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apriori algorithm-Based Learning Behavior Mining for Mobile Education Platforms

MOBILE NETWORKS & APPLICATIONS 2024年 1-13页

作者： Hong, Mei Alwadain, Ayed Alzahrani, Ahmed Ibrahim Changchun Univ Changchun 130000 Peoples R China King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia

In order to promote learners' learning effectiveness and improve the accuracy of learning behavior mining, this paper conducts research on the apriori algorithm based learning behavior mining on mobile education platforms. Firstly, web crawler technology is used to capture the behavioral information of learners during the learning process on the mobile education platform to construct learner profiles, and preprocess the sub-network set data of learning behaviors. Secondly, a Hash table is constructed to improve the apriori algorithm to extract the learning behavior characteristics of learners on the mobile education platform. Then, a Stacking ensemble learning model is built to determine four base learners for model training. Finally, the Stacking ensemble learning model is improved with a chain rule, and conducting k-fold cross-validation to achieve data mining of learning behaviors on the mobile education platform. Comparative experiments have proven that when using the method proposed in this paper for data mining of learning behaviors on the mobile education platform, the normalized difference precision is always above 90%, the mAP value is always above 93%, the mining scope coverage rate is maintained above 90%, and the comprehensiveness is kept above 90%. This indicates that applying the method proposed in this paper to the data mining of learning behaviors on the mobile education platform can improve the accuracy of data mining and has a good mining effect.

关键词： apriori algorithm Mobile education platform Learning behavior Data mining Hash table Stacking ensemble learning

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Exploring hierarchy shift of travelers' public transport dependence using an improved apriori algorithm

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2025年第1期17卷 72-85页

作者： Hu, Song Wang, Lin Weng, Jiancheng Zhou, Wei Qian, Yue Wang, Haipeng Res Inst Highway Minist Transport Natl Intelligent Transport Syst Ctr Engn & Technol Beijing Peoples R China Beijing Univ Technol Beijing Key Lab Traff Engn 100 Pingleyuan Beijing 100124 Peoples R China Minist Transport Peoples Republ China Comm Experts Beijing Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

Special external environments will lead to significant changes in the use behavior and dependence degree of different PT travellers, but it is difficult to analyze the mechanism of the hierarchy shift of travelers' public transportation (PT) dependence. Exploring travelers' dependence on PT is conducive to understanding individuals' travel choice behavior and optimizing PT operation organizations. Developing methods for analyzing the internal causal relationship between travelers' dependence on PT and the key influencing factors under the special condition is an issue. Therefore, the individual travel chains are constructed by associating and matching the multisource PT big data and travel survey data. Thereafter, the K-means algorithm and an improved apriori algorithm are developed to mine the frequent association rules of groups, and a framework of cross-hierarchy policy implications is derived based on the differences in association rules. Finally, the stated preference survey method is used to measure the effectiveness of the policies.

关键词： Public transport dependence K-means algorithm apriori algorithm association rules

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Influence factors on travel mode preference of working residents living far away from downtown area on workdays: A hybrid method integrating random parameter logit model and apriori algorithm

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2024年 190卷

作者： Sun, Zhiyuan Wang, Duo Wang, Jianyu Han, Lu Xing, Yuxuan Lu, Huapu Chen, Yanyan Beijing Univ Technol Beijing Key Lab Traff Engn Beijing 100124 Peoples R China Ordos Inst Technol Dept Mech & Traff Engn Ordos 017010 Peoples R China Beijing Univ Civil Engn & Architecture Sch Civil & Transportat Engn Beijing 100044 Peoples R China China Acad Urban Planning & Design 10CheGongZhuangXiLu Beijing 100037 Peoples R China Tsinghua Univ Inst Transportat Engn & Geomat Beijing 100084 Peoples R China

This study presents a detailed analysis on the characteristics of travel mode preference of working residents living far away from downtown area on workdays, using GPS-based activity travel diary data from Shangdi area (Beijing). A hybrid method integrating random parameter logit model with systematic heterogeneity (RPL-SH) and apriori algorithm is put forward to explore the influence factors and interaction effects affecting travel mode preference. First, the RPL-SH model is established to explore significant factors, and capture the unobserved random heterogeneity and systematic heterogeneity due to individual characteristics on the travel mode preference. Then, these significant factors are used to generate association rules by apriori algorithm to investigate statistical associations between the specific travel mode preference and these significant factors. Ten significant factors are found in the RPL-SH model, in which annual household income is normally distributed. The results of the apriori algorithm indicate that some factors combined with other factors could significantly influence working residents' travel mode preference. For example, the combination of lower annual household income and shorter distance between workplace and the nearest bus stop is highly associated with green travel mode preference. Moreover, the results show that the proposed hybrid method not only demonstrates the consistency of the results of the two methods, but also plays a complementary role in exploring more information on travel mode preference. This research hopes to give regulators a better understanding on how working residents living far away from downtown area choose their travel mode, so as to develop more effective and targeted measures for reducing private car use and alleviating workday traffic congestion.

关键词： Travel mode preference Random parameter logit model with systematic heterogeneity apriori algorithm Working residents Green travel mode

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Implementation of a Product-Recommender System in an IoT-Based Smart Shopping Using Fuzzy Logic and apriori algorithm

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 4940-4954页

作者： Yan, Shu-Rong Pirooznia, Sina Heidari, Arash Navimipour, Nima Jafari Unal, Mehmet Hunan Univ Sch Business Adm Changsha 410082 Peoples R China Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran Kadir Has Univ Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye Nisantasi Univ Dept Comp Engn Istanbul Turkiye

The Internet of Things (IoT) has recently become important in accelerating various functions, from manufacturing and business to healthcare and retail. A recommender system can handle the problem of information and data buildup in IoT-based smart commerce systems. These technologies are designed to determine users' preferences and filter out irrelevant information. Identifying items and services that customers might be interested in and then convincing them to buy is one of the essential parts of effective IoT-based smart shopping systems. Due to the relevance of product-recommender systems from both the consumer and shop perspectives, this article presents a new IoT-based smart product-recommender system based on an apriori algorithm and fuzzy logic. The suggested technique employs association rules to display the interdependencies and linkages among many data objects. The most common use of association rule discovery is "shopping cart analysis." Customers' buying habits and behavior are studied based on the numerous goods they place in their shopping carts. As a result, the association rules are generated using a fuzzy system. The apriori algorithm then selects the product based on the provided fuzzy association rules. The results revealed that the suggested technique had achieved acceptable results in terms of mean absolute error, root-mean-square error, precision, recall, diversity, novelty, and catalog coverage when compared to cutting-edge methods. Finally, themethod helps increase recommender systems' diversity in IoT-based smart shopping.

关键词： apriori algorithm filtering fuzzy logic Internet of things recommender systems shopping cart smartening

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Information literacy of college students from library education in smart classrooms: based on big data exploring data mining patterns using apriori algorithm

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SOFT COMPUTING 2024年第4期28卷 3571-3589页

作者： Chen, Si Xue, Ying Cui, Xiangzhe Xian Int Univ Coll Int Cooperat Xian 710077 Shaanxi Peoples R China Yanbian Univ Normal Coll Yanbian 133002 Jilin Peoples R China Sehan Univ Dept Educ Yeongam 58447 Jeollanam Do South Korea

The rapid advancement of IoT technology presents transformative opportunities across various sectors, with education being a prominent beneficiary. Smart classrooms, a product of IoT integration, are being widely adopted to create technology-enhanced, student-centric learning environments that cater to students' information literacy needs, particularly during events like pandemics. This widespread adoption generates substantial amounts of educational data, commonly known as big data, necessitating innovative solutions for analysis and utilization. To solve these challenges, this paper proposes utilizing the apriori algorithm-a data mining technique renowned for uncovering valuable patterns and associations within extensive datasets. This paper evaluates the impact of various information resources with differing quality, considering individuals' information literacy skills. Utilizing data mining techniques, it delves into university students' information literacy data, integrating it with the university library resources to establish a data-driven information literacy education model. It then focuses on criteria, components, and effective methods for instructing college students in information literacy. Finally, a diverse group of students, from first-year undergraduates to doctoral candidates at a specific university, is studied for their engagement in information literacy instruction. Based on the experimental findings, sophomore students exhibited the highest level of participation at 75.9% accuracy, while postgraduate students received more information literacy training than undergraduates and Ph.D. students. When comparing this method to others, such as SVM, KNN, LR, RF, and DT, it achieved superior performance. Additionally, the quality of information literacy training in university libraries was assessed through three dimensions: student learning, behavior, and achievements. Only junior, senior, and first-year graduate students scored above 4, with scores of 4

关键词： Smart classrooms Internet of things Big data University library Information literacy education Information-centric apriori algorithm

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Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using apriori algorithm

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SOFT COMPUTING 2023年第24期27卷 19159-19177页

作者： Song, Yang He, Yingwei Sichuan Int Studies Univ Chengdu Inst Sch Int Business Chengdu 611844 Sichuan Peoples R China Beijing Guantao Zhongmao Wuhan Law Firm Wuhan 430062 Hubei Peoples R China

In recent years, the rapid development of the Internet has promoted the continuous expansion of the scale of China's tourism industry, and the amount of tourism data has surged. However, tourists need help bringing personal interest and high-value data from the plethora of tourism information. The rise of artificial intelligence has transformed traditional tourism into an intelligent, data-driven industry. This shift has generated vast tourism data, offering both opportunities and challenges. The paper discusses an AI and IoT-based Intelligent Tourism Recommendation System (ITRS) that offers travelers predefined itineraries, personalized suggestions, and tourism insights. This system simplifies attraction discovery, unveiling hidden value within tourism data at the intersection of AI and IoT. The present study thoroughly investigates AI-based recommendation algorithms before delving into the system's architecture. It categorizes user-based, project-based, and article-based collaborative filtering methodologies tailored to specific goals. First, thoroughly examine AI-based recommendation algorithms before delving into the system architecture. Second, categorize collaborative filtering methods as user-based, project-based, and article-based, each tailored to specific objectives. Third, delve into the apriori algorithm's complexity within the context of weighted association rules and introduce an enhanced iteration for improved efficiency. The proposed scheme encompasses an elaborate ITRS plan featuring a user interest model and a client module, crucial for the computation and analysis of users' long-term and short-term interests. Rigorous performance testing confirms the ITRS's superiority across varying support levels, with experimental results demonstrating the apriori algorithm's exceptional accuracy, achieving a 94.3% improvement over other methods. The apriori algorithm is better than traditional recommendation algorithms such as Linear Regression, Logistic Regre

关键词： Internet Artificial intelligence Intelligent tourism recommendation system apriori algorithm

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Carbon reduction assessment of public buildings based on apriori algorithm and intelligent big data analysis

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SOFT COMPUTING 2023年 1-10页

作者： Shen, Xu Shandong Jianzhu Univ Sch Civil Engn Jinan 250101 Peoples R China

Today, with the continuous progress of urbanization, public buildings have many environmental problems. Their high carbon emissions and energy consumption have caused considerable environmental pollution. Based on the analysis of the whole life cycle of public buildings, it can be seen from the results that due to its long time span, the service life will cause more pollution to the environment, high energy consumption and carbon emissions. In this environment, this paper completes the design and construction of carbon reduction measurement system for public buildings by combining intelligent big data technology and apriori algorithm. The system mainly analyzes the whole life cycle of the building to calculate all energy consumption projects of the building, converts them into carbon footprint indicators, and uses the indicators to complete the quantitative assessment of environmental pollution level for public buildings in the whole life cycle, and obtains the carbon reduction assessment data of the building in the operating cycle in combination with the carbon emission factors of energy and electricity. The results of quantitative data analysis can be used for the design and arrangement of energy conservation and emission reduction policies, which can be realized by changing the lighting and ventilation, peripheral protection, shape coefficient and rainwater circulation of buildings. This paper conducts carbon reduction assessment for public buildings by integrating intelligent big data and apriori algorithm.

关键词： apriori algorithm Big data analysis Building energy conservation Carbon reduction assessment Conserve energy and reduce emissions

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A data mining-based analysis of cognitive intervention for college students' sports health using apriori algorithm

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SOFT COMPUTING 2023年第21期27卷 16353-16371页

作者： Hao, Qi Choi, Woong Jae Meng, Jie Pai Chai Univ Dept Leisure Sports Daejeon 35345 South Korea Linyi Univ Sch Sports & Hlth Linyi 276000 Shandong Peoples R China

In recent years, the importance of cognitive intervention promoting physical health and well-being among college students has gained significant attention. The cognitive dimension is pivotal in determining individuals' behaviors and decisions, especially in domains where personal choices significantly impact well-being. However, with increasing awareness concerning the advantages of exercise and healthy behaviors, many college students require assistance prioritizing their physical health because of various problems, including current academic schedules and the stress of postgraduate college admissions. To address these issues, this study presented data mining algorithms to assess the efficacy of cognitive treatments in improving college students' health and fitness through sports activity. It smoothly integrates sports education into a larger social development framework, emphasizing the vital significance of holistic well-being and the numerous benefits of rigorous fitness and physical training regimens. It analyzes the efficacy of college students' sports learning using data mining to clarify the apriori and decision tree algorithms. It addresses students' sports perceptions, cognitive interventions for sports health and health standards in college physical education. This paper evaluates the intervention's success based on sports value cognition, sports need cognition and sports attitude recognition to improve student performance. The analysis of undergraduate students' post-intervention scores revealed a considerable rise in these areas, with the experimental group scoring 14.2 and the control group scoring 11.53. The specific scores for sports value cognition were 17.86 and 15.7, while those for sports attitudes and cognition were 11.28 and 9.56, respectively. The impact of the cognitive intervention is further analyzed concerning body shape and physical fitness. The results reveal that a higher proportion of students in the intervention group engaged in physi

关键词： Data mining Cognitive intervention Sports health College students Physical health apriori algorithm Sports attitude recognition

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Big data technology for teaching quality monitoring and improvement in higher education - joint K-means clustering algorithm and apriori algorithm

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SYSTEMS AND SOFT COMPUTING 2024年 6卷

作者： Li, Yang Zhang, Haiyu Minzu Normal Univ Xingyi Sch Educ Sci Xingyi 562400 Peoples R China

With the development of big data technology, the field of monitoring and improving teaching quality in universities has ushered in new opportunities and challenges. Big data technology enables the capture and analysis of massive amounts of data generated during the teaching process, providing the possibility for a deeper understanding of teaching activities. However, how to extract useful information from these vast amounts of data and transform it into strategies for teaching improvement is a challenge. The research aims to propose a teaching quality monitoring and improvement method based on big data technology, which combines K-means clustering algorithm and association rule mining algorithm to improve the accuracy of teaching monitoring and the effectiveness of teaching improvement. In order to cope with these challenges, the study proposes a research method of big data technology based on joint K-mean clustering algorithm and association rule mining algorithm. The study first analyzes the teaching quality monitoring and evaluation indexes using the K-mean algorithm. Then the association rule mining algorithm is utilized to mine the data in the teaching quality monitoring indicators with association rules on the basis of the obtained cluster analysis. Finally, on the basis of association rule mining, the study constructs the assessment model of teaching quality monitoring indicators by utilizing the fused method. The outcomes revealed that the average of data analysis accuracy and the average of recall rate of the modeling method were 93.79 % and 91.95 %, respectively. Meanwhile, the evaluation time of the modeling method in the process of teaching quality monitoring data processing was 17.3 s, and the evaluation precision was 93.15 % respectively. Additionally, the process's overall confidence and enhancement are 95.01 % and 86.73 %, respectively, and the modeling method's performance is compared to other approaches. This shown that the approach may significant

关键词： Teaching quality Big data computing K -means clustering algorithm apriori algorithm Test scores

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apriori algorithm Based Exam Information Service Web App Design 1

Apriori Algorithm Based Exam Information Service Web App Des...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Hao, Huaidan Zhang, Lu School of Tourism Management Wuhan Business University Hubei Wuhan China School of Marxism Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9798350316469

This academic paper presents the design and implementation of an apriori algorithm-Based Exam Information Service Web App. The web app aims to provide personalized recommendations and valuable insights to users to enhance their exam preparation process. The system architecture, data preprocessing techniques, apriori algorithm implementation, and evaluation of system performance and user satisfaction are discussed in *** system architecture of the web app is designed to efficiently retrieve exam-related information and generate personalized recommendations based on user preferences. The components and functionality of the system are outlined, emphasizing the importance of data preprocessing, association rule mining, and recommendation *** results of the evaluation indicate that the apriori algorithm-Based Exam Information Service Web App shows promising performance. The recommendation system achieves high precision and recall, indicating the relevance and retrieval of relevant recommendations. The coverage metric demonstrates the system's ability to recommend items from a diverse set. User satisfaction ratings further validate the effectiveness of the system in meeting user needs and expectations. © 2023 IEEE.

关键词： apriori algorithm exam information personalized recommendations

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