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): 2024 Impact Factor 2.5; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2024): 4.4; SNIP (Source Normalized Impact per Paper 2024): 0.693; 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
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2509-2571, 2025, DOI:10.32604/cmes.2025.064283 - 30 June 2025
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract Object detection in occluded environments remains a core challenge in computer vision (CV), especially in domains such as autonomous driving and robotics. While Convolutional Neural Network (CNN)-based two-dimensional (2D) and three-dimensional (3D) object detection methods have made significant progress, they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging (LiDAR). This paper presents a comparative review of recent 2D and 3D detection models, focusing on their occlusion-handling capabilities and the impact of sensor modalities such More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2573-2614, 2025, DOI:10.32604/cmes.2025.064168 - 30 June 2025
Abstract Advanced artificial intelligence technologies such as ChatGPT and other large language models (LLMs) have significantly impacted fields such as education and research in recent years. ChatGPT benefits students and educators by providing personalized feedback, facilitating interactive learning, and introducing innovative teaching methods. While many researchers have studied ChatGPT across various subject domains, few analyses have focused on the engineering domain, particularly in addressing the risks of academic dishonesty and potential declines in critical thinking skills. To address this gap, this study explores both the opportunities and limitations of ChatGPT in engineering contexts through a two-part… More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2615-2673, 2025, DOI:10.32604/cmes.2025.064857 - 30 June 2025
Abstract Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks, sunglasses, and other obstructions. Addressing this issue is crucial for applications such as surveillance, biometric authentication, and human-computer interaction. This paper provides a comprehensive review of face detection techniques developed to handle occluded faces. Studies are categorized into four main approaches: feature-based, machine learning-based, deep learning-based, and hybrid methods. We analyzed state-of-the-art studies within each category, examining their methodologies, strengths, and limitations based on widely used benchmark datasets, highlighting their adaptability to partial and severe occlusions. The review… More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2675-2709, 2025, DOI:10.32604/cmes.2025.064854 - 30 June 2025
（This article belongs to the Special Issue: Data-driven Additive Manufacturing: Methodology, Fabrication, and Applications )
Abstract Nickel-based superalloys are indispensable for high-temperature engineering applications, yet their additive manufacturing (AM) is plagued by significant cracking defects. This review investigates crack failure mechanisms in AM nickel-based superalloys, emphasizing methodologies to evaluate crack sensitivity and compositional design strategies to mitigate defects. Key crack types—solidification, liquation, solid-state, stress corrosion, fatigue, and creep-fatigue cracks—are analyzed, with focus on formation mechanisms driven by thermal gradients, solute segregation, and microstructural heterogeneities. Evaluation frameworks such as the Rappaz-Drezet-Gremaud (RDG) criterion, Solidification Cracking Index (SCI), and Strain Age Cracking (SAC) index are reviewed for predicting crack susceptibility through integration of… More >
Open Access
REVIEW
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2711-2765, 2025, DOI:10.32604/cmes.2025.062709 - 30 June 2025
（This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
Abstract In recent years, feature selection (FS) optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification. This work reviews FS and classification methods that utilize evolutionary algorithms (EAs) for gene expression profiles in cancer or medical applications based on research motivations, challenges, and recommendations. Relevant studies were retrieved from four major academic databases–IEEE, Scopus, Springer, and ScienceDirect–using the keywords ‘cancer classification’, ‘optimization’, ‘FS’, and ‘gene expression profile’. A total of 67 papers were finally selected with key advancements identified as follows: (1) The majority of papers… More >
Graphic Abstract
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2767-2783, 2025, DOI:10.32604/cmes.2025.064459 - 30 June 2025
Abstract In order to help athletes optimize their performances in competitions while prevent overtraining and the risk of overuse injuries, it is important to develop science-based strategies for optimally designing training programs. The purpose of the present study is to develop a novel method by the combined use of optimal control theory and a training-performance model for designing optimal training programs, with the hope of helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining. The training-performance model used in the proposed optimal… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2785-2809, 2025, DOI:10.32604/cmes.2025.066001 - 30 June 2025
Abstract This study explores a sensitivity analysis method based on the boundary element method (BEM) to address the computational complexity in acoustic analysis with ground reflection problems. The advantages of BEM in acoustic simulations and its high computational cost in broadband problems are examined. To improve efficiency, a Taylor series expansion is applied to decouple frequency-dependent terms in BEM. Additionally, the Second-Order Arnoldi (SOAR) model order reduction method is integrated to reduce computational costs and enhance numerical stability. Furthermore, an isogeometric sensitivity boundary integral equation is formulated using the direct differentiation method, incorporating Cauchy principal value More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2811-2832, 2025, DOI:10.32604/cmes.2025.065318 - 30 June 2025
Abstract The precise computation of nanoelectromechanical switches’ (NEMS) multi-physical interactions requires advanced numerical models and is a crucial part of the development of micro- and nano-systems. This paper presents a novel compound numerical method to study the instability of a functionally graded (FG) beam-type NEMS, considering surface elasticity effects as stated by Gurtin-Murdoch theory in an Euler-Bernoulli beam. The presented method is based on a combination of the Method of Adjoints (MoA) together with the Bézier-based multi-step technique. By utilizing the MoA, a boundary value problem (BVP) is turned into an initial value problem (IVP). The… More >
Graphic Abstract
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2833-2846, 2025, DOI:10.32604/cmes.2025.064773 - 30 June 2025
Abstract Sliding electrical contact is a common phenomenon in electrical equipment and affects performance. In this paper, the mechanism of sliding electric contact pair transition and the moment when transition occurs are investigated using the railgun as an example. Transition is the phenomenon where the contact between the armature and rails changes from solid-solid contact to solid-arc-solid contact. In this paper, the finite element-boundary element coupling method (FE-BE method) is used to simulate the process of armature movement. Then, the force, thermal, and electrical parameters on the armature/rail contact surface are analyzed to explore the development… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2847-2865, 2025, DOI:10.32604/cmes.2025.068031 - 30 June 2025
Abstract The variable salinity in stored reservoirs connected by a long channel attracts the attention of scientists worldwide, having applications in environmental and geophysical engineering. This study explores the impact of Navier slip conditions on exchange flows within a long channel connecting two large reservoirs of differing salinity. These horizontal density gradients drive the flow. We modify the recent one-dimensional theory, developed to avoid runaway stratification, to account for the presence of uniform slip walls. By adjusting the parameters of the horizontal density gradient based on the slip factor, we resolve analytically various flow regimes ranging… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2867-2882, 2025, DOI:10.32604/cmes.2025.066444 - 30 June 2025
Abstract Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a life support intervention for patients with refractory cardiogenic shock or severe cardiopulmonary failure. However, the choice of cannulation strategy remains contentious, partly due to insufficient understanding of hemodynamic characteristics associated with the site of arterial cannulation. In this study, a geometrical multiscale model was built to offer a mathematical tool for addressing the issue. The outflow cannula of ECMO was inserted into the ascending aorta in the case of central cannulation, whereas it was inserted into the right subclavian artery (RSA) or the left iliac artery (LIA) in… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2883-2917, 2025, DOI:10.32604/cmes.2025.066442 - 30 June 2025
Abstract Existing power forecasting models struggle to simultaneously handle high-dimensional, noisy load data while capturing long-term dependencies. This critical limitation necessitates an integrated approach combining dimensionality reduction, temporal modeling, and robust prediction, especially for multi-day forecasting. A novel hybrid model, SLHS-TCN-XGBoost, is proposed for power demand forecasting, leveraging SLHS (dimensionality reduction), TCN (temporal feature learning), and XGBoost (ensemble prediction). Applied to the three-year electricity load dataset of Seoul, South Korea, the model’s MAE, RMSE, and MAPE reached 112.08, 148.39, and 2%, respectively, which are significantly reduced in MAE, RMSE, and MAPE by 87.37%, 87.35%, and 87.43%… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2919-2956, 2025, DOI:10.32604/cmes.2025.065098 - 30 June 2025
Abstract This research aims to address the challenges of fault detection and isolation (FDI) in digital grids, focusing on improving the reliability and stability of power systems. Traditional fault detection techniques, such as rule-based fuzzy systems and conventional FDI methods, often struggle with the dynamic nature of modern grids, resulting in delays and inaccuracies in fault classification. To overcome these limitations, this study introduces a Hybrid Neuro-Fuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic. The model’s performance was evaluated through extensive simulations on the… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2957-2990, 2025, DOI:10.32604/cmes.2025.065446 - 30 June 2025
Abstract Accelerated life tests play a vital role in reliability analysis, especially as advanced technologies lead to the production of highly reliable products to meet market demands and competition. Among these tests, progressive-stress accelerated life tests (PSALT) allow for continuous changes in applied stress. Additionally, the generalized progressive hybrid censoring (GPHC) scheme has attracted significant attention in reliability and survival analysis, particularly for handling censored data in accelerated testing. It has been applied to various failure models, including competing risks and step-stress models. However, despite its growing relevance, a notable gap remains in the literature regarding… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2991-3003, 2025, DOI:10.32604/cmes.2025.064789 - 30 June 2025
Abstract Scene graph prediction has emerged as a critical task in computer vision, focusing on transforming complex visual scenes into structured representations by identifying objects, their attributes, and the relationships among them. Extending this to 3D semantic scene graph (3DSSG) prediction introduces an additional layer of complexity because it requires the processing of point-cloud data to accurately capture the spatial and volumetric characteristics of a scene. A significant challenge in 3DSSG is the long-tailed distribution of object and relationship labels, causing certain classes to be severely underrepresented and suboptimal performance in these rare categories. To address… More >
Graphic Abstract
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3005-3031, 2025, DOI:10.32604/cmes.2025.065698 - 30 June 2025
Abstract The exponential expansion of the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Transportation Management of Things (TMoT) produces vast amounts of real-time streaming data. Ensuring system dependability, operational efficiency, and security depends on the identification of anomalies in these dynamic and resource-constrained systems. Due to their high computational requirements and inability to efficiently process continuous data streams, traditional anomaly detection techniques often fail in IoT systems. This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems. Extensive experiments were carried out on multiple real-world datasets, achieving… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3033-3064, 2025, DOI:10.32604/cmes.2025.065264 - 30 June 2025
Abstract Epilepsy is a long-term neurological condition marked by recurrent seizures, which result from abnormal electrical activity in the brain that disrupts its normal functioning. Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models, which inadequately represent the continuous dynamics of electroencephalogram (EEG) signals. To overcome this limitation, we introduce an innovative approach that employs Neural Ordinary Differential Equations (NODEs) to model EEG signals as continuous-time systems. This allows for effective management of irregular sampling and intricate temporal patterns. In contrast to conventional techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3065-3090, 2025, DOI:10.32604/cmes.2025.064783 - 30 June 2025
Abstract To improve small object detection and trajectory estimation from an aerial moving perspective, we propose the Aerial View Attention-PRB (AVA-PRB) model. AVA-PRB integrates two attention mechanisms—Coordinate Attention (CA) and the Convolutional Block Attention Module (CBAM)—to enhance detection accuracy. Additionally, Shape-IoU is employed as the loss function to refine localization precision. Our model further incorporates an adaptive feature fusion mechanism, which optimizes multi-scale object representation, ensuring robust tracking in complex aerial environments. We evaluate the performance of AVA-PRB on two benchmark datasets: Aerial Person Detection and VisDrone2019-Det. The model achieves 60.9% mAP@0.5 on the Aerial Person… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3091-3132, 2025, DOI:10.32604/cmes.2025.065471 - 30 June 2025
Abstract Breast cancer remains one of the most pressing global health concerns, and early detection plays a crucial role in improving survival rates. Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities. However, existing methodologies face persistent challenges, including low image contrast, noise interference, and inaccuracies in segmenting regions of interest. To address these limitations, this study introduces a novel computational framework for analyzing mammographic images, evaluated using the Mammographic Image Analysis Society (MIAS) dataset comprising 322 samples. The proposed methodology follows a structured three-stage approach. Initially,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3133-3166, 2025, DOI:10.32604/cmes.2025.064104 - 30 June 2025
Abstract In recent years, cyber threats have escalated across diverse sectors, with cybercrime syndicates increasingly exploiting system vulnerabilities. Traditional passive defense mechanisms have proven insufficient, particularly as Linux platforms—historically overlooked in favor of Windows—have emerged as frequent targets. According to Trend Micro, there has been a substantial increase in Linux-targeted malware, with ransomware attacks on Linux surpassing those on macOS. This alarming trend underscores the need for detection strategies specifically designed for Linux environments. To address this challenge, this study proposes a comprehensive malware detection framework tailored for Linux systems, integrating dynamic behavioral analysis with the… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3167-3195, 2025, DOI:10.32604/cmes.2025.064510 - 30 June 2025
（This article belongs to the Special Issue: Environment Modeling for Applications of Mobile Robots)
Abstract Reticular structures are the basis of major infrastructure projects, including bridges, electrical pylons and airports. However, inspecting and maintaining these structures is both expensive and hazardous, traditionally requiring human involvement. While some research has been conducted in this field of study, most efforts focus on faults identification through images or the design of robotic platforms, often neglecting the autonomous navigation of robots through the structure. This study addresses this limitation by proposing methods to detect navigable surfaces in truss structures, thereby enhancing the autonomous capabilities of climbing robots to navigate through these environments. The paper… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3197-3214, 2025, DOI:10.32604/cmes.2025.065041 - 30 June 2025
（This article belongs to the Special Issue: Recent Developments in Nonlocal Meshfree Particle Methods for Solids and Fluids )
Abstract As a critical component of pulse solid rocket motors (SRMs), the soft pulse separation device (PSD) is vital in enabling multi-pulse propulsion and has become a breakthrough in SRM engineering applications. To investigate the opening performance of the PSD, an axial PSD incorporating a star-shaped prefabricated defect was designed. The opening process was simulated using peridynamics, yielding the strain field distribution and the corresponding failure mode. A single-opening verification test was conducted. The simulation results showed good agreement with the experimental data, demonstrating the reliability of the peridynamic modeling approach. Furthermore, the effects of the… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3215-3231, 2025, DOI:10.32604/cmes.2025.066489 - 30 June 2025
（This article belongs to the Special Issue: Topology Optimization: Theory, Methods, and Engineering Applications)
Abstract As primary load-bearing components extensively utilized in engineering applications, beam structures necessitate the design of their microstructural configurations to achieve lightweight objectives while satisfying diverse mechanical performance requirements. Combining topology optimization with fully coupled homogenization beam theory, we provide a highly efficient design tool to access desirable periodic microstructures for beams. The present optimization framework comprehensively takes into account for key deformation modes, including tension, bending, torsion, and shear deformation, all within a unified formulation. Several numerical results prove that our method can be used to handle kinds of microstructure design for beam-like structures, e.g., More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3233-3251, 2025, DOI:10.32604/cmes.2025.065935 - 30 June 2025
（This article belongs to the Special Issue: Topology Optimization: Theory, Methods, and Engineering Applications)
Abstract Most material distribution-based topology optimization methods work on a relaxed form of the optimization problem and then push the solution toward the binary limits. However, when benchmarking these methods, researchers use known solutions to only a single form of benchmark problem. This paper proposes a comparison platform for systematic benchmarking of topology optimization methods using both binary and relaxed forms. A greyness measure is implemented to evaluate how far a solution is from the desired binary form. The well-known Zhou-Rozvany (ZR) problem is selected as the benchmarking problem here, making use of available global solutions… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3253-3277, 2025, DOI:10.32604/cmes.2025.065063 - 30 June 2025
（This article belongs to the Special Issue: Multiscale, Multifield, and Continuum-Discontinuum Analysis in Geomechanics )
Abstract The configuration of underground powerhouses is crucial in pumped-storage hydropower projects, which play a vital role in maintaining grid stability, facilitating the integration of renewable energy sources, and managing flood risks. However, geotechnical challenges, such as complex joint orientations, anisotropy in in-situ stress, and rock damage caused by excavation, require thorough stability assessments. This research employs the ubiquitous anisotropic joint model within FLAC3D to investigate the effects of joint dip angle, joint dip direction, and the alignment of in-situ stress on the stability of surrounding rock formations. The key parameters analyzed include joint cohesion, friction angle, More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3279-3327, 2025, DOI:10.32604/cmes.2025.065442 - 30 June 2025
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks (UPDNs), focusing on the complex 123-Bus test system. Three scenarios are investigated: (1) simultaneous power loss reduction and voltage profile improvement, (2) minimization of voltage and current unbalance indices under various operational cases, and (3) multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index, active power loss, and current unbalance index. Unlike previous research that oftensimplified system components, this work maintains all equipment, including capacitor banks, transformers, and voltage regulators, to ensure realistic results. The study evaluates twelve metaheuristic More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3329-3349, 2025, DOI:10.32604/cmes.2025.066025 - 30 June 2025
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract This study proposes a new component of the composite loss function minimised during training of the Super-Resolution (SR) algorithms—the normalised structural similarity index loss , which has the potential to improve the natural appearance of reconstructed images. Deep learning-based super-resolution (SR) algorithms reconstruct high-resolution images from low-resolution inputs, offering a practical means to enhance image quality without requiring superior imaging hardware, which is particularly important in medical applications where diagnostic accuracy is critical. Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity, visual artefacts may persist, making the design of… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3351-3375, 2025, DOI:10.32604/cmes.2025.064395 - 30 June 2025
（This article belongs to the Special Issue: Emerging Frontiers and Disruptive Technologies in Computer Science Engineering: Advancements in AI, Machine Learning, and Large Language Models to Shape Intelligent Systems)
Abstract Landslide hazard detection is a prevalent problem in remote sensing studies, particularly with the technological advancement of computer vision. With the continuous and exceptional growth of the computational environment, the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning. Furthermore, attention models, driven by human visual procedures, have become vital in natural hazard-related studies. Hence, this paper proposes an enhanced YOLOv5 (You Only Look Once version 5) network for improved satellite-based landslide detection, embedded with two popular attention modules: CBAM (Convolutional Block Attention Module) More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3377-3409, 2025, DOI:10.32604/cmes.2025.064245 - 30 June 2025
（This article belongs to the Special Issue: Analytical and Numerical Solution of the Fractional Differential Equation)
Abstract Asthma is the most common allergic disorder and represents a significant global public health problem. Strong evidence suggests a link between ascariasis and asthma. This study aims primarily to determine the prevalence of Ascaris lumbricoides infection among various risk factors, to assess blood parameters, levels of immunoglobulin E (IgE) and interleukin-4 (IL-4), and to explore the relationship between ascariasis and asthma in affected individuals. The secondary objective is to examine a fractal-fractional mathematical model that describes the four stages of the life cycle of Ascaris infection, specifically within the framework of the Caputo-Fabrizio derivative. A… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3411-3431, 2025, DOI:10.32604/cmes.2025.060855 - 30 June 2025
（This article belongs to the Special Issue: Analytical and Numerical Solution of the Fractional Differential Equation)
Abstract In 2022, Leukemia is the 13th most common diagnosis of cancer globally as per the source of the International Agency for Research on Cancer (IARC). Leukemia is still a threat and challenge for all regions because of 46.6% infection in Asia, and 22.1% and 14.7% infection rates in Europe and North America, respectively. To study the dynamics of Leukemia, the population of cells has been divided into three subpopulations of cells susceptible cells, infected cells, and immune cells. To investigate the memory effects and uncertainty in disease progression, leukemia modeling is developed using stochastic fractional… More >
Open Access
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3433-3462, 2025, DOI:10.32604/cmes.2025.064815 - 30 June 2025
（This article belongs to the Special Issue: Analytical and Numerical Solution of the Fractional Differential Equation)
Abstract In this article, we develop the Laplace transform (LT) based Chebyshev spectral collocation method (CSCM) to approximate the time fractional advection-diffusion equation, incorporating the Atangana-Baleanu Caputo (ABC) derivative. The advection-diffusion equation, which governs the transport of mass, heat, or energy through combined advection and diffusion processes, is central to modeling physical systems with nonlocal behavior. Our numerical scheme employs the LT to transform the time-dependent time-fractional PDEs into a time-independent PDE in LT domain, eliminating the need for classical time-stepping methods that often suffer from stability constraints. For spatial discretization, we employ the CSCM, where More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3463-3489, 2025, DOI:10.32604/cmes.2025.066299 - 30 June 2025
（This article belongs to the Special Issue: Advances in Mathematical Modeling: Numerical Approaches and Simulation for Computational Biology)
Abstract The numerical approximation of stochastic partial differential equations (SPDEs), particularly those including q-diffusion, poses considerable challenges due to the requirements for high-order precision, stability amongst random perturbations, and processing efficiency. Because of their simplicity, conventional numerical techniques like the Euler-Maruyama method are frequently employed to solve stochastic differential equations; nonetheless, they may have low-order accuracy and lower stability in stiff or high-resolution situations. This study proposes a novel computational scheme for solving SPDEs arising from a stochastic SEIR model with q-diffusion and a general incidence rate function. A proposed computational scheme can be used to… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3491-3521, 2025, DOI:10.32604/cmes.2025.061640 - 30 June 2025
（This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
Abstract This study investigates the dynamics of pneumococcal pneumonia using a novel fractal-fractional Susceptible-Carrier-Infected-Recovered model formulated with the Atangana-Baleanu in Caputo (ABC) sense. Unlike traditional epidemiological models that rely on classical or Caputo fractional derivatives, the proposed model incorporates nonlocal memory effects, hereditary properties, and complex transmission dynamics through fractal-fractional calculus. The Atangana-Baleanu operator, with its non-singular Mittag-Leffler kernel, ensures a more realistic representation of disease progression compared to classical integer-order models and singular kernel-based fractional models. The study establishes the existence and uniqueness of the proposed system and conducts a comprehensive stability analysis, including local More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3523-3555, 2025, DOI:10.32604/cmes.2025.066356 - 30 June 2025
（This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
Abstract Global security threats have motivated organizations to adopt robust and reliable security systems to ensure the safety of individuals and assets. Biometric authentication systems offer a strong solution. However, choosing the best security system requires a structured decision-making framework, especially in complex scenarios involving multiple criteria. To address this problem, we develop a novel quantum spherical fuzzy technique for order preference by similarity to ideal solution (QSF-TOPSIS) methodology, integrating quantum mechanics principles and fuzzy theory. The proposed approach enhances decision-making accuracy, handles uncertainty, and incorporates criteria relationships. Criteria weights are determined using spherical fuzzy sets,… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3557-3581, 2025, DOI:10.32604/cmes.2025.064733 - 30 June 2025
（This article belongs to the Special Issue: Data-Driven Artificial Intelligence and Machine Learning in Computational Modelling for Engineering and Applied Sciences)
Abstract This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional (1D) barcodes and Quick Response (QR) codes, addressing critical challenges in logistics operations. The proposed solution leverages an efficient Pix2Pix-based framework, a type of conditional Generative Adversarial Network (GAN) optimized for image-to-image translation tasks, enabling the recovery of degraded barcodes and QR codes with minimal computational overhead. A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments, such as low contrast, misalignment, physical wear,… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3583-3614, 2025, DOI:10.32604/cmes.2025.063957 - 30 June 2025
（This article belongs to the Special Issue: Data-Driven Artificial Intelligence and Machine Learning in Computational Modelling for Engineering and Applied Sciences)
Abstract The transportation and logistics sectors are major contributors to Greenhouse Gase (GHG) emissions. Carbon dioxide (CO2) from Light-Duty Vehicles (LDVs) is posing serious risks to air quality and public health. Understanding the extent of LDVs’ impact on climate change and human well-being is crucial for informed decision-making and effective mitigation strategies. This study investigates the predictability of CO2 emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers, their CO2 emission levels, and key influencing factors. Specifically, six Machine Learning (ML) algorithms, ranging from simple linear models to complex non-linear models, were applied under… More >
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CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3615-3638, 2025, DOI:10.32604/cmes.2025.064588 - 30 June 2025
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract Activity recognition is a challenging topic in the field of computer vision that has various applications, including surveillance systems, industrial automation, and human-computer interaction. Today, the demand for automation has greatly increased across industries worldwide. Real-time detection requires edge devices with limited computational time. This study proposes a novel hybrid deep learning system for human activity recognition (HAR), aiming to enhance the recognition accuracy and reduce the computational time. The proposed system combines a pre-trained image classification model with a sequence analysis model. First, the dataset was divided into a training set (70%), validation set… More >
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Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3639-3675, 2025, DOI:10.32604/cmes.2025.064668 - 30 June 2025
（This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract Multiple Sclerosis (MS) poses significant health risks. Patients may face neurodegeneration, mobility issues, cognitive decline, and a reduced quality of life. Manual diagnosis by neurologists is prone to limitations, making AI-based classification crucial for early detection. Therefore, automated classification using Artificial Intelligence (AI) techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages. This study developed hybrid systems integrating XGBoost (eXtreme Gradient Boosting) with multi-CNN (Convolutional Neural Networks) features based on Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS) algorithms for early… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3677-3707, 2025, DOI:10.32604/cmes.2025.058855 - 30 June 2025
（This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract Face liveness detection is essential for securing biometric authentication systems against spoofing attacks, including printed photos, replay videos, and 3D masks. This study systematically evaluates pre-trained CNN models— DenseNet201, VGG16, InceptionV3, ResNet50, VGG19, MobileNetV2, Xception, and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance. The models were trained and tested on NUAA and Replay-Attack datasets, with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability. Performance was evaluated using accuracy, precision, recall, FAR, FRR, HTER, and specialized spoof detection metrics (APCER, NPCER, ACER). Fine-tuning significantly improved detection accuracy, with DenseNet201 achieving the highest… More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3709-3741, 2025, DOI:10.32604/cmes.2025.065564 - 30 June 2025
（This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)
Abstract Early detection of Alzheimer’s disease (AD) is crucial, particularly in resource-constrained medical settings. This study introduces an optimized deep learning framework that conceptualizes neural networks as computational “sensors” for neurodegenerative diagnosis, incorporating feature selection, selective layer unfreezing, pruning, and algorithmic optimization. An enhanced lightweight hybrid DenseNet201 model is proposed, integrating layer pruning strategies for feature selection and bioinspired optimization techniques, including Genetic Algorithm (GA) and Harris Hawks Optimization (HHO), for hyperparameter tuning. Layer pruning helps identify and eliminate less significant features, while model parameter optimization further enhances performance by fine-tuning critical hyperparameters, improving convergence speed,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3743-3767, 2025, DOI:10.32604/cmes.2025.063427 - 30 June 2025
（This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)
Abstract Cardiovascular diseases (CVDs) remain one of the foremost causes of death globally; hence, the need for several must-have, advanced automated diagnostic solutions towards early detection and intervention. Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability. To counter this limitation, this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat. We employ FastAI vision-learner-based convolutional neural networks (CNNs) that include ResNet, DenseNet, VGG, ConvNeXt, SqueezeNet, and AlexNet to classify heart sound recordings. Instead of… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3769-3793, 2025, DOI:10.32604/cmes.2025.066298 - 30 June 2025
（This article belongs to the Special Issue: Computer Modeling for Future Communications and Networks)
Abstract As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions, efficient clustering mechanisms are vital to ensure stable and scalable communication. Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems (ITS). This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering (GOA-VNET) algorithm, an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks (VANETs), leveraging the Grasshopper Optimization Algorithm (GOA) to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems (ITS). The proposed GOA-VNET employs an… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3795-3838, 2025, DOI:10.32604/cmes.2025.066727 - 30 June 2025
（This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)
Abstract The accelerated global adoption of electric vehicles (EVs) is driving significant expansion and increasing complexity within the EV charging infrastructure, consequently presenting novel and pressing cybersecurity challenges. While considerable effort has focused on preventative cybersecurity measures, a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents, a gap exacerbated by system heterogeneity, distributed digital evidence, and inconsistent logging practices which hinder effective incident reconstruction and attribution. This paper addresses this critical need by proposing a novel, data-driven forensic framework tailored to the EV charging infrastructure, focusing on the systematic identification, classification,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3839-3859, 2025, DOI:10.32604/cmes.2025.065903 - 30 June 2025
（This article belongs to the Special Issue: Next-Generation Intelligent Networks and Systems: Advances in IoT, Edge Computing, and Secure Cyber-Physical Applications)
Abstract Wireless Sensor Networks (WSNs) play a critical role in automated border surveillance systems, where continuous monitoring is essential. However, limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time. To address this issue, this paper presents an innovative energy-efficient protocol based on deep Q-learning (DQN), specifically developed to prolong the operational lifespan of WSNs used in border surveillance. By harnessing the adaptive power of DQN, the proposed protocol dynamically adjusts node activity and communication patterns. This approach ensures optimal energy usage while maintaining high coverage, connectivity, and data accuracy. More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3861-3897, 2025, DOI:10.32604/cmes.2025.065833 - 30 June 2025
（This article belongs to the Special Issue: Next-Generation Intelligent Networks and Systems: Advances in IoT, Edge Computing, and Secure Cyber-Physical Applications)
Abstract The Tactile Internet of Things (TIoT) promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems. Yet TIoT’s stringent requirements for ultra-low latency, high reliability, and robust privacy present significant challenges. Conventional centralized Federated Learning (FL) architectures struggle with latency and privacy constraints, while fully distributed FL (DFL) faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous. To address these limitations, we propose a Clustered Distributed Federated Learning (CDFL) architecture tailored for a 6G-enabled TIoT environment. Clients are grouped into clusters based on… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3899-3920, 2025, DOI:10.32604/cmes.2025.064874 - 30 June 2025
（This article belongs to the Special Issue: Emerging Technologies in Information Security )
Abstract The emergence of Generative Adversarial Network (GAN) techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems (IDS). However, conventional GAN-based IDS models face several challenges, including training instability, high computational costs, and system failures. To address these limitations, we propose a Hybrid Wasserstein GAN and Autoencoder Model (WGAN-AE) for intrusion detection. The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model. The model was trained and evaluated using two recent benchmark datasets, 5GNIDD and IDSIoT2024. When trained on the 5GNIDD dataset,… More >
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3921-3941, 2025, DOI:10.32604/cmes.2025.065627 - 30 June 2025
（This article belongs to the Special Issue: Key Technologies and Applications of Blockchain Technology in Supply Chain Intelligence and Trust Establishment)
Abstract With the increasing importance of supply chain transparency, blockchain-based data has emerged as a valuable and verifiable source for analyzing procurement transaction risks. This study extends the mathematical model and proof of ‘the Overall Performance Characteristics of the Supply Chain’ to encompass multiple variables within blockchain data. Utilizing graph theory, the model is further developed into a single-layer neural network, which serves as the foundation for constructing two multi-layer deep learning neural network models, Feedforward Neural Network (abbreviated as FNN) and Deep Clustering Network (abbreviated as DCN). Furthermore, this study retrieves corporate data from the… More >
Graphic Abstract
Open Access
ARTICLE
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3943-3980, 2025, DOI:10.32604/cmes.2025.064997 - 30 June 2025
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract Pre-Authentication and Post-Connection (PAPC) plays a crucial role in realizing the Zero Trust security model by ensuring that access to network resources is granted only after successful authentication. While earlier approaches such as Port Knocking (PK) and Single Packet Authorization (SPA) introduced pre-authentication concepts, they suffer from limitations including plaintext communication, protocol dependency, reliance on dedicated clients, and inefficiency under modern network conditions. These constraints hinder their applicability in emerging distributed and resource-constrained environments such as AIoT and browser-based systems. To address these challenges, this study proposes a novel port-sequence-based PAPC scheme structured as a… More >
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