Qing Long¹, Bing Yi², Haiqiao Liu^3,*, Zhiling Peng¹, Xiang Liu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072058 - 12 January 2026

Abstract Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring. However, conventional detection approaches are highly susceptible to noise, illumination variations, and complex environmental conditions, which often reduce detection accuracy and real-time performance. To address these limitations, we propose Lightweight and Precise YOLO (LP-YOLO), a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid, built upon YOLOv8. First, to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks (CNNs), we design an enhanced backbone based on Wavelet Convolutions (WTConv), which expands the… More >

Nour El Houda Kaiber¹, Tahar Mekhaznia¹, Akram Bennour^1,*, Mohammed Al-Sarem^2,3,*, Zakaria Lakhdara⁴, Fahad Ghaban², Mohammad Nassef^5,6

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070337 - 12 January 2026

Abstract The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness. Deep learning has emerged as a promising solution, offering new avenues for improvements. However, building models from scratch is computationally expensive and requires large datasets. This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image. The core idea is to fine-tune a pre-trained model on specific object categories using new, unseen data, resulting in specialized versions of the model that are better adapted to reconstruct particular objects. The proposed approach utilizes a… More >

Xinwei Zhu, Jianxun Zhang^*, Huan Zeng

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068578 - 09 December 2025

Abstract Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering, color distortion, and detail blurring, limiting their application performance. Existing underwater image enhancement methods, although they can improve the image quality to some extent, often lead to problems such as detail loss and edge blurring. To address these problems, we propose FENet, an efficient underwater image enhancement method. FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra, respectively. Then, a distance… More >

Hefei Wang, Ruichun Gu^*, Jingyu Wang, Xiaolin Zhang, Hui Wei

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069251 - 10 November 2025

Abstract Graph Federated Learning (GFL) has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information. However, existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization, particularly in non-independent and identically distributed (NON-IID) scenarios where balancing global structural understanding and local node-level detail remains a challenge. To this end, this paper proposes a novel framework called GFL-SAR (Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement), which enhances the representation learning capability of graph data through a dual-branch… More >

Tian Tian¹, Huayi Wei^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-2, 2025, DOI:10.32604/icces.2025.011175

Abstract Brittle fracture is a critical failure mode in structural materials, and accurately simulating its evolution is essential for engineering design, material performance evaluation, and failure prediction. Traditional numerical methods, however, face significant challenges when dealing with higher-order fracture models and complex fracture behaviors. To overcome these challenges, this study proposes an innovative simulation framework based on higher-order finite element methods and adaptive mesh refinement, effectively balancing computational efficiency and simulation accuracy.
The research first develops a higher-order finite element method for the continuum damage fracture phase-field model. By incorporating higher-order finite element techniques, the proposed method… More >

Anshul Pandey, Sachin Kumar^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3251-3286, 2025, DOI:10.32604/cmes.2025.067858 - 30 September 2025

Abstract The phase field model can coherently address the relatively complex fracture phenomenon, such as crack nucleation, branching, deflection, etc. The model has been extensively implemented in the finite element package Abaqus to solve brittle fracture problems in recent studies. However, accurate numerical analysis typically requires fine meshes to model the evolving crack path effectively. A broad region must be discretized without prior knowledge of the crack path, further augmenting the computational expenses. In this proposed work, we present an automated framework utilizing a posteriori error-indicator (MISESERI) to demarcate and sufficiently refine the mesh along the… More >

Mohamed Elkawkagy¹, Ibrahim A. Elgendy^2,*, Ammar Muthanna^3,4, Reem Ibrahim Alkanhel⁵, Heba Elbeh¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 393-415, 2025, DOI:10.32604/cmc.2025.063766 - 09 June 2025

Abstract Hierarchical Task Network (HTN) planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures. However, achieving optimal solutions in HTN planning remains a challenge, especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently. This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization (ACO) algorithm into the refinement process. The Ant System algorithm, inspired by the foraging behavior of ants, is well-suited for addressing optimization problems by efficiently exploring solution spaces. By incorporating ACO… More >

Yuxin Gao¹, Xiaodong Duan^2,3, Qiguo Dai^2,3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 701-718, 2025, DOI:10.32604/cmc.2025.060137 - 26 March 2025

Abstract Graph convolutional network (GCN) as an essential tool in human action recognition tasks have achieved excellent performance in previous studies. However, most current skeleton-based action recognition using GCN methods use a shared topology, which cannot flexibly adapt to the diverse correlations between joints under different motion features. The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms. In this work, we propose a novel graph convolutional learning framework, called PCCTR-GCN, which integrates pose correction and channel topology refinement for skeleton-based human action… More >

Jiyang Xu¹, Qi Wang^1,*, Xin Xiong², Weidong Min^1,3, Jiang Luo⁴, Di Gai¹, Qing Han^1,3

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3921-3941, 2025, DOI:10.32604/cmc.2024.058586 - 06 March 2025

Abstract The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information. Due to the higher similarity in appearance between vehicles compared to pedestrians, pseudo-labels generated through clustering are ineffective in mitigating the impact of noise, and the feature distance between inter-class and intra-class has not been adequately improved. To address the aforementioned issues, we design a dual contrastive learning method based on knowledge distillation. During each iteration, we utilize a teacher model to randomly partition the entire dataset into two sub-domains based on clustering pseudo-label categories. By conducting contrastive… More >

Huakang Bian^1,3,*, Yufan Zhao^2,3, Akihiko Chiba³

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012491

Abstract Refining the Si phase in Al‒Si alloy has been a research interest for decades. Previous studies suggested many Al- and Si-enriched nano-segments (approximately 100 nm) can coexist in a melted Al–Si liquid solution when they were reheated to a temperature between 1080 and 1290 °C. These nano-segments could be retained to become crystal nuclei and grew into fine grains under a very fast cooling rate. Thus, this provides a novel approach of refining the microstructure of Al–Si alloy using electron beam melting (EBM) technology because the temperature exceeds 1500 °C in the melting pool with… More >