Pasquale De Luca^1,2,*, Livia Marcellino¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082664 - 30 June 2026

Abstract Osteosarcoma is the most common primary malignant bone tumor in pediatric populations. This work presents an extended Physics-Informed Neural Network framework that incorporates interferon-gamma (IFN-γ) as a fifth biological variable, complementing previous four-variable formulations with an explicit cytokine-mediated macrophage activation pathway. The model couples five biological fields with mechanical tissue response through Biot’s poroelastic theory over a two-dimensional domain. Four distinct initial macrophage distributions were investigated. Numerical stability was achieved across all scenarios, with total loss values between 0.056 and 0.158 and mechanical residuals below 3.2×10−5. The boundary-concentrated configuration yielded the lowest biological loss. More >

Jaffar Hussain¹, Tahira Nazir¹, Junaid Rashid^2,*, Jungeun Kim^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081617 - 30 June 2026

Abstract Early detection of diabetic retinopathy (DR), media haze (MH), optic disc cupping (ODC), and glaucoma is crucial for preventing vision loss. However, timely diagnosis is often constrained by limited specialist availability and high diagnostic costs. This study proposes a You Only Look Once (YOLO)-based deep learning (DL) framework for the automated classification of fundus images into disease-specific categories. We unified diverse annotations from the Retinal Fundus Multi-Disease image Dataset (RFMiD), RFMiD2.0, and the DR Fundus Image Dataset (DR-FID) by standardizing annotation files and class labels. A custom filtering module was used to isolate single-pathology cases,… More >

Anis Elgarduh¹, Anazida Zainal¹, Fuad A. Ghaleb², Sultan Noman Qasem^3,*, Abdullah M. Albarrak³, Faisal Saeed²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080862 - 30 June 2026

Abstract The rapid growth and increasing sophistication of malware pose significant challenges to traditional detection methods. Convolutional neural network (CNN)-based malware image classification methods have emerged as a promising approach by transforming binary files into visual representations and enabling automated feature extraction. To enhance discriminative learning, recent studies have incorporated attention mechanisms originally developed for natural image and natural language processing tasks. However, these mechanisms embed inductive biases that assume spatial coherence and visually salient semantics, assumptions that do not necessarily hold in malware image representations, where informative patterns may be subtle, structurally encoded, and globally… More > Graphic Abstract

Miguel A. Millán^1,*, Rubén Galindo², Fausto Molina-Gómez¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080219 - 30 June 2026

Abstract Low-density volcanic rocks have specific geomechanical properties that require complex laboratory tests and characterization that are not usually available in common geotechnical studies. A pyroclastic rock behaves at sufficiently “low” stress levels as if it were a conventional rock under the action of an external load, but when subjected to higher stresses, the bonds between its particles can break, leading to a sudden decrease in its volume and the reorganization of its particles, thus forming a more compact structure than the initial one. This process is known as “mechanical collapse” and involves a drastic change… More >

Yuzhen He¹, Zhiguo Qu^1,2,*, Le Sun¹

Journal of Quantum Computing, Vol.8, pp. 55-73, 2026, DOI:10.32604/jqc.2026.083993 - 26 June 2026

Abstract Quantum fuzzy neural networks (QFNNs) integrate fuzzy systems, neural networks, and quantum models, aiming to leverage their complementary strengths in handling uncertainty, parameter learning, and feature representation. However, a unified framework for effectively combining these three components remains lacking, and the existing literature reflects diverse and sometimes inconsistent modeling strategies. This paper provides a comprehensive review of the fundamental theories underlying QFNNs, including the core design principles and mathematical formulations, as well as the major categories of network architectures. Representative training strategies and typical application scenarios are also systematically examined. Furthermore, persistent issues in the More >

Xiaolan Li^1,2, Yanting Wang^1,2,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.076417 - 18 June 2026

Abstract To address the operational challenges of power systems with high renewable penetration, this research targets the non-stationarity and stochasticity of wind power. A novel hybrid framework for probabilistic forecasting and risk assessment is proposed. Initially, Empirical Mode Decomposition (EMD) adaptively decomposes the raw power signal into multi-scale Intrinsic Mode Functions (IMFs) and a residual trend, effectively segregating temporal features and reducing complexity. These components are then fused with historical data to form a comprehensive input. The core predictor is a Bidirectional Gated Recurrent Unit (BiGRU) network enhanced with a Temporal Attention (TA) mechanism. The BiGRU… More >

Danilo Greco^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082708 - 15 June 2026

Abstract The Enhanced Graph Neural Network Autoencoder (Enhanced GNN-AE), recently proposed for unsupervised cybersecurity monitoring in battery energy storage systems (BESSs), builds a multiscale k-nearest neighbour graph over measurement samples and learns compact latent representations via manifold-regularised training. Its spatial encoder, however, employs the original Graph Attention Network (GAT), which has been formally shown to compute a rank-1 attention function equivalent to graph convolutional networks on many graph structures. This work investigates whether replacing the GAT encoder with the strictly more expressive GATv2 formulation—which applies the attention vector after a joint, asymmetric linear transformation of source… More >

Pangwei Wang^1,*, Jie Wang¹, Zipeng Wang¹, Hangrui Dong², Li Wang¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080815 - 15 June 2026

Abstract Traffic holographic perception refers to the real-time, high-fidelity, and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors, including cameras, radars, and connected vehicle data. The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System (ITS). However, sensors are vulnerable to environmental interference, which can result in data loss at specific points or along arterial highways for certain periods, potentially undermining system safety and decision-making reliability. To address these challenges, a deep learning method based on Graph Convolutional Networks (GCN) and Gated… More >

Mohammed Shukur Alfaras^1,2,*, Oguz Karan³, Sefer Kurnaz¹, Ayca Kurnaz Turkben⁴

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079577 - 15 June 2026

Abstract Despite deep learning’s high precision in emotion identification, centralized training is associated with privacy and scalability concerns. The privacy-preserving federated learning model, Federated Hybrid-Optimized Emotion Recognition (Fed-HOER), introduced in this paper is an auto-tuning hyperparameters optimizer based on a hybrid Dung Beetle Optimizer-Fick’s Law Algorithm (DBO-FLA) optimizer. The global and local searches are optimized at two levels, and validation loss is minimized by 22%–24% without sharing raw data. The experiments on Extended Cohn–Kanade (CK+), Japanese Female Facial Expressions (JAFFE), and Karolinska Directed Emotional Faces (KDEF) exhibit a high generalization rate with a mean accuracy of More >

Li Chen¹, Fan Zhang^2,*, Guangwei Xie³, Yanzhao Gao¹, Xiaofeng Qi¹, Mingqian Sun²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078314 - 15 June 2026

Abstract Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery. Spiking Neural Networks (SNNs), benefiting from their implicit feedback-based and event-driven brain-inspired dynamics, offer a promising solution to alleviate the high energy consumption of conventional ANN-based detection models. However, existing SNN-based approaches for remote sensing object detection—particularly for small, arbitrarily rotated objects—are still in their infancy and suffer from a substantial performance gap compared with ANN counterparts. In this work, we draw inspiration from the hierarchical sparse perception mechanisms of biological vision and integrate dynamic receptive field modulation into… More >