Huihui Yang¹, Saif Ur Rehman Khan^2,*, Omair Bilal², Chao Chen^1,*, Ming Zhao²

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072148

Abstract Detecting Alzheimer’s disease is essential for patient care, as an accurate diagnosis influences treatment options. Classifying dementia from non-dementia in brain MRIs is challenging due to features such as hippocampal atrophy, while manual diagnosis is susceptible to error. Optimal computer-aided diagnosis (CAD) systems are essential for improving accuracy and reducing misclassification risks. This study proposes an optimized ensemble method (CEOE-Net) that initiates with the selection of pre-trained models, including DenseNet121, ResNet50V2, and ResNet152V2 for unique feature extraction. Each selected model is enhanced with the inclusion of a channel attention (CA) block to improve the feature… More >

Inga Telksniene¹, Tadas Telksnys², Romas Marcinkevičius³, Zenonas Navickas², Raimondas Čiegis¹, Minvydas Ragulskis^2,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072938

Abstract Fractional differential equations (FDEs) provide a powerful tool for modeling systems with memory and non-local effects, but understanding their underlying structure remains a significant challenge. While numerous numerical and semi-analytical methods exist to find solutions, new approaches are needed to analyze the intrinsic properties of the FDEs themselves. This paper introduces a novel computational framework for the structural analysis of FDEs involving iterated Caputo derivatives. The methodology is based on a transformation that recasts the original FDE into an equivalent higher-order form, represented as the sum of a closed-form, integer-order component and a residual fractional… More >

Kangjie Li, Wenjing Ye^*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072447

Abstract Recent progress in topology optimization (TO) has seen a growing integration of machine learning to accelerate computation. Among these, online learning stands out as a promising strategy for large-scale TO tasks, as it eliminates the need for pre-collected training datasets by updating surrogate models dynamically using intermediate optimization data. Stress-constrained lightweight design is an important class of problem with broad engineering relevance. Most existing frameworks use pixel or voxel-based representations and employ the finite element method (FEM) for analysis. The limited continuity across finite elements often compromises the accuracy of stress evaluation. To overcome this… More >

Bo Yang^1,2, Yu Zhou³, Jiawei Tian^4,*, Xiang Zhang², Fupei Guo², Shan Liu^5,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072031

Abstract Accurate calibration of surgical instruments and ultrasound probes is essential for achieving high precision in image guided minimally invasive procedures. However, existing methods typically treat the calibration of the needle tip and the ultrasound probe as two independent processes, lacking an integrated calibration mechanism, which often leads to cumulative errors and reduced spatial consistency. To address this challenge, we propose a joint calibration model that unifies the calibration of the surgical needle tip and the ultrasound probe within a single coordinate system. The method formulates the calibration process through a series of mathematical models and… More >

Fawad Zaman^1,#, Adeel Iqbal^2,#, Bakhtiar Ali¹, Abdul Khader Jilani Saudagar^3,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072638

Abstract Accurate estimation of the Direction-of-Arrival (DoA) of incident plane waves is essential for modern wireless communication, radar, sonar, and localization systems. Precise DoA information enables adaptive beamforming, spatial filtering, and interference mitigation by steering antenna array beams toward desired sources while suppressing unwanted signals. Traditional one-dimensional Uniform Linear Arrays (ULAs) are limited to elevation angle estimation due to geometric constraints, typically within the range . To capture full spatial characteristics in environments with multipath and angular spread, joint estimation of both elevation and azimuth angles becomes necessary. However, existing 2D and 3D array geometries often… More >

Hongrong Wang^1,2, Hang Geng^1,*, Jing Yuan¹, Wen Zhang², Hanmin Sheng¹, Qiuhua Wang³, Xinjian Li^4,5

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073513

Abstract Machine learning has emerged as a key approach in wildfire risk prediction research. However, in practical applications, the scarcity of data for specific regions often hinders model performance, with models trained on region-specific data struggling to generalize due to differences in data distributions. While traditional methods based on expert knowledge tend to generalize better across regions, they are limited in leveraging multi-source data effectively, resulting in suboptimal predictive accuracy. This paper addresses this challenge by exploring how accumulated domain expertise in wildfire prediction can reduce model reliance on large volumes of high-quality data. An active More >

Hongrong Wang^1,2, Xinjian Li^3,4, Xingjing She¹, Wenjian Ma^1,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072572

Abstract In modern complex systems, real-time regression prediction plays a vital role in performance evaluation and risk warning. Nevertheless, existing methods still face challenges in maintaining stability and predictive accuracy under complex conditions. To address these limitations, this study proposes an online prediction approach that integrates event tracking sensitivity analysis with machine learning. Specifically, a real-time event tracking sensitivity analysis method is employed to capture and quantify the impact of key events on system outputs. On this basis, a mutual-information–based self-extraction mechanism is introduced to construct prior weights, which are then incorporated into a LightGBM prediction More >

Božo Damjanović^*, Pejo Konjatić, Marko Katinić

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072256

Abstract Ensuring the structural integrity of piping systems is crucial in industrial operations to prevent catastrophic failures and minimize shutdown time. This study investigates a transportation-damaged pipe exposed to high-temperature conditions and cyclic loading, representing a realistic challenge in plant operation. The objective was to evaluate the service life and integrity assessment parameters of the damaged pipe, subjected to 22,000 operational cycles under two daily charge and discharge conditions. The flaw size in the damaged pipe was determined based on a failure assessment procedure, ensuring a conservative and reliable input. The damage was characterized as a… More >

Fei Wu¹, Ziyang Zeng^1,2, Kunliang Xie¹, Yuqiang Liu¹, Jiang Ding^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071256

Abstract The level set method (LSM) is renowned for producing smooth boundaries and clear geometric representations, facilitating integration with CAD environments. However, its inability to autonomously generate new holes during optimization makes the results highly dependent on the initial design. Although topological derivatives are often introduced to enable hole nucleation, their conversion into effective shape derivatives remains challenging, limiting topological evolution. To address this, a level set topology optimization method with autonomous hole formation (LSM-AHF) is proposed, integrating the material removal mechanism of the SIMP (Solid Isotropic Material with Penalization) method into the LSM framework. First,… More >

Meshari D. Alanazi¹, Gehan Elsayed^2,*, Turki M. Alanazi³, Anis Sahbani⁴, Amr Yousef^5,6

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.070726

Abstract Traffic congestion plays a significant role in intelligent transportation systems (ITS) due to rapid urbanization and increased vehicle concentration. The congestion is dependent on multiple factors, such as limited road occupancy and vehicle density. Therefore, the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment. Conventional prediction systems face difficulties in identifying highly congested areas, which leads to reduced prediction accuracy. The problem is addressed by integrating Graph Neural Networks (GNN) with the Lion Swarm Optimization (LSO) framework to tackle the congestion prediction problem. Initially, the traffic information is… More >