Abdu Salam¹, Mohammad Abrar², Raja Waseem Anwer³, Farhan Amin^4,*, Faizan Ullah⁵, Isabel de la Torre^6,*, Gerardo Mendez Mezquita⁷, Henry Fabian Gongora⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2457-2479, 2025, DOI:10.32604/cmes.2025.072765 - 26 November 2025

Abstract Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations. Due to the increase in precision image-based diagnostic tools, driven by advancements in artificial intelligence (AI) and deep learning, there has been potential to improve diagnostic accuracy, especially with Magnetic Resonance Imaging (MRI). However, traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation. Thus, our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model. The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification. The proposed model… More >

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

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2535-2550, 2025, DOI:10.32604/cmes.2025.072638 - 26 November 2025

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 [0, π]. 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… More >

I Wayan Adi Juliawan Pawana^1,2, Vincent Abella², Jhury Kevin Lastre², Yongho Ko², Ilsun You^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2733-2760, 2025, DOI:10.32604/cmes.2025.072611 - 26 November 2025

Abstract Roaming in 5G networks enables seamless global mobility but also introduces significant security risks due to legacy protocol dependencies, uneven Security Edge Protection Proxy (SEPP) deployment, and the dynamic nature of inter-Public Land Mobile Network (inter-PLMN) signaling. Traditional rule-based defenses are inadequate for protecting cloud-native 5G core networks, particularly as roaming expands into enterprise and Internet of Things (IoT) domains. This work addresses these challenges by designing a scalable 5G Standalone testbed, generating the first intrusion detection dataset specifically tailored to roaming threats, and proposing a deep learning based intrusion detection framework for cloud-native environments.… More > Graphic Abstract

Su Jin Shin¹, Sang Uk Shin^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2787-2819, 2025, DOI:10.32604/cmes.2025.072602 - 26 November 2025

Abstract To reconstruct vehicle accidents, data from the time of the incident—such as pre-collision speed and collision point—is essential. This data is collected and generated through various sensors installed in the vehicle. However, it may contain sensitive information about the vehicle owner. Consequently, vehicle owners tend to be reluctant to provide their vehicle data due to concerns about personal information exposure. Therefore, extensive research has been conducted on secure vehicle data trading models. Existing models primarily utilize centralized approaches, leading to issues such as single points of failure, data leakage, and manipulation. To address these problems,… More >

Sidra Naseem¹, Rashid Jahangir^1,*, Nazik Alturki², Faheem Shehzad³, Muhammad Sami Ullah⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2481-2501, 2025, DOI:10.32604/cmes.2025.072575 - 26 November 2025

Abstract Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies. While deep learning models have significantly advanced medical image analysis, challenges such as imbalanced datasets and redundant features persist. This study proposes a novel framework that customizes two deep learning models, NasNetMobile and ResNet50, by incorporating bottleneck architectures, named as NasNeck and ResNeck, to enhance feature extraction. The feature vectors are fused into a combined vector, which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power. The optimized feature vector is then classified using artificial… More >

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

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2039-2055, 2025, DOI:10.32604/cmes.2025.072572 - 26 November 2025

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 >

Bin Zhang¹, En-Cheng Liou^2,*, Yi-Chih Tung³, Muhammad Usman^2,4, Chiung-An Chen^2,4, Chao-Shun Yang^2,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2551-2571, 2025, DOI:10.32604/cmes.2025.072471 - 26 November 2025

Abstract Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication (ISAC) systems, enabling precise navigation in environments where Global Positioning System (GPS) signals are unavailable. Existing methods, such as map-based navigation or site-specific fingerprinting, often require intensive data collection and lack generalization capability across different buildings, thereby limiting scalability. This study proposes a cross-site, map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge. The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes, facilitating accurate… More >

Hansam Cho¹, Seokho Moon¹, Sunhyeok Hwang¹, Seoung Bum Kim^1,*, Younghoon Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1941-1967, 2025, DOI:10.32604/cmes.2025.072455 - 26 November 2025

Abstract Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment. However, existing approaches face three significant limitations: (1) reliance on predefined mathematical models that often fail to capture equipment-specific degradation, (2) offline optimization methods that assume access to future data, and (3) the absence of component-level guidance. To address these challenges, we propose a data-driven framework for component-level decision-making. The framework leverages streaming sensor data to predict the remaining useful life (RUL) without relying on mathematical models, employs an online optimization algorithm suitable for practical settings, and, through More >

Kangjie Li, Wenjing Ye^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1665-1688, 2025, DOI:10.32604/cmes.2025.072447 - 26 November 2025

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 >

Tae-hyeon Yun¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2707-2731, 2025, DOI:10.32604/cmes.2025.072357 - 26 November 2025

Abstract The dynamic, heterogeneous nature of Edge computing in the Internet of Things (Edge-IoT) and Industrial IoT (IIoT) networks brings unique and evolving cybersecurity challenges. This study maps cyber threats in Edge-IoT/IIoT environments to the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) framework by MITRE and introduces a lightweight, data-driven scoring model that enables rapid identification and prioritization of attacks. Inspired by the Factor Analysis of Information Risk model, our proposed scoring model integrates four key metrics: Common Vulnerability Scoring System (CVSS)-based severity scoring, Cyber Kill Chain–based difficulty estimation, Deep Neural Networks-driven detection scoring, and frequency… More >