Le Thi Hong Van^1,*, Le Duc Thuan¹, Pham Van Huong¹, Nguyen Hieu Minh²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075027 - 10 February 2026

Abstract Optimizing convolutional neural networks (CNNs) for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy. This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection. Unlike conventional single-objective approaches, the proposed method formulates a global multi-objective fitness function that integrates accuracy, precision, recall, and model size (speed/model complexity penalty) with adjustable weights. This design enables both single-objective and weighted-sum multi-objective optimization, allowing adaptive selection of optimal CNN configurations for diverse deployment… More >

Zeyuan Chen, Yafei Xie, Jinkun Li, Song Wang, Yingqiang Ding^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073846 - 10 February 2026

Abstract Stereo matching is a pivotal task in computer vision, enabling precise depth estimation from stereo image pairs, yet it encounters challenges in regions with reflections, repetitive textures, or fine structures. In this paper, we propose a Semantic-Guided Parallax Attention Stereo Matching Network (SGPASMnet) that can be trained in unsupervised manner, building upon the Parallax Attention Stereo Matching Network (PASMnet). Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets, facilitating robust training across diverse scene-specific datasets and enhancing generalization. SGPASMnet incorporates two novel components: a Cross-Scale Feature Interaction… More >

Lu Rong^1,#, Manyu Xu^2,3,#, Wenbo Zhu^2,*, Zhihao Yang^2,3, Chao Dong^1,4,5, Yunzhi Zhang^2,3, Kai Wang^1,2, Bing Zheng^1,4,5

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073442 - 10 February 2026

Abstract Deep learning has become integral to robotics, particularly in tasks such as robotic grasping, where objects often exhibit diverse shapes, textures, and physical properties. In robotic grasping tasks, due to the diverse characteristics of the targets, frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy, which presents a significant challenge for non-experts. Neural Architecture Search (NAS) provides a compelling method through the automated generation of network architectures, enabling the discovery of models that achieve high accuracy through efficient search algorithms. Compared to manually designed networks, NAS methods… More >

Noor Mueen Mohammed Ali Hayder^1,2, Seyed Amin Hosseini Seno^2,*, Hamid Noori², Davood Zabihzadeh³, Mehdi Ebady Manaa^4,5

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073236 - 10 February 2026

Abstract Distributed Denial of Service (DDoS) attacks are one of the severe threats to network infrastructure, sometimes bypassing traditional diagnosis algorithms because of their evolving complexity. Present Machine Learning (ML) techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times. However, such techniques sometimes fail to capture complicated relations among various traffic flows. In this paper, we present a new multi-scale ensemble strategy given the Graph Neural Networks (GNNs) for improving DDoS detection. Our technique divides traffic into macro- and micro-level elements, letting various GNN models to get… More >

Zeyu Wang¹, Jian-Hong Wang^1,*, Kuo-Chun Hsu^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072881 - 10 February 2026

Abstract In daily life, keyword spotting plays an important role in human-computer interaction. However, noise often interferes with the extraction of time-frequency information, and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge. To address this, we propose a novel time-frequency dual-branch parallel residual network, which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module. The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features, effectively avoiding the potential information loss caused by serial stacking, while enhancing information… More >

Su Zhang¹, Song Deng^1,*, Qingsheng Liu²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072777 - 10 February 2026

Abstract Accurate and reliable power system data are fundamental for critical operations such as grid monitoring, fault diagnosis, and load forecasting, underpinned by increasing intelligentization and digitalization. However, data loss and anomalies frequently compromise data integrity in practical settings, significantly impacting system operational efficiency and security. Most existing data recovery methods require complete datasets for training, leading to substantial data and computational demands and limited generalization. To address these limitations, this study proposes a missing data imputation model based on an improved Generative Adversarial Network (BAC-GAN). Within the BAC-GAN framework, the generator utilizes Bidirectional Long Short-Term… More >

Roshni Khedgaonkar¹, Pravinkumar Sonsare², Kavita Singh¹, Ayman Altameem³, Hameed R. Farhan⁴, Salil Bharany⁵, Ateeq Ur Rehman^6,*, Ahmad Almogren^7,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072651 - 10 February 2026

Abstract Recent studies indicate that millions of individuals suffer from renal diseases, with renal carcinoma, a type of kidney cancer, emerging as both a chronic illness and a significant cause of mortality. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have become essential tools for diagnosing and assessing kidney disorders. However, accurate analysis of these medical images is critical for detecting and evaluating tumor severity. This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images. The proposed framework fuses a customized U-Net and Mask R-CNN… More >

Evgenii V. Fomin^1,2, Ilya A. Bryukhanov¹, Natalya A. Grachyova², Alexander E. Mayer^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.071952 - 10 February 2026

Abstract It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements, including shock wave loading. However, the dependence of phase transformations in a wide range of crystallographic directions of shock loading has not been revealed. In this work, we calculated the shock Hugoniot for aluminum and copper in different crystallographic directions ([100], [110], [111], [112], [102], [114], [123], [134], [221] and [401]) of shock compression using molecular dynamics (MD) simulations. The results showed a high pressure (>160 GPa for Cu and >40 GPa for Al) of the FCC-to-BCC transition.… More >

Jianzhong Chen, Yunsheng Xu, Zirui Guo, Tianmin Liu, Ying Pan^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.071777 - 10 February 2026

Abstract The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields. As a key technology for knowledge representation and sharing, knowledge graphs play a crucial role by constructing structured networks of relationships among entities. However, data sparsity and numerous unexplored implicit relations result in the widespread incompleteness of knowledge graphs. In static knowledge graph completion, most existing methods rely on linear operations or simple interaction mechanisms for triple encoding, making it difficult to fully capture the deep semantic associations between entities and relations. Moreover, many methods… More >

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 >