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  • Open Access

    ARTICLE

    Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition

    Zhang Zhi1, Haiyu Huang2, Wei Xiong2, Yijia Zhou3, Mingyu Yan3,*, 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 >

  • Open Access

    ARTICLE

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

    Samar M. Alqhtani1, Toufique A. Soomro2,*, 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

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

  • Open Access

    ARTICLE

    Analyzing COVID-19 Discourse on Twitter: Text Clustering and Classification Models for Public Health Surveillance

    Pakorn Santakij1, Samai Srisuay2,*, 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 >

  • Open Access

    ARTICLE

    Path-Based Clustering Algorithm with High Scalability Using the Combined Behavior of Evolutionary Algorithms

    Leila Safari-Monjeghtapeh1, Mansour Esmaeilpour2,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 705-721, 2024, DOI:10.32604/csse.2024.044892

    Abstract Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree “MST”, and meta-heuristic algorithms, including Genetic Algorithm “GA” and Particle Swarm Optimization “PSO”. The Fast Path-based Clustering “FPC” algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and… More >

  • Open Access

    ARTICLE

    Fuzzy C-Means Algorithm Based on Density Canopy and Manifold Learning

    Jili Chen1,2, Hailan Wang2, Xiaolan Xie1,2,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 645-663, 2024, DOI:10.32604/csse.2023.037957

    Abstract Fuzzy C-Means (FCM) is an effective and widely used clustering algorithm, but there are still some problems. considering the number of clusters must be determined manually, the local optimal solutions is easily influenced by the random selection of initial cluster centers, and the performance of Euclid distance in complex high-dimensional data is poor. To solve the above problems, the improved FCM clustering algorithm based on density Canopy and Manifold learning (DM-FCM) is proposed. First, a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster… More >

  • Open Access

    ARTICLE

    Hyperspectral Image Based Interpretable Feature Clustering Algorithm

    Yaming Kang1,*, Peishun Ye1, Yuxiu Bai1, Shi Qiu2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2151-2168, 2024, DOI:10.32604/cmc.2024.049360

    Abstract Hyperspectral imagery encompasses spectral and spatial dimensions, reflecting the material properties of objects. Its application proves crucial in search and rescue, concealed target identification, and crop growth analysis. Clustering is an important method of hyperspectral analysis. The vast data volume of hyperspectral imagery, coupled with redundant information, poses significant challenges in swiftly and accurately extracting features for subsequent analysis. The current hyperspectral feature clustering methods, which are mostly studied from space or spectrum, do not have strong interpretability, resulting in poor comprehensibility of the algorithm. So, this research introduces a feature clustering algorithm for hyperspectral… More >

  • Open Access

    ARTICLE

    Density Clustering Algorithm Based on KD-Tree and Voting Rules

    Hui Du, Zhiyuan Hu*, Depeng Lu, Jingrui Liu

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3239-3259, 2024, DOI:10.32604/cmc.2024.046314

    Abstract Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets with uneven density. Additionally, they incur substantial computational costs when applied to high-dimensional data due to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset and compute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similarity matrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a vote for the point with the highest density among its KNN. By utilizing the vote counts More >

  • Open Access

    ARTICLE

    An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data

    Linlin Yuan1,2, Tiantian Zhang1,3, Yuling Chen1,*, Yuxiang Yang1, Huang Li1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1561-1579, 2024, DOI:10.32604/cmc.2023.046907

    Abstract The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’ privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based… More >

  • Open Access

    ARTICLE

    Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

    Jiao Wang, Bin Wu*, Hongying Zhang

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 143-160, 2024, DOI:10.32604/cmc.2023.046011

    Abstract Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the More >

  • Open Access

    ARTICLE

    A Web Application Fingerprint Recognition Method Based on Machine Learning

    Yanmei Shi1, Wei Yu2,*, Yanxia Zhao3,*, Yungang Jia4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 887-906, 2024, DOI:10.32604/cmes.2024.046140

    Abstract Web application fingerprint recognition is an effective security technology designed to identify and classify web applications, thereby enhancing the detection of potential threats and attacks. Traditional fingerprint recognition methods, which rely on preannotated feature matching, face inherent limitations due to the ever-evolving nature and diverse landscape of web applications. In response to these challenges, this work proposes an innovative web application fingerprint recognition method founded on clustering techniques. The method involves extensive data collection from the Tranco List, employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction. The core… More >

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