Tian Tian^1,2, Boyue Wang^1,2, Xiaxia He^1,2,*, Wentong Wang³, Meng Wang¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4885-4898, 2025, DOI:10.32604/cmc.2025.067795 - 23 October 2025

Abstract Attribute-graph clustering aims to divide the graph nodes into distinct clusters in an unsupervised manner, which usually encodes the node attribute feature and the corresponding graph structure into a latent feature space. However, traditional attribute-graph clustering methods often neglect the effect of neighbor information on clustering, leading to suboptimal clustering results as they fail to fully leverage the rich contextual information provided by neighboring nodes, which is crucial for capturing the intrinsic relationships between nodes and improving clustering performance. In this paper, we propose a novel Neighbor Dual-Consistency Constrained Attribute-Graph Clustering that leverages information from… More >

Kai Zhou¹, Yanan Bai², Yongli Hu³, Boyue Wang^3,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3873-3890, 2025, DOI:10.32604/cmc.2025.060918 - 06 March 2025

Abstract Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data, while the learned representation is difficult to maintain the underlying structure hidden in the origin samples, especially the high-order neighbor relationship between samples. To overcome the above challenges, this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model. We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module. By this design, the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix; then, More >

Wuqin Tang, Qiang Yang, Wenjun Yan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1423-1439, 2022, DOI:10.32604/cmes.2022.018313 - 30 December 2021

Abstract Defects detection with Electroluminescence (EL) image for photovoltaic (PV) module has become a standard test procedure during the process of production, installation, and operation of solar modules. There are some typical defects types, such as crack, finger interruption, that can be recognized with high accuracy. However, due to the complexity of EL images and the limitation of the dataset, it is hard to label all types of defects during the inspection process. The unknown or unlabeled create significant difficulties in the practical application of the automatic defects detection technique. To address the problem, we proposed… More >

K. S. Bhuvaneshwari¹, K. Venkatachalam², S. Hubálovský^3,*, P. Trojovský⁴, P. Prabu⁵

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5949-5965, 2022, DOI:10.32604/cmc.2022.020769 - 11 October 2021

Abstract With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because… More >

Lin Zhou^1,*, Kun Feng¹, Tianyi Wang¹, Yue Xu¹, Jingang Shi²

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 527-537, 2021, DOI:10.32604/iasc.2021.018414 - 11 August 2021

Abstract Neutral network (NN) and clustering are the two commonly used methods for speech separation based on supervised learning. Recently, deep clustering methods have shown promising performance. In our study, considering that the spectrum of the sound source has time correlation, and the spatial position of the sound source has short-term stability, we combine the spectral and spatial features for deep clustering. In this work, the logarithmic amplitude spectrum (LPS) and the interaural phase difference (IPD) function of each time frequency (TF) unit for the binaural speech signal are extracted as feature. Then, these features of… More >