Nader Karmous¹, Wadii Jlassi¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4363-4393, 2025, DOI:10.32604/cmes.2025.074178 - 23 December 2025

Abstract This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments, built upon a novel, synthetic multi-vector dataset generated in a Mininet-Ryu testbed using real-time flow-based labeling. The proposed model is based on the XGBoost algorithm, optimized with Principal Component Analysis for dimensionality reduction, utilizing lightweight flow-level features extracted from OpenFlow statistics to classify attacks across critical IoT protocols including TCP, UDP, HTTP, MQTT, and CoAP. The model employs lightweight flow-level features extracted from OpenFlow statistics to ensure low computational overhead and fast processing. Performance was rigorously… More >

Damodharan Palaniappan¹, Tan Kuan Tak², K. Vijayan³, Balajee Maram⁴, Pravin R Kshirsagar⁵, Naim Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4107-4127, 2025, DOI:10.32604/cmes.2025.073387 - 23 December 2025

Abstract A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy (DR) stratification. A curated cohort of 1198 patients (2915 UWF_RI and 17,854 UWF_FA images) with strict registration quality supports training across three angiographic phases (initial, mid, final). The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature, and is evaluated using MAE, PSNR, SSIM, and MS-SSIM on held-out pairs. Quantitatively, the mid phase achieves the lowest MAE (98.76 ± 42.67), while SSIM remains high across phases. Expert review shows substantial agreement (Cohen’s More >

Mirza Murad Baig¹, Muhammad Rehan Faheem^2,*, Lal Khan^3,*, Hannan Adeel², Syed Asim Ali Shah⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2895-2935, 2025, DOI:10.32604/cmes.2025.073172 - 23 December 2025

Abstract With growing urban areas, the climate continues to change as a result of growing populations, and hence, the demand for better emergency response systems has become more important than ever. Human Behaviour Classification (HBC) systems have started to play a vital role by analysing data from different sources to detect signs of emergencies. These systems are being used in many critical areas like healthcare, public safety, and disaster management to improve response time and to prepare ahead of time. But detecting human behaviour in such stressful conditions is not simple; it often comes with noisy… More > Graphic Abstract

Mubashir Ali^1,2, Jingzhen Li¹, Zedong Nie^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4153-4177, 2025, DOI:10.32604/cmes.2025.073048 - 23 December 2025

Abstract Diabetes imposes a substantial burden on global healthcare systems. Worldwide, nearly half of individuals with diabetes remain undiagnosed, while conventional diagnostic techniques are often invasive, painful, and expensive. In this study, we propose a noninvasive approach for diabetes detection using photoplethysmography (PPG), which is widely integrated into modern wearable devices. First, we derived velocity plethysmography (VPG) and acceleration plethysmography (APG) signals from PPG to construct multi-channel waveform representations. Second, we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal, statistical, and nonlinear features with recursive feature elimination and deep feature extraction using… More >

Jeongsu Park¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3913-3948, 2025, DOI:10.32604/cmes.2025.072261 - 23 December 2025

Abstract Time series anomaly detection is critical in domains such as manufacturing, finance, and cybersecurity. Recent generative AI models, particularly Transformer- and Autoencoder-based architectures, show strong accuracy but their robustness under noisy conditions is less understood. This study evaluates three representative models—AnomalyTransformer, TranAD, and USAD—on the Server Machine Dataset (SMD) and cross-domain benchmarks including the Soil Moisture Active Passive (SMAP) dataset, the Mars Science Laboratory (MSL) dataset, and the Secure Water Treatment (SWaT) testbed. Seven noise settings (five canonical, two mixed) at multiple intensities are tested under fixed clean-data training, with variations in window, stride, and More > Graphic Abstract

Ke Li¹, Bendong Liu¹, Yulong Han², Yafeng Zhang³, Chunqi Yang¹, Dawei Yin², Yazhou Zhang³, Wantao Ding^4,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2779-2793, 2025, DOI:10.32604/fdmp.2025.072671 - 01 December 2025

Abstract Traditional cement-based slurries are often constrained by excessive cement consumption, prolonged setting times, and limited controllability, which hinder their broader engineering applications. To overcome these challenges, this study focuses on optimizing ordinary cement-based slurry through the incorporation of targeted additives and rational adjustment of mix proportions, with the aim of developing a rapid-setting, early-strength cementitious system. In particular, a series of comparative and orthogonal experiments were conducted to systematically examine the evolution of the slurry’s macroscopic properties. In addition, the response surface methodology (RSM) was introduced to reveal the interaction mechanisms among key parameters, thereby… More >

Zhenzhen Shen^1,2, Kang Yang², Dengfeng Wei², Quansheng Liang², Zhenpeng Ma², Hong Wang², Keyu Wang², Chunwei Zhang², Xiaohu Yang^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2741-2760, 2025, DOI:10.32604/fdmp.2025.070395 - 01 December 2025

Abstract The rapid prediction of seepage mass flow in soil is essential for understanding fluid transport in porous media. This study proposes a new method for fast prediction of soil seepage mass flow by combining mesoscopic modeling with deep learning. Porous media structures were generated using the Quartet Structure Generation Set (QSGS) method, and a mesoscopic-scale seepage calculation model was applied to compute flow rates. These results were then used to train a deep learning model for rapid prediction. The analysis shows that larger average pore diameters lead to higher internal flow velocities and mass flow More >

Chaonan Xin^*, Keqing Xu

Journal of Cyber Security, Vol.7, pp. 483-503, 2025, DOI:10.32604/jcs.2025.071627 - 28 November 2025

Abstract Intrusion Detection Systems (IDS) have achieved high accuracy on benchmark datasets, yet models often fail to generalize across different network environments. In this paper, we propose Transformer-IDS, a transformer-based network intrusion detection model designed for cross-dataset generalization. The model incorporates a classification token, multi-head self-attention, and embedding layers to learn versatile features, and it introduces a calibration module and an AUC-oriented optimization objective to improve reliability and ranking performance. We evaluate Transformer-IDS on three prominent datasets (NSL-KDD, UNSW-NB15, CIC-IDS2017) in both within-dataset and cross-dataset scenarios. Results demonstrate that while conventional deep IDS models (e.g., CNN-LSTM More >

Yang Xu^1,#, Yaqi Zhang^2,#, Meijiao Zhu³, Pengcheng Xue⁴, Siyu Ma¹, Di Yu¹, Liang Hu¹, Yuxi Zhang¹, Wei Peng¹, Jirong Qi¹, Xuyun Wen⁴, Ming Yang³, Xuming Mo^1,2,5,*

Congenital Heart Disease, Vol.20, No.5, pp. 559-570, 2025, DOI:10.32604/chd.2025.072242 - 30 November 2025

Abstract Background: The life-course management of children with tetralogy of Fallot (TOF) has focused on demonstrating brain structural alterations, developmental trajectories, and cognition-related changes that unfold over time. Methods: We introduce an magnetic resonance imaging (MRI) dataset comprising TOF children who underwent brain MRI scanning and cross-sectional neurocognitive follow-up. The dataset includes brain three-dimensional T1-weighted imaging (3D-T1WI), three-dimensional T2-weighted imaging (3D-T2WI), and neurodevelopmental evaluations using the Wechsler Preschool and Primary Scale of Intelligence–Fourth Edition (WPPSI-IV). Results: Thirty-one children with TOF (age range: 4–33 months; 18 males) were recruited and completed corrective surgery at the Children’s Hospital of Nanjing More >

Zhe Liu^1,2,3,*, Sijia Zhu⁴, Yulong Huang^1,*, Tapan Senapati^5,6,7, Xiangyu Li⁸, Wulfran Fendzi Mbasso⁹, Himanshu Dhumras¹⁰, Mehdi Hosseinzadeh^11,12,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2157-2188, 2025, DOI:10.32604/cmes.2025.072352 - 26 November 2025

Abstract Uncertainty and ambiguity are pervasive in real-world intelligent systems, necessitating advanced mathematical frameworks for effective modeling and analysis. Fermatean fuzzy sets (FFSs), as a recent extension of classical fuzzy theory, provide enhanced flexibility for representing complex uncertainty. In this paper, we propose a unified parametric divergence operator for FFSs, which comprehensively captures the interplay among membership, non-membership, and hesitation degrees. The proposed operator is rigorously analyzed with respect to key mathematical properties, including non-negativity, non-degeneracy, and symmetry. Notably, several well-known divergence operators, such as Jensen-Shannon divergence, Hellinger distance, and χ²-divergence, are shown to be special cases More >