Zengyu Cai^1,*, Yuming Dai¹, Jianwei Zhang^2,3,*, Yuan Feng⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3335-3350, 2025, DOI:10.32604/cmc.2025.061206 - 16 April 2025

Abstract The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic, highlighting the growing importance of network security. Intrusion Detection Systems (IDS) are essential for safeguarding network integrity. To address the low accuracy of existing intrusion detection models in identifying network attacks, this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network (SA-ResNet). Utilizing residual connections can effectively capture local features in the data; by introducing a spatial attention mechanism, the global dependency relationships of intrusion features can be extracted, enhancing the intrusion More >

Yaojin Fan, Huaqing Dong^*, Zixuan Zong, Tingting Long, Qianglin Huang, Guoqiang Huang

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 731-769, 2025, DOI:10.32604/sdhm.2025.061056 - 03 April 2025

Abstract This study presents a comprehensive investigation of residual strength in corroded pipelines within the Yichang-Qianjiang section of the Sichuan-East Gas Pipeline, integrating advanced numerical simulation with experimental validation. The research methodology incorporates three distinct parameter grouping approaches: a random group based on statistical analysis of 389 actual corrosion defects detected during 2023 MFL inspection, a deviation group representing historically documented failure scenarios, and a structural group examining systematic parameter variations. Using ABAQUS finite element software, we developed a dynamic implicit analysis model incorporating geometric nonlinearity and validated it through 1:12.7 scaled model testing, achieving prediction… More >

Shijie Xiang, Dong Zhou, Dan Tian^*, Zihao Wang

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 497-515, 2025, DOI:10.32604/cmc.2025.060244 - 26 March 2025

Abstract Real-time semantic segmentation tasks place stringent demands on network inference speed, often requiring a reduction in network depth to decrease computational load. However, shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy. Therefore, balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation. To address these challenges, this paper proposes a lightweight bilateral dual-residual network. By introducing a novel residual structure combined with feature extraction and fusion modules, the proposed network significantly enhances representational capacity while reducing computational costs. Specifically, an improved compound… More >

Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4161-4179, 2025, DOI:10.32604/cmc.2025.061497 - 06 March 2025

Abstract A healthy brain is vital to every person since the brain controls every movement and emotion. Sometimes, some brain cells grow unexpectedly to be uncontrollable and cancerous. These cancerous cells are called brain tumors. For diagnosed patients, their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans. Nowadays, Physicians and radiologists rely on Magnetic Resonance Imaging (MRI) pictures for their clinical evaluations of brain tumors. These evaluations are time-consuming, expensive, and require expertise with high skills to provide an accurate diagnosis. Scholars and industrials have recently partnered to implement… More >

Guorong Qi¹, Jian Mao^1,*, Kai Huang¹, Zhengxian You², Jinliang Lin²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2159-2176, 2025, DOI:10.32604/cmc.2024.058396 - 17 February 2025

Abstract Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and global features, as well as the lack of effective mechanisms to capture complex interactions between features; Additionally, when increasing the receptive field to obtain deeper feature representations, the reliance on increasing network depth leads to a significant increase in computational resource consumption, affecting the efficiency and performance of detection. Based on these issues, firstly, this paper proposes a network traffic anomaly detection… More >

Firdaus Firdaus, Siti Nurmaini^*, Anggun Islami, Annisa Darmawahyuni, Ade Iriani Sapitri, Muhammad Naufal Rachmatullah, Bambang Tutuko, Akhiar Wista Arum, Muhammad Irfan Karim, Yultrien Yultrien, Ramadhana Noor Salassa Wandya

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3419-3441, 2025, DOI:10.32604/cmc.2024.055906 - 17 February 2025

Abstract Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated… More >

Afaq Khattak^1,*, Pak-wai Chan², Feng Chen³, Abdulrazak H. Almaliki⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1565-1584, 2025, DOI:10.32604/cmes.2025.059914 - 27 January 2025

Abstract Intense wind shear (I-WS) near airport runways presents a critical challenge to aviation safety, necessitating accurate and timely classification to mitigate risks during takeoff and landing. This study proposes the application of advanced Residual Network (ResNet) architectures including ResNet34 and ResNet50 for classifying I-WS and Non-Intense Wind Shear (NI-WS) events using Doppler Light Detection and Ranging (LiDAR) data from Hong Kong International Airport (HKIA). Unlike conventional models such as feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), ResNet provides a distinct advantage in addressing key challenges such as capturing intricate… More >

Lanxi Zhang¹, Yuming Peng¹, Yudong Wu^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.1, pp. 205-220, 2025, DOI:10.32604/fdmp.2024.056516 - 24 January 2025

Abstract The challenge of aerodynamic noise is a key obstacle in the advancement of low-pressure tube ultra-high-speed maglev transportation, demanding urgent resolution. This study utilizes a broadband noise source model to perform a quantitative analysis of the aerodynamic noise produced by ultra-high-speed maglev trains operating in low-pressure environments. Initially, an external flow field calculation model for the ultra-high-speed maglev train is presented. Subsequently, numerical simulations based on the broadband noise source model are used to examine the noise characteristics. The impact of the train speed and pressure level on noise generation is investigated accordingly. Subsequently, a… More >

Xiaoyun Chen¹, Lanyao Zhang¹, Xiaoling Chen¹, Yigang Cen², Linna Zhang^1,*, Fugui Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 521-542, 2025, DOI:10.32604/cmc.2024.058063 - 03 January 2025

Abstract Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules. In the production process, defect samples occur infrequently and exhibit random shapes and sizes, which makes it challenging to collect defective samples. Additionally, the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions. This paper proposes a novel Lightweight Multi-scale Feature Fusion network (LMFF) to address these challenges. The network comprises a feature extraction network, a multi-scale feature fusion module (MFF), and a segmentation network. Specifically, a feature extraction network is proposed to obtain… More >

Dong-Wook Lee^1,*, Heungjo An², Tae Yeon Kim³, Sungmun Lee⁴, Jide Oyebanji¹, Prabakaran Balasubramanian¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011286

Abstract This work analyzes the problems of material elastic constants identification and residual stresses determination in an orthotropic materials using hole drilling method. These problems are very important to understand mechanical performance of materials. A lot of optical method such as Moiré, laser speckle interferometry, digital image correlation or photoelasticity is developed to estimate displacement (or strain) fields or applied loads (or stresses) from images. These methods require a very complicated techniques, skill, and efforts to analysis images. But deep learning method based on a convolution neural network shows better performance in image analysis problems such… More >