Manish Kewalramani¹, Refka Ghodhbani², Arsalan Mahmoodzadeh^3,*, Abdulaziz Alghamdi⁴, Faten Khalid Karim⁵, Abed Alanazi⁶, Abdullah Alqahtani⁶, Shtwai Alsubai⁶, Mounir Ltifi⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084696 - 30 June 2026

Abstract Concrete manufacturing consumes vast quantities of natural resources and contributes significantly to environmental degradation and carbon emissions. Therefore, integrating recycled waste substances into concrete has become a crucial approach to fostering eco-friendly building practices and supporting circular economy concepts. This study investigates the potential of incorporating recycled aluminum beverage can crumbs (RABCC) as a partial replacement for natural coarse aggregates (NCA) in concrete mixtures, focusing on its impact on compressive strength (CS) and the feasibility of its application in structural concrete. A comprehensive experimental program was conducted to assess the mechanical properties of concrete with… More >

Wenbing Zhao^1,*, Pan Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084481 - 30 June 2026

Abstract This article has no abstract. More >

Salman Khan^*, Mai Alzamel^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084282 - 30 June 2026

Abstract Traditional threat detection has proven ineffective in large-scale, moving data in the era of ever-more complex adversarial techniques and interconnected systems. The challenge becomes even more complex when high-volume, unstructured data continuously streams from social media platforms, requiring them to process the data efficiently and intelligently to provide timely security insights. Considering the big data security, the present study presents a scalable deep-learning-based system for real-time cyber threat detection, which has been developed and validated especially for distributed big data processing environments. A hybrid embedding approach that combines Word2Vec and Iterated Dilated Convolutional Neural Networks… More > Graphic Abstract

Siamak Talatahari^*, Amin Beheshti

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084097 - 30 June 2026

Abstract This article has no abstract. More >

Ulysses Lam^1,*, Jin-Hee Cho², Hyuk Lim³, Terrence Moore⁴, Frederica Free-Nelson⁴, Hyunjae Kang¹, Dan Dongseong Kim¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084062 - 30 June 2026

Abstract Autonomous vehicles are potentially more vulnerable to cyber-attacks compared to traditional human-driven ones, as they employ electronic sensors to enable self-driving. Cybersecurity for autonomous vehicles will be crucial in the near future. However, intrusion detection systems (IDSes) for vehicles are still in the early stages. Many IDS models that claim to work for vehicles are actually built with traditional Internet datasets rather than those with real vehicle data, which is impractical in reality. In this paper, IDS models are developed with Federated Learning (FL) with the Car-Hacking and CAN-MIRGU datasets, which are obtained from real More >

Anita Desiani^1,*, Indri Ramayanti², Sigit Priyanta³, Bambang Suprihatin¹, Muhammad Arhami⁴, Deshinta Arrova Dewi⁵, Puspa Sari¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083951 - 30 June 2026

Abstract Image segmentation is essential in medical image analysis for glaucoma screening. Accurate delineation of the optic disc (OD) and optic cup (OC) in retinal fundus images is required for reliable clinical assessment. Manual segmentation is time-consuming and suffers from interobserver variability, which leads to inconsistent results. To address these limitations, this study proposes ASPP Inception Attention U-Net (ASPPIAU-Ne), an enhanced encoder-decoder architecture that integrates Atrous Spatial Pyramid Pooling (ASPP), Inception modules, and attention gates for feature selection in skip connections. The ASPP module is applied after the encoder to capture multi-scale contextual information and improve… More >

Chenghao Kou¹, Zunyi Duan^2,*, Shengjie Wang¹, Jun Ma¹, Zhongwei Yang¹, Xudong Tang¹, Rongchun Hu²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083699 - 30 June 2026

Abstract Piezoelectric actuators are widely used in precision positioning systems. However, their inherent nonlinear behaviors, particularly hysteresis and output saturation, degrade modeling accuracy and limit control performance. Existing studies have generally used either black-box models or traditional physical models. The former typically lack physical interpretability, while the latter can exhibit limited accuracy when the actuator response includes coupled nonlinear effects. To address this issue, this paper proposes a hybrid physics-informed neural network (Hybrid-PINN) framework. An equivalent attenuation model, with a calibrated attenuation coefficient, is first established to describe output saturation and provide a nominal physical reference.… More >

Soran Manguri^1,2, Kasim Mermerdaş¹, Briar Esmail^3,4, Ahmed Manguri^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083537 - 30 June 2026

Abstract Geopolymer materials are increasingly recognized as sustainable alternatives to conventional cementitious materials due to their lower environmental impact and promising engineering performance. Recent studies have demonstrated that incorporating nanomaterials can further enhance the properties of geopolymer systems. In particular, nano-silica has been reported to significantly improve the mechanical performance of geopolymer materials. However, accurate prediction of compressive strength remains challenging because of the complex nonlinear interactions among mix design parameters, activator chemistry, and curing conditions. This study develops a machine learning framework to predict the 28-day compressive strength of nanosilica-modified hybrid geopolymer mortar using a… More >

Aminah Almehmadi¹, Ayman Noor¹, Aziza I. Noor², Hanan Almukhalfi¹, Talal H. Noor^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083503 - 30 June 2026

Abstract Fire emergencies have long posed a serious threat to people’s lives, real estate assets, and environmental sustainability in civilized societies, especially when combustible events are detected at late stages of development. Recent advancements in computer vision–based fire detection have enabled automated real-time monitoring; however, most solutions either detect the existence of fire/smoke or employ binary decision-making, which limits visual monitoring systems from being risk-aware. This work introduces a severity-aware fire/smoke detection model that supports intelligent monitoring systems in detecting visual hazards. The goal is to identify varying levels of fire intensity and smoke density and… More >

Yuyan Ning^1,2, Haiyun Huang¹, Legend Zhang³, Wei Wei⁴, Hao Quan⁵, Bo Yang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083345 - 30 June 2026

Abstract Hybrid CNN-Transformer models are widely used in medical image segmentation because they combine CNN-based local feature extraction with Transformer-based global context modeling. Despite their popularity, these models face several challenges, including computational complexity, noise blurring, and information loss. This paper proposes an enhanced convolutional attention network (ECANet) for liver segmentation. ECANet uses a U-shaped architecture with efficient channel-attention-based skip connections. Both the encoder and decoder are constructed using enhanced convolutional Transformer (ECT) blocks, where group convolution is integrated into the convolutional attention module for efficient Token embedding and channel disentanglement, and a Token-wise multi-layer perceptron More >