Arvind Prasad^1,*, Ibrahim Aljubayri², Mohammad Zubair Khan^3,*, Abdulfattah Noorwali⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082021 - 30 June 2026

Abstract Intrusion detection in large-scale IoT deployments becomes particularly challenging during ongoing attack scenarios, where malicious traffic may temporarily dominate benign traffic. In such conditions, streaming network data exhibits severe class imbalance in favor of attack traffic, while device behavior remains heterogeneous, non-identically distributed (non-IID), and temporally evolving. Within federated learning environments, this imbalance can destabilize early aggregation rounds, dominant attack gradients bias the global model, distort decision boundaries, and degrade reliable discrimination of residual benign behavior. Since the server has no access to raw data, these effects can persist across communication rounds if not addressed… More >

Shafidah Shafian^1,*, Azlan Ismail^2,3

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080623 - 15 June 2026

Abstract Organic solar cells (OSCs) have progressed rapidly in recent years, driven by advances in donor polymers, non-fullerene acceptors, and increasingly complex binary and multicomponent blend architectures. Despite these achievements, device performance remains governed by strongly coupled molecular, morphological, and processing variables, making materials optimization inherently multidimensional and difficult to navigate using conventional trial-and-error approaches. The growing availability of experimental data and computational descriptors has therefore encouraged the integration of machine learning (ML) techniques into OSC research as a complementary strategy for accelerating materials discovery and device optimization. Among the available ML strategies, ensemble learning has… More >

Zafer Serin^1,*, Cihan Karakuzu², Uğur Yüzgeç²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079254 - 27 May 2026

Abstract This study proposes SWAGE-3D (Spectral Wasserstein Attention Generative Ensemble), an enhanced 3D-VAE-GAN framework for single-view 3D object reconstruction using voxel-based representations. The proposed model integrates RGB-D encoding, Wasserstein adversarial learning with hybrid Lipschitz regularization, and a self-attention–augmented generator to improve structural coherence and training stability. By combining variational latent modeling with stabilized Wasserstein optimization, the framework aims to address common challenges in 3D generative modeling, including mode collapse, unstable convergence, and insufficient global consistency. The encoder employs a depth-aware feature extraction strategy, while the discriminator utilizes a hybrid spectral normalization and gradient penalty mechanism to More > Graphic Abstract

Menahil Rahman¹, Waqas Ishtiaq², Amerah Alabrah^3,*, Arif Mehmood⁴, Rana Faraz Ahmed⁴, Iqra Khalid⁵, Farhan Amin^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078549 - 27 May 2026

Abstract Access to safe drinking water is a fundamental determinant of global health. The presence of contaminated water affects the citizens’ health. Per- and polyfluoroalkyl substances (PFAS) are often referred to as forever chemicals. They pose a persistent and growing threat to drinking water. In the literature, machine learning methods are used to identify the forever chemicals in water. However, traditional methods are not efficient and scalable. Thus, to solve this issue. This study develops a large-scale machine-learning framework for PFAS risk screening in US public water systems. The proposed framework incorporates data ingestion, preprocessing, and More >

Rafael de Oliveira Silva^1,2,*, Roberto Outa³, Fábio Roberto Chavarette⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080607 - 27 April 2026

Abstract The reliable detection of cracks in engineering materials remains a fundamental challenge in nondestructive testing, especially in applications that require automated inspection, reduced instrumentation costs, and robustness under noisy operational conditions. Traditional nondestructive evaluation techniques often rely on complex sensing setups or expert-dependent interpretation, which can limit scalability and real-time applicability. In this context, this study addresses the scientific problem of achieving reliable and automated crack detection using simplified sensing architectures combined with intelligent data-driven analysis. This work proposes an intelligent signal classification framework for crack detection in polymeric materials based on machine learning and… More >

Sultan Kahla¹, Zuping Zhang^1,*, Majed Alsafyani², Ahmed Emara^3,*, Mohammod Abdullah Bin Hossain⁴, Abdulwahab Osman Sheikhdon¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076402 - 09 April 2026

Abstract The effective diagnosis and treatment planning require the correct classification of the cerebral neoplasia, such as glioma, meningioma, and pituitary tumors. The recent developments in the deep learning field have made a significant contribution to the field of image analysis in medicine; however, Vision Transformers (ViTs) have achieved good results but are computationally complex. This paper presents NeuroTriad-ViT, a proprietary large-scale Vision Transformer of 235 million parameters, which is represented as a high-performance teacher model to classify brain tumors. Knowledge distillation is applied in an attempt to transfer the representations that the teacher learned to… More >

Hatim Madkhali^1,2,*, Abdullah Sheneamer², Linh Nguyen³, Gnana Bharathy¹, Ritu Chauhan⁴, Mukesh Prasad^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074444 - 09 April 2026

Abstract Consumer electronics, with 62 million tons of electronic waste (e-waste) generated in 2022 and e-waste expected to grow to 82 million tons annually by 2030, pose critical challenges when it comes to national infrastructure and circular economy policies. This paper compares forecasting approaches using sparse panel data for 32 European countries (2005–2018, Eurostat/Waste Electrical and Electronic Equipment (WEEE) Directive), focusing on leakage-safe prospective validation to guarantee true predictive performance. We make one-step-ahead predictions with conservative features (primarily lagged values) to account for temporal autocorrelation but with reduced multicollinearity (Variance Inflation Factor (VIF) ≈ 1.0). Cross-paradigm comparisons… More >

Daniyal Asif^1,*, Nabil Kerdid², Muhammad Shoaib Arif³, Mairaj Bibi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077334 - 30 March 2026

Abstract Ovarian cancer (OC) is one of the leading causes of death related to gynecological cancer, with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers. Machine learning (ML) has the potential to process complex datasets and support decision-making in OC diagnosis. Nevertheless, traditional ML models tend to be biased, overfitting, noisy, and less generalized. Moreover, their black-box nature reduces interpretability and limits their practical clinical applicability. In this study, we introduce an explainable ensemble learning (EL) model, TreeX-Stack, based on a stacking architecture that employs tree-based learners such as Decision… More >

Faisal Albalwy^1,*, Muhannad Almohaimeed²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075098 - 12 March 2026

Abstract Intrusion detection in Internet of Things (IoT) environments presents challenges due to heterogeneous devices, diverse attack vectors, and highly imbalanced datasets. Existing research on the ToN-IoT dataset has largely emphasized binary classification and single-model pipelines, which often show strong performance but limited generalizability, probabilistic reliability, and operational interpretability. This study proposes a stacked ensemble deep learning framework that integrates random forest, extreme gradient boosting, and a deep neural network as base learners, with CatBoost as the meta-learner. On the ToN-IoT Linux process dataset, the model achieved near-perfect discrimination (macro area under the curve = 0.998),… More >

Long Yu^1,*, Xianghua Pan², Rui Sun¹, Yuan Li³, Wenjia Hao⁴

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.070023 - 27 February 2026

Abstract Distribution transformers play a vital role in power distribution systems, and their reliable operation is crucial for grid stability. This study presents a simulation-based framework for active fault diagnosis and early warning of distribution transformers, integrating Sample Ensemble Learning (SEL) with a Self-Optimizing Support Vector Machine (SO-SVM). The SEL technique enhances data diversity and mitigates class imbalance, while SO-SVM adaptively tunes its hyperparameters to improve classification accuracy. A comprehensive transformer model was developed in MATLAB/Simulink to simulate diverse fault scenarios, including inter-turn winding faults, core saturation, and thermal aging. Feature vectors were extracted from voltage,… More >