Jingyao Liu^1,2, Qinghe Feng⁴, Jiashi Zhao^2,3, Yu Miao^2,3, Wei He², Weili Shi^2,3, Zhengang Jiang^2,3,*

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 >

Shiwei Su^1,2, Guangyong Hu², Xianghua Li³, Xin Li², Wei Xiong^2,*

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

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 >

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 >

Yinghao Nie¹, Shan Tang^1,*, Gengdong Cheng^1,*

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 >

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 >

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 >

Lei Ling¹, Lijun Huang², Jie Wang², Li Zhang², Yue Wu², Yizhang Jiang¹, Kaijian Xia^2,3,*

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 >

Mona Jamjoom¹, Ahmed Elhadad², Hussein Abulkasim^3,*, Safia Abbas⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 367-382, 2023, DOI:10.32604/cmc.2023.037310

Abstract Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, including Phytophthora infestans, Fusarium graminearum,… More >

Muhammad Yasir Khan¹, Muhammad Adnan^1,2, Jawaid Iqbal³, Noor ul Amin¹, Byeong-Hee Roh⁴, Jehad Ali^4,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1185-1208, 2023, DOI:10.32604/csse.2023.039352

Abstract An Information-Centric Network (ICN) provides a promising paradigm for the upcoming internet architecture, which will struggle with steady growth in data and changes in access models. Various ICN architectures have been designed, including Named Data Networking (NDN), which is designed around content delivery instead of hosts. As data is the central part of the network. Therefore, NDN was developed to get rid of the dependency on IP addresses and provide content effectively. Mobility is one of the major research dimensions for this upcoming internet architecture. Some research has been carried out to solve the mobility issues, but it still has… More >