Wenfeng Zheng¹, Guangyu Xu², Siyu Lu³, Junmin Lyu⁴, Feng Bao^5,*, Lirong Yin^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075741 - 29 January 2026

Abstract Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways More >

Yuyao Huang, Hui Shu, Fei Kang^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071552 - 12 January 2026

Abstract Message structure reconstruction is a critical task in protocol reverse engineering, aiming to recover protocol field structures without access to source code. It enables important applications in network security, including malware analysis and protocol fuzzing. However, existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery, resulting in imprecise and incomplete reconstructions. In this paper, we propose ProRE, a novel method for reconstructing protocol field structures based on program execution slice embedding. ProRE extracts code slices from protocol parsing at runtime, converts them into embedding vectors using a data flow-sensitive assembly language model, More >

Yuang Chen^1,2, Yong Li^1,*, Fang Lin^1,2, Shuhan Lv^1,2, Jiaze Jiang^1,2

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.070990 - 09 December 2025

Abstract Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet, this paper proposes a novel lightweight neural network model called ResghostNet. This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks, which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations. Specifically, ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow, and designs a weight self-attention mechanism combined with SE blocks to enhance feature 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 >

Yu Tao¹, Tian Yin², Yang Jie^1,*

Journal on Artificial Intelligence, Vol.7, pp. 549-570, 2025, DOI:10.32604/jai.2025.072727 - 01 December 2025

Abstract To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units (IMU), this paper proposes a multi-sensor fusion attitude estimation method based on an improved Error-State Kalman Filter (ESKF). Several adaptive mechanisms are introduced within the standard ESKF framework: first, the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions; second, the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time, mitigating disturbances caused by external accelerations and magnetic fields. Additionally,… More >

Zhi-Yun Zhang^1,*, Yaqiong Wu¹, Chenshi Deng², Peng Wang³, Weiguaju Nong^4,*

International Journal of Mental Health Promotion, Vol.27, No.10, pp. 1533-1551, 2025, DOI:10.32604/ijmhp.2025.070883 - 31 October 2025

Abstract Objectives: Medical students often rely on recreational internet media to relieve the stress caused by immense academic and life pressures, and among these media, short-form videos, which are an emerging digital medium, have gradually become the mainstream choice of students to relieve their stress. However, the addiction caused by their usage has attracted the widespread attention of both academia and society, which is why the purpose of this study is to systematically explore the underlying mechanisms that link perceived stress, entertainment gratification, emotional gratification, short-form video usage intensity, and short-form video addiction based on multiple… More >

Sara H. Elsayed¹, Rodaina Abdelsalam¹, Mahmoud A. Ismail Shoman², Raed Alotaibi^3,*, Omar Reyad^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4995-5013, 2025, DOI:10.32604/cmc.2025.068429 - 23 October 2025

Abstract Ensuring the secure transmission of secret messages, particularly through video—one of the most widely used media formats—is a critical challenge in the field of information security. Relying on a single-layered security approach is often insufficient for safeguarding sensitive data. This study proposes a triple-lightweight cryptographic and steganographic model that integrates the Hill Cipher Technique (HCT), Rotation Left Digits (RLD), and Discrete Wavelet Transform (DWT) to embed secret messages within video frames securely. The approach begins with encrypting the secret text using a private key matrix (PK₁) of size 2 × 2 up to 6 × 6… More >

Kadriye Simsek Alan^*, Ayca Durgut, Helin Doga Demirel

Journal on Artificial Intelligence, Vol.7, pp. 397-415, 2025, DOI:10.32604/jai.2025.071133 - 20 October 2025

Abstract Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures. While earlier research has emphasized CPU utilization forecasting, joint prediction of CPU and memory usage under real workload conditions remains underexplored. This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset. The framework combines Extreme Gradient Boosting (XGBoost) with a MultiOutputRegressor (MOR) to capture nonlinear interactions across multiple resource dimensions, supported by a leakage-safe imputation strategy that prevents bias from missing values. Nested… More >

Sunghyun Yu, Yoojae Won^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3943-3980, 2025, DOI:10.32604/cmes.2025.064997 - 30 June 2025

Abstract Pre-Authentication and Post-Connection (PAPC) plays a crucial role in realizing the Zero Trust security model by ensuring that access to network resources is granted only after successful authentication. While earlier approaches such as Port Knocking (PK) and Single Packet Authorization (SPA) introduced pre-authentication concepts, they suffer from limitations including plaintext communication, protocol dependency, reliance on dedicated clients, and inefficiency under modern network conditions. These constraints hinder their applicability in emerging distributed and resource-constrained environments such as AIoT and browser-based systems. To address these challenges, this study proposes a novel port-sequence-based PAPC scheme structured as a… More >

Zichen Li^1,#, Jukui Ma^2,#, Minjun Liu³, Guowei Geng^1,*, Hongxia Zhang^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1841-1856, 2025, DOI:10.32604/phyton.2025.066148 - 27 June 2025

Abstract Sweet potatoes are significant cash crops, however, their yield and quality are greatly compromised by viral diseases. In this study, the complete genomic sequences of two Sweet Potato Virus 2 (SPV2) isolates from infected sweet potato leaves in the Shandong (designated as SPV2-SDYT, GenBank No. PQ855660.1) and Jiangsu (designated as SPV2-JSXZ, GenBank No. PQ855661.1) provinces in China were obtained using 5^′ RACE and RT-PCR amplification. Consistency, phylogeny, codon usage bias, recombination, and selection pressure analyses were conducted using the SPV2-SDYT and SPV2-JSXZ genome sequences. The complete genome sequences of SPV2-SDYT and SPV2-JSXZ were 10561 nucleotides (nt)… More >