Open Access

ARTICLE

Minjie Shao, Tielin Shi, Qi Xia^*, Shiyuan Liu^*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2685-2703, 2025, DOI:10.32604/cmes.2025.068078 - 30 September 2025
Abstract A data-driven model of multiple variable cutting (M-VCUT) level set-based substructure is proposed for the topology optimization of lattice structures. The M-VCUT level set method is used to represent substructures, enriching their diversity of configuration while ensuring connectivity. To construct the data-driven model of substructure, a database is prepared by sampling the space of substructures spanned by several substructure prototypes. Then, for each substructure in this database, the stiffness matrix is condensed so that its degrees of freedom are reduced. Thereafter, the data-driven model of substructures is constructed through interpolation with compactly supported radial basis More >

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EDITORIAL

Wenfeng Zheng¹, Chao Liu², Lirong Yin^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2705-2707, 2025, DOI:10.32604/cmes.2025.072137 - 30 September 2025
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract This article has no abstract. More >

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REVIEW

Yujie Wu¹, Xing Deng^1,*, Haijian Shao¹, Ke Cheng¹, Ming Zhang¹, Yingtao Jiang², Fei Wang¹
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2709-2778, 2025, DOI:10.32604/cmes.2025.066647 - 30 September 2025
Abstract The application of generative artificial intelligence (AI) is bringing about notable changes in anime creation. This paper surveys recent advancements and applications of diffusion and language models in anime generation, focusing on their demonstrated potential to enhance production efficiency through automation and personalization. Despite these benefits, it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models. We conduct an in-depth survey of cutting-edge generative AI technologies, encompassing models such as Stable Diffusion and GPT, and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics. Review of the surveyed literature… More >

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REVIEW

Puranam Revanth Kumar¹, Gouse Baig Mohammad², Pallati Narsimhulu³, Dharnisha Narasappa⁴, Lakshmana Phaneendra Maguluri⁵, Subhav Singh^6,7,8, Shitharth Selvarajan^9,10,11,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2779-2818, 2025, DOI:10.32604/cmes.2025.066365 - 30 September 2025
（This article belongs to the Special Issue: Key Technologies and Applications of Blockchain Technology in Supply Chain Intelligence and Trust Establishment)
Abstract Blockchain Technology (BT) has emerged as a transformative solution for improving the efficacy, security, and transparency of supply chain intelligence. Traditional Supply Chain Management (SCM) systems frequently have problems such as data silos, a lack of visibility in real time, fraudulent activities, and inefficiencies in tracking and traceability. Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues; it facilitates trust, security, and the sharing of data in real-time among all parties involved. Through an examination of critical technologies, methodology, and applications, this paper delves deeply into computer modeling based-blockchain framework… More >

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639

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ARTICLE

Suppakit Eiadtrong^1,2,#, Tan N. Nguyen^3,#,*, Mohamed-Ouejdi Belarbi⁴, Nuttawit Wattanasakulpong^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2819-2848, 2025, DOI:10.32604/cmes.2025.069481 - 30 September 2025
Abstract An improved meshfree moving-Kriging (MK) formulation for free vibration analysis of functionally graded material-functionally graded carbon nanotube-reinforced composite (FGM-FGCNTRC) sandwich shells is first proposed in this article. The proposed sandwich structure consists of skins of FGM layers and an FGCNTRC core. This structure possesses all the advantages of FGM and FGCNTRC, including high electrical or thermal insulating properties, high fatigue resistance, good corrosion resistance, high stiffness, low density, high strength, and high aspect ratios. Such sandwich structures can be used to replace conventional FGM structures. The present formulation has been established by using an improved More >

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302

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ARTICLE

Jin Zhu^1,2,*, Yanxin Xu¹
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2849-2872, 2025, DOI:10.32604/cmes.2025.070287 - 30 September 2025
Abstract Ice accretion on structures such as aircraft wings and wind turbine blades poses serious risks to aerodynamic performance and operational safety, particularly in cold and humid environments. This study conducts numerical simulations of ice formation on thin flat plates using CFD and FENSAP-ICE, exploring how air temperature, wind velocity, and angle of attack (AOA) affect icing behavior and aerodynamic characteristics. Results indicate that ice thickness increases linearly over time. Rime ice forms at low temperatures due to immediate droplet freezing, whereas glaze ice develops at higher temperatures when a water film forms and subsequently refreezes… More >

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782

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546

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ARTICLE

Yi Zhang¹, Wenliang Zhu^1,*, Hanbin Wang¹, Chun Tian², Jiajun Zhou²
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2873-2891, 2025, DOI:10.32604/cmes.2025.068951 - 30 September 2025
Abstract Aiming to address the challenge of directly measuring the real-time adhesion coefficient between wheels and rails, this paper proposes an online estimation algorithm for the adhesion coefficient based on parameter estimation. Firstly, a force analysis of the single-wheel pair model of the train is conducted to derive the calculation relationship for the wheel-rail adhesion coefficient in train dynamics. Then, an estimator based on parameter estimation is designed, and its stability is verified. This estimator is combined with the wheelset force analysis to estimate the wheel-rail adhesion coefficient. Finally, the approach is validated through joint simulations… More >

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526

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327

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ARTICLE

Seunghwan Seo^1,2,*, Moonkyung Chung¹
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2893-2922, 2025, DOI:10.32604/cmes.2025.069668 - 30 September 2025
Abstract Excavation-induced deformations of earth-retaining walls (ERWs) can critically affect the safety of surrounding structures, highlighting the need for reliable prediction models to support timely decision-making during construction. This study utilizes traditional statistical ARIMA (Auto-Regressive Integrated Moving Average) and deep learning-based LSTM (Long Short-Term Memory) models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models. The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time, whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within… More >

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ARTICLE

Miguel A. Millán^1,*, Rubén A. Galindo², Fausto Molina-Gómez¹
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2923-2941, 2025, DOI:10.32604/cmes.2025.069356 - 30 September 2025
Abstract Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes, resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns. This complexity poses significant challenges for slope stability analysis, requiring the development of specialized techniques to address these issues. This research presents a numerical methodology that incorporates spatial variability, nonlinear material characterization, and probabilistic analysis using a Monte Carlo framework to address this issue. The heterogeneous structure is represented by randomly assigning different lithotypes across the slope, while maintaining predefined global proportions. This contrasts with the more common approach… More >

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346

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ARTICLE

Sen Yang¹, Jie Zhong¹, Boyu Gan¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2943-2967, 2025, DOI:10.32604/cmes.2025.067282 - 30 September 2025
Abstract Foam concrete is widely used in engineering due to its lightweight and high porosity. Its compressive strength, a key performance indicator, is influenced by multiple factors, showing nonlinear variation. As compressive strength tests for foam concrete take a long time, a fast and accurate prediction method is needed. In recent years, machine learning has become a powerful tool for predicting the compressive strength of cement-based materials. However, existing studies often use a limited number of input parameters, and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.… More >

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ARTICLE

Chih-Yu Liu^1,2, Cheng-Yu Ku^1,2,*, Ming-Han Tsai¹, Jia-Yi You³
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2969-2990, 2025, DOI:10.32604/cmes.2025.070663 - 30 September 2025
Abstract Amid growing typhoon risks driven by climate change with projected shifts in precipitation intensity and temperature patterns, Taiwan faces increasing challenges in flood risk. In response, this study proposes a geographic information system (GIS)-based artificial intelligence (AI) model to assess flood susceptibility in Keelung City, integrating geospatial and hydrometeorological data collected during Typhoon Krathon (2024). The model employs the random forest (RF) algorithm, using seven environmental variables excluding average elevation, slope, topographic wetness index (TWI), frequency of cumulative rainfall threshold exceedance, normalized difference vegetation index (NDVI), flow accumulation, and drainage density, with the number of… More >

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598

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ARTICLE

Okechukwu J. Obulezi^1,*, Hatem E. Semary², Sadia Nadir³, Chinyere P. Igbokwe⁴, Gabriel O. Orji¹, A. S. Al-Moisheer², Mohammed Elgarhy⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2991-3027, 2025, DOI:10.32604/cmes.2025.069553 - 30 September 2025
Abstract This study introduces the type-I heavy-tailed Burr XII (TIHTBXII) distribution, a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness, heavy tails, and diverse hazard behaviors. We meticulously develop the TIHTBXII’s mathematical foundations, including its probability density function (PDF), cumulative distribution function (CDF), and essential statistical properties, crucial for theoretical understanding and practical application. A comprehensive Monte Carlo simulation evaluates four parameter estimation methods: maximum likelihood (MLE), maximum product spacing (MPS), least squares (LS), and weighted least squares (WLS). The simulation results consistently show… More >

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814

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329

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ARTICLE

Jian Li^1,*, Qing Zhang², Tong Yang², Yu’an Chen², Yongzhong Zhan³
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3029-3051, 2025, DOI:10.32604/cmes.2025.068644 - 30 September 2025
Abstract In the deployment of wireless networks in two-dimensional outdoor campus spaces, aiming at the problem of efficient coverage of the monitoring area by limited number of access points (APs), this paper proposes a deployment method of multi-objective optimization with virtual force fusion bat algorithm (VFBA) using the classical four-node regular distribution as an entry point. The introduction of Lévy flight strategy for bat position updating helps to maintain the population diversity, reduce the premature maturity problem caused by population convergence, avoid the over aggregation of individuals in the local optimal region, and enhance the superiority… More >

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575

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309

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ARTICLE

Mian Muhammad Kamal^1,*, Syed Zain Ul Abideen², M. A. Al-Khasawneh^3,4, Alaa M. Momani⁴, Hala Mostafa⁵, Mohammed Salem Atoum⁶, Saeed Ullah⁷, Jamil Abedalrahim Jamil Alsayaydeh^8,*, Mohd Faizal Bin Yusof⁹, Suhaila Binti Mohd Najib⁸
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3053-3083, 2025, DOI:10.32604/cmes.2025.067430 - 30 September 2025
Abstract Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue. However, low resolution, occlusion, and background interference make small object detection a complex and demanding task. One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities. This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations, such as Hyperspectral–Multispectral (HS-MS), Hyperspectral–Synthetic Aperture Radar (HS-SAR), and HS-SAR–Digital Surface Model (HS-SAR-DSM). The detection process is done by the proposed Jaccard Deep Q-Net (JDQN), which More >

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ARTICLE

Ayman Noor^1,2, Hanan Almukhalfi^1,2, Arthur Souza^2,3, Talal H. Noor^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3085-3111, 2025, DOI:10.32604/cmes.2025.068393 - 30 September 2025
Abstract People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces. Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial awareness to users. This research work represents a feasibility study for the wearable IoT-based indoor object detection assistant system architecture that employs a real-time indoor object detection approach to help visually impaired users recognize indoor objects. The system architecture includes four main layers: Wearable Internet of Things (IoT), Network, Cloud, and Indoor Object Detection Layers. The wearable hardware prototype is assembled using a Raspberry Pi 4, while the… More >

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282

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ARTICLE

Mohammad Soleimani Amiri¹, Sahbi Boubaker^2,3,*, Rizauddin Ramli^4,*, Souad Kamel²
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3113-3133, 2025, DOI:10.32604/cmes.2025.069167 - 30 September 2025
Abstract Disability is defined as a condition that makes it difficult for a person to perform certain vital activities. In recent years, the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent. However, controlling an exoskeleton for rehabilitation presents challenges due to their non-linear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton. To remedy these problems, this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons, addressing the challenges of nonlinear dynamics and external disturbances. The proposed controller integrated… More >

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349

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ARTICLE

Areeba Masood Siddiqui^1,2,*, Hyder Abbas^3,4, Muhammad Asim^5,6,*, Abdelhamied A. Ateya⁵, Hanaa A. Abdallah⁷
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3135-3168, 2025, DOI:10.32604/cmes.2025.069926 - 30 September 2025
Abstract Timely and accurate diagnosis of skin diseases is crucial as conventional methods are time-consuming and prone to errors. Traditional trial-and-error approaches often aggregate multiple models without optimization by resulting in suboptimal performance. To address these challenges, we propose a novel Squid Game Optimization-Dimension Reduction-based Ensemble (SGO-DRE) method for the precise diagnosis of skin diseases. Our approach begins by selecting pre-trained models named MobileNetV1, DenseNet201, and Xception for robust feature extraction. These models are enhanced with dimension reduction blocks to improve efficiency. To tackle the aggregation problem of various models, we leverage the Squid Game Optimization… More >

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ARTICLE

Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025
Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >

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ARTICLE

Myoung-oh Choi¹, Mincheol Shin¹, Hyonjun Kang¹, Ka Lok Man², Mucheol Kim^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3191-3212, 2025, DOI:10.32604/cmes.2025.068723 - 30 September 2025
Abstract The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification. To address this challenge, we propose Vulnerability2Vec, a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience. Vulnerability2Vec converts Common Vulnerabilities and Exposures (CVE) text explanations to semantic graphs, where nodes represent CVE IDs and key terms (nouns, verbs, and adjectives), and edges capture co-occurrence relationships. Then, it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram More >

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ARTICLE

Ramadan Abdul-Rashid¹, Mohd Amiruddin Abd Rahman^1,*, Abdulaziz Yagoub Barnawi²
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3213-3250, 2025, DOI:10.32604/cmes.2025.066589 - 30 September 2025
Abstract Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks (WSNs), especially in security-critical, time-sensitive applications. However, most existing protocols degrade substantially under malicious interference. We introduce iSTSP, an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust, precise synchronization even in hostile environments: (1) trust preprocessing that filters node participation using behavioral trust scoring; (2) anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time; (3) reliability-weighted consensus that prioritizes high-trust nodes during time aggregation; and (4) convergence-optimized synchronization… More >

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ARTICLE

Anshul Pandey, Sachin Kumar^*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3251-3286, 2025, DOI:10.32604/cmes.2025.067858 - 30 September 2025
（This article belongs to the Special Issue: Advances in Computational Fracture Mechanics: Theories, Techniques, and Applications)
Abstract The phase field model can coherently address the relatively complex fracture phenomenon, such as crack nucleation, branching, deflection, etc. The model has been extensively implemented in the finite element package Abaqus to solve brittle fracture problems in recent studies. However, accurate numerical analysis typically requires fine meshes to model the evolving crack path effectively. A broad region must be discretized without prior knowledge of the crack path, further augmenting the computational expenses. In this proposed work, we present an automated framework utilizing a posteriori error-indicator (MISESERI) to demarcate and sufficiently refine the mesh along the… More >

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886

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ARTICLE

Yuhang Liu¹, Xiaoying Zhuang^1,2,*, Huilong Ren¹
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3287-3305, 2025, DOI:10.32604/cmes.2025.070018 - 30 September 2025
（This article belongs to the Special Issue: Advanced Computational Methods in Multiphysics Phenomena)
Abstract Shallow-buried thick sand strata present considerable local instability risks during diaphragm wall trenching construction. However, this critical issue has not been extensively studied, despite its serious safety consequences. This paper proposes an automatic identification model for shallow-buried thick sand strata, integrating three-dimensional limit equilibrium theory with a genetic algorithm to precisely identify the most potentially dangerous local instability mass and determine its minimum safety factor. The model establishes three undetermined parameters: failure angle, upper boundary, and thickness of the local instability mass. These parameters define the search space for the local instability mass. The effectiveness… More >

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509

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ARTICLE

Jin Xue, Boyun Guo^*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3307-3328, 2025, DOI:10.32604/cmes.2025.069909 - 30 September 2025
（This article belongs to the Special Issue: Computer Modeling of Fluid Seepage in Porous Media with Ultra-low Permeabilities)
Abstract Fluid imbibition from hydraulic fractures into shale formations is mainly affected by a combination of capillary forces and viscous resistance, both of which are closely related to the pore geometry. This study established five self-imbibition models with idealized pore structures and conducted a comparative analysis of these models. These models include circular, square, and equilateral triangular capillaries; a triangular star-shaped cross-section formed by three tangent spherical particles; and a traditional porous medium representation method. All these models are derived based on Newton’s second law, where capillary pressure is described by the Young-Laplace equation and viscous… More >

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ARTICLE

Geunil Lee¹, Daeil Kwon^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3329-3342, 2025, DOI:10.32604/cmes.2025.069402 - 30 September 2025
（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 Nondestructive testing (NDT) methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control, but they remain limited in their ability to detect defect characteristics. Visual inspection depends strongly on operator experience, while ultrasonic testing requires physical contact and stable coupling conditions that are difficult to maintain in production lines. These constraints become more pronounced when defect-related information is scarce or when background noise interferes with signal acquisition in manufacturing processes. This study presents a non-contact acoustic method for diagnosing defects in scroll compressors during the manufacturing process. The diagnostic approach… More >

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ARTICLE

Yi Guan¹, Pengpeng Zhi^2,3,*, Zhonglai Wang^1,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3343-3368, 2025, DOI:10.32604/cmes.2025.069515 - 30 September 2025
（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 Variable-fidelity (VF) surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity (HF) simulations with reduced computational power. A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity (LF) and/or HF samples. The additional samples must enhance the model accuracy while maximizing the computational efficiency. We propose ISMA-VFEEI, a global optimization framework that integrates an Improved Slime-Mould Algorithm (ISMA) and a Variable-Fidelity Expected Extension Improvement (VFEEI) learning function to construct a VF surrogate model efficiently. First, A cost-aware VFEEI More >

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Carlos Quiterio Gómez Muñoz¹, Fausto Pedro García Márquez^2,*, Jorge Bernabé Sanjuán³
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3369-3386, 2025, DOI:10.32604/cmes.2025.069225 - 30 September 2025
（This article belongs to the Special Issue: Advanced Data Analysis Techniques in Renewable Energy)
Abstract Due to the continuous increase in global energy demand, photovoltaic solar energy generation and associated maintenance requirements have significantly expanded. One critical maintenance challenge in photovoltaic installations is detecting hot spots, localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage. Traditional methods for detecting these defects rely on manual inspections using thermal imaging, which are costly, labor-intensive, and impractical for large-scale installations. This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture. The first convolutional neural network efficiently detects More >

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Bassel Weiss¹, Segundo Esteban^2,*, Matilde Santos³
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3387-3418, 2025, DOI:10.32604/cmes.2025.070070 - 30 September 2025
（This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)
Abstract Anomaly detection in wind turbines involves emphasizing its ability to improve operational efficiency, reduce maintenance costs, extend their lifespan, and enhance reliability in the wind energy sector. This is particularly necessary in offshore wind, currently one of the most critical assets for achieving sustainable energy generation goals, due to the harsh marine environment and the difficulty of maintenance tasks. To address this problem, this work proposes a data-driven methodology for detecting power generation anomalies in offshore wind turbines, using normalized and linearized operational data. The proposed framework transforms heterogeneous wind speed and power measurements into… More >

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Hongwei Ma¹, Yingqian Tian^2,*, Fuzhang Wang^3,*, Quanfu Lou⁴, Lijuan Yu⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3419-3432, 2025, DOI:10.32604/cmes.2025.070791 - 30 September 2025
（This article belongs to the Special Issue: Meshless Methods and Its Applications in Porous Media Problems)
Abstract This study introduces a novel single-layer meshless method, the space-time collocation method based on multiquadric-radial basis functions (MQ-RBF), for solving the Benjamin-Bona-Mahony-Burgers (BBMB) equation. By reconstructing the time variable as a space variable, this method establishes a combined space-time structure that can eliminate the two-step computational process required in traditional grid methods. By introducing shape parameter-optimized MQ-RBF, high-precision discretization of the nonlinear, dispersive, and dissipative terms in the BBMB equation is achieved. The numerical experiment section validates the effectiveness of the proposed method through three benchmark examples. This method shows significant advantages in computational efficiency, More >

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Ali Raza^1,*, Asad Ullah², Eugénio M. Rocha¹, Dumitru Baleanu³, Hala H. Taha⁴, Emad Fadhal^5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3433-3461, 2025, DOI:10.32604/cmes.2025.069655 - 30 September 2025
（This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
Abstract This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations (SDDEs). A delayed stochastic model is formulated by dividing the population into five distinct compartments: susceptible, exposed, infected, environmental irritants, and recovered individuals. The model undergoes thorough analytical examination, addressing key dynamical properties including positivity, boundedness, existence, and uniqueness of solutions. Local and global stability around the equilibrium points is studied with respect to the basic reproduction number. The existence of a unique global positive solution for the stochastic delayed model is established. In addition, a stochastic nonstandard finite difference scheme is More >

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Azhar Iqbal Kashif Butt^1,*, Tariq Ismaeel^2,*, Sara Khan², Muhammad Imran³, Waheed Ahmad², Ismail Abdulrashid⁴, Muhammad Sajid Riaz⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3463-3492, 2025, DOI:10.32604/cmes.2025.069277 - 30 September 2025
（This article belongs to the Special Issue: Advances in Mathematical Modeling: Numerical Approaches and Simulation for Computational Biology)
Abstract Malaria is a significant global health challenge. This devastating disease continues to affect millions, especially in tropical regions. It is caused by Plasmodium parasites transmitted by female Anopheles mosquitoes. This study introduces a nonlinear mathematical model for examining the transmission dynamics of malaria, incorporating both human and mosquito populations. We aim to identify the key factors driving the endemic spread of malaria, determine feasible solutions, and provide insights that lead to the development of effective prevention and management strategies. We derive the basic reproductive number employing the next-generation matrix approach and identify the disease-free and… More >

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Sagvan Y. Musa^1,2, Zanyar A. Ameen^3,*, Wafa Alagal⁴, Baravan A. Asaad^5,6
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3493-3529, 2025, DOI:10.32604/cmes.2025.070019 - 30 September 2025
（This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
Abstract Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility, cost-effectiveness, and environmental impact, often under uncertainty and incomplete information. These complex decisions require input from various stakeholders, including planners, policymakers, engineers, and community representatives, whose opinions may differ or contradict. Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations. To address these challenges, we propose a novel decision-making framework based on Pythagorean fuzzy N-bipolar soft expert sets. This model allows experts to express both positive and negative opinions on a multinary scale, capturing nuanced judgments with higher accuracy. It… More >

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Zhe Liu^1,2,*, Jiahao Shi³, Dania Santina⁴, Yulong Huang¹, Nabil Mlaiki⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3531-3555, 2025, DOI:10.32604/cmes.2025.071145 - 30 September 2025
（This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
Abstract The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations. However, traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data, as they rely on a single-dimensional membership value. To overcome these limitations, we propose an auto-weighted multi-view neutrosophic fuzzy clustering (AW-MVNFC) algorithm. Our method leverages the neutrosophic framework, an extension of fuzzy sets, to explicitly model imprecision and ambiguity through three membership degrees. The core novelty of AW-MVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions More >

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Muhammad Salman Qamar^1,2,*, Muhammad Fahad Munir², Athar Waseem²
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3557-3584, 2025, DOI:10.32604/cmes.2025.067447 - 30 September 2025
（This article belongs to the Special Issue: Advances in Deep Learning for Time Series Forecasting: Research and Applications)
Abstract Air pollution, specifically fine particulate matter (PM2.5), represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems. Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks; however, the inherent nonlinearity and dynamic variability of air quality data present significant challenges. This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and the hybrid CNN-LSTM as well as statistical models, AutoRegressive Integrated Moving Average (ARIMA) and Maximum Likelihood Estimation (MLE) for hourly PM2.5 forecasting. Model performance is… More >

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Sudhakar Tummala^1,2,*, Sajjad Hussain Chauhdary³, Vikash Singh⁴, Roshan Kumar⁵, Seifedine Kadry⁶, Jungeun Kim^7,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3585-3605, 2025, DOI:10.32604/cmes.2025.069731 - 30 September 2025
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things (IoMT). Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer. Therefore, this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention (ResMHCNN) blocks to classify medical images. We introduced three novel lightweight deep learning models (BT-Net, LCC-Net, and BC-Net) utilizing the ResMHCNN block as their backbone. These models were cross-validated and tested on three publicly available medical image datasets:… More >

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Mohammed Albekairi¹, Radhia Khdhir^2,*, Amina Magdich³, Somia Asklany^4,*, Ghulam Abbas⁵, Amr Yousef ^6,7
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3607-3644, 2025, DOI:10.32604/cmes.2025.069681 - 30 September 2025
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation, which lead to pixel displacements. This article introduces a Displacement Region Recognition Method (DR2M) to address such a problem. This method operates on displaced features compared to the training input observed throughout definite time frames. The technique focuses on detecting features that remain relatively stable under haze, using a frame-based analysis to isolate edges minimally affected by visual noise. The edge detection failures are identified using a bilateral neural network through displaced feature training. The training converges bilaterally… More >

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Emad Sami Jaha^*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3645-3678, 2025, DOI:10.32604/cmes.2025.068681 - 30 September 2025
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract The human ear has been substantiated as a viable nonintrusive biometric modality for identification or verification. Among many feasible techniques for ear biometric recognition, convolutional neural network (CNN) models have recently offered high-performance and reliable systems. However, their performance can still be further improved using the capabilities of soft biometrics, a research question yet to be investigated. This research aims to augment the traditional CNN-based ear recognition performance by adding increased discriminatory ear soft biometric traits. It proposes a novel framework of augmented ear identification/verification using a group of discriminative categorical soft biometrics and deriving… More >

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Lakshmi Alekhya Jandhyam^1,*, Ragupathy Rengaswamy¹, Narayana Satyala²
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3679-3714, 2025, DOI:10.32604/cmes.2025.068941 - 30 September 2025
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract Human Activity Recognition (HAR) has become increasingly critical in civic surveillance, medical care monitoring, and institutional protection. Current deep learning-based approaches often suffer from excessive computational complexity, limited generalizability under varying conditions, and compromised real-time performance. To counter these, this paper introduces an Active Learning-aided Heuristic Deep Spatio-Textural Ensemble Learning (ALH-DSEL) framework. The model initially identifies keyframes from the surveillance videos with a Multi-Constraint Active Learning (MCAL) approach, with features extracted from DenseNet121. The frames are then segmented employing an optimized Fuzzy C-Means clustering algorithm with Firefly to identify areas of interest. A deep ensemble More >

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Florentin Smarandache¹, Saleh I. Alzahrani², Sulaiman Al Amro³, Ijaz Ahmad⁴, Mubashir Ali^5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3715-3735, 2025, DOI:10.32604/cmes.2025.068736 - 30 September 2025
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body. Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes, where abnormal hemoglobin levels can indicate significant health issues. Traditional methods for hemoglobin measurement are invasive, causing pain, risk of infection, and are less convenient for frequent monitoring. PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure, sleep, blood glucose, and stress analysis. In this work, we propose a hemoglobin estimation method using an adaptive lightweight… More >

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Soumia Zertal^1,2,*, Asma Saighi^1,2, Sofia Kouah^1,2, Souham Meshoul^3,*, Zakaria Laboudi^2,4
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3737-3782, 2025, DOI:10.32604/cmes.2025.068558 - 30 September 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) continue to present a leading cause of mortality worldwide, emphasizing the importance of early and accurate prediction. Electrocardiogram (ECG) signals, central to cardiac monitoring, have increasingly been integrated with Deep Learning (DL) for real-time prediction of CVDs. However, DL models are prone to performance degradation due to concept drift and to catastrophic forgetting. To address this issue, we propose a real-time CVDs prediction approach, referred to as ADWIN-GFR that combines Convolutional Neural Network (CNN) layers, for spatial feature extraction, with Gated Recurrent Units (GRU), for temporal modeling, alongside adaptive drift detection and… More >

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Faareh Ahmed¹, Babar Mansoor², Muhammad Awais Javed³, Abdul Khader Jilani Saudagar^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3783-3804, 2025, DOI:10.32604/cmes.2025.069430 - 30 September 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 Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content. However, retrieving infotainment data from remote servers often introduces high delays, degrading the Quality of Service (QoS). To overcome this, caching frequently requested content at fog-enabled Road Side Units (RSUs) reduces communication latency. Yet, the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity. This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction. The scheme is evaluated against Intelligent Content Caching (ICC) and Random Caching More >

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Shengjia Chang, Baojiang Cui^*, Shaocong Feng
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3805-3827, 2025, DOI:10.32604/cmes.2025.067756 - 30 September 2025
（This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)
Abstract With the rapid advancement of mobile communication networks, key technologies such as Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) have enhanced the quality of service for 5G users but have also significantly increased the complexity of network threats. Traditional static defense mechanisms are inadequate for addressing the dynamic and heterogeneous nature of modern attack vectors. To overcome these challenges, this paper presents a novel algorithmic framework, SD-5G, designed for high-precision intrusion detection in 5G environments. SD-5G adopts a three-stage architecture comprising traffic feature extraction, elastic representation, and adaptive classification. Specifically, an enhanced Concrete… More >

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