José Arturo Ramírez-Fernández¹, Henevith G. Méndez-Figueroa¹, Sebastián Ossandón^2,*, Ricardo Galván-Martínez³, Miguel Ángel Hernández-Pérez³, Ricardo Orozco-Cruz³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072707 - 12 January 2026

Abstract In this study, artificial neural networks (ANNs) were implemented to determine design parameters for an impressed current cathodic protection (ICCP) prototype. An ASTM A36 steel plate was tested in 3.5% NaCl solution, seawater, and NS4 using electrochemical impedance spectroscopy (EIS) to monitor the evolution of the substrate surface, which affects the current required to reach the protection potential (Eprot). Experimental data were collected as training datasets and analyzed using statistical methods, including box plots and correlation matrices. Subsequently, ANNs were applied to predict the current demand at different exposure times, enabling the estimation of electrochemical More >

Chee Chin Lim^1,2,*, Hui Wen Tiu¹, Qi Wei Oung^1,3, Chiew Chea Lau⁴, Xiao Jian Tan^2,5

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070468 - 12 January 2026

Abstract Breast cancer remains one of the leading causes of cancer mortality world-wide, with accurate molecular subtyping is critical for guiding treatment and improving patient outcomes. Traditional molecular subtyping via immuno-histochemistry (IHC) test is invasive, time-consuming, and may not fully represent tumor heterogeneity. This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes. Four pretrained models, including two Convolutional Neural Networks (MobileNet_V3_Large and VGG-16) and two Vision Transformers (ViT_B_16 and ViT_Base_Patch16_Clip_224) were fine-tuned to classify images into HER2-enriched, Luminal, Normal-like, and Triple Negative subtypes. Hyperparameter tuning,… More >

Haoxin Sun¹, Xiao Yu^1,*, Jiale Li¹, Yitong Xu¹, Jie Yu¹, Huanhuan Li¹, Yuanzhang Li², Yu-An Tan²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070930 - 09 December 2025

Abstract Since the advent of smart contracts, security vulnerabilities have remained a persistent challenge, compromsing both the reliability of contract execution and the overall stability of the virtual currency market. Consequently, the academic community has devoted increasing attention to these security risks. However, conventional approaches to vulnerability detection frequently exhibit limited accuracy. To address this limitation, the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks (GNNs). The proposed method first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts. More >

Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >

Nikhil S. Mane¹, Sheetal Kumar Dewangan^2,*, Sayantan Mukherjee³, Pradnyavati Mane⁴, Deepak Kumar Singh¹, Ravindra Singh Saluja⁵

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.072090 - 10 November 2025

Abstract The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids. Researchers rely on experimental investigations to explore nanofluid properties, as it is a necessary step before their practical application. As these investigations are time and resource-consuming undertakings, an effective prediction model can significantly improve the efficiency of research operations. In this work, an Artificial Neural Network (ANN) model is developed to predict the thermal conductivity of metal oxide water-based nanofluid. For this, a comprehensive set of 691 data points was collected from the literature. This dataset is split More >

Yadong Wang¹, Zhiwei Zhang^1,*, Pengpeng Qiao², Ye Yuan¹, Guoren Wang¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.068590 - 10 November 2025

Abstract Graph Neural Networks (GNNs) have proven highly effective for graph classification across diverse fields such as social networks, bioinformatics, and finance, due to their capability to learn complex graph structures. However, despite their success, GNNs remain vulnerable to adversarial attacks that can significantly degrade their classification accuracy. Existing adversarial attack strategies primarily rely on label information to guide the attacks, which limits their applicability in scenarios where such information is scarce or unavailable. This paper introduces an innovative unsupervised attack method for graph classification, which operates without relying on label information, thereby enhancing its applicability… More >

Lei Zheng^1,*, Hong Pan^2,3, Yuelei Ruan^2,4, Guoxin Zhang¹, Lei Zhang^1,*, Jianda Xin¹, Zhenyang Zhu¹, Jianyao Zhang^2,5, Wei Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3217-3241, 2025, DOI:10.32604/cmes.2025.074651 - 23 December 2025

Abstract As a critical material in construction engineering, concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency. This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron (MLP) neural network, dynamically fused via a weighted strategy to achieve high-precision temperature estimation. Experimental results on an independent test set demonstrated the superior performance of the fused model, with a root mean square error (RMSE) of 1.59°C and a mean absolute error (MAE) of 1.23°C, representing a 25.3% RMSE reduction compared to More >

Jia Mi¹, Zhikang Liu¹, Chang Li², Jing Wan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4089-4106, 2025, DOI:10.32604/cmes.2025.074364 - 23 December 2025

Abstract Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies. Despite notable progress, existing computational techniques struggle to represent the interplay between sequence-derived and structure-dependent protein features. To overcome this limitation, we introduce GLM-EP, a unified framework that fuses protein language models with equivariant graph neural networks. By merging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations, GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification. Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional More >

Arshad Riaz^1,*, Misbah Ilyas¹, Muhammad Naeem Aslam², Safia Akram³, Sami Ullah Khan⁴, Ghaliah Alhamzi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3481-3509, 2025, DOI:10.32604/cmes.2025.072513 - 23 December 2025

Abstract Peristaltic transport of non-Newtonian nanofluids with double diffusion is essential to biological engineering, microfluidics, and manufacturing processes. The authors tackle the key problem of Sisko nanofluids under double diffusion convection with thermal radiations and electroosmotic effects. The study proposes a solution approach by using Morlet-Wavelet Neural Networks that can effectively solve this complex problem by their superior ability in the capture of nonlinear dynamics. These convergence analyses were calculated across fifty independent runs. Theil’s Inequality Coefficient and the Mean Squared Error values range from 10⁻⁷ to 10⁻⁵ and 10⁻⁷ to 10⁻¹⁰, respectively. These values showed the proposed More >

Carlos Torres-Aguilar^1,*, Pedro Moreno^2,*, Diego Rossit³, Sergio Nesmachnow⁴, Karla M. Aguilar-Castro¹, Edgar V. Macias-Melo¹, Luis Hernández-Callejo⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3859-3881, 2025, DOI:10.32604/cmes.2025.069996 - 23 December 2025

Abstract Solar chimneys are renewable energy systems designed to enhance natural ventilation, improving thermal comfort in buildings. As passive systems, solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way. The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions, both of which are critical for optimal performance. This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys. The dataset includes information from 2784 solar chimney configurations, which encompasses various factors such as chimney height, channel thickness, More > Graphic Abstract