Research on geological hazard risk assessment based on the cloud fuzzy clustering algorithm

Yang, Yanguo; Yu, Jiaqi; Fu, Yubin; Hu, Jiangtao

doi:10.3233/jifs-179311

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…The applications of big data clustering algorithms are mainly concentrated in the fields of graph processing, pattern matching, and market analysis. There are various difficulties in cluster analysis research of big data, and these difficulties are determined by the characteristics of big data itself [9]. After entering the era of big data, the amount of data that needs to be processed has increased dramatically, and the traditional clustering method using serial data analysis has been difficult to adapt to the data processing requirements in the current cloud computing network environment, the author adopts a parallel method to study and optimize the big data clustering algorithm with the prediction strength as the starting point.…”

Section: Article Contributionmentioning

confidence: 99%

Research and Application of Big Data Clustering Algorithm based on AI Technology in Cloud Environment

Huang,

Zhang

2024

SCPE

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Traditional big data filling algorithms often give inaccurate results due to vulnerabilities to different data types.To solve this problem, this study presents a new big data clustering algorithm powered by AI technology in a cloud environment. The study proposes an advanced Big Data clustering algorithm that leverages AI technology in a cloud environment. It optimizes clustering based on predicted strength using parallel processing. The research focuses on optimizing the clustering algorithm based on the predicted intensity through parallel processing. Experimental results demonstrate that image clustering stability is achieved when the number of clusters exceeds 4, indicating reduced sensitivity to random factors. Although it was not possible to precisely determine the optimal number of clusters, the use of an optimization algorithm showed that at four clusters the prediction intensity reached its peak, ensuring more accurate cluster identification. Through rigorous testing, the optimal number of clusters was determined to be 4. Clustering results show that visitors characterized by certain attributes show higher interest in most columns. This algorithm makes it easier to cluster incomplete large data, improves clustering speed, and improves the accuracy of filling in missing data. Compared to existing methods, this algorithm leverages AI technology in the cloud environment to optimize clustering based on prediction intensity, providing improved accuracy and efficiency during processing in big data management.

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Section: Article Contributionmentioning

confidence: 99%

Research and Application of Big Data Clustering Algorithm based on AI Technology in Cloud Environment

Huang,

Zhang

2024

SCPE

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“…Different roles correspond to different management paths. Students and teachers need functions such as inquiring and entering educational information, while the Academic Affairs Office needs functions such as reviewing and maintaining educational information [22]. Figure 10 signifies the specific software architecture.…”

Section: Database Engine Report Service Analysis Servicesmentioning

confidence: 99%

Design and Implementation of Intelligent Educational Administration System Using Fuzzy Clustering Algorithm

Liu

2021

Scientific Programming

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The present work aims to solve the problems that the traditional educational administration management system has, such as low efficiency in analyzing big data, and the analysis results have low value, which is based on manual rules definition in big data analysis and processing. The work proposes a student achievement prediction model FCM-CF based on Fuzzy C-means (FCM) and Collaborative Filtering (CF). The work also introduces it into the research of educational administration management to construct an intelligent educational administration management system. At the beginning, the FCM-CF model is described in detail. Then, the system requirements and specific design methods are described in detail. Eventually, with the students’ performance prediction as an example, the performance of the system is tested by designed simulation experiments. The result shows that the students’ achievement in study is closely related to their daily study performance such as preparation before class, classroom performance, attendance, extracurricular study, and homework completion. Generally, the examination scores of students are significant to their daily performances. Under the same experimental conditions, the prediction error of the FCM-CF model proposed here is less than 10.8% of that of other algorithms. The model has better prediction performance and is more suitable for the prediction of middle school students’ examination scores in educational administration management system. The innovation of intelligent educational administration management system is that, in addition to the basic information management function, it also has two other functions: students’ performance prediction analysis and teacher evaluation prediction. It can provide data support for improving teaching quality. The research purpose is to provide important technical support for more intelligent educational administration and reduce the loss of human resources in educational administration.

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“…Article [36] studies the GIS-based risk assessment and regionalization of Taihang Mountain drought and flood disasters. In order to improve the comprehensiveness and accuracy of geological hazard risk assessment, a cloud fuzzy clustering algorithm is constructed in article [39], which can effectively estimate and evaluate uncertain variables. Article [40] constructs a model of competition between two manufacturers (large farmers) and two retailers (pork wholesalers) in the pig breeding industry under fuzzy environment.…”

mentioning

confidence: 99%

Special issue on fuzzy theoretical model analysis for signal processing

Balas

Hong

Lin

2019

IFS

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Research on geological hazard risk assessment based on the cloud fuzzy clustering algorithm

Cited by 7 publications

References 14 publications

Research and Application of Big Data Clustering Algorithm based on AI Technology in Cloud Environment

Research and Application of Big Data Clustering Algorithm based on AI Technology in Cloud Environment

Design and Implementation of Intelligent Educational Administration System Using Fuzzy Clustering Algorithm

Special issue on fuzzy theoretical model analysis for signal processing

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