Open Access
ISSN:1526-1492 (print)
ISSN:1526-1506 (online)
Publication Frequency:Monthly
This journal publishes original research papers of reasonable permanent intellectual value, in the areas of computer modeling in engineering & Sciences, including, but not limited to computational mechanics, computational materials, computational mathematics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua spanning from various spatial length scales (quantum, nano, micro, meso, and macro), and various time scales (picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. Novel computational approaches and state-of-the-art computation algorithms, such as soft computing, artificial intelligence-based machine learning methods, and computational statistical methods are welcome.
Science Citation Index (Web of Science): 2025 Impact Factor 2.9; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2025): 5.3; SNIP (Source Normalized Impact per Paper 2025): 0.836; RG Journal Impact (average over last three years); Engineering Index (Compendex); Applied Mechanics Reviews; Cambridge Scientific Abstracts: Aerospace and High Technology, Materials Sciences & Engineering, and Computer & Information Systems Abstracts Database; CompuMath Citation Index; INSPEC Databases; Mathematical Reviews; MathSci Net; Mechanics; Science Alert; Science Navigator; Zentralblatt fur Mathematik; Portico, etc...
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080428 - 30 June 2026
Abstract In industrial and service robotics, autonomous mobile robots must achieve accurate trajectory tracking while maintaining low energy consumption to avoid frequent recharging and performance degradation. Energy efficiency is particularly critical because locomotion accounts for 45%–65% of total power consumption, directly limiting operational range and autonomy. This paper proposes an energy-aware trajectory tracking framework that optimizes a fractional-order proportional-integral-derivative (FOPID) controller using an Enhanced Whale Optimization Algorithm (E-WOA). The key contributions are threefold: (1) the E-WOA hybridizes Differential Evolution (DE)’s global exploration with WOA’s local exploitation to overcome premature convergence in high-dimensional FOPID parameter spaces; (2)… More >
Open Access
EDITORIAL
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084481 - 30 June 2026
（This article belongs to the Special Issue: Computer Modeling for Future Communications and Networks)
Abstract This article has no abstract. More >
Open Access
EDITORIAL
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084097 - 30 June 2026
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract This article has no abstract. More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082535 - 30 June 2026
Abstract Ferroelectric materials, integral to modern sensors, actuators, and transducers, exhibit complex fracture behavior under coupled electromechanical loading due to the intrinsic interplay between cracks, domain structures, and microstructural features. Linear piezoelectric fracture mechanics provides a foundational framework but fails to capture nonlinearities induced by domain switching and microstructure. This review synthesizes advances in computational modeling of ferroelectric fracture, with a focus on the unifying capabilities of the phase-field method (PFM). We first establish the fundamentals, including fracture toughness anisotropy and the crack-tip flexoelectric effect. We then critically assess traditional approaches like cohesive zone models and… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080567 - 30 June 2026
Abstract The theory of thermomechanical-electromagnetic coupling was constructed. The finite element analysis of thermo-visco-elastic-plastic-electromagnetic continuum was formulated. Then the problem of wave propagation in this continuum was solved in two stages. In Stage I, a nearly static thermomechanical solution of a hollow cylinder, subject to twist and temperature gradient, was obtained. Then, in Stage II, the problem of wave propagation of scalar and vector potentials, due to deformation and temperature gradient, was solved. In the second approach, in Stage I, the static electric field and static magnetic field are obtained through static scalar and vector potentials, More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081333 - 30 June 2026
（This article belongs to the Special Issue: Advances in Computational Fracture Mechanics: Theories, Techniques, and Applications)
Abstract The t′ phase of yttria-stabilized zirconia (t′-YSZ) is the most extensively used top coat material in thermal barrier coatings (TBCs). Its relatively high fracture toughness is among the most important factors that enable t′-YSZ to stand out from other candidate ceramics. Unveiling the toughening mechanisms of t′-YSZ is conducive to the development of next-generation top-coat materials. In this paper, a coupled phase field model is proposed to study crack growth and domain evolution in polycrystalline t′-YSZ. Two distinct polycrystal microstructures are considered to investigate the impact of the initial domain structure on the toughening behavior. In Polycrystal I,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082244 - 30 June 2026
（This article belongs to the Special Issue: Computational Modelling of Advanced Polymeric Materials and Structures)
Abstract Glass-fiber-reinforced polymer (GFRP) pipes are increasingly used in aggressive environments due to their high corrosion resistance and favorable mechanical properties. However, long-term exposure to saline environments and elevated temperatures can lead to degradation of their structural performance. This study investigates the influence of accelerated saltwater aging on the tensile behavior and structural characteristics of GFRP pipes and proposes machine-learning-based predictive models for the ultimate tensile strength (UTS). Experimental specimens were immersed in a 3.5% NaCl solution under controlled temperature and exposure time conditions. Tensile testing revealed that the unexposed samples exhibited a maximum UTS of… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083227 - 30 June 2026
（This article belongs to the Special Issue: Advanced Computational Methods in Multiphysics Phenomena)
Abstract Constitutive modeling for geomaterials remains challenging because of limited data availability, strong nonlinearity, pressure sensitivity, and the non-smooth characteristics of commonly used yield surfaces. This study presents a deep-learning-based constitutive method for geomaterials that incorporates a neural stress-integration procedure based on the cutting plane algorithm (CPA). Two compact fully connected networks are trained to learn the yield function and its stress gradient from an augmented stress-state dataset. The trained networks are then incorporated into a cutting plane return-mapping procedure, in which only first-order information is required for the plastic stress return. This avoids explicit analytical More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084696 - 30 June 2026
Abstract Concrete manufacturing consumes vast quantities of natural resources and contributes significantly to environmental degradation and carbon emissions. Therefore, integrating recycled waste substances into concrete has become a crucial approach to fostering eco-friendly building practices and supporting circular economy concepts. This study investigates the potential of incorporating recycled aluminum beverage can crumbs (RABCC) as a partial replacement for natural coarse aggregates (NCA) in concrete mixtures, focusing on its impact on compressive strength (CS) and the feasibility of its application in structural concrete. A comprehensive experimental program was conducted to assess the mechanical properties of concrete with… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083537 - 30 June 2026
Abstract Geopolymer materials are increasingly recognized as sustainable alternatives to conventional cementitious materials due to their lower environmental impact and promising engineering performance. Recent studies have demonstrated that incorporating nanomaterials can further enhance the properties of geopolymer systems. In particular, nano-silica has been reported to significantly improve the mechanical performance of geopolymer materials. However, accurate prediction of compressive strength remains challenging because of the complex nonlinear interactions among mix design parameters, activator chemistry, and curing conditions. This study develops a machine learning framework to predict the 28-day compressive strength of nanosilica-modified hybrid geopolymer mortar using a… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080930 - 30 June 2026
（This article belongs to the Special Issue: Machine Learning, Data-Driven and Novel Approaches in Computational Mechanics)
Abstract In bearing fault diagnosis for Prognostics and Health Management (PHM), the overall performance of data-driven models is strongly influenced by the coupled effects of preprocessing, model configuration, and decision fusion. However, these components are often optimized independently, resulting in fragmented workflows that limit global optimality, reproducibility, and computational efficiency of the model. This study presents a computationally unified three-stage sequential optimization framework that systematically coordinates the preprocessing selection, model hyperparameter optimization, and decision-level fusion within a consistent surrogate-based optimization architecture. In the first stage, candidate preprocessing schemes reflecting physical fault mechanisms—outer race, inner race, rolling… More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082594 - 30 June 2026
（This article belongs to the Special Issue: Machine Learning-Assisted Structural Integrity Assessment and Design Optimization under Uncertainty)
Abstract Over the past decades, surrogate model-aided reliability analysis approaches grounded in active learning have undergone extensive development. However, Gaussian process models like Kriging suffer from severe computational burdens when handling high-dimensional problems or large samples. Conversely, machine learning algorithms such as extreme learning machines exhibit high computational efficiency but lack variance output and stability, making them difficult to employ for adaptive active learning strategies. To address these limitations, this study proposes a population Monte Carlo method based on an adaptive closed neuron extreme learning machine. First, a closed neuron strategy uses a consistency metric to… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080387 - 30 June 2026
Abstract Parallel mechanisms are extensively employed in industrial logistics, food processing, and medical applications. Due to the strong nonlinearity and cross-axis coupling inherent in closed-chain kinematics, fault diagnostic performance is highly sensitive to signal perturbations and class imbalance under noisy measurement conditions. Furthermore, diagnostic models trained under single-fault scenarios often exhibit notable performance degradation when transferred to compound fault conditions as a result of distribution shift. In this study, a Delta 3D printer, as a representative parallel mechanism, is adopted as the experimental platform. An interpretable three-stage diagnostic framework is proposed, in which compound fault diagnosis… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083272 - 30 June 2026
Abstract Accurate aircraft engine diagnostics is essential for ensuring operational safety and enabling predictive maintenance under heterogeneous operating conditions. Although deep learning models can effectively capture high-dimensional multivariate sensor dynamics, purely data-driven approaches often entangle operating-condition variability with degradation-sensitive patterns, which limits robustness and generalization. This paper introduces PRIME, a physics-guided residual integrated framework for multi-task aircraft engine diagnostics. Rather than embedding explicit thermodynamic equations or physical constraints into the optimization process, PRIME relies on a physically motivated residual decomposition strategy that separates operating-condition-driven nominal behavior from degradation-sensitive sensor deviations. Specifically, nominal responses are estimated from operating-condition… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082376 - 30 June 2026
（This article belongs to the Special Issue: Intelligent Dynamics Modeling, Predictive Operations & Maintenance, and Control Optimization for Complex Systems)
Abstract Structural health monitoring (SHM) of ship piping systems is a core component of predictive maintenance strategies for complex marine engineering systems. During the detection of ship T-shaped pipes using ultrasonic guided waves, signal overlap arises from the diffusion of guided wave branches. To address this issue, an intelligent wave-guidance mechanism based on acoustic metamaterials is proposed for dynamic propagation control of ultrasonic guided waves. First, a metamaterial unit composed of a stainless steel substrate and a copper column is designed. The control of bandgap characteristics by lattice constant, column diameter, and column height is systematically… More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081925 - 30 June 2026
（This article belongs to the Special Issue: Recent Developments in SPH and CFD Methods for Complex Flow Simulations)
Abstract Fluid–structure interaction (FSI) plays a critical role in civil engineering applications, directly influencing structural safety, resilience, and performance. However, the inherent multiphysics complexity of FSI problems presents significant challenges for numerical modeling, particularly under highly dynamic flow conditions. This study presents a fully Lagrangian mesh-free framework for FSI based on the moving particle semi-implicit (MPS) method. The approach couples an enhanced weakly compressible MPS (WC-MPS) fluid solver with a hybrid total–updated Lagrangian (TL–UL) MPS formulation for elastic solids. In the solid phase, strains are evaluated in the reference configuration, while momentum balance is enforced in… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082583 - 30 June 2026
（This article belongs to the Special Issue: Numerical Methods for Multiphase Flow and Cavitating Flow)
Abstract With the sharp increase in the heat flux of high-power electronic devices, efficient thermal management has become critically important. Boiling heat transfer in parallel small channels, which utilizes latent heat efficiently, has emerged as a key enabling technology for next-generation cooling solutions. However, parallel channel systems are extremely susceptible to flow instabilities, resulting in severely uneven distributions of flow rate and heat transfer among the channels. This unevenness often leads to local overheating, which in turn restricts the system’s reliability and limits its practical application. In this paper, a three-dimensional transient numerical simulation method was… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081660 - 30 June 2026
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-III)
Abstract Rock fracture behavior under stress is vital for risk evaluation in underground engineering excavation because the presence of water can significantly increase the extent of cracks and fractures in rock, leading to structural damage. This can result in catastrophic failures, including rock bursts, coal bursts, and water inrush. Hence, reliable prediction of rock damage and fracture processes is still lacking, which, in turn, enables the safe and efficient conduct of engineering projects in rock-mass environments. Thus, this study examines both dry and saturated sandstone samples under loading using Infrared Radiation (IR), Acoustic Emission (AE) monitoring,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080219 - 30 June 2026
（This article belongs to the Special Issue: Soft Computing Applications of Civil Engineering including AI-based Optimization and Prediction)
Abstract Low-density volcanic rocks have specific geomechanical properties that require complex laboratory tests and characterization that are not usually available in common geotechnical studies. A pyroclastic rock behaves at sufficiently “low” stress levels as if it were a conventional rock under the action of an external load, but when subjected to higher stresses, the bonds between its particles can break, leading to a sudden decrease in its volume and the reorganization of its particles, thus forming a more compact structure than the initial one. This process is known as “mechanical collapse” and involves a drastic change… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083699 - 30 June 2026
Abstract Piezoelectric actuators are widely used in precision positioning systems. However, their inherent nonlinear behaviors, particularly hysteresis and output saturation, degrade modeling accuracy and limit control performance. Existing studies have generally used either black-box models or traditional physical models. The former typically lack physical interpretability, while the latter can exhibit limited accuracy when the actuator response includes coupled nonlinear effects. To address this issue, this paper proposes a hybrid physics-informed neural network (Hybrid-PINN) framework. An equivalent attenuation model, with a calibrated attenuation coefficient, is first established to describe output saturation and provide a nominal physical reference.… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081475 - 30 June 2026
（This article belongs to the Special Issue: Data-Driven and Physics-Informed Machine Learning for Digital Twin, Surrogate Modeling, and Model Discovery, with An Emphasis on Industrial Applications)
Abstract Maintaining overhead distribution facilities inherently involves high risks for operators, where ensuring worker safety and operational efficiency remains a paramount challenge. In particular, automating the positioning of aerial work platforms is crucial to mitigate electrocution hazards during live-line maintenance tasks. This paper proposes a novel autonomous framework for detecting jumper lines that could be employed to estimate the optimal bucket position in live-line maintenance of overhead distribution systems. The proposed framework comprises three core modules to form a unified pipeline for autonomous field inspection: a 4D multi-modal map, Sparse-dense fusion network (SDFNet), and Rotational multi-pyramid… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082366 - 30 June 2026
Abstract The increasing penetration of solar photovoltaic (PV) systems into power grids poses challenges due to their inherent intermittency and variability, which can compromise grid stability and reliability. Hybridizing solar PV with wind energy and battery energy storage system (BESS) offers a promising solution by leveraging resource complementarity and providing fast frequency response. This study presents a techno-economic and environmental assessment of a hybrid renewable energy system. Wind turbines and a BESS are integrated with the existing 7.5 MW Sirajganj Solar PV Power Plant in Bangladesh. The proposed hybrid configuration is evaluated using real-world operational data… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080276 - 30 June 2026
Abstract Voltage instability and reactive power fluctuations represent major challenges for DFIG-based wind turbines under load variations and grid disturbances. This paper proposes an integrated fault ride-through enhancement scheme based on a STATCOM supported by a Battery Energy Storage System (BESS) and a braking chopper (BC). The STATCOM regulates the DFIG terminal voltage through dynamic reactive power compensation using a coordinated outer voltage loop and inner synchronous dq-axis current control. The BESS supports the STATCOM DC side and enables fast bidirectional power exchange, while the BC suppresses overvoltage in the DFIG back-to-back converter DC-link during fault… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081630 - 30 June 2026
（This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
Abstract This paper presents the development of a digital twin of a heavy-duty electric truck powered by a hybrid energy system based on a hydrogen fuel cell and a battery pack. The objective of the model is to analyze different energy management strategies and to determine how the power demand of a real route can be shared between both energy sources, while keeping the fuel cell within safe operating limits to preserve its service life. The digital twin simulates vehicle dynamics, traction, and regenerative braking, and the main operational constraints of the fuel cell, including minimum… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081247 - 30 June 2026
（This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)
Abstract Accurate modelling of power production in wind power systems is essential for optimizing their real-time operation and meeting technical or economic objectives. However, the precise modelling of wind turbine power output remains challenging, particularly when relying on conventional parametric models, which often struggle to capture complex or non-linear behaviors. This paper compares three modelling approaches to estimate the power produced by a real wind turbine (a Senvion MM82/2050 located in France): one parametric, based on analytical expressions of the power coefficient CP(λ, β); another nonparametric, which uses Gaussian processes (GP) to probabilistically model the relationship between… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082514 - 30 June 2026
（This article belongs to the Special Issue: Explainable AI, Digital Twin, and Hybrid Deep Learning Approaches for Urban–Regional Hydrology, Water Quality, and Risk Modeling under Uncertainty)
Abstract Accurate prediction of seepage discharge in earth-rock dams remains challenging due to the strong non-stationary and nonlinear characteristics, limited robustness of individual models, and poor interpretability of black-box approaches. To address these issues, this paper proposes an interpretable hybrid model that integrates Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM) networks, and Support Vector Machine (SVM). The model first decomposes the seepage discharge sequence and relevant lagged features using VMD. The LSTM network then captures temporal dependencies of the decomposed components, while the SVM performs regression on the original sequences and features. An adaptive fusion… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082664 - 30 June 2026
（This article belongs to the Special Issue: Advances in Mathematical Modeling: Numerical Approaches and Simulation for Computational Biology)
Abstract Osteosarcoma is the most common primary malignant bone tumor in pediatric populations. This work presents an extended Physics-Informed Neural Network framework that incorporates interferon-gamma (IFN-
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080973 - 30 June 2026
（This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
Abstract A global health concern, neurodegenerative disorders like Parkinson’s and Alzheimer’s impact both mental and physical functioning. The complex interplay among immunological response, protein accumulation, and brain health necessitates sophisticated mathematical modeling. This study introduces a fractional-order mathematical model using the Mittag-Leffler derivative to describe the dynamics of neurodegeneration, incorporating key biological factors such as functioning and infected neurons, extracellular alpha-synuclein, microglia, and T-cells. A fundamental assumption of the model is that neuronal deterioration is influenced by memory effects, where past states impact current disease progression, making fractional-order calculus more suitable than traditional integer-order models. The… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081129 - 30 June 2026
（This article belongs to the Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics-III)
Abstract This article presents a Hierarchical Pathway-Masked Attention Autoencoder (H-PAAE), a biologically inspired representation-learning framework that enables explainable AI-guided cancer diagnosis. The model directly integrates the curated MSigDB Hallmark pathways, introducing pathway-constrained information flow and mechanistic interpretability through multi-level attention mechanisms. Based on TCGA RNA-seq data from 33 tumor types, H-PAAE compresses approximately 20,000 genes into a 128-dimensional latent space while preserving biologically meaningful structure. When used with XGBoost classification, H-PAAE delivers 92.37% test accuracy and 99.38% macro-AUROC with robust cross-validation results (92.5
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.077921 - 30 June 2026
（This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)
Abstract Spinal disorders, such as disk hernia and spondylolisthesis, affect millions worldwide, leading to chronic pain and reduced quality of life due to disruptions in biomechanical alignment. Traditional diagnostic methods often overlook the viscoelastic memory effects in spinal tissues, necessitating advanced models that integrate machine learning with fractional calculus for improved accuracy and interpretability. The research introduces a new fractional-order machine learning system that analyzes vertebral column abnormalities through biomechanical motion analysis by using the University of California, Irvine (UCI) vertebral column dataset. The system selects the best machine learning model from Random Forest (RF), Gradient… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081411 - 30 June 2026
（This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)
Abstract Reliable multi-object detection and tracking play a critical role in Unmanned Aerial Vehicles-based aerial surveillance applications operating under challenging real-world conditions. This study presents a mathematically grounded, model-driven tracking framework named TopoEKF, which integrates an enhanced Adaptive Extended Kalman Filter with Topological Data Analysis to improve both tracking robustness and anomaly detection performance. Unlike prior approaches that primarily focus on refining object detection architectures, this work emphasizes the predictive power of iterative Bayesian filtering, optimal state estimation, and adaptive error minimization within a unified mathematical framework. The proposed system employs a carefully optimized YOLOv12 detector… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082245 - 30 June 2026
Abstract Classical image denoising methods remain relevant in practical scenarios where training data or noise models are unavailable, yet their performance is highly sensitive to parameter selection. Non-Local Means (NLM) is a representative example whose effectiveness depends critically on smoothing strength, patch size, and search window configuration. This paper formulates NLM parameter selection as a black-box optimization problem under unknown noise conditions and employs adaptive metaheuristic optimization strategies for this task. We propose an adaptive optimization framework that integrates rank-based perturbation, opposition-based learning, Lévy-flight exploration, and noise-aware parameter constraints to improve robustness and convergence. The proposed More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081473 - 30 June 2026
（This article belongs to the Special Issue: Machine Learning and Data Fusion for Autonomous Control and Surveillance Systems)
Abstract This study presents a mobile expert system for on-device detection and short-horizon forecasting of aggression using affordable edge hardware. The proposed framework combines lightweight on-body and ambient signals, compact sequential predictors, and an interpretable fuzzy decision layer that converts calibrated probabilities into actionable and auditable alerts. In a subject-held-out pilot study with 10 independent participants, the system achieved a macro-averaged F1 score of 98.3% and an area under the receiver operating characteristic curve of 0.998 on the held-out test split. These results should be interpreted as pilot-scale held-out estimates rather than as definitive evidence of… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082801 - 30 June 2026
Abstract This study investigates the influence of autonomous vehicle (AV) front-end geometry on pedestrian injury biomechanics using PC-Crash multibody simulations. While emerging vehicles promise improved urban safety through automation and collision avoidance technologies, their unconventional front-end architectures introduce new passive safety challenges. The research compares classical passenger vehicles with van-type and symmetric flat-front autonomous platforms under standardized impact conditions at 40 km/h. Results reveal a clear redistribution of injury mechanisms depending on vehicle geometry. Conventional sloped front-end vehicles, super-mini and compact class, generate higher Head Injury Criterion (HIC) values due to wrap-around kinematics, where pedestrians rotate… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080403 - 30 June 2026
Abstract Real-time video streams now flood everything from security cameras to social media, yet current summarization systems still stumble when audio, visual, and semantic cues unfold with tangled cause–and–effect patterns. Most cross-modal transformers treat correlations as if time were a flat canvas, ignoring how an early sound might trigger a later visual event in the process. They also lack mechanisms to predict tracking uncertainty, adapt to narrative shifts, or evolve their own evaluation criteria, leaving summaries brittle and often incoherent in process. To address these gaps, we propose a Cross-Modal Context Fusion framework built from five… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082506 - 30 June 2026
Abstract Time-series forecasting can significantly aid decision-making in fields in which immediate action is required, such as power demand forecasting, financial market analysis, and traffic flow management. Transformer-based models achieve high forecasting accuracy by learning complex temporal patterns; however, their extensive parameters and substantial computational costs make practical deployment difficult in latency-sensitive environments. Therefore, lightweight models based on linear layers have recently been studied for improved efficiency. However, existing linear-based models have difficulty capturing local patterns and fail to reflect sudden volatility or fine-grained local trends, limiting their overall representational capacity. In this paper, SegTSF is… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083503 - 30 June 2026
Abstract Fire emergencies have long posed a serious threat to people’s lives, real estate assets, and environmental sustainability in civilized societies, especially when combustible events are detected at late stages of development. Recent advancements in computer vision–based fire detection have enabled automated real-time monitoring; however, most solutions either detect the existence of fire/smoke or employ binary decision-making, which limits visual monitoring systems from being risk-aware. This work introduces a severity-aware fire/smoke detection model that supports intelligent monitoring systems in detecting visual hazards. The goal is to identify varying levels of fire intensity and smoke density and… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083259 - 30 June 2026
Abstract Diabetes remains a major global health challenge and requires diagnostic systems capable of handling uncertainty and sometimes conflicting clinical evidence. In this study, a Disc T-Spherical Fuzzy (DT-SF) TOPSIS framework is proposed for diabetes risk assessment, where the radius parameter is used to encode the confidence associated with each diagnostic attribute. The methodology also integrates the Analytic Hierarchy Process (AHP) to determine the relative importance of several key risk factors, including blood glucose, body mass index, family history, lifestyle factors, and clinical symptoms. One important feature of the proposed approach is the ternary classification scheme,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081152 - 30 June 2026
Abstract Epilepsy is a chronic neurological disorder characterized by recurrent seizures, posing significant challenges to patients’ quality of life. Accurate classification of seizure states is crucial for effective intervention. This paper presents a deep learning-based approach for epileptic seizure classification by integrating multi-feature analysis of electroencephalogram (EEG) signals. The proposed method begins with signal preprocessing, including denoising, segmentation, and label construction. Subsequently, a comprehensive set of temporal, spectral, and wavelet-based features—such as signal mean, power, heart rate, and wavelet coefficients—is extracted. Feature selection is then performed using the Maximal Information Coefficient (MIC) to identify the most… More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083345 - 30 June 2026
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Hybrid CNN-Transformer models are widely used in medical image segmentation because they combine CNN-based local feature extraction with Transformer-based global context modeling. Despite their popularity, these models face several challenges, including computational complexity, noise blurring, and information loss. This paper proposes an enhanced convolutional attention network (ECANet) for liver segmentation. ECANet uses a U-shaped architecture with efficient channel-attention-based skip connections. Both the encoder and decoder are constructed using enhanced convolutional Transformer (ECT) blocks, where group convolution is integrated into the convolutional attention module for efficient Token embedding and channel disentanglement, and a Token-wise multi-layer perceptron More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.078738 - 30 June 2026
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Hyperspectral image (HSI) denoising is a crucial preprocessing step that significantly enhances the performance of downstream applications, such as object detection and classification. Whereas deep neural networks have achieved remarkable performance in HSI denoising, many existing models rely mostly on vanilla convolutions, which often fail to capture fine-grained noise patterns and structural details in real-time HSIs. To address these limitations, we propose a novel Center-Difference Convolutional Network (CDCN) designed to effectively suppress various noise types while preserving the inherent structure of HSIs. By leveraging center-difference convolution (CDC), our model captures both gradient and intensity information… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.079104 - 30 June 2026
（This article belongs to the Special Issue: Advanced Image Segmentation and Object Detection: Innovations, Challenges, and Applications)
Abstract Background: Examining peripheral blood smears is a vital diagnostic tool in hematology. Although deep learning-based automated systems have improved the accuracy of blood cell detection, most current methods depend on fully supervised learning and need extensive annotations to identify unusual cell shapes. Clinical practice often lacks these complete annotations, limiting the ability to generalize to rare or unseen abnormalities. To address this issue of incomplete annotated data, this paper introduces a hybrid framework that recognizes anomalies for reliable and clear analysis of peripheral blood smears. Methods: The proposed framework combines supervised blood cell detection with… More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081705 - 30 June 2026
（This article belongs to the Special Issue: Advances in Deep Learning and Computer Vision for Intelligent Systems: Methods, Applications, and Future Directions)
Abstract Cardiac magnetic resonance imaging (MRI) segmentation is an essential aspect of quantitative cardiovascular analysis, facilitating accurate evaluation of ventricular volumes, myocardial mass, and functional parameters. Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC, but they remain challenging to deploy in real-world multi-centre settings. Data privacy laws make it hard to share data across institutions, and differences in imaging protocols and patient populations mean that data is not always distributed in the same way (non-IID). This can have a big impact on how well models work together and how well… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081617 - 30 June 2026
Abstract Early detection of diabetic retinopathy (DR), media haze (MH), optic disc cupping (ODC), and glaucoma is crucial for preventing vision loss. However, timely diagnosis is often constrained by limited specialist availability and high diagnostic costs. This study proposes a You Only Look Once (YOLO)-based deep learning (DL) framework for the automated classification of fundus images into disease-specific categories. We unified diverse annotations from the Retinal Fundus Multi-Disease image Dataset (RFMiD), RFMiD2.0, and the DR Fundus Image Dataset (DR-FID) by standardizing annotation files and class labels. A custom filtering module was used to isolate single-pathology cases,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083951 - 30 June 2026
Abstract Image segmentation is essential in medical image analysis for glaucoma screening. Accurate delineation of the optic disc (OD) and optic cup (OC) in retinal fundus images is required for reliable clinical assessment. Manual segmentation is time-consuming and suffers from interobserver variability, which leads to inconsistent results. To address these limitations, this study proposes ASPP Inception Attention U-Net (ASPPIAU-Ne), an enhanced encoder-decoder architecture that integrates Atrous Spatial Pyramid Pooling (ASPP), Inception modules, and attention gates for feature selection in skip connections. The ASPP module is applied after the encoder to capture multi-scale contextual information and improve… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081849 - 30 June 2026 Graphic Abstract
Abstract We present QFedFormer, a federated transformer for dynamic electric vehicle (EV)-charging price prediction that combines quantization-aware training, SHAP-guided explainability, and blockchain-based incentives. The framework trains across distributed charging stations without centralizing user data, and programmable contracts set tariffs from forecasted demand and user-declared flexibility, while token rewards are derived from SHAP-based utility scores and anchored on-chain via Merkle proofs. On a real-world dataset, QFedFormer attains an energy-demand RMSE of
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084282 - 30 June 2026
（This article belongs to the Special Issue: Applied Machine Learning for FAIR and Responsible Modelling)
Abstract Traditional threat detection has proven ineffective in large-scale, moving data in the era of ever-more complex adversarial techniques and interconnected systems. The challenge becomes even more complex when high-volume, unstructured data continuously streams from social media platforms, requiring them to process the data efficiently and intelligently to provide timely security insights. Considering the big data security, the present study presents a scalable deep-learning-based system for real-time cyber threat detection, which has been developed and validated especially for distributed big data processing environments. A hybrid embedding approach that combines Word2Vec and Iterated Dilated Convolutional Neural Networks… More >
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Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082021 - 30 June 2026
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract Intrusion detection in large-scale IoT deployments becomes particularly challenging during ongoing attack scenarios, where malicious traffic may temporarily dominate benign traffic. In such conditions, streaming network data exhibits severe class imbalance in favor of attack traffic, while device behavior remains heterogeneous, non-identically distributed (non-IID), and temporally evolving. Within federated learning environments, this imbalance can destabilize early aggregation rounds, dominant attack gradients bias the global model, distort decision boundaries, and degrade reliable discrimination of residual benign behavior. Since the server has no access to raw data, these effects can persist across communication rounds if not addressed… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080862 - 30 June 2026
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract The rapid growth and increasing sophistication of malware pose significant challenges to traditional detection methods. Convolutional neural network (CNN)-based malware image classification methods have emerged as a promising approach by transforming binary files into visual representations and enabling automated feature extraction. To enhance discriminative learning, recent studies have incorporated attention mechanisms originally developed for natural image and natural language processing tasks. However, these mechanisms embed inductive biases that assume spatial coherence and visually salient semantics, assumptions that do not necessarily hold in malware image representations, where informative patterns may be subtle, structurally encoded, and globally… More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081720 - 30 June 2026
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract Federated learning (FL) has emerged as a promising approach for enabling collaborative model training across distributed Internet of Medical Things (IoMT) devices without sharing sensitive data. Existing FL frameworks face significant challenges in healthcare settings, including vulnerability to adversarial attacks, lack of verifiable update integrity, and limited robustness under heterogeneous data distributions. These limitations hinder reliable deployment in critical medical applications. To address these challenges, this paper proposes RP-IoMT, a robust and privacy-preserving FL framework that integrates secure multi-party computation (MPC), zero-knowledge proof-based gradient verification, and robust aggregation mechanisms. The objective of this work is… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080871 - 30 June 2026
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things (IoT)-enabled smart contract systems. The integration of IoT, edge computing, and blockchain introduces significant challenges, including limited device capacity, high verification cost, and scalability constraints. Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices, resulting in increased latency, energy consumption, and transaction costs. To address these issues, this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup (Z-FLOR) framework, an adaptive and energy-efficient model designed to enable secure and verifiable… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082504 - 30 June 2026
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract The advent of 5th Generation (5G) mobile networks has introduced Network Slicing as a core mechanism for supporting heterogeneous vertical services—such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC) over a shared physical infrastructure, thereby significantly expanding the attack surface at the User Plane Function (UPF). Securing this multi-slice environment requires intrusion detection systems that can simultaneously accommodate the statistical heterogeneity of per-slice traffic and the stringent Quality of Service (QoS) constraints of real-time slices, yet the practical cost of obtaining high-quality labeled traffic in operational 5G cores remains… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083308 - 30 June 2026
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract The evolution of the Future Mobile Internet, driven by large-scale connectivity and heterogeneous device ecosystems, introduces significant challenges for securely integrating devices into operational environments. Existing onboarding mechanisms primarily focus on authentication and credential provisioning, while security policy enforcement is typically deferred, creating a temporal gap during which devices may operate without appropriate constraints. This paper addresses this limitation by enabling policy enforcement during onboarding. To this end, we propose a model-driven approach that integrates the Device Security Passport (DSP) with the FIDO Device Onboard (FDO) protocol. The DSP is a lifecycle-aware model that aggregates… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084062 - 30 June 2026
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract Autonomous vehicles are potentially more vulnerable to cyber-attacks compared to traditional human-driven ones, as they employ electronic sensors to enable self-driving. Cybersecurity for autonomous vehicles will be crucial in the near future. However, intrusion detection systems (IDSes) for vehicles are still in the early stages. Many IDS models that claim to work for vehicles are actually built with traditional Internet datasets rather than those with real vehicle data, which is impractical in reality. In this paper, IDS models are developed with Federated Learning (FL) with the Car-Hacking and CAN-MIRGU datasets, which are obtained from real More >
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