检索结果-内蒙古大学图书馆

19th IEEE International Conference on Mobile Ad Hoc and Smart Systems (MASS)

作者： Kim, Haesik VTT Tech Res Ctr Finland POB 1100 FI-90570 Oulu Finland

ISBN: (数字)9781665471800

ISBN: (纸本)9781665471800

The IoT systems enable us to create various new services and offer new solutions. They play an important role in 5G and 6G systems. In order to operate IoT systems efficiently and support scalability, they should be grouped properly. There are many clustering and association ways for IoT systems. Clustering algorithms of machine learning are techniques for finding similarity groups in data. In this paper, we propose two-steps IoT device association method by combining Fuzzy C-Means clustering algorithm and apriori algorithm. Fuzzy C-Means clustering as a soft clustering approach assigns data points to probability score and allocates them to clusters. apriori algorithm is used in finding frequent sets and relevant association rules. As combining the clustering technique and the association rule mining technique, we can obtain multiple benefits including handover cost reduction. The performances of the proposed method are evaluated and compared with K mean clustering algorithm in terms of energy efficiency and handover frequency.

关键词： IoT Fuzzy C-Means Clustering apriori algorithm Handover Clustering Association Energy efficiency 5G and 6G systems etc

来源：评论

学校读者我要写书评

暂无评论

The Research on Analyzing Risk Factors of Type 2 Diabetes Mellitus Based on Improved apriori algorithm

The Research on Analyzing Risk Factors of Type 2 Diabetes Me...

引用

2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015)

作者： Wang yuzhen Jiang donghong Wei Zhe Ye Guangjian Lanzhou General Hospital Lanzhou Military Area Command School of Electrical Engineering and Information Engineering Lanzhou University of Technology

ISBN: (纸本)9781510813465

Purpose: We do it to improve the efficiency of analyzing risk factors of Type 2 Diabetes. Method: We use the patients' data from the information department of one tertiary referral hospital in Lanzhou which include course note of disease and their health record form January 2009 to March *** find out one improved algorithm applies to analyze risk factors of Type 2 Diabetes based on original apriori algorithm and it's requirement. And we analyze the efficiency by programming both of the algorithms with C#. Result: We can analyze the chart of frequent item and support degree, time and number of records, time and support degree. Conclusion: This new improved apriori algorithm has a high efficiency in analyzing risk factors of Type 2 Diabetes.

关键词： data mining apriori algorithm association rules improved algorithm

来源：评论

学校读者我要写书评

暂无评论

The Improved apriori algorithm was Applied in the System of Elective Courses in Colleges and Universities and Analysis of Carbon Emissions

The Improved Apriori Algorithm was Applied in the System of ...

引用

2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2015)

作者： She Wei Tan Yuyin Xie Huijuan Hainan University Hainan College of Economics and Business

ISBN: (纸本)9781510806481

apriori algorithm is one of classical algorithms in data mining, can mine frequent item sets what the association rules needed. A classic application is shopping analysis in the supermarket, such as "beer" and "diaper". Aiming at the system of professional elective courses in the colleges and universities, this paper proposes a modified apriori algorithm that can analyze what the combination of each professional elective course that influenced the employment can provide basis for decision making for the college professional elective course system and provide some suggestions for the employment of students. At the same time, the association rules can also provide the analysis of carbon emissions in the environmental protection, and provide decisions to the low-carbon for China's government.

关键词： Data Mining apriori algorithm Professional Elective System low-carbon

来源：评论

学校读者我要写书评

暂无评论

Application Analysis of Nursing Students'Grades in Course Relevance Based on Association Rule Mining algorithm apriori

引用

Journal of Contemporary Educational Research 2024年第2期8卷 213-223页

作者： Xuemei Li Edward CJimenez Angeles University Foundation PhD Educ-EMMacArthur HwyAngeles2009 PampangaPhilippines Shandong College of Traditional Chinese Medicine Educational Technology CenterYantai 264199Shandong ProvinceChina

By analyzing the correlation between courses in students’grades,we can provide a decision-making basis for the revision of courses and syllabi,rationally optimize courses,and further improve teaching *** the help of IBM SPSS Modeler data mining software,this paper uses apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine,and to explore the correlation between professional basic courses and professional core ***,according to the detailed analysis of the mining results,valuable curriculum information will be found from the actual teaching data.

关键词： Grade analysis apriori algorithm Course relevance Data mining

来源：评论

学校读者我要写书评

暂无评论

Agricultural price forecasting based on the spatial and temporal influences factors under spillover effects

引用

KYBERNETES 2025年第3期54卷 1321-1343页

作者： Tang, Dezhao Cai, Qiqi Nie, Tiandan Zhang, Yuanyuan Wu, Jinghua Sichuan Agr Univ Coll Informat Engn Yaan Peoples R China Sichuan Agr Univ Coll Sci Yaan Peoples R China

PurposeIntegrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the ***/methodology/approachTo explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data;second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price;then, the apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features;finally, the N-Beats model was selected as the prediction model for futures *** the apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different ***/valueThis paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricult

关键词： Artificial intelligence apriori algorithm Granger causality test N-Beats model Quantitative investment

来源：评论

学校读者我要写书评

暂无评论

Web log mining techniques to optimize apriori association rule algorithm in sports data information management

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-8页

作者： Li, Tiantian Liu, Fang Chen, Xiaobin Ma, Chao Bengbu Med Univ Dept Sports & Arts Bengbu 233000 Anhui Peoples R China

To optimize the current college sports data information management system, this study combines the apriori association rule algorithm with web application development technology to upgrade the management system. Firstly, this study explores novel log mining techniques in genetic algorithms and web application development technology. Secondly, by integrating log mining techniques to optimize the apriori algorithm, associations between sports data and information are discovered. Through the optimized algorithm, this study identifies key association rules of sports data information and validates the optimized system's reliability and effectiveness through experiments. The experimental results show that the running time of the traditional apriori algorithm exponentially grows with the increase in information volume, while the optimized execution efficiency is improved by approximately 10-15%. Additionally, the average retrieval accuracy of this optimized system can reach 98.3%, although the retrieval time also increased by 23%. Therefore, the technology and algorithms proposed in this study have certain application value in the sports information management system and contribute to the optimization of data information management in this field.

关键词： Web log mining technology apriori algorithm Sports data Information management Retrieval time

来源：评论

学校读者我要写书评

暂无评论

Dynamic Scene Segmentation and Sentiment Analysis for Danmaku

引用

APPLIED SCIENCES-BASEL 2025年第8期15卷 4435-4435页

作者： Li, Limin Jing, Jie Shi, Peng Univ Sci & Technol Beijing Natl Ctr Mat Serv Safety 12 Kunlun Rd Beijing 100083 Peoples R China

Danmaku analysis is important for understanding video content and user interactions. However, current methods often look at separate comments and do not see the complex links between Danmaku and the video's context. This paper presents a new approach that combines advanced shot segmentation techniques, using Deep Convolutional Neural Networks (DDCNN), with an analysis of feelings based on the MacBERT model. First, videos are cut into clear scenes based on detected scene changes. Then, a large group of Danmaku comments are collected and studied to make a complete dictionary of feelings for this field. With this as a base, a new Danmaku-E model is made to find and group seven different emotional categories within Danmaku comments. The model shows significantly improved performance, with accuracy increasing from 94.58% to 95.37% and F1 score going from 94.92% to 95.66%, helped by the improved dictionary of feelings. Experimental results show the good effects of the expanded dictionary in helping model performance in different structures. Also, the apriori algorithm is used to find and explain links between Danmaku comments and video content, providing a deeper understanding into user participation and emotional reactions.

关键词： Danmaku sentiment analysis scene segmentation apriori algorithm macbert

来源：评论

学校读者我要写书评

暂无评论

action-rules: GPU-accelerated Python package for counterfactual explanations and recommendations

引用

SOFTWAREX 2025年 29卷

作者： Sykora, Lukas Kliegr, Tomas Prague Univ Econ & Business Dept Informat & Knowledge Engn W Churchill Sq 1938-4 Prague 130 67 Zizkov Czech Republic

The action-rules package provides an efficient method for mining action rules using the Action-apriori algorithm, a modification of the traditional apriori algorithm tailored specifically for action rule mining. Designed to generate counterfactual explanations, this Python package enables researchers and practitioners to discover actionable insights by integrating user-defined parameters directly into the rule generation process, reducing computational overhead. The action-rules package supports optional GPU acceleration to further speedup processing on large datasets. The package provides a user-friendly API, as well as a command-line interface for versatile use. The package supports the customization of stable and flexible attributes, as well as separate minimum support and confidence thresholds for both the desired and undesired components of the rules. Comprehensive documentation, including a Jupyter Notebook example, is provided to facilitate ease of use for both novice and expert users.

关键词： Action rules apriori algorithm Data mining Counterfactual explanations Python

来源：评论

学校读者我要写书评

暂无评论

Automated Population and Validation of Geologic Logging Fields: An Approach to Autopopulate Select Logging Parameters and Rapidly Identify Mis-Logged Interval Candidates

引用

MINING METALLURGY & EXPLORATION 2024年第6期41卷 3641-3658页

作者： Hutson, Lynnette L. Barton, Isabel Hill, Logan Stavast, William Youn, Seokjun Univ Arizona Dept Min & Geol Engn Tucson AZ 85721 USA Freeport McMoRan Inc Phoenix AZ USA Univ Arizona Dept Management Informat Syst Tucson AZ USA

Geological characterization of drillholes is a crucial source of exploration data, but is a time-consuming, laborious, and manual process. This paper tests two ways that automation can help speed up drill core logging and assessing core log accuracy: (1) autopopulation of interpretive parameters from entered descriptions and (2) autovalidation, or automatic identification and flagging of potentially mis-logged intervals. These two approaches were tested using geologic logging data from 15 + years of drilling at the Morenci copper porphyry deposit. Autopopulation of an interpretive field (rock type) from the values entered in a descriptive field, including, e.g., grain size, color, texture, and mineralogy, with a Complementary Naive Bayes classifier resulted in 89% accuracy. The autovalidation test applied two methods. The first used text mining to extract the rock type description from within a comment field and cross-referenced it to the manually logged rock code parameter, testing the rock code for inconsistency with the rock type description. The second used the apriori algorithm to develop association rules ranking the commonness of mineral combinations in the same logging interval. The rarest mineral combinations may be erroneous and were automatically flagged for further review. Both autovalidation methods combined resulted in approximately 4300 logged intervals identified as potentially inaccurate. Eliminating typographical errors and similar minor problems narrowed this down to similar to 1000 potentially mis-logged intervals. Review by geologists confirmed that 350 of them were incorrectly logged. This study demonstrates the viability of using automation as a complement to the conventional manual core logging process, even in the absence of sophisticated automated logging systems. While the benefits of such approaches are incremental, they may still yield significant time savings for the operation given the volume of core and time and labor intensity of log

关键词： Core logging Autopopulation Autovalidation Text mining apriori algorithm

来源：评论

学校读者我要写书评

暂无评论

Intelligent Attitude Control Method for Shield Tunneling Machines Considering a Rectifying Mechanism: A Case Study of the Chengdu Subway

引用

INTERNATIONAL JOURNAL OF GEOMECHANICS 2024年第8期24卷

作者： Xiao, Haohan Cao, Ruilang Feng, Shangxin China Inst Water Resources & Hydropower Res Key Lab Simulat & Regulat Water Cycle River Basin Beijing 100038 Peoples R China Xian Univ Sci & Technol Sch Architecture & Civil Engn Xian 710054 Shaanxi Peoples R China

During tunnel construction with earth pressure balance (EPB) shield machine, the machine operators determine the attitude correction parameters depending only on their own experiences. Inappropriate parameter setting may lead to more and more deviation of equipment attitude, delay of construction period, or even ground collapse. An attitude correction method for an EPB shield machine is proposed in this study to assist new machine operators in determining suitable operating parameters in advance considering the previous correction experience. The proposed method first reconstructs the tunneling parameters and attitude parameters according to experienced drivers. Then, using these parameters, an association model based on multiple machine algorithms is established to refine the important association rules of EPB shield machine attitude. Finally, the optimal range of deviation correction parameters corresponding to different attitude deviation is generated. This assisted attitude control method was examined using the data from the Chengdu Subway project in China. Essentially, this study can be helpful for the equipment attitude correction and the determination of correction parameters, paving the way for ideal track driving in harsh tunneling environments.

关键词： EPB shield machine Attitude correction method Tunneling parameters Association model apriori algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：