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 >

Boram Kim, Guebin Choi^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073500 - 10 February 2026

Abstract Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships, as in Euclidean data such as time series or image grids. However, real-world data frequently involve irregular, interconnected structures, requiring a shift toward non-Euclidean approaches. This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals. By leveraging graph convolution filters, we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods. Utilizing the Graph Convolutional Networks (GCN) framework, we integrate graph-based embeddings with conventional anomaly detection models, 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 >

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 >

Monalisa Jena¹, Noman Khan^2,*, Mi Young Lee^3,*, Seungmin Rho³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075119 - 29 January 2026

Abstract Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises. When such risks go undetected, consequences can escalate to self-harm, long-term disability, reduced productivity, and significant societal and economic burden. Despite recent advances, detecting risk from online text remains challenging due to heterogeneous language, evolving semantics, and the sequential emergence of new datasets. Effective solutions must encode clinically meaningful cues, reason about causal relations, and adapt to new domains without forgetting prior knowledge. To address these challenges, this paper presents a Continual Neuro-Symbolic Graph… More >

Abdul Rahaman Wahab Sait^1,*, Yazeed Alkhurayyif²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072481 - 12 January 2026

Abstract Malware poses a significant threat to the Internet of Things (IoT). It enables unauthorized access to devices in the IoT environment. The lack of unique architectural standards causes challenges in developing robust malware detection (MD) models. The existing models demand substantial computational resources. This study intends to build a lightweight MD model to detect anomalies in IoT networks. The authors develop a transformation technique, converting the malware binaries into images. MobileNet V2 is fine-tuned using improved grey wolf optimization (IGWO) to extract crucial features of malicious and benign samples. The ResNeXt model is combined with… More >

Qian Yu^1,2, Gui Zhang^2,*, Ying Wang¹, Xin Wu², Jiangshu Xiao², Wenbing Kuang¹, Juan Zhang²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072172 - 12 January 2026

Abstract Detecting small forest fire targets in unmanned aerial vehicle (UAV) images is difficult, as flames typically cover only a very limited portion of the visual scene. This study proposes Context-guided Compact Lightweight Network (CCLNet), an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources. CCLNet employs a three-stage network architecture. Its key components include three modules. C3F-Convolutional Gated Linear Unit (C3F-CGLU) performs selective local feature extraction while preserving fine-grained high-frequency flame details. Context-Guided Feature Fusion Module (CGFM) replaces plain concatenation with triplet-attention interactions to… More >