Jieren Cheng^1,3, Jimei Li^1,3,*, Faqiang Zeng^1,3, Zhicong Tao^1,3, Yue Yang^2,3

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 281-298, 2024, DOI:10.32604/cmc.2024.051816

Abstract Contrastive learning is a significant research direction in the field of deep learning. However, existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods. To address these challenges, we propose the Efficient Clustering Network based on Matrix Factorization (ECN-MF). Specifically, we design a batched low-rank Singular Value Decomposition (SVD) algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data. Additionally, we design a Mutual Information-Enhanced More >

Hongchi Liu¹, Xing Deng^1,*, Haijian Shao^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2397-2424, 2024, DOI:10.32604/cmes.2024.049737

Abstract The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach… More > Graphic Abstract

Zhenyu Qian¹, Yizhang Jiang¹, Zhou Hong¹, Lijun Huang², Fengda Li³, KhinWee Lai⁶, Kaijian Xia^4,5,6,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4741-4762, 2024, DOI:10.32604/cmc.2024.050920

Abstract In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction… More > Graphic Abstract

Imtiaz Ahmad¹, Tariq Hussain², Babar Shah³, Altaf Hussain⁴, Iqtidar Ali¹, Farman Ali^5,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3585-3629, 2024, DOI:10.32604/cmc.2024.050596

Abstract Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost. One of them is a sensor network with embedded sensors working as the primary nodes, termed Wireless Sensor Networks (WSNs), in which numerous sensors are connected to at least one Base Station (BS). These sensors gather information from the environment and transmit it to a BS or gathering location. WSNs have several challenges, including throughput, energy usage, and network lifetime concerns. Different strategies have been applied to get over these… More >

Xin Xiong¹, Jing Zhang¹, Sanli Yi¹, Chunwu Wang², Ruixiang Liu³, Jianfeng He^1,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4659-4681, 2024, DOI:10.32604/cmc.2024.050528

Abstract The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for… More >

Jing Gao^*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158

Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

Jian Yu^1,2,*, Gaofeng Yu³, Xiangmei Xiao^1,2, Zhixing Lin^1,2

Journal of Cyber Security, Vol.6, pp. 41-68, 2024, DOI:10.32604/jcs.2024.049658

Abstract With the rapid advancement of the Internet, network attack methods are constantly evolving and adapting. To better identify the network attack behavior, a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms. First, the algorithm designated the cluster set as vacant, with the introduction of a new object. Subsequently, a new cluster based on the given object was constructed. The dissimilarities between it and each existing cluster were calculated using a defined difference measure. The minimum dissimilarity was selected. Through comparing the proposed algorithm with the traditional Back More >

Zhang Zhi¹, Haiyu Huang², Wei Xiong², Yijia Zhou³, Mingyu Yan^3,*, Shaolian Xia², Baofeng Jiang², Renbin Su², Xichen Tian⁴

Energy Engineering, Vol.121, No.6, pp. 1557-1576, 2024, DOI:10.32604/ee.2024.047401

Abstract Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existing scenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios, which threatens the robustness of stochastic unit commitment and hinders its application. This paper provides a stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming and Benders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouples the primal problem into the master problem and two types of subproblems. In the master problem, the committed generator is determined, while the feasibility and… More >

Samar M. Alqhtani¹, Toufique A. Soomro^2,*, Faisal Bin Ubaid³, Ahmed Ali⁴, Muhammad Irfan⁵, Abdullah A. Asiri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475

Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More > Graphic Abstract

Pakorn Santakij¹, Samai Srisuay^2,*, Pongporn Punpeng¹

Computer Systems Science and Engineering, Vol.48, No.3, pp. 665-689, 2024, DOI:10.32604/csse.2024.045066

Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.… More >