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Study on comprehensive evaluation of environmental pollution in tourist attractions based on fcm algorithm

INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT 2025年第1-3期28卷 174-189页

作者： Pan, Yurong Jia, Chaoyong Bengbu Univ Sch Math & Phys Bengbu 233000 Peoples R China

To address the shortcomings of traditional evaluation approaches for environmental pollution in tourist destinations, such as their limited precision, accuracy, and reliability, we must seek innovative strategies, a comprehensive evaluation method of environmental pollution in tourist attractions based on fcm algorithm is proposed. The comprehensive evaluation index system of environmental pollution in tourist attractions is established, and the evaluation index data are clustered by fcm algorithm. The improved principal component analysis is improved by logarithmic processing, so that the improved principal component analysis can process the evaluation index data with high quality, and environmental pollution evaluation results are obtained by combining the factor load matrix. The empirical findings indicate that the average precision of the evaluation index stands at an impressive 97.36%, the evaluation accuracy is between 96.5% and 98.3%, and the average reliability of the evaluation result is 0.97, which can realise the accurate evaluation of environmental pollution.

关键词： fcm algorithm tourist attractions environmental pollution comprehensive evaluation logarithmic processing improved principal component analysis

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Application of data mining in female sports behavior prediction based on fcm algorithm

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SOFT COMPUTING 2023年第14期27卷 10045-10055页

作者： Yuan, Liyun Cao, Jiaxing Tianjin Univ Sport Tianjin Peoples R China Tianjin Sports Dev Co LTD Tianjin Peoples R China

Data mining is the process of continuously optimizing the problem to the goal. It has to meet the needs of the user, which means it has to unearth content that is useful or interesting to the user. This paper analyzes the data mining of female sports behavior prediction based on fcm algorithm. We modified the standard speech frequencies of the data into high-level descriptions, used various data mining methods to determine speech rules, and obtained good experimental results. Data mining technology is used to mine valuable information and knowledge hidden in a large amount of test security data, and make the acquired expertise into understandable rules or patterns, and then use the rules or patterns that have been created to effectively detect Guidelines for any activity suspected of attacking network data. The simulation results show that the data mining system in this paper can effectively predict and analyze the data. The results show that there are many differences among working women in terms of average annual consumption of sports and leisure activities, participation and consumption of sports and leisure activities. In addition, based on the results of the study, we will take more effective and specific references and measures to expand the participation of professional women in sports and leisure activities.

关键词： Data mining Language processing fcm algorithm Sports research

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Optimization of overprint error detection based on the fcm algorithm

Optimization of overprint error detection based on the FCM a...

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International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)

作者： Wang, Yuchuan Qi, Yusheng Zhang, Yongbin Du, Yunfei Beijing Inst Graph Commun Beijing Peoples R China

ISBN: (纸本)9781665464680

Because the printing accuracy of printed matter needs to be higher and higher, it is necessary to strengthen the accuracy of the overprint error detection. However, in the current machine vision detection methods, when separating the four primary colors of CMYK, there is still the phenomenon of four colors overlapping, which decrease the detection accuracy. In this paper, based on the existing machine vision detection methods, The fcm algorithm is used to cluster image colors, so as to improve the accuracy of color segmentation and decrease the impact of four-color overlap. Through this method, the identifier image can be clustered to improve the detection accuracy. The results indicate that this method can reduce the interaction between the four colors to a certain extent and provide convenience for successive color segmentation.

关键词： fcm algorithm Machine vision inspection CMYK color space Quadratic segmentation algorithm

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Research on Online Reading User Clustering Method and Application based on fcm algorithm 22

Research on Online Reading User Clustering Method and Applic...

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4th Blockchain and Internet of Things Conference (BIOTC)

作者： Hu, Xuewei Shandong Management Univ Sch Informat Engn Jinan Peoples R China

ISBN: (纸本)9781450396622

Reading is one of the important ways for people to absorb knowledge and improve their ability. With more and more extensive network information and more and more popular digital reading, people's reading media is no longer limited to paper text. According to the analysis of various surveys, it is found that online reading has a certain impact on the traditional paper book reading. In this paper, online reading users as the research object, and through offline interviews and questionnaires in the form of 362 valid data. The data is processed, the quantitative data is imported into Matlab, and fcm algorithm is used to cluster similar users according to the characteristics of users. The experimental results show that the clustering effect is the best when SSE index determines that the clustering results are four categories, and the characteristics of the four categories of readers are significantly different. This paper analyzes the characteristics of all kinds of readers, makes corresponding recommendations according to the characteristics of different readers and users, and puts forward corresponding promotion countermeasures, so as to improve users' online reading satisfaction and the use frequency of online reading platform.

关键词： fcm algorithm Digital reading Online reading User analysis

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Extraction of plateau lake water bodies based on an improved fcm algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期41卷 1727-1740页

作者： Li, Yingxin Li, Shihua Peng, Shuangyun Zhao, Shoulu Yang, Wenxian Qiu, Lidan Yunnan Normal Univ Fac Geog Kunming Yunnan Peoples R China Yunnan Prov Geomat Ctr Kunming Yunnan Peoples R China Yunnan Univ Sch Earth Sci Kunming Yunnan Peoples R China

Changes in plateau body lake water are an important indicator of global ecosystem changes, and a timely and accurate grasp of this change information can provide a scientific reference for the formulation of relevant policies. The traditional fuzzy C-means clustering (fcm) algorithm takes into account the ambiguity of the classification of the ground object pixels but does not consider the rich spectral information of the neighboring pixels and is very sensitive to the background noise" of the remote sensing image, resulting in low water extraction accuracy. Aiming to compensate for the shortcomings of the traditional fcm algorithm, this paper proposes an improved fcm algorithm. This algorithm replaces the Euclidean distance of the traditional fcm algorithm with a combination of the Mahalanobis distance and spectral angle matching (SAM) to fully take into account the spectral information of neighboring pixels and improve the clustering accuracy. The study selected Sentinel-2 images of the Fuxian Lake and Xingyun Lake basins during normal, wet, and dry periods as the data source. Under the same conditions, the clustering accuracy was compared with the traditional fcm algorithm, improved fcm algorithm, K-means clustering method and iterative self-organizing data analysis (ISODATA) clustering method. The experimental results show that the improved fcm algorithm has a higher water extraction accuracy than the traditional fcm algorithm, K-means clustering method and ISODATA clustering method. The kappa coefficient and overall accuracy (OA) of the improved fcm algorithm can be increased by 5.56%-9.45% and 2.66%-5.32%, respectively, and the omission error and commission error can be reduced by 1.72%-4.55% and 12.14%-22.10%, respectively. When the improved fcm algorithm is used, the extraction accuracy is higher for plateau deep lakes than for plateau shallow lakes, and the extraction effect for lakes with poor water environments is more significant than that of other metho

关键词： Remote sensing fuzzy clustering fcm algorithm mahalanobis distance spectral angle matching

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Clustering Optimization for Triple-Frequency Combined Observations of BDS-3 Based on Improved PSO-fcm algorithm

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REMOTE SENSING 2022年第15期14卷 3713-3713页

作者： Qian, Zhaoyong Cao, Yuhua Sun, Xiaoshuang Ni, Lei Wang, Zhiyu Chen, Xiaowei Space Engn Univ Dept Space Support Beijing 101416 Peoples R China Natl Def Univ Joint Serv Coll Beijing 100858 Peoples R China

The triple-frequency linear combination method can provide combinations with different characteristics and is one of the important methods to improve the performance of navigation services. Due to the large number of combinations and different combination performances, combinatorial clustering optimization is very important, and the efficiency of manual screening is very low. Firstly, based on the basic model, the objective equations are derived. Secondly, based on the fuzzy c-means (fcm) algorithm, three improved PSO-fcm algorithms are proposed, namely the S-PSO-fcm algorithm, L-PSO-fcm algorithm, and LOG-PSO-fcm algorithm. Thirdly, according to the different combination characteristics, the two datasets whose combined coefficients sum to 0 and 1 are emphatically discussed. Finally, the effectiveness of the improved PSO-fcm algorithms is studied based on the public dataset and the measured BeiDou-3 navigation satellite system (BDS-3) data of short baseline, long baseline, and ultra-long baseline. The results show that the performance of the proposed algorithm is better than that of the fcm algorithm, especially in short baseline and long baseline cases.

关键词： triple frequency clustering optimization combination fcm algorithm improved PSO-fcm algorithm

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Improved Iteration fcm algorithm for MapReduce Research 2018

Improved Iteration FCM Algorithm for MapReduce Research

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2nd International Conference on Telecommunications and Communication Engineering (ICTCE)

作者： Kiki, Mesmin J. Mbyamm Zhang Jianbiao Kouassi, Adolphe Bonzou Beijing Univ Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Univ Technol Sch Elect Informat & Control Engn Beijing Peoples R China

ISBN: (纸本)9781450365857

Aiming at the iterative characteristics of the clustering process of fuzzy C-means clustering (CLUSTERING, fcm) algorithm, an iterative MapReduce model is used for fcm. The algorithm is optimized, the map function calculates the membership degree of each sample to the cluster center, the reduce function receives the new cluster center of the middle output of the map function, and the transfer module transmits the newest cluster center to the original map task node for the new round of mapreduce job;iterative The MapReduce model adds a transfer module to the MapReduce basic model, which effectively solves the deficiencies of the basic model in dealing with the iterative problem. In the Hadoop platform, we use fcm based on the iterative MapReduce and MapReduce basic models, respectively. The algorithm is used to diagnose the transformer;the experimental results show that the diagnostic speed of fcm algorithm based on iterative mapreduce is more than 12 times of the MapReduce basic model algorithm, the misjudgment rate is lower 12 to 15 percent, and the diagnostic efficiency of fcm algorithm is improved effectively.

关键词： Cloud Environment iteration fcm algorithm MapReduce

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Customer Churn Prediction Using Improved fcm algorithm 3

Customer Churn Prediction Using Improved FCM Algorithm

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3rd International Conference on Information Management (ICIM)

作者： Cui, Shaoying Ding, Ning Dalian Maritime Univ Transportat Management Coll Dalian Peoples R China

ISBN: (纸本)9781509063062

Data mining can provide support for bank managers to effectively analyze and predict customer churn in the era of big data. After analyzing the reasons for the bank customer churn and the defects of fcm algorithm as a data mining algorithm, a new method of calculating the effectiveness function to improve the fcm algorithm was raised. At the same time, it has been applied to predict bank customer churn. Through data mining experiments of customer information conducted on a commercial bank, it's found out the clients have been lost and will be lost. Contrast of confusion matrixes shows that the improved fcm algorithm has high accuracy, which can provide new ideas and new methods for the analysis and prediction of bank customer churn.

关键词： Bank customers churn Empirical analysis fcm algorithm Validity function

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Real-time Segmentation of Yarn Images Based on an fcm algorithm and Intensity Gradient Analysis

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FIBRES & TEXTILES IN EASTERN EUROPE 2016年第4期24卷 45-50页

作者： Li, Zhongjian Pan, Ruru Wang, Jing'an Wang, Ziyu Li, Bianbian Gao, Weidong Jiangnan Univ Sch Text & Clothing Wuxi 214122 Peoples R China

This paper presents a new method for real-time segmentation of yarn images which are captured by a real-time image acquisition device. The first frame of the images is clustered by the local average intensity and entropy of the image based on the fcm (Fuzzy C-means) algorithm to obtain a segmentation threshold value. The pixels with an intensity below the threshold value in each column of the image are convolved with a convolve template to construct an intensity gradient curve. The points of maximum value and minimum value in the curve are considered as the upper and lower edge points of yarn. A robust real-time segmentation algorithm of yarn images is obtained for evaluating yarn diameter more precisely. Finally two indices of SE (Segmentation Error) in % and ADE (Average Diameter Error) in % are proposed to evaluate the segmentation method, which is then compared with the manual method.

关键词： real-time segmentation image processing yarn evenness fcm algorithm gradient analysis

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Resource Scheduling Based on Improved fcm algorithm for Mobile Cloud Computing 22

Resource Scheduling Based on Improved FCM Algorithm for Mobi...

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22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS)

作者： Wu Hong-Qiang Li Xiao-Yong Fang Bin-Xing Wang Yi-Ping Beijing Univ Posts & Telecommun Sch Comp Sci & Technol Minist Educ Key Lab Trustworthy Distributed Comp & Serv BUPT Beijing Peoples R China

ISBN: (纸本)9781509044573

With the development of mobile devices, mobile cloud computing is becoming increasingly important. One of the basic questions in mobile cloud computing is how to match user demand with cloud server resources. Based on Improved fcm (IGAfcm) algorithm, this paper proposes a scheduling scheme which is provided for mobile resources to cluster continuously, so as to reduce the size of the matching requirements during the search. Moreover, Experiments have proved that matching strategy is dynamically adjusted according to the matching score and feedback training.

关键词： Mobile cloud computing resource scheduling resource clustering fcm algorithm

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