design, and task suitability. This review provides a paradigm-oriented overview of recent 3D MRI-based computer vision studies, focusing on the relationships among datasets, input representations,… More >

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Shuaiqi Cheng^1,2, Yuxi Chen², Bo Yang^1,*, Legend Zhang³, Junmin Lyu³, Guangyu Xu⁴, Chao Liu⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.085787 - 28 September 2026
（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 >

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Abdelzahir Abdelmaboud¹, Ibrahim Hameed², Salmah Fattah³, Ashraf Osman Ibrahim⁴, Salil Bharany⁵, Ateeq Ur Rehman^6,7,8, SeongKi Kim^9,10,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.088032 - 28 September 2026
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract This paper provides a thorough, systematic review of explainable, context-sensitive Artificial Intelligence (AI) and Digital Twin (DT) technologies in healthcare, emphasizing their potential to transform the medical system into an intelligent, patient-centred system of care. The PRISMA framework helped identify 1842 records from the largest scientific databases, of which 97 high-quality studies were selected for a comprehensive analysis. The review analyses in detail the development of AI from traditional machine learning to modern deep learning and generative models with a focus on their use in disease diagnosis and predictive analytics, personalized medicine, and healthcare management.… More >

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REVIEW

I Wayan Adi Juliawan Pawana^1,2, Vincent Abella², Eldridge Aaron Miole², Hoonyong Park³, Ilsun You^2,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087445 - 28 September 2026
（This article belongs to the Special Issue: The Evolution of Cybersecurity and AI: Surveys and Tutorials)
Abstract Fifth- and sixth-generation cellular networks remain exposed to a spectrum of threats ranging from the stealthy and targeted false base station (FBS) to the systemic distributed denial-of-service (DDoS) and signaling-storm attacks. The elevation of artificial intelligence (AI) to a native network capability by the International Telecommunication Union Radiocommunication Sector (ITU-R) framework for International Mobile Telecommunications-2030 (IMT-2030) has made learning-based defense the dominant response. This survey examines that response across the three threats jointly, advancing as a design position the view that effective defense is best served when AI is integrated into the radio access network… More >

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ARTICLE

Ante Jurčević¹, Tomislav Lesičar², Zdenko Tonković², Jurica Sorić^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087199 - 28 September 2026
Abstract Accurate and computationally efficient numerical modelling of damage and fracture in heterogeneous materials requires the incorporation of a multiscale approach. However, the information transfer between the lower and upper scale with the presence of a specific damage algorithm represents a significant challenge. This paper presents a robust two-scale concurrent multiscale approach for modelling damage and fracture in brittle and ductile heterogeneous materials. The developed multiscale procedure utilises the self-consistent clustering analysis (SCA) at the microlevel, and a phase-field (PF) fracture method at the macrolevel in order to meet the main criteria of an accurate and… More >

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ARTICLE

Hashem Al-Madwami^1,2, Amira Abo Kaf ³, Haibin Yin^1,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086319 - 28 September 2026
（This article belongs to the Special Issue: Mechanical Applications of Computational Methods and Machine Learning)
Abstract A mechanics-based analytical framework is developed for estimating the steady bending force and shaft torque in central-axis bending of reinforcing bars (RBs). Analytical expressions are derived for the sectional bending moment and are subsequently linked to the machine-level force and torque through the roller-system load-transfer geometry. Three constitutive descriptions are considered, namely elastic-perfectly plastic, bilinear hardening, and power-law hardening, to examine the influence of post-yield material response on bending-demand estimation. The analytical formulations are assessed using a section-level pure-bending finite element model, a process-level three-dimensional finite element model with tool-bar contact, and reported smooth round-bar… More >

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101

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ARTICLE

Xin-Hui Zhou^1,2, Chao Yang^1,2, Yu-Xiang Cai^1,3, Yanfeng Zheng^1,2, Jin Guo⁴, Yao-Zhi Luo^1,2, Yan-Bin Shen^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086395 - 28 September 2026
Abstract In this study, the two-dimensional discrete element method (DEM) was employed to investigate how the frequency of cyclic loading affects the liquefaction behavior of granular materials. A stress-controlled undrained cyclic biaxial loading method was implemented, and a series of numerical simulations were carried out across a wide range of loading frequencies. The simulation results reveal that cyclic loading frequency has negligible influence on the material’s behavior prior to liquefaction. However, it delays the development of excess pore water pressure and axial strain during and after liquefaction, a phenomenon referred to as the “delay effect”. This… More >

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Yuanyuan Zeng¹, Yuanxie Shen², Shixue Liang^1,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087037 - 28 September 2026
（This article belongs to the Special Issue: AI-Enhanced Computational Methods in Engineering and Physical Science)
Abstract Punching shear failure at a single slab–column joint can trigger rapid load redistribution and cascading damage in reinforced concrete flat-slab systems, making component-level reliability assessment insufficient for evaluating progressive-collapse risk. Moreover, direct Monte Carlo simulation (MCS) coupled with nonlinear finite element (FE) analysis is computationally prohibitive for rare-event system reliability problems involving multiple uncertainties. To address these challenges, this study proposes a two-stage machine learning–finite element (ML–FE) framework for system failure assessment of reinforced concrete flat-slab structures. In Stage I, an ML surrogate trained on 610 experimental slab–column joint tests predicts punching shear resistance and… More >

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15

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ARTICLE

Zhixi Chen¹, Jie Zhong¹, Qingsong Li¹, Sen Yang¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.089217 - 28 September 2026
Abstract Recycled aggregate concrete (RAC) enables construction waste reuse, but surface micro-cracks on recycled aggregates (RA) and multiple ITZs from old adhered mortar create extra sulfate transport paths, undermining RAC’s sulfate resistance. Current studies largely examine single modification methods’ effects on durability, yet lack sufficient predictive power for how such modifications influence final material properties. This study evaluated nine concrete mixtures comprising 297 cube specimens and employed sulfate wet-dry cycling experiments combined with XRD and SEM microscopic characterization to investigate the coupled influence of carbonation-treated RA and fly ash addition on the compressive strength development and… More >

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ARTICLE

Yan-Ping Liang¹, Zhiqiang Wan^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.088012 - 28 September 2026
（This article belongs to the Special Issue: AI-Enhanced Computational Mechanics and Structural Optimization Methods)
Abstract Initial geometric imperfections critically affect cylindrical-shell buckling, yet the influence of imperfection morphology remains difficult to separate from that of amplitude. This study develops an interpretable machine-learning-assisted framework for stochastic buckling assessment of cylindrical shells with random geometric imperfections. Circumferentially continuous Gaussian imperfection fields with different normalized correlation lengths are generated under a fixed root-mean-square (RMS) amplitude. Nonlinear Riks analyses are performed to construct a finite-element database of the corresponding buckling responses. Feature diagnosis is used to identify response-relevant descriptors of imperfection morphology. Compact surrogate models are subsequently evaluated for sample-level prediction and group-level statistical More >

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13

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ARTICLE

Hoang Minh Dang¹, Van Thanh Tien Nguyen¹, Van Binh Phung^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086495 - 28 September 2026
（This article belongs to the Special Issue: AI-Enhanced Computational Mechanics and Structural Optimization Methods)
Abstract Evaluating objectives and constraints in complex dynamical or multi-physics systems can take minutes to hours per design point, which motivates the use of artificial neural network (ANN) surrogates; in practice, however, their architectures and hyperparameters are still largely chosen by trial and error. This study proposes the Adaptive Multi-scale Grid Search with Ensemble Integration (AMGSE), an automated framework that constructs ANN surrogates from a fixed, previously generated dataset under an explicit training-time budget and without any additional calls to the expensive model. AMGSE couples four explicit decision rules: budget-tiered search scales, dataset-size-triggered complexity scaling, multi-restart… More >

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Xiaoyuan Li¹, Ming Xu¹, Shibin Yao¹, Su Wang^2,*, Jian Zhou^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086451 - 28 September 2026
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-III)
Abstract Accurate prediction of crest settlement in Concrete-Faced Rockfill Dam (CFRD) is of great significance for safety during its construction and operational phases. In this study, the Squirrel Search Algorithm (SSA) was used to optimize Random Forest (RF), EXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) to improve model performance. The original dataset, containing 74 cases, was augmented to train and test the models. The final results showed that among all the developed models, the XGBoost model optimized by SSA with a population size of 25 achieved the best performance, with an R² of 0.936, More >

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ARTICLE

Faiza Rasheed¹, Jinchuan Zheng², Luis Eduardo Cofré Lizama^3,4, Suzanne Martin⁵, Kwong Ming Tse^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086130 - 28 September 2026
Abstract The estimation of lower limb joint kinematics has great potential to be used for different applications, for instance, gait analysis, diagnosis of any joint injury, diagnosis of any other lower limb injury, prosthesis control, etc. The natural and controlled interaction between human and lower limb prosthesis is very important. Hybrid Stacked Long Short-Term Memory-Multilayer Perceptron (HS-LSTM-MLP) network is hypothesized to estimate knee joint angle individually over five different surfaces: level ground, ramp ascent, ramp descent, stair ascent, and stair descent. The spatial and temporal information extracted from reflective markers is used as input features, and… More >

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91

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ARTICLE

Maopeng Tian¹, Fan Cao^1,2,*, Xinlei He², Caicheng Zhu³
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087682 - 28 September 2026
Abstract Weakly compressible smoothed particle hydrodynamics (WCSPH) is widely used for computational modeling of violent free surface flows, but the equation of state pressure is sensitive to density error, deficient wall support, and local particle disorder. We develop an adaptive density regularization method for pressure stabilization in WCSPH. The method computes the raw summation density, a local kernel averaged density, and a bounded blended density used only for pressure evaluation through the Tait equation of state. The transport density definition, particle mass, and diagnostic density are retained; particle motion is affected only through the pressure values… More >

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ARTICLE

Bin Zhang^1,*, Xuanyan Lu¹, Zhigang Qin², Yibo Mo³, Chenwei Wang¹, Sihui Hao¹, Yixiang Song¹, Jianyun Xu¹, Zhifeng Zhang⁴, Xu Long^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086481 - 28 September 2026
Abstract Nanofluid-cooled microchannel heat sinks (NCMHS) feature high heat dissipation efficiency and serve as a critical thermal management solution for electronic devices. This study employs a computationally efficient multi-layer modeling approach to conduct three-layer topological optimisation of the NCMHS. The flow and heat transfer within the NCMHS are described using a single-phase nanofluid-based thermofluid model that accounts for temperature-sensitive fluid properties. On this basis, a three-layer thermofluid model of the NCMHS is constructed by introducing assumptions regarding the velocity profile and an adaptive temperature profile in the thickness direction, together with the interlayer coupled heat flux… More >

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ARTICLE

Jiwon Hwang, Sangjun Lee^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086869 - 28 September 2026
Abstract Computational Fluid Dynamics (CFD) has become the benchmark approach for analyzing hydrogen explosions because it accurately resolves transient shock-wave propagation and complex blast-wave interactions. However, high-fidelity CFD simulations remain computationally expensive for repeated safety evaluation and large-scale parametric studies. To address this limitation, this study proposes a deep learning-based spatiotemporal surrogate framework for reconstructing transient three-dimensional ($3\mathrm{D}$) hydrogen explosion overpressure fields. High-resolution CFD datasets were generated using the OpenFOAM-based radXiFoam solver by systematically varying blast-wall height and setback distance, producing approximately 20.4 million spatiotemporal pressure samples from 1859 monitoring locations. Three representative sequential deep learning… More >

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ARTICLE

Jyun-Kai Yang, Ya-Sin Yang, Hsin-Fu Yeh^*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086972 - 28 September 2026
（This article belongs to the Special Issue: Advanced Numerical and Data-Driven Modeling for Geoengineering and Subsurface Systems)
Abstract Traditional rainfall intensity–duration (I–D) thresholds for shallow landslides are predominantly empirical and lack explicit linkage to internal slope hydrological processes, limiting their reliability under variable rainfall conditions. This study establishes a physically based early warning framework by integrating critical suction stress–depth profiles with rainfall I–D thresholds derived from limit equilibrium analysis and unified effective stress theory. Requiring only rainfall data, the framework does not depend on real-time subsurface monitoring and thus remains applicable in data-limited regions. Antecedent rainfall effects are incorporated through an antecedent rainfall duration estimation method, enabling a physically interpretable definition of the… More >

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Konstantinos N. Sioutas^*, Andreas Benardos
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087755 - 28 September 2026
（This article belongs to the Special Issue: Advances in Artificial Intelligence for Geotechnical Engineering)
Abstract Tunnel operations performed with Tunnel Boring Machines (TBMs) require the selection of feasible setpoints under changing ground and site constraints. In this study, an inverse Machine Learning (ML) setup was constructed to infer operating parameters from geotechnical context and target performance thresholds for Penetration Rate (PR) and the utilization (UTIL) of the machine. Inputs included geological and geotechnical descriptors, relative depth, a categorical geological profile, harmonic time features, and the two targets (PR and UTIL). Outputs comprised Cutterhead Rotation and Torque, Total Thrust, Screw-conveyor Rotation and Working Pressure, Excavating Rate, and Earth Pressure on the More >

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Yuxin Chen¹, Jian Zhou¹, Su Wang^2,*, Mohammad Rezaei³, Danial Jahed Armaghani^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087513 - 28 September 2026
Abstract Rapid prediction of the plastic zone depth (PZD) at key points in the rock mass around underground powerhouse caverns is important for surrounding-rock stability assessment. However, existing machine-learning studies have primarily focused on forward prediction under prescribed conditions, with insufficient attention to model stability, prediction uncertainty, and the statistical significance of performance differences. Moreover, model interpretation has rarely been integrated with target-oriented cavern-layout parameter screening. To address these limitations, this study develops a metaheuristic-optimized XGBoost framework based on a numerical database containing 1920 two-dimensional finite-element results. The Runge-Kutta optimizer (RUN) and the weighted mean of… More >

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Hani Albalawi^1,2, Rab Nawaz³, Abdul Wadood^1,2,*, Anwar Ul Haq³, Shahbaz Khan^1,2, Bakht Muhammad Khan¹, Aadel Mohammed Alatwi^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.084732 - 28 September 2026
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems, 2nd Edition)
Abstract Ensuring the reliability of Ultra-High Voltage (UHV) power systems remains a critical challenge, as series compensation improves stability while introducing complex fault dynamics. Although machine learning and deep learning methods have advanced fault diagnosis, existing approaches often depend on computationally intensive preprocessing and struggle with data scarcity, limiting real-time applicability. This study proposes a streamlined One-Dimensional Convolutional Neural Network (1D CNN) optimized via Bayesian learning for efficient and robust fault classification in a 735 kV, 32-bus UHV system. The model operates directly on raw time-series signals, eliminating the need for domain-specific transformations while preserving the… More >

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Mohammad Barr¹, Tawfeeq Shawly², Ahmed A. Alsheikhy^3,*, Shaaban M. Shaaban⁴, Aws AbuEid⁵, Yahia Said⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.085040 - 28 September 2026
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems, 2nd Edition)
Abstract The worldwide shift toward renewable energy has generated considerable interest in harnessing the vast potential of ocean energy sources, including wave, tidal, and offshore wind energy. Nevertheless, current marine energy technologies face challenges stemming from unpredictable environmental conditions, low energy-conversion efficiency, and high maintenance costs. It is essential to tackle these issues to realize sustainable and large-scale marine energy solutions. To confront these challenges, we present a novel AI-driven biomimetic energy farm that integrates nature-inspired modular designs with a hierarchical AI control system, enabling an autonomous and self-optimizing marine energy network. This network comprises three… More >

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Guanglin Sha¹, Xinwei Cong¹, Yunzhao Wu¹, Dinghong Chen¹, Bo Wang², Hengrui Ma^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.081823 - 28 September 2026
（This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)
Abstract To address the challenges of significant nonlinearity, intricate temporal interdependencies, and the tendency to get stuck in local optima during parameter tuning in short-term photovoltaic (photovoltaic, PV) power forecasting, this paper puts forward a hybrid model called IWOA-TCN-BiGRU-MATT. This model fuses the IWOA (Improved Whale Optimization Algorithm, IWOA) with the TCN (Temporal Convolutional Network, TCN), BiGRU (Bidirectional Gated Recurrent Unit, BiGRU), and MATT. By leveraging IWOA to boost parameter optimization efficiency and integrating TCN’s ability to extract multi-scale features, BiGRU’s bidirectional temporal modeling, and MATT’s emphasis on key features, the model aims to realize high-precision More >

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Vae Onalethata¹, Boniface Kgosidintsi¹, Bokani Nthaba¹, Elisha M. Shemang¹, Abid Yahya², Mohamed Yasin Abdul Salam^3,*, Yar Muhammad⁴, Enerst Edozie⁵, Asiimwe Eva⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.084977 - 28 September 2026
（This article belongs to the Special Issue: Recent Advances in Geospatial Artificial Intelligence (GeoAI) Models, Approaches, and Applications)
Abstract Conventional geophysical inversion approaches typically have a limited capacity to map disseminated sulphide mineralization in complex greenstone belts. Here, we describe GeoPhysML, a physics-informed machine-learning approach that combines aeromagnetic and IP–ERI data to map sulphide mineralization in the Maitengwe Greenstone Belt, Botswana. Labels constrained by boreholes MTW1 and MTW2, including the mineralised intervals (MTW1: 110–160 m; MTW2: 118–153 m), produced 247 labelled grid cells (78 positive and 169 negative) that were split using a 70/15/15 train/validation/test split with spatial blocking. Borehole MTW4 was reserved exclusively for independent validation. GeoPhysML uses a three-hidden-layer neural network with… More >

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Rasha Alyousef¹, Amal S. Hassan², Omar A. Saudi³, Ohud A. Alqasem⁴, Mohammed Elgarhy^5,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.085404 - 28 September 2026
（This article belongs to the Special Issue: Computer Modeling in Statistics)
Abstract It is of utmost importance to develop probability models that can cope with asymmetry for an effective analysis of asymmetrical real-world data. In this context, the current paper proposes a new unit asymmetric probability distribution for the interval (0, 1). The sine unit inverse exponentiated Pareto probability distribution is developed through the application of the sine-G family of transformations to the unit inverse exponentiated Pareto probability distribution. The inherent flexibility of the proposed distribution makes it have high potential for practical applications in the analysis of asymmetry in real-life data sets. Explicit formulas for some… More >

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Bruno Buonomo^1,*, Eleonora Messina¹, Claudia Panico¹, Mario Pezzella², Gaetano Zanghirati³
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.084828 - 28 September 2026
（This article belongs to the Special Issue: Advances in Mathematical Modeling: Numerical Approaches and Simulation for Computational Biology)
Abstract EPITIME, a computational framework for the simulation of two classes of integral epidemic models, namely an age of infection model and an information-dependent behavioural model, is presented. The main contributions of this work are the design and implementation of a modular MATLAB/Python software environment built upon previously developed structure-preserving non-standard finite difference discretizations. The solvers are complemented by input parsing and validation routines, performance indicators, reproducibility tools and user-oriented graphical interfaces. The preserved structures are specific to the underlying model and include positivity, monotonicity, final-size behaviour and extinction of infectivity for the age of infection… More >

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Hai-Xiang Wang¹, Chu-Xiang Li², Zi-Jia Wang^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086077 - 28 September 2026
（This article belongs to the Special Issue: Advances in Computational Intelligence for Complex Systems)
Abstract Evolutionary multitasking optimization (EMTO) is an emerging research direction in evolutionary computation (EC), with its core objective being the collaborative solution of multiple problems through inter-task knowledge transfer (KT). In classical EMTO algorithms, KT typically relies on the direct exchange or crossover of individuals between populations. However, such transfer strategies often follow singular rules or direct transplantation, which struggle to adequately adapt to the dynamically evolving distributional differences between tasks, and may lead to inefficient transfer or even negative transfer. To tackle this issue, this study presents HKTMTO, a multitask differential evolution algorithm built upon More >

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Omar Guillén-Fernández¹, Carlos Alejandro Velázquez-Morales¹, Luis Gerardo de la Fraga², Sajad Jafari^3,4, Esteban Tlelo-Cuautle^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086260 - 28 September 2026
（This article belongs to the Special Issue: Computational Modeling, Simulation, and Algorithmic Methods for Dynamical Systems)
Abstract The reconstruction of chaotic time series can be considered fundamental for modeling nonlinear dynamical systems and enabling real-time processing applications. However, their hardware implementation remains challenging due to the inherent complexity and high sensitivity to initial conditions exhibited by chaotic systems. The proposed work shows that artificial neural networks based on multilayer perceptrons (MLPs) provide an efficient approach for approximating nonlinear dynamics and are well suited for implementation on reconfigurable hardware platforms such as Field Programmable Gate Arrays (FPGAs). In this manner, an MLP is trained in software using Python and the scikit-learn library to… More >

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Jasmine Batra¹, Kiranbir Kaur¹, Fuad Ali Mohammed Al-Yarimi², Abdulrahman Mohammed Alamoudi³, Salil Bharany⁴, Ateeq Ur Rehman^5,*, Jaeyoung Choi^5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086048 - 28 September 2026
（This article belongs to the Special Issue: Digital Twin-Enabled Intelligent Transportation Systems: Computational Modeling, AI Integration, and Smart Mobility Applications)
Abstract Flying Ad Hoc Networks (FANETs) are emerging as a key enabler for intelligent transportation systems, smart aerial mobility, disaster response, surveillance, and environmental monitoring. However, their highly dynamic topology, rapid node mobility, intermittent connectivity, and limited energy resources pose major challenges for reliable routing. Existing routing protocols largely depend on instantaneous network information and lack predictive intelligence, leading to unstable links, increased overhead, and degraded performance in dynamic environments. To address these issues, this study proposes a Digital Twin-driven Trust-Aware PSO-based routing framework (DT-TAPSO) for UAV-assisted smart mobility and disaster-aware FANETs. The framework employs a… More >

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Huaran Zhou¹, Gaoteng Yuan², Ping Qiu^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086179 - 28 September 2026
Abstract Accurate vehicle trajectory prediction and lane-changing intention recognition are essential for autonomous driving and advanced driver-assistance systems, as they support motion planning, collision avoidance, and risk assessment. Existing deep learning methods often use high-dimensional trajectory variables without explicitly evaluating their relevance, which may introduce redundant information and reduce computational efficiency. In addition, local motion variations and long-range temporal dependencies are frequently modeled in isolation, although both are important for representing lane-changing behavior. To address these limitations, this paper proposes a joint intention-and-trajectory prediction framework that combines mutual-information-guided feature selection with a hybrid convolutional neural network… More >

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François G. Landry^*, Moulay A. Akhloufi
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086105 - 28 September 2026
Abstract With the introduction of vehicles with autonomous capabilities on public roads, predicting pedestrian crossing intention has emerged as an active area of research. The task of predicting pedestrian crossing intention involves determining whether pedestrians in the scene are likely to cross the road or not. In this work, we propose TrajFusionNet, a novel transformer-based model that leverages future pedestrian trajectory and vehicle speed predictions as priors for predicting crossing intention. TrajFusionNet comprises two branches: a Sequence Attention Module (SAM) and a Visual Attention Module (VAM). The SAM branch learns from a sequential representation of the More >

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Assel Ospan¹, Madina Mansurova¹, Aisha Sailau¹, Talshyn Sarsembayeva^1,*, Amir Mosavi^1,2,3,4
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086051 - 28 September 2026
Abstract This study addresses the automated conversion of unstructured Kazakh journalistic text into structured tabular representations. Existing text-to-table approaches are commonly developed for high-resource languages, rely on fixed schemas, or require computationally expensive full-model fine-tuning. These limitations reduce their applicability to morphologically rich low-resource languages and heterogeneous news collections. We propose a morphology-aware dual-regime framework for Kazakh text-to-table generation. The framework extends a Text-Tuple-Table pipeline with semantic chunking based on cosine-similarity thresholds, thematic grouping using $k$-means clustering, anchor-based factual cue detection, and large language model-based table generation. It supports both a static regime with a predefined… More >

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Yucheng Huang^1,*, Xingyu Gao², Jianze Wei², Hong Yan¹
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.088326 - 28 September 2026
Abstract Current 3D Human Pose and Shape estimation methods predominantly focus on numerical coordinate regression, treating pose analysis as a geometric mapping task rather than a structured reasoning problem. This reliance on quantitative output creates an interpretability gap, as these models fail to provide high-level, qualitative justifications for their predictions, such as why a specific pose is physically unstable or biomechanically incorrect. To bridge this gap, we propose a novel framework that leverages neuro-symbolic reasoning for the analysis of human poses from multimodal inputs. Our approach integrates symbolic expressions and logical deduction rules into a large… More >

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Hongliang Tian¹, Chenying Pei^1,*, Jin Lei², Xiaoke Liu¹, Xin Ma³
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087611 - 28 September 2026
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition, 2nd Edition)
Abstract Infrared ship detection is an important deep learning-based pattern recognition task for maritime visual perception, where accurate target recognition under complex thermal backgrounds is essential for intelligent monitoring and real-time decision support. However, low target-background contrast, sea-wave thermal textures, coastline heat-source interference, and specular thermal reflections in infrared maritime imaging weaken discriminative ship patterns and reduce recognition reliability in complex scenes. To address these challenges, we propose an edge-oriented infrared ship detection method for real-time maritime monitoring. The proposed method reconstructs the feature pyramid by integrating a Wavelet-Frequency Enhancement Module (WFEM) with a Dynamic Multi-Scale… More >

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Farhan Ullah¹, Irfan Ullah², Khalil Khan³, Sarra Ayouni⁴, Quan Wang^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087860 - 28 September 2026
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition, 2nd Edition)
Abstract Hyperspectral image (HSI) classification requires models that can effectively capture long-range contextual dependencies while preserving fine-grained spectral–spatial variations under strict computational constraints. Recent transformer-based approaches, particularly Swin Transformers, have shown strong performance by leveraging localized self-attention; however, their reliance on generic attention mechanisms often overlooks frequency-sensitive information that is critical for discriminating spectrally similar materials. Moreover, existing frequency-aware designs typically introduce heavy parameterization or explicit spectral transforms, limiting their efficiency and practical deployment. In this paper, we propose FEAM-Swin, a lightweight frequency-aware Swin Transformer designed for efficient HSI classification. The proposed model introduces a novel… More >

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Rashid Jahangir^1,*, Nazik Alturki², Mohammed Alreshoodi³
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086137 - 28 September 2026
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition, 2nd Edition)
Abstract Facial emotion recognition (FER) remains difficult in real-world settings. Inter-subject variability, lighting changes, occlusion, and class imbalance all limit performance. Most FER systems rely on one convolutional or transformer backbone. This narrows the features available for classification. This paper presents Dual-Stream FERNet. It is a carefully evaluated integration of an EfficientNetV2-S backbone with a Swin Transformer Tiny backbone, joined by a learnable sigmoid-gated fusion module. Before fine-tuning, both branches undergo SimCLR-style self-supervised pre-training on two augmented views. This gives a stronger initialization without extra labels. An Evidential Deep Learning head then produces class probabilities and… More >

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Hikmat Yar^1,2, Zulfiqar Ahmad Khan³, Samee Ullah Khan⁴, Habib Khan⁵, Sung Wook Baik^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.084156 - 28 September 2026
Abstract In the realm of animal voice recognition, this work introduces AVRNet, a task-specific integration framework that combines separable convolutions, hierarchical deep supervision through auxiliary classifiers, skip connections, and a dual-pooling channel-spatial attention mechanism configured for spectrogram-based animal vocalization recognition. The combination is designed to provide a favorable trade-off between recognition accuracy and computational cost. Together, these modules enhance the interpretability, training efficiency, and overall performance of the model, making a significant contribution to the development of animal voice recognition technology. In the existing methods for animal voice recognition, researchers used attention mechanisms with average or… More >

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Areeba Gul¹, Muhammad Ramzan¹, Romana Aziz^2,*, Qaiser Abbas³, Ala Saleh Alluhaidan², Summair Raza¹, Mahwish Ilyas⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.084222 - 28 September 2026
Abstract Gastrointestinal diseases (GI) are serious diseases that affect people of all ages. Early and accurate diagnosis helps reduce complications and the subsequent impact on patients. Precise diagnoses by endoscopy are important for reducing complications and mortality rates. Manual interpretation of endoscopic images is time-consuming, highly dependent on the specialist’s clinical judgment, and subject to variability in multi-class classification tasks. To overcome these limitations, this study proposes GastroNetV4, an explainable hybrid deep learning model for multi-class classification of gastrointestinal diseases and a urinary tract-related class included in the endoscopic dataset used in this study.GastroNetV4 employs an… More >

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Sarfaraz Natha^1,*, Mohammad Siraj^2,*, Mohammed Muflih Alamer³, Aaqid Syed⁴, Ayesha Shafique⁵, Kashan Memon⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087211 - 28 September 2026
（This article belongs to the Special Issue: Artificial Intelligence in Healthcare: Current Challenges, Emerging Trends, and Future Directions)
Abstract Gastrointestinal (GI) tract cancers are a serious health concern worldwide due to their high mortality rates. Wireless Capsule Endoscopy (WCE) provides a valuable non-invasive approach for detecting gastrointestinal abnormalities that may be associated with cancer. Despite WCE examinations generating many images, manual assessment is time-consuming. Therefore, automated methods capable of accurate and efficient lesion classification are highly desirable. Deep Learning (DL) techniques have demonstrated considerable potential for medical image analysis. However, many existing deep learning methods struggle to capture both broader contextual relationships and suitable patterns at the same time. While many are limited to… More >

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Nedaa Almansour^1,2,*, Azizi Abdullah², Dheeb Albashish^3,4, Shahnorbanun Sahran^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086450 - 28 September 2026
Abstract Data augmentation (DA) techniques are widely used in convolutional neural networks (CNNs) to artificially expand the size of training datasets. This is particularly true for medical imaging tasks, such as Diabetic Retinopathy (DR) image classification, where the training data are often limited and imbalanced. Various DA techniques are utilized in CNN models, including horizontal and vertical flipping, rotation, and zoom. Combining distinct methods increases the diversity of the produced images and allows the CNNs to handle the complex details in the images. Manually designed or heuristically selected augmentation combinations may generate redundant or highly similar… More >

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Legend Zhang¹, Junmin Lyu¹, Guan Yao², Wei Wei³, Jiawei Tian^4,*, Bo Yang^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.086568 - 28 September 2026
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Accurate disparity estimation of soft tissue surfaces in endoscopic cardiac imaging is critical for minimally invasive surgical navigation and robotic surgery. Traditional geometric models, such as thin-plate splines, and recent learning-based stereo matching networks often struggle to balance nonlinear modeling capacity, computational cost, and robustness in dynamic surgical environments. We propose TTO-StyleGAN3 (Test-Time Optimization with a simplified StyleGAN3), a hybrid framework combining pseudo-label-supervised prior learning with self-supervised test-time latent optimization. A simplified StyleGAN3 generator is first pre-trained on pseudo-disparity maps generated by a teacher estimator and subsequently serves as a learned prior over plausible cardiac… More >

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Yuxin Zhang^1,#, Bin Li^1,#,*, Riqiang Liao², Lianfang Tian¹
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087646 - 28 September 2026
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Chest X-ray multi-label prediction aims to identify multiple thoracic findings from radiographs and generate structured disease information for clinical analysis. Conventional image-only supervised learning uses disease labels primarily as prediction targets and may underutilize the cross-sample relational structure contained in multi-label disease annotations, thereby limiting disease-aware representation learning. Structured disease-label-guided contrastive pre-training provides a promising solution by encoding report-derived disease-label vectors as an auxiliary training view and aligning them with image representations through contrastive learning, while preserving image-only inference. In this formulation, the binary disease-label vector serves as both the structured input to the auxiliary… More >

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Fazliddin Makhmudov¹, Jamshid Khamzaev², Jakhongir Karimberdiyev², Mekhriddin Rakhimov², Nigora Djurayeva², Islambek Saymanov^3,4, Abdulla Almuradov⁵, Alpamis Kutlimuratov^6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.085457 - 28 September 2026
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision, 2nd Edition)
Abstract The left ventricular ejection fraction (LVEF) is one of the most important quantitative indicators in cardiovascular medicine. It serves as a key measure for diagnosing, assessing risk, and guiding treatment decisions in conditions such as heart failure, cardiomyopathies, and coronary artery disease. However, obtaining accurate LVEF values using echocardiography remains a major challenge. The process is often affected by differences between observers (8%–15%), time-consuming manual measurements that take several minutes per case, dependence on the operator’s skill, and sensitivity to image quality. To address these challenges, this study proposes a Temporal Attention Network (TAN)—a deep… More >

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Bao Quoc Vuong^1,2, Kien Dinh Vu^1,2, Kien Trang^1,2,*, An Hoang Nguyen^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087493 - 28 September 2026
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision, 2nd Edition)
Abstract Brain tumor detection from magnetic resonance imaging (MRI) is an important task for supporting early diagnosis and treatment planning. However, accurate detection is still challenging because tumor regions often have weak boundaries, variety of sizes, and similar intensity. To address these issues, we propose AMASA-YOLO, which is an adaptive spectral and sparse-guided attention framework for MRI brain tumor detection. Our model is built on a YOLO-based architecture and introduces two main modules. First, the Adaptive Spectral Mamba-Inspired Attention (ASMA) block is used to improve feature extraction by combining spatial attention with spectral feature refinement. This… More >

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Bakhytzhan Omarov¹, Balnur Kenjayeva^2,*, Daniyar Sultan^3,*, Zhanseri Ikram³
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.088762 - 28 September 2026
Abstract Brain tumor localization in magnetic resonance imaging remains challenging for small, low-contrast lesions. This study presents Small Brain Lesion Deep Q Network (SBL-DQN), a two-dimensional lesion-wise reinforcement-learning framework that refines an axis-aligned region of interest on a co-registered axial multi-channel MRI slice. In the current evaluation protocol, voxel reference masks are resampled, processed by 26-connected-component analysis, and filtered using a maximum in-plane diameter of 10 mm; one axial episode is then executed for each reference-enumerated component. The mask and component coordinates are not included in the DQN state, but ground-truth annotations are required to generate… More >

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Chia-Nan Wang¹, Tsei-Hsuan Chen^2,*, Syuan-Yun Wang^3,*, Chung-Nan Cheng²
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.085454 - 28 September 2026
Abstract In email-centric healthcare environments, social engineering attacks increasingly exploit human psychology, organizational trust relationships, and persuasive communication strategies to bypass conventional cybersecurity defenses. While existing email security controls are effective at blocking many malicious messages, they remain vulnerable to whitelist-failure scenarios in which compromised or seemingly legitimate communications evade detection and reach end users. Under limited analyst capacity and increasing alert volumes, the operational challenge is no longer solely identifying phishing emails but determining which socially engineered communications should be reviewed first. To address this problem, this study proposes a governance-oriented human-factor risk prioritization framework… More >

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217

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Mohammad Kamrul Hasan^1,*, A. K. M. Ahasan Habib¹, Shayla Islam^2,*, A. K. M. Zakir Hossain^3,*, Masrullizam Mat Ibrahim³, Rosilah Hassan¹, Rahul Thakkar⁴, Nguyen Vo⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.083521 - 28 September 2026
Abstract Dynamic security assessment (DSA) of power system devices in smart grid (SG) power systems is currently essential for minimizing widespread blackouts and preventing cyberattacks. For stability processes that are difficult to perform in real time, security evaluation approaches for current SG devices may therefore require extensive historical domain training. Given that predictions are instantaneous, machine learning (ML) can be used to predict DSA. To classify and predict the time margin and transient energy margin (TEM) for a specific fault position and operating condition, input features, reactive power outputs, SG device transient energy function (TEF) terms,… More >

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Martin Parmar¹, Het Khatusuriya¹, Dharmendra Chauhan², Mrugendra Rahevar¹, Bimal Patel³, Agbotiname Lucky Imoize⁴, Chun-Ta Li^5,*, Hiren Mewada⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.087402 - 28 September 2026
Abstract The rapid growth of Internet of Things (IoT) devices in smart urban areas—health monitoring, urban infrastructure, environmental sensing, and energy management—creates attack surfaces that centralized security architectures cannot adequately safeguard. Traditional approaches impose inadequate latency, introduce a single point of failure, and scale poorly to the distributed, resource-constrained nature of IoT ecosystems. This work proposes a formally modeled, hardware-validated four-tier blockchain-edge security framework that integrates a permissioned Hyperledger Fabric network (Raft ordering), tiered AES-128/256 cryptography, and IoT-specific smart contracts implementing Decentralized Identifier (DID) and OAuth2-based access control. Experimental evaluation on Raspberry Pi 4 and NVIDIA More >

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Hedi Hamdi^1,*, Zaki Brahmi², Sabeur Lajili³, Nabil Almashfi⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.3, 2026, DOI:10.32604/cmes.2026.083334 - 28 September 2026
Abstract The increasing adoption of virtual hospitals and Internet of Medical Things (IoMT)-driven clinical workflows demands service composition mechanisms that can operate reliably under strict privacy constraints and heterogeneous provider ecosystems. Traditional trust-based composition approaches, such as majority voting or Bayesian aggregation, struggle to remain robust in large-scale, non-Independent and Identically Distributed (IID), and privacy-sensitive environments. This paper addresses these limitations by introducing PSCP-FL, a Federated Learning–Driven Privacy- and Trust-Aware Service Composition Framework for cloud- and edge-enabled virtual hospitals. We first define a comprehensive set of medical service trust attributes and construct a Federated Learning–based Trust More >

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