Yuchao Hou^1,2, Biaobiao Bai³, Shuai Zhao³, Yue Wang³, Jie Wang³, Zijian Li^4,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-30, 2026, DOI:10.32604/cmc.2025.069789 - 09 December 2025

Abstract Recently, large-scale deep learning models have been increasingly adopted for point cloud classification. However, these methods typically require collecting extensive datasets from multiple clients, which may lead to privacy leaks. Federated learning provides an effective solution to data leakage by eliminating the need for data transmission, relying instead on the exchange of model parameters. However, the uneven distribution of client data can still affect the model’s ability to generalize effectively. To address these challenges, we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework (FDASS-MRFCF).… More >

Tiantian Wang^1,2, Zhihua Hu^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2357-2381, 2025, DOI:10.32604/cmes.2025.073640 - 26 November 2025

Abstract Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks. We present a novel image deraining framework built on a three-stage dual-residual architecture that progressively restores rain-degraded content while preserving fine structural details. Each stage begins with a multi-scale feature extractor and a channel attention module that adaptively emphasizes informative representations for rain removal. The core restoration is achieved via enhanced dual-residual blocks, which stabilize training and mitigate feature degradation across layers. To further refine representations, we integrate cross-dimensional spatial attention supervised by ground-truth guidance, ensuring that More >

Jawad Ali^1,*, Ioan-Lucian Popa^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 857-889, 2025, DOI:10.32604/cmes.2025.064604 - 30 October 2025

Abstract With the rapid growth of urbanization, smart city development has become a strategic priority worldwide, requiring complex and uncertain decision-making processes. In this context, advanced decision-support tools are essential to evaluate and prioritize competing initiatives effectively. To support effective prioritization of smart city initiatives under uncertainty, this study introduces a robust decision-making framework based on the t-arbicular fuzzy (t-AF) set—a recent extension of the t-spherical fuzzy set that incorporates an additional parameter, the radius , to enhance the representation of uncertainty. Dombi-based operational laws are formulated within this context, leading to the development of four… More >

Zengyao Tian^1,2, Li Lv^1,*, Wenchen Deng¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5359-5376, 2025, DOI:10.32604/cmc.2025.069427 - 23 October 2025

Abstract Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems. While graph neural networks show promise for this task, existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions. To address these dual challenges, we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning. Its core innovations are two synergistic modules: (1) The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations. It employs an information-driven optimization loss within a… More >

Jingwen Wang, Ming Xu, Deming Nie^*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.9, pp. 2225-2251, 2025, DOI:10.32604/fdmp.2025.068327 - 30 September 2025

Abstract This study employs the fluctuating-lattice Boltzmann method to investigate temperature-gradient-driven aggregation of microswimmers, specifically, pulling-type (pullers) and pushing-type (pushers), within a fluid confined by two channel walls. The analysis incorporates the Brownian motion of both swimmer types and introduces key dimensionless parameters, including the swimming Reynolds, Prandtl, and Lewis numbers, to characterize the influences of self-propulsion strength, thermal diffusivity, and Brownian diffusivity on aggregation efficiency and behavior. Our findings reveal that pushers tend to aggregate either along the channel centerline or near the channel walls under conditions of thermal gradients imposed by heated or cooled More >

Yiming Zhang¹, Wei Zhang^1,2,*, Cong Shen³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3185-3211, 2025, DOI:10.32604/cmc.2025.067563 - 23 September 2025

Abstract Distributed data fusion is essential for numerous applications, yet faces significant privacy security challenges. Federated learning (FL), as a distributed machine learning paradigm, offers enhanced data privacy protection and has attracted widespread attention. Consequently, research increasingly focuses on developing more secure FL techniques. However, in real-world scenarios involving malicious entities, the accuracy of FL results is often compromised, particularly due to the threat of collusion between two servers. To address this challenge, this paper proposes an efficient and verifiable data aggregation protocol with enhanced privacy protection. After analyzing attack methods against prior schemes, we implement… More >

Jianchang Hou¹, Zhanpeng Jiang¹, Fenghe Wu¹, Hui Lian¹, Zhaohua Wang², Zijian Liu³, Weicheng Li^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1545-1572, 2025, DOI:10.32604/cmes.2025.068482 - 31 August 2025

Abstract In this paper, a topology optimization method for coordinated stiffness and strength design is proposed under mass constraints, utilizing the Solid Isotropic Material with Penalization approach. Element densities are regulated through sensitivity filtering to mitigate numerical instabilities associated with stress concentrations. A p-norm aggregation function is employed to globalize local stress constraints, and a normalization technique linearly weights strain energy and stress, transforming the multi-objective problem into a single-objective formulation. The sensitivity of the objective function with respect to design variables is rigorously derived. Three numerical examples are presented, comparing the optimized structures in terms More >

Chuanchuan Wang^1,2, Ahmad Sufril Azlan Mohamed^2,*, Xiao Yang ², Hao Zhang ², Xiang Li¹, Mohd Halim Bin Mohd Noor ²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 855-874, 2025, DOI:10.32604/cmc.2025.066343 - 29 August 2025

Abstract Classroom behavior recognition is a hot research topic, which plays a vital role in assessing and improving the quality of classroom teaching. However, existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures, and inconsistent objects. To address this challenge, we proposed an effective, lightweight object detector method called the RFNet model (YOLO-FR). The YOLO-FR is a lightweight and effective model. Specifically, for efficient multi-scale feature extraction, effective feature pyramid shared convolutional (FPSC) was designed to improve the feature extract performance by leveraging convolutional… More >

Yu-Shiuan Tsai^*, Zhen-Rong Wu, Jian-Zhi Liu

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3431-3457, 2025, DOI:10.32604/cmc.2025.066509 - 03 July 2025

Abstract Few-shot learning has emerged as a crucial technique for coral species classification, addressing the challenge of limited labeled data in underwater environments. This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection. The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity, effectively capturing both feature magnitude and directional relationships. This approach achieves a notable accuracy of 71.8% under a 5-way 5-shot evaluation, outperforming state-of-the-art models such as Prototypical Networks, FEAT, and ESPT by up to 10%. Notably, the model demonstrates high… More >

Peizheng Lai¹, Minqing Zhang^1,2,*, Yixin Tang¹, Ya Yue¹, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2935-2957, 2025, DOI:10.32604/cmc.2025.065887 - 03 July 2025

Abstract Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing. However, its reliance on a server introduces critical security vulnerabilities: malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results. Consequently, achieving verifiable aggregation without compromising client privacy remains a critical challenge. To address these problem, we propose a reversible data hiding in encrypted domains (RDHED) scheme, which designs joint secret message embedding and extraction mechanism. This approach enables clients to embed secret messages… More >