Open Access
ARTICLE
Nedaa Almansour1,2,*, Azizi Abdullah2, Dheeb Albashish3,4, Shahnorbanun Sahran2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086450
Abstract Data augmentation (DA) techniques are widely used in convolutional neural networks (CNNs) to artificially expand the size of training datasets. This is particularly true for medical imaging tasks, such as Diabetic Retinopathy (DR) image classification, where the training data are often limited and imbalanced. Various DA techniques are utilized in CNN models, including horizontal and vertical flipping, rotation, and zoom. Combining distinct methods increases the diversity of the produced images and allows the CNNs to handle the complex details in the images. Manually designed or heuristically selected augmentation combinations may generate redundant or highly similar… More >
Open Access
ARTICLE
Hashem Al-Madwami1,2, Amira Abo Kaf 3, Haibin Yin1,4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086319
Abstract A mechanics-based analytical framework is developed for estimating the steady bending force and shaft torque in central-axis bending of reinforcing bars (RBs). Analytical expressions are derived for the sectional bending moment and are subsequently linked to the machine-level force and torque through the roller-system load-transfer geometry. Three constitutive descriptions are considered, namely elastic-perfectly plastic, bilinear hardening, and power-law hardening, to examine the influence of post-yield material response on bending-demand estimation. The analytical formulations are assessed using a section-level pure-bending finite element model, a process-level three-dimensional finite element model with tool-bar contact, and reported smooth round-bar… More >
Open Access
ARTICLE
Abdelouahid Derhab1,*, Adlen Kerboua2, Noureddine Seddari3,4, Anis Haniche5, Mohammad Mehedi Hassan6
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086180
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract Machine learning has radically transformed network security, enabling intrusion detection systems capable of identifying malicious traffic with near-perfect accuracy on standard benchmarks. However, these systems remain critically vulnerable to adversarial examples—subtly manipulated inputs designed to escape detection—where performance can severely drop under minimal perturbation. This paper introduces the Hierarchical Adversarially-Driven Escalation System (
Open Access
ARTICLE
Hai-Xiang Wang1, Chu-Xiang Li2, Zi-Jia Wang2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086077
（This article belongs to the Special Issue: Advances in Computational Intelligence for Complex Systems)
Abstract Evolutionary multitasking optimization (EMTO) is an emerging research direction in evolutionary computation (EC), with its core objective being the collaborative solution of multiple problems through inter-task knowledge transfer (KT). In classical EMTO algorithms, KT typically relies on the direct exchange or crossover of individuals between populations. However, such transfer strategies often follow singular rules or direct transplantation, which struggle to adequately adapt to the dynamically evolving distributional differences between tasks, and may lead to inefficient transfer or even negative transfer. To tackle this issue, this study presents HKTMTO, a multitask differential evolution algorithm built upon More >
Open Access
ARTICLE
Jasmine Batra1, Kiranbir Kaur1, Fuad Ali Mohammed Al-Yarimi2, Abdulrahman Mohammed Alamoudi3, Salil Bharany4, Ateeq Ur Rehman5,*, Jaeyoung Choi5,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.086048
（This article belongs to the Special Issue: Digital Twin-Enabled Intelligent Transportation Systems: Computational Modeling, AI Integration, and Smart Mobility Applications)
Abstract Flying Ad Hoc Networks (FANETs) are emerging as a key enabler for intelligent transportation systems, smart aerial mobility, disaster response, surveillance, and environmental monitoring. However, their highly dynamic topology, rapid node mobility, intermittent connectivity, and limited energy resources pose major challenges for reliable routing. Existing routing protocols largely depend on instantaneous network information and lack predictive intelligence, leading to unstable links, increased overhead, and degraded performance in dynamic environments. To address these issues, this study proposes a Digital Twin-driven Trust-Aware PSO-based routing framework (DT-TAPSO) for UAV-assisted smart mobility and disaster-aware FANETs. The framework employs a… More >
Open Access
ARTICLE
Muhammad Shamrooz Aslam1,#, Wen-Jer Chang2,*, Hazrat Bilal3,*, Vyacheslav Gulvanskii4, Dmitrii Perevertaylo4, Dmitrii Kaplun1,4,5, Muhammad Hashim Bukhari6, Muhammad Aamir Aman7,#,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084958
Abstract In a multi-agent system, platoon vehicles receive a huge collection regarding autonomous models coordinating and their actions to improve traffic flow, lower fuel consumption, and boost safety. This paper examines the distributed consensus control problem for heterogeneous multi–agent systems (MASs) containing both first-order and second-order agents, under constrained network communication resources. Secondly, a novel event–triggered approach is proposed to tackle the problems of information transmission restrictions and bandwidth contention. Unlike conventional state-independent triggering methods, the proposed trigger condition depends on both the agent’s own state update error and the information mismatches between neighboring agents, enabling… More >
Open Access
ARTICLE
Areeba Gul1, Muhammad Ramzan1, Romana Aziz2,*, Qaiser Abbas3, Ala Saleh Alluhaidan2, Summair Raza1, Mahwish Ilyas4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.084222
Abstract Gastrointestinal diseases (GI) are serious diseases that affect people of all ages. Early and accurate diagnosis helps reduce complications and the subsequent impact on patients. Precise diagnoses by endoscopy are important for reducing complications and mortality rates. Manual interpretation of endoscopic images is time-consuming, highly dependent on the specialist’s clinical judgment, and subject to variability in multi-class classification tasks. To overcome these limitations, this study proposes GastroNetV4, an explainable hybrid deep learning model for multi-class classification of gastrointestinal diseases and a urinary tract-related class included in the endoscopic dataset used in this study.GastroNetV4 employs an… More >
Open Access
ARTICLE
Chia-Nan Wang1, Tsei-Hsuan Chen2,*, Syuan-Yun Wang3,*, Chung-Nan Cheng2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085454
Abstract In email-centric healthcare environments, social engineering attacks increasingly exploit human psychology, organizational trust relationships, and persuasive communication strategies to bypass conventional cybersecurity defenses. While existing email security controls are effective at blocking many malicious messages, they remain vulnerable to whitelist-failure scenarios in which compromised or seemingly legitimate communications evade detection and reach end users. Under limited analyst capacity and increasing alert volumes, the operational challenge is no longer solely identifying phishing emails but determining which socially engineered communications should be reviewed first. To address this problem, this study proposes a governance-oriented human-factor risk prioritization framework… More >
Open Access
ARTICLE
Rasha Alyousef1, Amal S. Hassan2, Omar A. Saudi3, Ohud A. Alqasem4, Mohammed Elgarhy5,6,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085404
（This article belongs to the Special Issue: Computer Modeling in Statistics)
Abstract It is of utmost importance to develop probability models that can cope with asymmetry for an effective analysis of asymmetrical real-world data. In this context, the current paper proposes a new unit asymmetric probability distribution for the interval (0, 1). The sine unit inverse exponentiated Pareto probability distribution is developed through the application of the sine-G family of transformations to the unit inverse exponentiated Pareto probability distribution. The inherent flexibility of the proposed distribution makes it have high potential for practical applications in the analysis of asymmetry in real-life data sets. Explicit formulas for some… More >
Open Access
ARTICLE
Motab F. Alenezi1, Fahad M. Alotaibi1, Badraddin Alturki2, Ahmad J. Tayeb2, Abdulaziz A. Alsulami1,*, Abdullah Alhejaili1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085678
（This article belongs to the Special Issue: Emerging Technologies in Information Security: Modeling, Algorithms, and Applications)
Abstract Machine learning-based intrusion detection for Internet of Things (IoT) networks remains difficult because modern traffic is highly imbalanced and attack behaviors are heterogeneous. Evaluation pipelines can also overestimate performance when preprocessing is performed before train-test separation. We propose a family-aware hierarchical intrusion detection framework for attack-family prediction. The proposed approach first separates normal and attack traffic, then routes attack samples into empirically defined majority and minority attack-family branches, and finally performs branch-specific family classification. Within each cross-validation fold, training-label counts define the majority/minority routing branches, while scaling, weighting, model fitting, stage diagnostics, and metric computation… More >
Open Access
REVIEW
Shuaiqi Cheng1,2, Yuxi Chen2, Bo Yang1,*, Legend Zhang3, Junmin Lyu3, Guangyu Xu4, Chao Liu5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.085787
（This article belongs to the Special Issue: The Collection of the Latest Reviews on Advances and Challenges in AI)
Abstract Computed tomography (CT) is an essential medical imaging technique that produces high-resolution cross-sectional images, but delivers substantial radiation dose to patients. Sparse-view CT (SVCT) reduces radiation dose by decreasing projection views, but causes streak artifacts that degrade image quality. Deep learning has emerged as a powerful tool to address this challenge. Although prospective paired acquisitions for low-current/voltage CT may be constrained by radiation-dose management and clinical workflow considerations, SVCT provides a practical way to construct paired training data through retrospective angular downsampling of full-view projections. This survey provides a systematic review of deep learning–based SVCT, More >
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