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International Conference on Informatics Education and Computer Technology Applications (IECA)

作者： Zhou, Haiyan Jilin Animat Inst Changchun Peoples R China

ISBN: (纸本)9798350375343;9798350375336

This study aims to explore how to utilize ASP NET technology and k-means clustering algorithm are used to analyze the humanistic quality evaluation data of private university students. Firstly, by introducing ASP We have explained the basic principles and functions of NET, and its application in efficiently processing and managing large amounts of educational data. Next, the working principle of k-means algorithm and its effectiveness in data clustering were introduced in detail. In the study, we constructed a humanistic quality evaluation system that includes multiple evaluation indicators, aiming to comprehensively evaluate the humanistic quality of students. By collecting relevant data on students from private universities and utilizing ASP NET is used for data preprocessing and management, and then k-means algorithm is applied for data clustering analysis. The results revealed the distribution of different dimensions of humanistic literacy and identified key influencing factors, providing data support for further improving education quality and student humanistic literacy. In addition, this study also discussed the limitations of the method and made suggestions for future research directions. Through this study, we demonstrated ASP The practicality of NET technology and k-means algorithm in educational data analysis provides a new perspective for private universities to evaluate and enhance students' humanistic qualities.

关键词： ASP NET technology k-means algorithm private universities humanistic qualities

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Optimized k-means algorithm-Based Piecewise Behavioral Model for DPD of RF PAs 16

Optimized K-means Algorithm-Based Piecewise Behavioral Model...

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16th International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Chang, Hao Han, Renlong Jiang, Chengye Yang, Guichen Zhang, Qianqian Wang, Junsen Liu, Falin Univ Sci & Technol China Dept EEIS Hefei 230027 Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

ISBN: (纸本)9798350389968

For the k-means algorithm-based piecewise behavior model for digital predistortion (DPD), an optimization idea based on weighted feature matrix is proposed in this paper, and two optimization methods are given respectively. The first one is attenuation function-based k-means optimization, and the common exponential attenuation function is chosen in this paper. The second one is genetic algorithm-based k-means optimization, which finds the optimal weight coefficients through a series of operations such as initializing population, two-point crossover, random mutation and calculating fitness. Neither of these optimization methods increase the complexity of the model. The experiments show that this k-means optimization idea based on weighted feature matrix is feasible, and the performance is significantly improved.

关键词： Behavioral modeling genetic algorithm (GA) k-means algorithm piecewise model power amplifiers (PAs) running complexity

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Voltage transformer secondary circuit anomaly detection based on k-means algorithm 3

Voltage transformer secondary circuit anomaly detection base...

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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Sun, Jianqiao Chen, Xiao Zhang, Wei Ding, Xunan Du, Junchi State Grid Zhejiang Mkt Ctr Hangzhou Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350375145;9798350375138

In the secondary circuit of voltage transformer in substation, there will be poor contact phenomenon, which will affect the accuracy of measurement and threaten the safe operation of power system. At present, regular inspection is used to check the power metering equipment, but the fault cannot be discovered immediately. In order to realize real-time detection of the voltage transformer's secondary circuit poor contact fault, this paper analyzes the data characteristics of the secondary circuit poor contact fault from the data, and establishes the secondary circuit abnormal detection model based on k-means algorithm to complete the voltage transformer's secondary circuit poor contact fault detection. The experimental results show that the secondary circuit anomaly detection method based on k-means algorithm proposed in this paper can effectively detect the number of anomalies, and the detection accuracy rate is more than 90%, which has a good detection effect and quality.

关键词： voltage transformer secondary circuit poor contact k-means algorithm anomaly detection

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Efficient Customer Segmentation Using Silhouette Based k-means algorithm 4

Efficient Customer Segmentation Using Silhouette Based K-Mea...

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4th International Conference on Artificial Intelligence and Signal Processing

作者： Chilla, Pavan karthik Nahak, Sunil kumar Deepika, Mudem Veera Venkata Rohit, Balivada Sai Hemalatha, Tankala Dalai, Asish kumar KL Univ Dept Comp Sci Vaddeswaram Andhra Pradesh India NIST Univ Dept Comp Sci & Engn Berhampur Odisha India Vijaya Inst Technol Women Dept Elect & Commun Engn Vijayawada Andhra Pradesh India VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Customer segmentation is now essential for firms seeking to maintain and optimize revenue from consumers. The present research investigates the potential of utilizing behavioral variables such as expenses and income to successfully segment clients, beyond conventional demographic methods. The k-means clustering procedure, which is an unsupervised machine learning methodology, is utilized using the Elbow method and Silhouette score to ascertain the most suitable number of clusters. A Python application, with basic scaling, examines a dataset from a retail mall to visually represent similarities among customers and categorize them into clusters. The project aims to segment customers based on their purchase behavior using the silhouette-based k-means algorithm. The seaborn library will be used for implementation. The objective is to create a resilient consumer segmentation approach that assists organizations in comprehending target markets and enhancing marketing strategies suitably. By employing this algorithm, organizations acquire knowledge about client behavior, facilitating customized marketing campaigns and enhanced income generat- ing.

关键词： Customer segmentation clustering k-means algorithm silhouette analysis machine learning data analysis marketing strategy behavioral traits

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Construction of an Online English Essay Self-Scoring Model Based on Improved k-means algorithm 3

Construction of an Online English Essay Self-Scoring Model B...

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3rd International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Zhang, Hanyue Xiong, Li School of Education and Foreign Languages Wuhan Donghu University Hubei Wuhan430212 China

ISBN: (纸本)9798350376173

English short essay questions play a crucial role in comprehensively evaluating students' English proficiency. Although paperless scoring methods and electronic correction methods have been used, ratings are still influenced by subjective factors, resulting in significant differences in scoring standards. The focus of this study is to introduce self-assessment techniques to construct an English essay self-assessment model to ensure consistency in the scoring criteria. In order to address the limitations of large training set data requirements and knowledge transfer breadth and depth faced by autonomous evaluation technology in practical applications, I adopted a new method based on expert assistance and successfully implemented a more feasible scoring model by integrating the k-means algorithm in artificial intelligence technology. This innovative solution provides valuable insights into overcoming the issue of consistency in grading and provides substantial direction and methods for developing an English essay self-assessment system. © 2024 IEEE.

关键词： Autonomous Assessment Technology Electronic Grading English Writing Expert Assistance k-means algorithm

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An Enhancement in k-means algorithm for Automatic Ultrasound Image Segmentation 1

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2nd International Conference on Biomedical Engineering Science and Technology (ICBEST) - Roadway from Laboratory to Market

作者： Panigrahi, Lipismita Panigrahi, Raghab Ranjan Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA SOA Univ Dept Comp Sci & Engn Bhubaneswar India

ISBN: (数字)9783031545474

ISBN: (纸本)9783031545467;9783031545474

Breast malignancy is a relatively frequent disease that affects people all over the world. When interpreting the lesion component of medical images, inter- and intra-observer errors frequently happen, leading to considerable diversity in result interpretations. To combat this variability, computer-aided diagnosis (CAD) systems are essential. Automatic segmentation is an essential and critical step inCADsystems toward boundary detection, feature extraction, and classification. The aim of this study is to incorporate an Ant colony optimization (ACO) to initialize the cluster center and replace the Euclidean distance (ED) with the Manhattan distance (MD), in the traditional k-means algorithm to segment the BUS images with maximal area preservation. The Jaccard index (JI), Dice similarity (DS), and Area difference (AD) are the cluster validation measures used to compare the efficiency of the proposed method with other state-of-the-art methods. A total of 1293 BUS images are used in this study. According to the quantitative experimental findings, the suggested method can successfully segment the BUS images with an accuracy of 91.66%. Compared to existing methods, the proposed approach accomplishes segmentation more quickly and accurately.

关键词： BUS images segmentation ACO Vector field Convolution k-means algorithm

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A k-means algorithm for Digital Management of Urban Planning Engineering 3rd

A K-means Algorithm for Digital Management of Urban Planning...

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3rd World Conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Wang, Chenlin Xi ‘an Jiaotong University City College Xi ‘an China

ISBN: (纸本)9789819791231

The background of digital management of urban planning engineering comes from the acceleration of urbanization and the continuous rise of population density, which brings challenges to urban governance. At the same time, the development of digital technology has also provided new solutions for urban governance. The digital management system of urban planning engineering has serious inefficiency and management error rate. In this paper, k-means algorithm is used to establish the digital management system of urban planning engineering, and the conclusion is drawn through the experiment. The algorithm greatly reduces the error rate, and its accuracy rate reaches 98.6, which improves the efficiency of digital management of urban planning engineering. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital management k-means algorithm Machine learning Quality assessment Urban planning engineering

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Nonparametric k-means algorithm with applications in economic and functional data

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2022年第2期51卷 537-551页

作者： Feng, Zhangmei Zhang, Jiamin Shanghai Univ Finance & Econ Shanghai Peoples R China

Inspired by the well-known relationship between k-means algorithm and Expectation-Maximization (EM) algorithm for mixture models, we propose nonparametric k-means algorithm for estimation of nonparametric mixture of regressions and mixture of Gaussian processes. The proposed methods are illustrated by extensive numerical simulations, comparisons, and analysis of two real datasets. Simulation studies and applications demonstrate that our method is an effective and competitive procedure for modified EM algorithm in nonparametric mixture settings.

关键词： k-means algorithm mixture models nonparametric regression Gaussian processes EM algorithm

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Cluster Analysis of Poverty Data in Cities/Districts in Indonesia Using k-means algorithm for the Years 2019–2022 8th

Cluster Analysis of Poverty Data in Cities/Districts in Indo...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Salomo, Julian Siregar, Bakti Matana University Kab. Tangerang Indonesia

ISBN: (纸本)9789819738588

This article focuses on clustering 514 cities/districts in Indonesia based on their poverty levels from the pre-COVID-19 period (2019) until the near-end of the COVID-19 period (2022). Using publicly available data from the Central Statistics Agency (BPS) for 2019–2022, encompassing 11 poverty-related variables, the study employs unsupervised learning techniques. The process involves testing for multicollinearity;normalizing the data and applying Principal Component Analysis to reduce the variables to 2 principal components;determining the optimal number of clusters (3) is done using the elbow method, silhouette index, and gap statistic algorithm;the k-means algorithm is then applied to cluster the cities/districts each year. The analysis reveals three clusters: cluster 1 represents areas with low poverty levels, cluster 2 comprises regions with moderate levels, and cluster 3 includes areas with high poverty rates. The number of cities/districts in cluster 3 increased from 36 in 2019 to 40 in 2022, with 32 consistently belonging to this cluster. These findings highlight the importance of targeted government assistance to these areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cluster optimization k-means algorithm PCA Poverty data Unsupervised learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第26期81卷 37007-37032页

作者： Gupta, Manoj kumar Chandra, Pravin Guru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol Sect 16C Delhi 110078 India Trinity Inst Innovat Profess Studies Greater Noida 201310 Uttar Pradesh India

Recently, Internet of Things (IoT) and multimedia are gaining popularity because of their usages in various applications. Numerous sensors and automated devices are generating huge volumes of data. Therefore, it is required to efficiently and effectively analyze this voluminous data. It can be achieved by using appropriate machine leaning techniques such as clustering. Among the clustering techniques, the k-means method/algorithm is one of the simplest, effective and commonly used methods. For making the cluster, it uses a measure of similarity/distance among the data observations. Nearby/similar data observations are placed within the same cluster whereas distant/dis-similar data observations are placed in other clusters. Hence, the similarity/distance metric plays a major role on the performance and accuracy of the k-means. Therefore, using an appropriate similarity/distance metric, the performance and accuracy of the k-means can be improved. k-means algorithm is majorly implemented using Euclidean distance metric. With the objective to explore the better and/or alternate similarity/distance metric(s) for k-means, a case study, based on empirical evaluation, of thirteen different similarity/distance metrics on six well-known datasets is performed and presented in this paper. By using the efficient and effective similarity / distance metrics, the performance and accuracy of the k-means algorithm can be improved which leads to formation of good clusters of various data observations or things or images etc. The results of the empirical study are analyzed and compared on the basis of widely used statistical clustering evaluation/validation measures. Based on the comparative results, these metrics are assigned with the ranks. Overall, the results demonstrate that Manhattan and Minkowski distance metrics gives better results for k-means algorithm.

关键词： Internet of things Data analytics and multimedia Similarity metrics Distance metrics k-means algorithm Clustering Machine learning

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