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 >

Haihao He^1,2, Xianzhou Dong^1,*, Shuangshuang Wang¹, Chengzhang Zhu¹, Xiaotong Zhao^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069513 - 10 November 2025

Abstract Clock synchronization has important applications in multi-agent collaboration (such as drone light shows, intelligent transportation systems, and game AI), group decision-making, and emergency rescue operations. Synchronization method based on pulse-coupled oscillators (PCOs) provides an effective solution for clock synchronization in wireless networks. However, the existing clock synchronization algorithms in multi-agent ad hoc networks are difficult to meet the requirements of high precision and high stability of synchronization clock in group cooperation. Hence, this paper constructs a network model, named DAUNet (unsupervised neural network based on dual attention), to enhance clock synchronization accuracy in multi-agent wireless ad hoc… More >

Haoran Wang¹, Shuhong Yang², Kuan Shao², Tao Xiao², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069404 - 10 November 2025

Abstract With the rapid development of the Artificial Intelligence of Things (AIoT), convolutional neural networks (CNNs) have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks. However, the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices. Therefore, this paper proposes an efficient privacy-preserving CNN framework (i.e., EPPA) based on the Fully Homomorphic Encryption (FHE) scheme for AIoT application scenarios. In the plaintext domain, we verify schemes with different activation structures to determine the… More >

Kemahyanto Exaudi^1,2, Deris Stiawan^3,*, Bhakti Yudho Suprapto¹, Hanif Fakhrurroja⁴, Mohd. Yazid Idris⁵, Tami A. Alghamdi⁶, Rahmat Budiarto⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.069377 - 10 November 2025

Abstract Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by… More >

Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >

Kwok Tai Chui^1,*, Varsha Arya¹, Brij B. Gupta^2,3,4,*, Miguel Torres-Ruiz⁵, Razaz Waheeb Attar⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068842 - 10 November 2025

Abstract Parkinson’s disease (PD) is a debilitating neurological disorder affecting over 10 million people worldwide. PD classification models using voice signals as input are common in the literature. It is believed that using deep learning algorithms further enhances performance; nevertheless, it is challenging due to the nature of small-scale and imbalanced PD datasets. This paper proposed a convolutional neural network-based deep support vector machine (CNN-DSVM) to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets. A customized kernel function reduces the impact… More >

Jaeho Shin¹, Jaekwang Kim^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068767 - 10 November 2025

Abstract Network attacks have become a critical issue in the internet security domain. Artificial intelligence technology-based detection methodologies have attracted attention; however, recent studies have struggled to adapt to changing attack patterns and complex network environments. In addition, it is difficult to explain the detection results logically using artificial intelligence. We propose a method for classifying network attacks using graph models to explain the detection results. First, we reconstruct the network packet data into a graphical structure. We then use a graph model to predict network attacks using edge classification. To explain the prediction results, we… More >

Vinh Truong Hoang^1,*, Nghia Dinh¹, Viet-Tuan Le¹, Kiet Tran-Trung¹, Bay Nguyen Van¹, Kittikhun Meethongjan^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-40, 2026, DOI:10.32604/cmc.2025.068733 - 10 November 2025

Abstract The Financial Technology (FinTech) sector has witnessed rapid growth, resulting in increasingly complex and high-volume digital transactions. Although this expansion improves efficiency and accessibility, it also introduces significant vulnerabilities, including fraud, money laundering, and market manipulation. Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data. Graph Neural Networks (GNNs), capable of modeling intricate interdependencies among entities, have emerged as a powerful framework for detecting subtle and sophisticated anomalies. However, the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability, performance, and 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 >

Jing Yang¹, Zhiming Liu^1,*, Xingchao Li², Zhongyao Wang³, Beitong Li¹, Kaiyang Liu¹, Wang Long⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068353 - 10 November 2025

Abstract Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service. However, due to the complexity of fatigue failure mechanisms, achieving accurate multiaxial fatigue life predictions remains challenging. Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions, making it difficult to maintain reliable life prediction results beyond these constraints. This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life, using Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Fully Connected Neural… More >