Open Access

ARTICLE

Yanwen Zhu^1,#, Chen Zhao^2,#, Yishuo Xu², Zheyang Wu³, Akiko Maehara⁴, Liang Wang¹, Dirui Zhang², Ming Zeng², Rui Lv⁵, Xiaoya Guo⁶, Mengde Huang¹, Minglong Chen⁷, Gary S. Mintz⁴, Dalin Tang^1,3,*, Haibo Jia², Bo Yu^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1249-1270, 2025, DOI:10.32604/cmes.2025.067039 - 31 August 2025
Abstract Image-based computational models have been used for vulnerable plaque progression and rupture predictions, and good results have been reported. However, mechanisms and predictions for plaque erosion are under-investigated. Patient-specific fluid-structure interaction (FSI) models based on optical coherence tomography (OCT) follow-up data from patients with plaque erosion and who received conservative antithrombotic treatment (using medication, no stenting) to identify risk factors that could be used to predict the treatment outcome. OCT and angiography data were obtained from 10 patients who received conservative antithrombotic treatment. Five participants had worse outcomes (WOG, stenosis severity ≥ 70% at one-year… More >

Graphic Abstract

View

2393

Download

1673

Open Access

REVIEW

Shubham Desai, Sai Sidhardh^*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1271-1334, 2025, DOI:10.32604/cmes.2025.068141 - 31 August 2025
Abstract The increasing integration of small-scale structures in engineering, particularly in Micro-Electro-Mechanical Systems (MEMS), necessitates advanced modeling approaches to accurately capture their complex mechanical behavior. Classical continuum theories are inadequate at micro- and nanoscales, particularly concerning size effects, singularities, and phenomena like strain softening or phase transitions. This limitation follows from their lack of intrinsic length scale parameters, crucial for representing microstructural features. Theoretical and experimental findings emphasize the critical role of these parameters on small scales. This review thoroughly examines various strain gradient elasticity (SGE) theories commonly employed in literature to capture these size-dependent effects… More >

View

1802

Download

1537

Open Access

REVIEW

Wan Mohd Faizal^1,2,*, Nurul Musfirah Mazlan^1,*, Shazril Imran Shaukat^3,4, Chu Yee Khor², Ab Hadi Mohd Haidiezul², Abdul Khadir Mohamad Syafiq²
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1335-1369, 2025, DOI:10.32604/cmes.2025.068660 - 31 August 2025
Abstract Conventional oncology faces challenges such as suboptimal drug delivery, tumor heterogeneity, and therapeutic resistance, indicating a need for more personalized, and mechanistically grounded and predictive treatment strategies. This review explores the convergence of Computational Fluid Dynamics (CFD) and Machine Learning (ML) as an integrated framework to address these issues in modern cancer therapy. The paper discusses recent advancements where CFD models simulate complex tumor microenvironmental conditions, like interstitial fluid pressure (IFP) and drug perfusion, and ML enhances simulation workflows, automates image-based segmentation, and enhances predictive accuracy. The synergy between CFD and ML improves scalability and More >

View

6158

Download

1504

Open Access

REVIEW

Ahmad Fazlizan^1,*, Wan Khairul Muzammil², Najm Addin Al-Khawlani¹
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1371-1437, 2025, DOI:10.32604/cmes.2025.067854 - 31 August 2025
Abstract This review provides a comprehensive and systematic examination of Computational Fluid Dynamics (CFD) techniques and methodologies applied to the development of Vertical Axis Wind Turbines (VAWTs). Although VAWTs offer significant advantages for urban wind applications, such as omnidirectional wind capture and a compact, ground-accessible design, they face substantial aerodynamic challenges, including dynamic stall, blade–wake interactions, and continuously varying angles of attack throughout their rotation. The review critically evaluates how CFD has been leveraged to address these challenges, detailing the modelling frameworks, simulation setups, mesh strategies, turbulence models, and boundary condition treatments adopted in the literature.… More >

View

2188

Download

1526

Open Access

REVIEW

Hamed Alqahtani¹, Gulshan Kumar^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1439-1479, 2025, DOI:10.32604/cmes.2025.067738 - 31 August 2025
Abstract Domain Generation Algorithms (DGAs) continue to pose a significant threat in modern malware infrastructures by enabling resilient and evasive communication with Command and Control (C&C) servers. Traditional detection methods—rooted in statistical heuristics, feature engineering, and shallow machine learning—struggle to adapt to the increasing sophistication, linguistic mimicry, and adversarial variability of DGA variants. The emergence of Large Language Models (LLMs) marks a transformative shift in this landscape. Leveraging deep contextual understanding, semantic generalization, and few-shot learning capabilities, LLMs such as BERT, GPT, and T5 have shown promising results in detecting both character-based and dictionary-based DGAs, including… More >

View

1978

Download

1528

Open Access

REVIEW

Cecilia Castro¹, Victor Leiva^2,*, Franco Basso^2,3
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1481-1543, 2025, DOI:10.32604/cmes.2025.067992 - 31 August 2025
Abstract Metaverse technologies are increasingly promoted as game-changers in transport planning, connected-autonomous mobility, and immersive traveler services. However, the field lacks a systematic review of what has been achieved, where critical technical gaps remain, and where future deployments should be integrated. Using a transparent protocol-driven screening process, we reviewed 1589 records and retained 101 peer-reviewed journal and conference articles (2021–2025) that explicitly frame their contributions within a transport-oriented metaverse. Our review reveals a predominantly exploratory evidence base. Among the 101 studies reviewed, 17 (16.8%) apply fuzzy multi-criteria decision-making, 36 (35.6%) feature digital-twin visualizations or simulation-based testbeds,… More >

Graphic Abstract

View

1885

Download

1453

Open Access

ARTICLE

Jianchang Hou¹, Zhanpeng Jiang¹, Fenghe Wu¹, Hui Lian¹, Zhaohua Wang², Zijian Liu³, Weicheng Li^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1545-1572, 2025, DOI:10.32604/cmes.2025.068482 - 31 August 2025
Abstract In this paper, a topology optimization method for coordinated stiffness and strength design is proposed under mass constraints, utilizing the Solid Isotropic Material with Penalization approach. Element densities are regulated through sensitivity filtering to mitigate numerical instabilities associated with stress concentrations. A p-norm aggregation function is employed to globalize local stress constraints, and a normalization technique linearly weights strain energy and stress, transforming the multi-objective problem into a single-objective formulation. The sensitivity of the objective function with respect to design variables is rigorously derived. Three numerical examples are presented, comparing the optimized structures in terms More >

View

6387

Download

1596

Open Access

ARTICLE

Ala’a R. Al-Shamasneh¹, Faten Khalid Karim², Arsalan Mahmoodzadeh^3,*, Abdulaziz Alghamdi⁴, Abdullah Alqahtani⁵, Shtwai Alsubai⁵, Abed Alanazi⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1573-1606, 2025, DOI:10.32604/cmes.2025.068887 - 31 August 2025
Abstract The fracture energy of fiber-reinforced concrete (FRC) affects the durability and structural performance of concrete elements. Advancements in experimental studies have yet to overcome the challenges of estimating fracture energy, as the process remains time-intensive and costly. Therefore, machine learning techniques have emerged as powerful alternatives. This study aims to investigate the performance of machine learning techniques to predict the fracture energy of FRC. For this purpose, 500 data points, including 8 input parameters that affect the fracture energy of FRC, are collected from three-point bending tests and employed to train and evaluate the machine… More >

View

8329

Download

1695

Open Access

ARTICLE

Yingui Qiu¹, Enming Li^1,2,*, Pablo Segarra², Bin Xi³, Jian Zhou¹
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1607-1629, 2025, DOI:10.32604/cmes.2025.068211 - 31 August 2025
Abstract With the growing demand for sustainable development in the mining industry, cemented paste backfill (CPB) materials, primarily composed of tailings, play a crucial role in mine backfilling and underground support systems. To enhance the mechanical properties of CPB materials, fiber reinforcement technology has gradually gained attention, though challenges remain in predicting its performance. This study develops a hybrid model based on the adaptive equilibrium optimizer (adap-EO)-enhanced XGBoost method for accurately predicting the uniaxial compressive strength of fiber-reinforced CPB. Through systematic comparison with various other machine learning methods, results demonstrate that the proposed hybrid model exhibits… More >

View

1781

Download

1509

Open Access

ARTICLE

Shen-Haw Ju^*, Yi-Chen Huang
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1631-1654, 2025, DOI:10.32604/cmes.2025.067679 - 31 August 2025
Abstract This study focuses on determining the second-order irregular wave loads in the time domain without using the Inverse Fast Fourier Transform (IFFT). Considering the substantial displacement effects that Floating Offshore Wind Turbine (FOWT) support structures undergo when subjected to wave loads, the time-domain wave method is more suitable, while the frequency-domain method requiring IFFT cannot be used for moving bodies. Nonetheless, the computational challenges posed by the considerable computer time requirements of the time-domain wave method remain a significant obstacle. Thus, the paper incorporates various numerical schemes, including parallel computing and extrapolation of wave forces… More >

View

1526

Download

1447

Open Access

ARTICLE

Yan Xu¹, Dietmar Gohlich², Sangyoung Park², William Zhendong Liu³, Ziwei Zhou¹, Haiyang Qu⁴, Weidong Zhu^5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1655-1676, 2025, DOI:10.32604/cmes.2025.068771 - 31 August 2025
Abstract The emissions from traditional fossil heavy-duty trucks have become a conspicuous issue worldwide. The electrical road system (ERS) can offer a viable solution for achieving zero CO₂ emissions and has high energy efficiency in long-distance road cargo transport. While many kinds of pantograph structures have been developed for the ERS, their corresponding pantograph-catenary dynamic characteristics under different road conditions have not been investigated. This work performs a numerical study on the dynamics of the pantograph-catenary interaction of an ERS considering different pantograph structures. First, a pantograph-catenary-truck-road model is proposed. The reduced catenary model and reduced-plate model… More >

View

8453

Download

1452

Open Access

ARTICLE

Yunus Dogan¹, Goksu Tuysuzoglu¹, Elife Ozturk Kiyak², Bita Ghasemkhani³, Kokten Ulas Birant^1,4, Semih Utku¹, Derya Birant^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1677-1715, 2025, DOI:10.32604/cmes.2025.069255 - 31 August 2025
Abstract Accurate traffic flow prediction (TFP) is vital for efficient and sustainable transportation management and the development of intelligent traffic systems. However, missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision. This study introduces REPTF-TMDI, a novel method that combines a Reduced Error Pruning Tree Forest (REPTree Forest) with a newly proposed Time-based Missing Data Imputation (TMDI) approach. The REPTree Forest, an ensemble learning approach, is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urban mobility solutions. Meanwhile, the TMDI approach exploits temporal patterns… More >

View

8450

Download

7890

Open Access

ARTICLE

He Huang, Jin Zhu^*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1717-1748, 2025, DOI:10.32604/cmes.2025.068384 - 31 August 2025
Abstract Automated guided vehicles (AGVs) are key equipment in automated container terminals (ACTs), and their operational efficiency can be impacted by conflicts and battery swapping. Additionally, AGVs have bidirectional transportation capabilities, allowing them to move in the opposite direction without turning around, which helps reduce transportation time. This paper aims at the problem of AGV scheduling and bidirectional conflict-free routing with battery swapping in automated terminals. A bi-level mixed integer programming (MIP) model is proposed, taking into account task assignment, bidirectional conflict-free routing, and battery swapping. The upper model focuses on container task assignment and AGV… More >

View

6561

Download

1626

Open Access

ARTICLE

Abubakar Elsafi^*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1749-1766, 2025, DOI:10.32604/cmes.2025.066449 - 31 August 2025
Abstract Domain randomization is a widely adopted technique in deep reinforcement learning (DRL) to improve agent generalization by exposing policies to diverse environmental conditions. This paper investigates the impact of different reset strategies, normal, non-randomized, and randomized, on agent performance using the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3) algorithms within the CarRacing-v2 environment. Two experimental setups were conducted: an extended training regime with DDPG for 1000 steps per episode across 1000 episodes, and a fast execution setup comparing DDPG and TD3 for 30 episodes with 50 steps per episode under constrained computational… More >

View

3104

Download

2043

Open Access

ARTICLE

Alex Varghese¹, Achin Jain², Mohammed Inamur Rahman³, Mudassir Khan^4,*, Arun Kumar Dubey², Iqrar Ahmad⁵, Yash Prakash Narayan¹, Arvind Panwar⁶, Anurag Choubey⁷, Saurav Mallik^8,9,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1767-1791, 2025, DOI:10.32604/cmes.2025.067999 - 31 August 2025
Abstract Skin cancer remains a significant global health challenge, and early detection is crucial to improving patient outcomes. This study presents a novel deep learning framework that combines Convolutional Neural Networks (CNNs), Transformers, and Gated Recurrent Units (GRUs) for robust skin cancer classification. To address data set imbalance, we employ StyleGAN3-based synthetic data augmentation alongside traditional techniques. The hybrid architecture effectively captures both local and global dependencies in dermoscopic images, while the GRU component models sequential patterns. Evaluated on the HAM10000 dataset, the proposed model achieves an accuracy of 90.61%, outperforming baseline architectures such as VGG16 More >

View

1856

Download

1510

Open Access

ARTICLE

Farhan A. Alenizi¹, Faten Khalid Karim^2,*, Alaa R. Al-Shamasneh³, Mohammad Hossein Shakoor⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1793-1829, 2025, DOI:10.32604/cmes.2025.066023 - 31 August 2025
Abstract Deep neural networks provide accurate results for most applications. However, they need a big dataset to train properly. Providing a big dataset is a significant challenge in most applications. Image augmentation refers to techniques that increase the amount of image data. Common operations for image augmentation include changes in illumination, rotation, contrast, size, viewing angle, and others. Recently, Generative Adversarial Networks (GANs) have been employed for image generation. However, like image augmentation methods, GAN approaches can only generate images that are similar to the original images. Therefore, they also cannot generate new classes of data.… More >

View

1759

Download

1477

Open Access

ARTICLE

Yufei Shi, Ziwen He^*, Teng Jin, Haochen Tong, Zhangjie Fu
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1831-1848, 2025, DOI:10.32604/cmes.2025.067658 - 31 August 2025
Abstract This article proposes an innovative adversarial attack method, AMA (Adaptive Multimodal Attack), which introduces an adaptive feedback mechanism by dynamically adjusting the perturbation strength. Specifically, AMA adjusts perturbation amplitude based on task complexity and optimizes the perturbation direction based on the gradient direction in real time to enhance attack efficiency. Experimental results demonstrate that AMA elevates attack success rates from approximately 78.95% to 89.56% on visual question answering and from 78.82% to 84.96% on visual reasoning tasks across representative vision-language benchmarks. These findings demonstrate AMA’s superior attack efficiency and reveal the vulnerability of current visual More >

View

1862

Download

1477

Open Access

ARTICLE

Singamaneni Krishnapriya^1,2,*, Sukhvinder Singh¹
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1849-1881, 2025, DOI:10.32604/cmes.2025.068326 - 31 August 2025
Abstract The explosive expansion of the Internet of Things (IoT) systems has increased the imperative to have strong and robust solutions to cyber Security, especially to curtail Distributed Denial of Service (DDoS) attacks, which can cripple critical infrastructure. The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabled mitigation to make intelligent, decentralized, and real-time DDoS countermeasures in an IoT network. The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms, which makes them more accurate… More >

Graphic Abstract

View

2476

Download

1695

Open Access

ARTICLE

Yezhi Qin^*, Qinglin Chen^*, Ying Wang, Yingqiang Cai
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1883-1906, 2025, DOI:10.32604/cmes.2025.066844 - 31 August 2025
（This article belongs to the Special Issue: Theoretical and Computational Modeling of Advanced Materials and Structures-II)
Abstract The penetration of ogival-nosed projectiles into ship plates represents a complex impact dynamics issue essential for analyzing structural failure mechanisms. Although stiffened plates are vital in ship construction, few studies have addressed the issue of model equivalence under penetration loading. This study employs numerical simulation to validate an experiment with an ogival-nosed projectile penetrating a Q345 steel plate. Four equivalent stiffened plate methods are proposed based on the area, flexural modulus, moment of inertia, and thickness. The results indicate that thickness equivalence (DM4) is unsuitable for penetration-loaded stiffened plates, except under low-speed, non-penetrating through impacts, More >

View

1567

Download

1466

Open Access

ARTICLE

Hang Zhou^*, Xiaojun Ding, Song Chen, Qijun Zhang
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1907-1933, 2025, DOI:10.32604/cmes.2025.069198 - 31 August 2025
（This article belongs to the Special Issue: Machine Learning-Assisted Structural Integrity Assessment and Design Optimization under Uncertainty)
Abstract Structural Reliability-Based Topology Optimization (RBTO), as an efficient design methodology, serves as a crucial means to ensure the development of modern engineering structures towards high performance, long service life, and high reliability. However, in practical design processes, topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions, which traditional methods often struggle accommodate. Therefore, this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization (DHPSO) algorithm. By leveraging… More >

View

8796

Download

7933

Open Access

ARTICLE

Pasqualino Corigliano^*, Giulia Palomba
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1935-1955, 2025, DOI:10.32604/cmes.2025.067094 - 31 August 2025
（This article belongs to the Special Issue: Advances in Fatigue Life Prediction and Reliability Assessment)
Abstract Experimental tests are essential for evaluating S-N curves and assessing the fatigue life of welded joints. However, in the case of complex geometries, experimental tests often cannot provide the necessary stress-strain data for specific materials and welded joints. Therefore, finite element (FE) analyses are frequently utilized to assess fatigue behavior in complex geometries and address the discontinuities induced by welding processes. In this study, the fatigue properties of titanium welded joints, produced using an innovative laser source and welded without the use of filler materials, were analyzed through numerical methods. Two different FE methods were… More >

View

1593

Download

1441

Open Access

ARTICLE

Yan Li¹, Bailong Sun², Tian Li^2,*, Weihua Zhang²
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1957-1970, 2025, DOI:10.32604/cmes.2025.069159 - 31 August 2025
Abstract High-speed maglev trains represent a key direction for the future development of rail transportation. As operating speeds increase, they face increasingly severe aerodynamic challenges. The streamlined aerodynamic shape of a maglev train is a critical factor influencing its aerodynamic performance, and optimizing its length plays a significant role in improving the overall aerodynamic characteristics of the train. In this study, a numerical simulation model of a high-speed maglev train was established based on computational fluid dynamics (CFD) to investigate the effects of streamline length on the aerodynamic performance of the train operating on an open… More >

View

8247

Download

5740

Open Access

ARTICLE

Asad Khan¹, Fathia Moh. Al Samman^2,*, Waqar Ul Hassan³, Mohammed M. A. Almazah⁴, A. Y. Al-Rezami⁵, Azmat Ullah Khan Niazi^3,*, Adnan Manzor⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1971-1999, 2025, DOI:10.32604/cmes.2025.068240 - 31 August 2025
（This article belongs to the Special Issue: Intelligent Control and Machine Learning for Renewable Energy Systems and Industries)
Abstract This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems, focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots. Each follower robot has unknown dynamics and control inputs, which expose it to the risks of both sensor and actuator attacks. The leader robot, described by a second-order, time-varying nonlinear model, transmits its position, velocity, and acceleration information to follower robots through a wireless connection. To handle the complex setup and communication among robots in the… More >

Graphic Abstract

View

2020

Download

1530

Open Access

ARTICLE

Anis Ben Ghorbal^1,*, Azedine Grine¹, Marwa M. Eid^2,3,*, El-Sayed M. El-Kenawy^4,5
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2001-2028, 2025, DOI:10.32604/cmes.2025.068212 - 31 August 2025
（This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
Abstract Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes. This study introduces a hybrid approach integrating Long Short-Term Memory (LSTM) networks with the Hybrid Greylag Goose and Particle Swarm Optimization (GGPSO) algorithm to optimize preterm birth classification using Electrohysterogram signals. The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings, capturing key physiological features such as contraction patterns, entropy, and statistical variations. Statistical analysis and feature selection methods are applied to identify the most relevant predictors and More >

Graphic Abstract

View

1881

Download

1467

Open Access

ARTICLE

Hadeel AlQadi¹, Fatimah M. Alghamdi², Hamada H. Hassan³, Mohamed E. Mead⁴, Ahmed Z. Afify^5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2029-2061, 2025, DOI:10.32604/cmes.2025.069801 - 31 August 2025
（This article belongs to the Special Issue: Frontiers in Parametric Survival Models: Incorporating Trigonometric Baseline Distributions, Machine Learning, and Beyond)
Abstract Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine. While the log-logistic distribution is popular for its simplicity and closed-form expressions, it often lacks the flexibility needed to capture complex hazard patterns. In this article, we propose a novel extension of the classical log-logistic distribution, termed the new exponential log-logistic (NExLL) distribution, designed to provide enhanced flexibility in modeling time-to-event data with complex failure behaviors. The NExLL model incorporates a new exponential generator to expand the shape adaptability of the baseline log-logistic distribution, allowing it to capture a… More >

View

8356

Download

7851

Open Access

ARTICLE

Zohra A. Esaadi¹, Rabab S. Gomaa¹, Beih S. El-Desouky¹, Ehab M. Almetwally², Alia M. Magar^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2063-2097, 2025, DOI:10.32604/cmes.2025.065604 - 31 August 2025
（This article belongs to the Special Issue: Frontiers in Parametric Survival Models: Incorporating Trigonometric Baseline Distributions, Machine Learning, and Beyond)
Abstract A new extended distribution called the Odd Exponential Generalized Exponential-Exponential distribution is proposed based on generalization of the odd generalized exponential family (OEGE-E). The statistical properties of the proposed distribution are derived. The study evaluates the accuracy of six estimation methods under complete samples. Estimation techniques include maximum likelihood, ordinary least squares, weighted least squares, maximum product of spacing, Cramer von Mises, and Anderson-Darling methods. Two methods of estimation for the involved parameters are considered based on progressively type II censored data (PTIIC). These methods are maximum likelihood and maximum product of spacing. The proposed More >

View

1889

Download

1566

Open Access

ARTICLE

Sijia Zhu¹, Yuhan Li², Prasanalakshmi Balaji^3,*, Akila Thiyagarajan³, Rajanikanth Aluvalu⁴, Zhe Liu^5,6,7,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2099-2121, 2025, DOI:10.32604/cmes.2025.068734 - 31 August 2025
（This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
Abstract While interval-valued picture fuzzy sets (IvPFSs) provide a powerful tool for modeling uncertainty and ambiguity in various fields, existing divergence measures for IvPFSs remain limited and often produce counterintuitive results. To address these shortcomings, this paper introduces two novel divergence measures for IvPFSs, inspired by the Jensen-Shannon divergence. The fundamental properties of the proposed measures—non-degeneracy, symmetry, triangular inequality, and boundedness—are rigorously proven. Comparative analyses with existing measures are conducted through specific cases and numerical examples, clearly demonstrating the advantages of our approach. Furthermore, we apply the new divergence measures to develop an enhanced interval-valued picture More >

View

8482

Download

1492

Open Access

ARTICLE

Ikhtesham Ullah¹, Muhammad Sajjad Ali Khan², Fawad Hussain¹, Madad Khan³, Kamran^4,*, Ioan-Lucian Popa^5,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2123-2164, 2025, DOI:10.32604/cmes.2025.064832 - 31 August 2025
（This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
Abstract Multi-criteria decision-making (MCDM) is essential for handling complex decision problems under uncertainty, especially in fields such as criminal justice, healthcare, and environmental management. Traditional fuzzy MCDM techniques have failed to deal with problems where uncertainty or vagueness is involved. To address this issue, we propose a novel framework that integrates group and overlap functions with Aczel-Alsina (AA) operational laws in the intuitionistic fuzzy set (IFS) environment. Overlap functions capture the degree to which two inputs share common features and are used to find how closely two values or criteria match in uncertain environments, while the… More >

View

1869

Download

1552

Open Access

ARTICLE

Junseo Lee¹, Bongsoo Jang¹, Umer Saeed^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2165-2189, 2025, DOI:10.32604/cmes.2025.069023 - 31 August 2025
（This article belongs to the Special Issue: Analytical and Numerical Solution of the Fractional Differential Equation)
Abstract In recent years, variable-order fractional partial differential equations have attracted growing interest due to their enhanced ability to model complex physical phenomena with memory and spatial heterogeneity. However, existing numerical methods often struggle with the computational challenges posed by such equations, especially in nonlinear, multi-term formulations. This study introduces two hybrid numerical methods—the Linear-Sine and Cosine (L1-CAS) and fast-CAS schemes—for solving linear and nonlinear multi-term Caputo variable-order (CVO) fractional partial differential equations. These methods combine CAS wavelet-based spatial discretization with L1 and fast algorithms in the time domain. A key feature of the approach is More >

View

8231

Download

5772

Open Access

ARTICLE

Benjamin Wacker¹, Jan Christian Schlüter^2,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2191-2229, 2025, DOI:10.32604/cmes.2025.067397 - 31 August 2025
（This article belongs to the Special Issue: Advances in Mathematical Modeling: Numerical Approaches and Simulation for Computational Biology)
Abstract In this work, we re-investigate a classical mathematical model of untreated HIV infection suggested by Kirschner and introduce a novel non-standard finite-difference method for its numerical solution. As our first contribution, we establish non-negativity, boundedness of some solution components, existence globally in time, and uniqueness on a time interval for an arbitrary for the time-continuous problem which extends known results of Kirschner’s model in the literature. As our second analytical result, we establish different equilibrium states and examine their stability properties in the time-continuous setting or discuss some numerical tools to evaluate this question. Our More >

View

1881

Download

1494

Open Access

ARTICLE

Yann-Horng Lin¹, Wen-Jer Chang^1,*, Yi-Chen Lee^2,*, Muhammad Shamrooz Aslam³, Cheung-Chieh Ku⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2231-2262, 2025, DOI:10.32604/cmes.2025.067464 - 31 August 2025
（This article belongs to the Special Issue: Computational Models and Applications of Multi-Agent Systems in Control Engineering and Information Science)
Abstract An Interval Type-2 (IT-2) fuzzy controller design approach is proposed in this research to simultaneously achieve multiple control objectives in Nonlinear Multi-Agent Systems (NMASs), including formation, containment, and collision avoidance. However, inherent nonlinearities and uncertainties present in practical control systems contribute to the challenge of achieving precise control performance. Based on the IT-2 Takagi-Sugeno Fuzzy Model (T-SFM), the fuzzy control approach can offer a more effective solution for NMASs facing uncertainties. Unlike existing control methods for NMASs, the Formation and Containment (F-and-C) control problem with collision avoidance capability under uncertainties based on the IT-2 T-SFM… More >

View

1574

Download

1481

Open Access

ARTICLE

Zeeshan Ali¹, Jihoon Moon², Saira Gillani³, Sitara Afzal⁴, Maryam Bukhari⁵, Seungmin Rho^6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2263-2286, 2025, DOI:10.32604/cmes.2025.067743 - 31 August 2025
（This article belongs to the Special Issue: Advanced Image Segmentation and Object Detection: Innovations, Challenges, and Applications)
Abstract Surveillance systems can take various forms, but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation. In the existing studies, several approaches have been suggested for gait recognition; nevertheless, the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions, clothing changes, walking speed, and varying camera viewpoints. Furthermore, most existing research focuses on single-person gait recognition; however, counting, tracking, detecting, and recognizing individuals in dual-subject settings with occlusions remains a challenging task. Therefore, this research proposed a… More >

View

1675

Download

1494

Open Access

ARTICLE

Shitong Ye¹, Bo Yang^2,*, Hao Quan³, Shan Liu⁴, Minyi Tang⁵, Jiawei Tian^6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2287-2306, 2025, DOI:10.32604/cmes.2025.066195 - 31 August 2025
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract Computer-aided surgical navigation technology helps and guides doctors to complete the operation smoothly, which simulates the whole surgical environment with computer technology, and then visualizes the whole operation link in three dimensions. At present, common image-guided surgical techniques such as computed tomography (CT) and X-ray imaging (X-ray) will cause radiation damage to the human body during the imaging process. To address this, we propose a novel Extended Kalman filter-based model that tracks the puncture needle-point using an ultrasound probe. To address the limitations of Kalman filtering methods based on position and velocity, our method of More >

View

2185

Download

1586

Open Access

ARTICLE

Gaurav Saxena¹, Kiran Napte², Neeraj Kumar Shukla^3,4, Sushma Parihar^5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2307-2323, 2025, DOI:10.32604/cmes.2025.067530 - 31 August 2025
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract The ill-posed character of haze or fog makes it difficult to remove from a single image. While most existing methods rely on a transmission map refined through depth estimation and assume a constant scattering coefficient, this assumption limits their effectiveness. In this paper, we propose an enhanced transmission map that incorporates spatially varying scattering information inherent in hazy images. To improve linearity, the model utilizes the ratio of the difference between intensity and saturation to their sum. Our approach also addresses critical issues such as edge preservation and color fidelity. In terms of qualitative as More >

View

1888

Download

1586

Open Access

ARTICLE

Fatma M. Talaat^1,2,*, Mohamed Salem¹, Mohamed Shehata^3,4,*, Warda M. Shaban⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2325-2358, 2025, DOI:10.32604/cmes.2025.067195 - 31 August 2025
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists. The rise in patient numbers has substantially elevated the data processing volume, making conventional methods both costly and inefficient. Recently, Artificial Intelligence (AI) has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame. Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors. This paper proposes a new model based on AI, called the Brain Tumor Detection (BTD)… More >

View

1979

Download

1590

Open Access

ARTICLE

Thaer Thaher^1,*, Muhammed Saffarini², Majdi Mafarja³, Abdulaziz Alashbi⁴, Abdul Hakim Mohamed⁵, Ayman A. El-Saleh⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2359-2394, 2025, DOI:10.32604/cmes.2025.065388 - 31 August 2025
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract Face detection is a critical component in modern security, surveillance, and human-computer interaction systems, with widespread applications in smartphones, biometric access control, and public monitoring. However, detecting faces with high levels of occlusion, such as those covered by masks, veils, or scarves, remains a significant challenge, as traditional models often fail to generalize under such conditions. This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients (HOG) and Canny edge detection with modern deep learning models. The goal is to improve face detection accuracy under occlusions. The… More >

View

1754

Download

1511

Open Access

ARTICLE

Abdullah G. M. Almansour^1,*, Faisal Alshomrani², Abdulaziz T. M. Almutairi³, Easa Alalwany⁴, Mohammed S. Alshuhri¹, Hussein Alshaari⁵, Abdullah Alfahaid⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2395-2418, 2025, DOI:10.32604/cmes.2025.069661 - 31 August 2025
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract The early and precise identification of Alzheimer’s Disease (AD) continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases. This study presents a novel Deformable Attention Vision Transformer (DA-ViT) architecture that integrates deformable Multi-Head Self-Attention (MHSA) with a Multi-Layer Perceptron (MLP) block for efficient classification of Alzheimer’s disease (AD) using Magnetic resonance imaging (MRI) scans. In contrast to traditional vision transformers, our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions, markedly diminishing processing demands while improving localized feature representation. DA-ViT contains only More >

View

8281

Download

7893

Open Access

ARTICLE

Khalil Ibrahim Lairedj¹, Zouaoui Chama¹, Amina Bagdaoui¹, Samia Larguech², Younes Menni^3,4,*, Nidhal Becheikh⁵, Lioua Kolsi^6,*, Badr M. Alshammari⁷
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2419-2443, 2025, DOI:10.32604/cmes.2025.069396 - 31 August 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 Brain tumor segmentation from Magnetic Resonance Imaging (MRI) supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans, making it a crucial yet challenging task. Supervised models such as 3D U-Net perform well in this domain, but their accuracy significantly improves with appropriate preprocessing. This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model (GGMM) to T1 contrast-enhanced MRI scans from the BraTS 2020 dataset. The Expectation-Maximization (EM) algorithm is employed to estimate parameters for four tissue classes, generating a More >

View

8451

Download

7917

Open Access

ARTICLE

Essam H. Houssein^1,*, Eman Saber¹, Nagwan Abdel Samee²
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2445-2480, 2025, DOI:10.32604/cmes.2025.069184 - 31 August 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 The diagnosis of Dry Eye Disease (DED), however, usually depends on clinical information and complex, high-dimensional datasets. To improve the performance of classification models, this paper proposes a Computer Aided Design (CAD) system that presents a new method for DED classification called (IAOO-PSO), which is a powerful Feature Selection technique (FS) that integrates with Opposition-Based Learning (OBL) and Particle Swarm Optimization (PSO). We improve the speed of convergence with the PSO algorithm and the exploration with the IAOO algorithm. The IAOO is demonstrated to possess superior global optimization capabilities, as validated on the IEEE Congress on More >

View

8288

Download

5852

Open Access

ARTICLE

Sami Alrabie^#,*, Ahmed Barnawi^#
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2481-2519, 2025, DOI:10.32604/cmes.2025.067977 - 31 August 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 In the effort to enhance cardiovascular diagnostics, deep learning-based heart sound classification presents a promising solution. This research introduces a novel preprocessing method: iterative k-means clustering combined with silhouette score analysis, aimed at downsampling. This approach ensures optimal cluster formation and improves data quality for deep learning models. The process involves applying k-means clustering to the dataset, calculating the average silhouette score for each cluster, and selecting the cluster with the highest score. We evaluated this method using 10-fold cross-validation across various transfer learning models from different families and architectures. The evaluation was conducted on… More >

View

1899

Download

1564

Open Access

ARTICLE

Phu Tran Tin¹, Minh-Sang Van Nguyen², Quy-Anh Bui¹, Agbotiname Lucky Imoize³, Byung-Seo Kim^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2521-2543, 2025, DOI:10.32604/cmes.2025.066669 - 31 August 2025
（This article belongs to the Special Issue: Artificial Intelligence for 6G Wireless Networks)
Abstract Digital content such as games, extended reality (XR), and movies has been widely and easily distributed over wireless networks. As a result, unauthorized access, copyright infringement by third parties or eavesdroppers, and cyberattacks over these networks have become pressing concerns. Therefore, protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention. The Intelligent Reflecting Surface (IRS) is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment. This study investigates the security performance of an uplink Non-Orthogonal Multiple Access (NOMA)… More >

View

2182

Download

1564

Open Access

ARTICLE

Zesheng Xi^1,2,#, Chuan He^1,3,#, Yunfan Wang^1,3,#, Bo Zhang^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2545-2565, 2025, DOI:10.32604/cmes.2025.066822 - 31 August 2025
（This article belongs to the Special Issue: Computer Modeling for Future Communications and Networks)
Abstract The increasing reliance on interconnected Internet of Things (IoT) devices has amplified the demand for robust anonymization strategies to protect device identities and ensure secure communication. However, traditional anonymization methods for IoT networks often rely on static identity models, making them vulnerable to inference attacks through long-term observation. Moreover, these methods tend to sacrifice data availability to protect privacy, limiting their practicality in real-world applications. To overcome these limitations, we propose a dynamic device identity anonymization framework using Moving Target Defense (MTD) principles implemented via Software-Defined Networking (SDN). In our model, the SDN controller periodically… More >

View

1618

Download

1432

Open Access

ARTICLE

Shaocong Feng, Baojiang Cui^*, Shengjia Chang, Meiyi Jiang
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2567-2590, 2025, DOI:10.32604/cmes.2025.067326 - 31 August 2025
（This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)
Abstract Service-Based Architecture (SBA) of 5G network introduces novel communication technology and advanced features, while simultaneously presenting new security requirements and challenges. Commercial 5G Core (5GC) networks are highly secure closed systems with interfaces defined through the 3rd Generation Partnership Project (3GPP) specifications to fulfill communication requirements. However, the 5GC boundary, especially the access domain, faces diverse security threats due to the availability of open-source cellular software suites and Software Defined Radio (SDR) devices. Therefore, we systematically summarize security threats targeting the N2 interfaces at the 5GC boundary, which are categorized as Illegal Registration, Protocol attack,… More >

View

2325

Download

1711

Open Access

ARTICLE

Tamara Zhukabayeva^1,2,*, Vasily Desnitsky³, Assel Abdildayeva^1,4
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2591-2625, 2025, DOI:10.32604/cmes.2025.067142 - 31 August 2025
（This article belongs to the Special Issue: Emerging Technologies in Information Security )
Abstract Wireless Sensor Networks (WSN) have gained significant attention over recent years due to their extensive applications in various domains such as environmental monitoring, healthcare systems, industrial automation, and smart cities. However, such networks are inherently vulnerable to different types of attacks because they operate in open environments with limited resources and constrained communication capabilities. The paper addresses challenges related to modeling and analysis of wireless sensor networks and their susceptibility to attacks. Its objective is to create versatile modeling tools capable of detecting attacks against network devices and identifying anomalies caused either by legitimate user… More >

View

2078

Download

1658

Open Access

ARTICLE

Madini O. Alassafi, Syed Hamid Hasan^*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2627-2645, 2025, DOI:10.32604/cmes.2025.064926 - 31 August 2025
（This article belongs to the Special Issue: Emerging Technologies in Information Security )
Abstract Over the past few years, Malware attacks have become more and more widespread, posing threats to digital assets throughout the world. Although numerous methods have been developed to detect malicious attacks, these malware detection techniques need to be more efficient in detecting new and progressively sophisticated variants of malware. Therefore, the development of more advanced and accurate techniques is necessary for malware detection. This paper introduces a comprehensive Dual-Channel Attention Deep Bidirectional Long Short-Term Memory (DCA-DBiLSTM) model for malware detection and risk mitigation. The Dual Channel Attention (DCA) mechanism improves the model’s capability to concentrate… More >

View

1785

Download

1472

Open Access

ARTICLE

Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal², Heba G. Mohamed³, Muhammad Sheraz⁴, Teong Chee Chuah^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2647-2681, 2025, DOI:10.32604/cmes.2025.068849 - 31 August 2025
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract The Internet of Things (IoT) ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027, operating in distributed networks with resource limitations and diverse system architectures. The current conventional intrusion detection systems (IDS) face scalability problems and trust-related issues, but blockchain-based solutions face limitations because of their low transaction throughput (Bitcoin: 7 TPS (Transactions Per Second), Ethereum: 15–30 TPS) and high latency. The research introduces MBID (Multi-Tier Blockchain Intrusion Detection) as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection, which… More >

View

8458

Download

1491

Open Access

CORRECTION

Zhuoqun Xia¹, Hangyu Hu¹, Wenjing Li^2,3, Qisheng Jiang¹, Lan Pu¹, Yicong Shu¹, Arun Kumar Sangaiah^4,5,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2683-2683, 2025, DOI:10.32604/cmes.2025.069871 - 31 August 2025
（This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
Abstract This article has no abstract. More >

View

8034

Download

7825