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  • Open Access


    Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing

    Zhihong Ouyang*, Lei Xue, Feng Ding, Yongsheng Duan

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 983-1008, 2023, DOI:10.32604/cmc.2023.042222

    Abstract Affinity propagation (AP) is a widely used exemplar-based clustering approach with superior efficiency and clustering quality. Nevertheless, a common issue with AP clustering is the presence of excessive exemplars, which limits its ability to perform effective aggregation. This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters, without changing the similarity matrix or customizing preference parameters, as done in existing enhanced approaches. An automatic aggregation enhanced affinity propagation (AAEAP) clustering algorithm is proposed, which combines a dependable partitioning clustering approach with AP to achieve this purpose. The partitioning clustering approach generates an additional set… More >

  • Open Access


    Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

    Xueting Cheng1, Ziqi Zhang2,*, Yueshuang Bao1, Huiping Zheng1

    Energy Engineering, Vol.120, No.11, pp. 2517-2529, 2023, DOI:10.32604/ee.2023.042633

    Abstract At present, the proportion of new energy in the power grid is increasing, and the random fluctuations in power output increase the risk of cascading failures in the power grid. In this paper, we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering (DEC) algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids. First, considering the real-time operation status and system structure of new energy power grids, the scenario cascading failure risk indicator is established. Based on… More >

  • Open Access


    K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

    Rufus Gikera1,*, Jonathan Mwaura2, Elizaphan Muuro3, Shadrack Mambo3

    Journal on Artificial Intelligence, Vol.5, pp. 75-112, 2023, DOI:10.32604/jai.2023.043229

    Abstract k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this issue, may be… More >

  • Open Access


    Image Steganalysis Based on Deep Content Features Clustering

    Chengyu Mo1,2, Fenlin Liu1,2, Ma Zhu1,2,*, Gengcong Yan3, Baojun Qi1,2, Chunfang Yang1,2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2921-2936, 2023, DOI:10.32604/cmc.2023.039540

    Abstract The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image.… More >

  • Open Access


    Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis

    Jingyao Liu1,2, Qinghe Feng4, Jiashi Zhao2,3, Yu Miao2,3, Wei He2, Weili Shi2,3, Zhengang Jiang2,3,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2649-2665, 2023, DOI:10.32604/cmc.2023.038891

    Abstract The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method was used for feature extraction… More >

  • Open Access


    Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

    Shiwei Su1,2, Guangyong Hu2, Xianghua Li3, Xin Li2, Wei Xiong2,*

    Energy Engineering, Vol.120, No.10, pp. 2343-2368, 2023, DOI:10.32604/ee.2023.028500

    Abstract As new power systems and dual carbon policies develop, virtual power plant cluster (VPPC) provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems. To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant, a multi-virtual power plant (VPP) electricity-carbon interaction optimal scheduling model considering integrated demand response (IDR) is proposed. Firstly, a multi-VPP electricity-carbon interaction framework is established. The interaction of electric energy and carbon quotas can realize energy complementarity, reduce energy waste and promote low-carbon operation. Secondly, in order to coordinate the multiple types of energy and… More > Graphic Abstract

    Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

  • Open Access


    Evaluation of Pre-Harvest Sprouting (PHS) Resistance and Screening of High-Quality Varieties from Thirty-Seven Quinoa (Chenopodium quinoa Willd.) Resources in Chengdu Plain

    Xin Pan, Ya Gao, Fang Zeng, Chunmei Zheng, Wenxuan Ge, Yan Wan, Yanxia Sun, Xiaoyong Wu*

    Phyton-International Journal of Experimental Botany, Vol.92, No.10, pp. 2921-2936, 2023, DOI:10.32604/phyton.2023.029853

    Abstract Pre-harvest sprouting (PHS) will have a serious effect both on the yield and quality of quinoa (Chenopodium quinoa Willd.). It is crucial to select and breed quinoa varieties with PHS resistance and excellent agronomic traits for guidance production and utilization of quinoa. A comprehensive evaluation of the PHS resistance and agronomic traits of 37 species of quinoa resources was conducted in Chengdu Plain. The evaluation used various methods, including grain germination rate (GR), grain germination index (GI), total spike germination rate (SR), total grain germination index (SI), grey correlation analysis (GCA), cluster analysis and correlation analysis. Results showed significant differences… More >

  • Open Access


    Genetic Algorithm Combined with the K-Means Algorithm: A Hybrid Technique for Unsupervised Feature Selection

    Hachemi Bennaceur, Meznah Almutairy, Norah Alhussain*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2687-2706, 2023, DOI:10.32604/iasc.2023.038723

    Abstract The dimensionality of data is increasing very rapidly, which creates challenges for most of the current mining and learning algorithms, such as large memory requirements and high computational costs. The literature includes much research on feature selection for supervised learning. However, feature selection for unsupervised learning has only recently been studied. Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate. This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS, which combines the genetic algorithm (GA) approach with the classical k-Means algorithm. In the proposed algorithm, a new… More >

  • Open Access


    Prediction of Effective Properties for Hyperelastic Materials with Large Deformation Behavior vis FEM-Cluster Based Analysis (FCA)

    Yinghao Nie1, Shan Tang1,*, Gengdong Cheng1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.1, pp. 1-2, 2023, DOI:10.32604/icces.2023.09603

    Abstract Advanced heterogeneous materials are widely used in many fields because of their excellent properties, especially those with hyperelastic properties and significant deformation behavior. Highly efficient numerical prediction methods of nonlinear mechanical properties of heterogeneous material provide essential tools for two-scale material and structural analysis, data-driven material design, and direct application in various engineering fields. Recently, the Clustering-based Reduced Order Model (CROM) methods [1-6] have proven effective in many nonlinear homogenization problems. However, some CROM methods would need help predicting significant large deformation behavior with more than 50% true strain. This presentation introduces the FEM-Cluster based Analysis (FCA: one of the… More >

  • Open Access


    Fully Automated Density-Based Clustering Method

    Bilal Bataineh*, Ahmad A. Alzahrani

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1833-1851, 2023, DOI:10.32604/cmc.2023.039923

    Abstract Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, and inconsistent performance concerning data size and structure. To address these challenges, a novel clustering algorithm called the fully automated density-based clustering method (FADBC) is proposed. The FADBC method consists of two stages: parameter selection and cluster extraction. In the first stage, a proposed method extracts optimal parameters for the dataset, including the epsilon size and a minimum number of points thresholds. These parameters are then used in a… More >

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