Open Access
ARTICLE
Li-Ling Peng1, Tong Li1, Guo-Feng Fan1, Xin-Yu Yang1, Wei-Chiang Hong2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087031
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems, 2nd Edition)
Abstract Accurate load forecasting has become a critical foundation for ensuring the stable operation of power systems, optimizing generation scheduling, and supporting the efficient functioning of electricity markets. In this paper, the cross-scale and meso-scale characteristics of the complexity of power loads are analyzed, and the meteorological factors, the impact of the emergence law, and the uncertainty are also considered simultaneously. The short-term coupled forecasting model of power loads based on AI technology is proposed. Firstly, the cross-scale and meso-scale characteristics of short-term power loads are explored, and the nonlinear effects between different scales are analyzed… More >
Open Access
ARTICLE
Yan-Ping Liang1, Shixue Liang2, Xiaodan Ren3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.088525
Abstract Random geometric imperfections strongly influence the buckling resistance of thin cylindrical shells. In random field imperfection modeling, Euclidean and geodesic distances may generate different spatial correlation structures on closed cylindrical shells. This study examines whether such distance model differences are transmitted to nonlinear stochastic buckling response. A paired stochastic finite element framework is developed, in which Euclidean- and geodesic-distance imperfection fields are generated from common nodal white noise inputs and rescaled to the same prescribed root-mean-square (RMS) amplitude. Eight normalized correlation lengths are considered, with 200 paired samples for each case, and the corresponding critical More >
Open Access
ARTICLE
Farhan Ullah1, Irfan Ullah2, Khalil Khan3, Sarra Ayouni4, Quan Wang1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087860
（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 >
Open Access
ARTICLE
Hongliang Tian1, Chenying Pei1,*, Jin Lei2, Xiaoke Liu1, Xin Ma3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087611
（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 >
Open Access
ARTICLE
Yuxin Chen1, Jian Zhou1, Su Wang2,*, Mohammad Rezaei3, Danial Jahed Armaghani4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087513
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 >
Open Access
ARTICLE
Xiaoyuan Li1, Ming Xu1, Shibin Yao1, Su Wang2,*, Jian Zhou1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086451
（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 >
Open Access
ARTICLE
Rashid Jahangir1,*, Nazik Alturki2, Mohammed Alreshoodi3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086137
（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 >
Open Access
ARTICLE
Guanglin Sha1, Xinwei Cong1, Yunzhao Wu1, Dinghong Chen1, Bo Wang2, Hengrui Ma2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081823
（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 >
Open Access
ARTICLE
Jyun-Kai Yang, Ya-Sin Yang, Hsin-Fu Yeh*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086972
（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 >
Open Access
ARTICLE
Rihem Farkh1, Ghislain Oudinet2, Alaeddine Moussa3, Yasser Fouad4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085610
Abstract Autonomous UAV navigation in safety-critical environments requires more than accurate prediction of future states; it also requires the ability to interpret action-conditioned futures as actionable safety risk. This paper presents a predictive and explainable UAV navigation framework that combines a latent world model with a Future Interpretation Module (FIM), a safety-constrained decision layer, and a hybrid explanation module. The world model predicts action-conditioned latent futures, while the FIM converts these rollouts into interpretable descriptors including time-to-collision, minimum vertical clearance, route-commitment risk, and risk trend. These descriptors are used jointly for decision-making, safety filtering, and explanation… More >
Open Access
ARTICLE
Ante Jurčević1, Tomislav Lesičar2, Zdenko Tonković2, Jurica Sorić2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087199
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 >
Open Access
ARTICLE
Bin Zhang1,*, Xuanyan Lu1, Zhigang Qin2, Yibo Mo3, Chenwei Wang1, Sihui Hao1, Yixiang Song1, Jianyun Xu1, Zhifeng Zhang4, Xu Long1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086481
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 >
Open Access
ARTICLE
Mohammad Barr1, Tawfeeq Shawly2, Ahmed A. Alsheikhy3,*, Shaaban M. Shaaban4, Aws AbuEid5, Yahia Said4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085040
（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 >
Open Access
ARTICLE
Hani Albalawi1,2, Rab Nawaz3, Abdul Wadood1,2,*, Anwar Ul Haq3, Shahbaz Khan1,2, Bakht Muhammad Khan1, Aadel Mohammed Alatwi1,2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084732
（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 >
Open Access
ARTICLE
Liqiang Jiang1,2, Ziyi Kong1, Fengqi Guo1,*, Wei Guo1,2, Lizhong Jiang1,2, Yijun Liu1, Wangbao Zhou1,2, Peng Jiang3, Lijie Han3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086915
Abstract High-speed railway bridges are critical infrastructure in seismically active regions; however, their complex track-bridge interaction poses significant challenges to conventional seismic protection systems. To mitigate seismic damage risks and enhance post-earthquake recoverability of high-speed railway bridges, this paper proposes a combined curved steel damper (CCSD) that incorporates a novel porous energy-absorbing material, which achieves stable energy dissipation through bending deformation. The hysteretic energy dissipation characteristics of the damper were investigated through refined finite element simulations. The results indicate that the damper exhibits stable energy dissipation efficiency under cyclic loading, with plump, continuous hysteresis loops and… More >
Open Access
ARTICLE
Muammar Shahrear Famous1,2, Samia Sayed1,2, Rashed Mazumder1, Risala T. Khan1, M. Shamim Kaiser1, Mohammad Shahadat Hossain3, Karl Andersson4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084699
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract Counterfeit pharmaceuticals, fragmented data management, and the absence of transparent verification mechanisms continue to threaten the integrity of modern healthcare drug supply chains. Although blockchain-based traceability frameworks improve decentralization and auditability, existing approaches frequently lack formally verified authentication guarantees and interpretable security decision mechanisms. To address these limitations, this paper proposes a unified blockchain-enabled healthcare supply-chain framework integrating nonce-based cryptographic authentication, formal security verification, Solidity smart contracts, and SHAP-based explainable artificial intelligence (XAI). The proposed protocol employs elliptic-curve digital signatures, nonce-based challenge–response authentication, and blockchain validation mechanisms to ensure secure communication among manufacturers, distributors, pharmacies,… More >
Open Access
ARTICLE
Wibby Aldryani Astuti Praditasari1,2,*, Hyejin Yoon3, Seunghwan Yun4, Changuk Jang4, Okyeon Yi1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084050
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract Satellite-based Quantum Key Distribution (QKD) provides a foundation for achieving information-theoretic security in long-distance communication systems. In practical satellite-to-ground scenarios, however, dynamic channel conditions significantly affect key validity, availability, and the stability of rekeying processes at higher communication layers. This work proposes a feasibility-aware adaptive control framework that enforces operation only within conditions where secure key generation remains valid, thereby preventing the use of cryptographically unreliable keys. The control mechanism is formulated as a constrained decision process and implemented using a Deep Q-Network (DQN), which dynamically adjusts basis selection in response to time-varying channel conditions.… More >
Graphic Abstract
Open Access
REVIEW
Xianjian Jin1,2,*, Yinchen Tao1, Yuhuai Zhang1, Haoze Wu1, Jianning Lu1, Nonsly Valerienne Opinat Ikiela1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.082644
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 >
Open Access
ARTICLE
Bao Quoc Vuong1,2, Kien Dinh Vu1,2, Kien Trang1,2,*, An Hoang Nguyen1,2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087493
（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 >
Graphic Abstract
Open Access
ARTICLE
Sarfaraz Natha1,*, Mohammad Siraj2,*, Mohammed Muflih Alamer3, Aaqid Syed4, Ayesha Shafique5, Kashan Memon6
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087211
（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 >
Open Access
ARTICLE
Konstantinos N. Sioutas*, Andreas Benardos
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087755
（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 >
Open Access
REVIEW
Jiawei Tian1, Kyungtae Kang2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087740
（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 >
Open Access
ARTICLE
Jiwon Hwang, Sangjun Lee*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086869
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 () hydrogen explosion overpressure fields. High-resolution CFD datasets were generated using the OpenFOAM-based
Open Access
ARTICLE
Huaran Zhou1, Gaoteng Yuan2, Ping Qiu1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086179
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 >
Open Access
ARTICLE
Faiza Rasheed1, Jinchuan Zheng2, Luis Eduardo Cofré Lizama3,4, Suzanne Martin5, Kwong Ming Tse1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086130
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 >
Open Access
ARTICLE
Maopeng Tian1, Fan Cao1,2,*, Xinlei He2, Caicheng Zhu3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087682
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 >
Open Access
ARTICLE
Martin Parmar1, Het Khatusuriya1, Dharmendra Chauhan2, Mrugendra Rahevar1, Bimal Patel3, Agbotiname Lucky Imoize4, Chun-Ta Li5,*, Hiren Mewada6
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.087402
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 >
Open Access
ARTICLE
Vae Onalethata1, Boniface Kgosidintsi1, Bokani Nthaba1, Elisha M. Shemang1, Abid Yahya2, Mohamed Yasin Abdul Salam3,*, Yar Muhammad4, Enerst Edozie5, Asiimwe Eva5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084977
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 >
Open Access
ARTICLE
Hikmat Yar1,2, Zulfiqar Ahmad Khan3, Samee Ullah Khan4, Habib Khan5, Sung Wook Baik1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084156
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 >
Open Access
ARTICLE
Bruno Buonomo1,*, Eleonora Messina1, Claudia Panico1, Mario Pezzella2, Gaetano Zanghirati3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084828
（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 >
Open Access
ARTICLE
Legend Zhang1, Junmin Lyu1, Guan Yao2, Wei Wei3, Jiawei Tian4,*, Bo Yang2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086568
（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 >
Open Access
ARTICLE
François G. Landry*, Moulay A. Akhloufi
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086105
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 >
Open Access
ARTICLE
Mohammad Kamrul Hasan1,*, A. K. M. Ahasan Habib1, Shayla Islam2,*, A. K. M. Zakir Hossain3,*, Masrullizam Mat Ibrahim3, Rosilah Hassan1, Rahul Thakkar4, Nguyen Vo4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083521
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 >
Open Access
ARTICLE
Nedaa Almansour1,2,*, Azizi Abdullah2, Dheeb Albashish3,4, Shahnorbanun Sahran2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086450
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 >
Open Access
ARTICLE
Hashem Al-Madwami1,2, Amira Abo Kaf 3, Haibin Yin1,4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086319
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 >
Open Access
ARTICLE
Abdelouahid Derhab1,*, Adlen Kerboua2, Noureddine Seddari3,4, Anis Haniche5, Mohammad Mehedi Hassan6
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086180
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract Machine learning has radically transformed network security, enabling intrusion detection systems capable of identifying malicious traffic with near-perfect accuracy on standard benchmarks. However, these systems remain critically vulnerable to adversarial examples—subtly manipulated inputs designed to escape detection—where performance can severely drop under minimal perturbation. This paper introduces the Hierarchical Adversarially-Driven Escalation System (
Open Access
ARTICLE
Hai-Xiang Wang1, Chu-Xiang Li2, Zi-Jia Wang2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086077
（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 >
Open Access
ARTICLE
Jasmine Batra1, Kiranbir Kaur1, Fuad Ali Mohammed Al-Yarimi2, Abdulrahman Mohammed Alamoudi3, Salil Bharany4, Ateeq Ur Rehman5,*, Jaeyoung Choi5,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086048
（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 >
Open Access
ARTICLE
Muhammad Shamrooz Aslam1,#, Wen-Jer Chang2,*, Hazrat Bilal3,*, Vyacheslav Gulvanskii4, Dmitrii Perevertaylo4, Dmitrii Kaplun1,4,5, Muhammad Hashim Bukhari6, Muhammad Aamir Aman7,#,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084958
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 >
Open Access
ARTICLE
Areeba Gul1, Muhammad Ramzan1, Romana Aziz2,*, Qaiser Abbas3, Ala Saleh Alluhaidan2, Summair Raza1, Mahwish Ilyas4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084222
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 >
Open Access
ARTICLE
Chia-Nan Wang1, Tsei-Hsuan Chen2,*, Syuan-Yun Wang3,*, Chung-Nan Cheng2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085454
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 >
Open Access
ARTICLE
Rasha Alyousef1, Amal S. Hassan2, Omar A. Saudi3, Ohud A. Alqasem4, Mohammed Elgarhy5,6,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085404
（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 >
Open Access
ARTICLE
Motab F. Alenezi1, Fahad M. Alotaibi1, Badraddin Alturki2, Ahmad J. Tayeb2, Abdulaziz A. Alsulami1,*, Abdullah Alhejaili1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085678
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract Machine learning-based intrusion detection for Internet of Things (IoT) networks remains difficult because modern traffic is highly imbalanced and attack behaviors are heterogeneous. Evaluation pipelines can also overestimate performance when preprocessing is performed before train-test separation. We propose a family-aware hierarchical intrusion detection framework for attack-family prediction. The proposed approach first separates normal and attack traffic, then routes attack samples into empirically defined majority and minority attack-family branches, and finally performs branch-specific family classification. Within each cross-validation fold, training-label counts define the majority/minority routing branches, while scaling, weighting, model fitting, stage diagnostics, and metric computation… 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 >
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