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  • Open Access

    ARTICLE

    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

    ARTICLE

    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

    ARTICLE

    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

    ARTICLE

    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

    ARTICLE

    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

    ARTICLE

    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

    PROCEEDINGS

    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

    ARTICLE

    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 >

  • Open Access

    ARTICLE

    Analysis of CLARANS Algorithm for Weather Data Based on Spark

    Jiahao Zhang, Honglin Wang*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2427-2441, 2023, DOI:10.32604/cmc.2023.038462

    Abstract With the rapid development of technology, processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming, which cannot meet the demands of scientific research and business. Therefore, this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search (CLARANS) clustering algorithm on the Spark cloud computing platform to cluster China’s climate regions using meteorological data from 1988 to 2018. The aim is to address the challenge of applying clustering algorithms to large datasets. In this paper, the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance,… More >

  • Open Access

    ARTICLE

    An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

    Lei Ling1, Lijun Huang2, Jie Wang2, Li Zhang2, Yue Wu2, Yizhang Jiang1, Kaijian Xia2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2353-2379, 2023, DOI:10.32604/cmes.2023.028828

    Abstract In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum; the clustering effect… More >

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