Search

In recent years, the athlete mental health problems have become more and more prominent. Compared with high school, college athlete students are in a more complex environment and face more diversified difficulties, such as the constant emergence of problems in academics, interpersonal interactions, emotional life, employment, etc., which make college athletes overwhelmed, and the accumulation of pressures from many sides leads to a variety of psychological problems. Therefore, it is important to pay attention to the individual athlete mental health problems, and to detect and guide the development of athletes' mental health in time, which is crucial for the management of athlete students in colleges and universities. The purpose of this paper is to use data mining principles and methods to explore the factors leading to psychological problems of college athletes, so as to carry out psychological intervention purposefully. In this paper, we use data mining technology to set up a questionnaire with 90 questions reflecting mental health, and take SCL-90 Symptom Self-assessment Scale as the measurement standard to collect data, establish a data set, analyse the data using improved Apriori algorithm, and establish a decision model of obsessive-compulsive symptoms using decision tree ID3 algorithm, which provides a theoretical basis for the evaluation of the athletes mental health status of college athletes and decision-making of health checkups. It provides a theoretical basis for the evaluation of the mental health status of athletes and the decision-making of health examination. [ABSTRACT FROM AUTHOR]

In recent years, China has been paying more and more attention to the development of the sports industry, and many sports are no longer seen as a mere sport, but can be developed into an industry and play an important role in the development of the economy. This paper examines the application of multi-source big data mining techniques in the analysis of basketball economic management. Firstly, through multi-source big data mining technology, we collect various factors that influence the development of basketball economic industrialization, use Hash Tree-based Apriori algorithm to mine various influencing factors for basketball economic industrialization, and analyze the correlation between each influencing factor. The association rule mining results are then used to analyze the relationship between the key influencing factors and the industrialization of the basketball economy. This paper examines various aspects of the Chinese basketball league market, including the management system, market operations, and talent flow, and compares them with the foreign basketball industry models, in order to analyze the operation of China's basketball industrialization and develop corresponding countermeasures to improve basketball economic management based on the results of the study. [ABSTRACT FROM AUTHOR]

Nowadays, supermarkets and retail stores all use software systems with databases to store customer transactions. Over time, the volume of data is also increasing and it contributes a lot of hidden value in this data warehouse, mining data from historical transactions will find out the buying patterns and behavior of consumers, which can assist in improving sales by reaching customers more precisely. Data-mining techniques allow us to exploit synthetic information in many aspects, such as association rules for statistics and decision support in many fields. Most users of e-commerce systems or web platforms are concerned about privacy protection, such as privacy requirements for name, occupation, age, interests, residence, or sales transactions on the e-commerce system. Therefore, protecting the privacy of electronic service users in data mining is also an important factor that needs to be considered. For those important reasons, the Apriori algorithm was researched and extrapolated into a new S-Apriori algorithm for the concept of seasonal shopping. This paper applied the S-Apriori, ORM model, SQL language, and C# to build the libraries for the forecast of Seasonal Consumption Behavior of Consumers. Also, a new Thanh and Huh Cryptography algorithm for privacy-preserving filters is proposed for data-mining processing privacy protection. The paper experimented on two datasets based on a small dataset with 37 records and the Adventure large dataset of Microsoft with 172,459 records, while the software provides association rules with the corresponding confidence ratio for users to easily make decisions. In addition, the model will be packaged and published to the Microsoft Nuget ecosystem, developers and researchers can use it to develop association rule mining systems or further extend it based on the new S-Apriori model. [ABSTRACT FROM AUTHOR]

One of the significant challenges in the sports industry is identifying the factors influencing match results and their respective weightage. For appropriate recommendations to the team management and the team players, there is a need to predict the match and quantify the important factors for which prediction models need to be developed. The second thing required is identifying talented and emerging players and performing an associative analysis of the important factors to the match-winning outcome. This paper formulates a hybrid machine learning-clustering-associative rules model. This paper also implements the framework for cricket matches, one of the most popular sports globally watched by billions around the world. We predict the match outcome for One day Internationals (ODIs) and Twenty 20 s (T20s) (two formats of Cricket representing fifty over and twenty over versions respectively) adopting state-of-the-art machine learning algorithms, Random Forest, Gradient Boosting, and Deep neural networks. The variable importance is computed using machine-learning techniques and further statistically validated through the regression model. The emerging talented players are identified by clustering. Association rules are generated for determining the best possible winning outcome. The results show that environmental conditions are equally crucial for determining a match result, as are internal quantitative factors. The model is thus helpful for both team management and for players to improve their winning strategy and also for discovering emerging players to form an unbeatable team. [ABSTRACT FROM AUTHOR]

Equipment quality-related data contains valuable information. Data mining technology seems to be an efficient method for extracting knowledge from large amounts of data. In this paper, a general method for equipment quality information mining based on association rule is proposed for complex equipment. Due to the shortcomings of classical association rule mining algorithms such as long running time and high memory consumption, the candidate itemset generation process is optimized, and an improved Apriori algorithm is proposed. Taking five experimental data sets as the object, the performance of the algorithms is tested using time complexity and spatial complexity as evaluation criteria. Comparative experiments show that the improved algorithm had advantages. To further implement data processing and information representation, a matrix-based strong association rule extraction algorithm was proposed. Taking a certain type of equipment as an example, a simulation experiment was conducted using the method proposed in this article in reliability test data sets, and some interesting knowledge was obtained through mining, verifying the effectiveness of the method. The research in this article seems promising with respect to improving the scientific level of equipment support. [ABSTRACT FROM AUTHOR]

This paper takes the physical fitness test data and the physical health self-assessment data as the research objects. The decision tree algorithm is used to construct a decision tree model for students who fail to meet the physical test. Thus, the classification of students with different physical qualities is realized. The association rule Apriori algorithm is used to mine the association of physical fitness test indexes so as to judge the hidden law between students' physical fitness and behavior habits and get the correlation information of various physical health indexes. The back propagation (BP) neural network algorithm is used to establish the physical fitness test prediction model. By using these data mining models, this paper explores the hidden association information in college students' physical test data, which can provide more scientific and effective guidance for students' physical tests. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology and the arrival of the era of big data, the rapid expansion of network information resources has formed massive information. Massive information resources have brought great convenience to people's lives. However, it becomes more and more difficult to find the content that interests you, which is the phenomenon of "information overload." In order to solve this problem, a solution based on personalized recommendation technology is proposed. In personalized recommendation technology, collaborative filtering algorithm is the most widely used technology. Clustering technology can effectively divide objects into groups, so that the similarity of attributes between objects in the same group is high, and the similarity of objects in different groups is low. The core step of the filtering recommendation algorithm is to find the similar neighbors of the target user by calculating the similarity. Applying the clustering technology to the recommendation can effectively improve the performance of the recommendation system. Aiming at the real-time problem of collaborative filtering recommendation, this paper introduces a method of firstly clustering users on the user item rating matrix, and finding the nearest neighbors in the clusters with high similarity with the target user, which effectively reduces the query space and improves the recommendation. This paper proposes a method to measure the user's preference for item attributes, which is used in the above clustering process to improve the recommendation accuracy while retaining the advantage of reducing the query space. Aiming at the problem of poor recommendation accuracy, this paper proposes a fuzzy-improved K-means algorithm to cluster items in the product attribute matrix, and then fuses the similarity of the belongingness of items to clusters in the fuzzy clustering. The similarity calculated on the score matrix shows that this method is better than the traditional hybrid recommendation in accuracy. [ABSTRACT FROM AUTHOR]

Standardization of educational resources and advanced use of appropriate computer knowledge to construct an efficient educational management system is integral for the modern system of education in the age of digitalized education. In the context of this need, this study is aimed at analyzing the Apriori algorithm of education resource management system and its implementation. The Apriori algorithm is analyzed and studied in this paper. Firstly, the background and function of the Apriori algorithm are summarized. Secondly, the content of the Apriori algorithm is introduced in detail. The educational resources managed by this system all follow the Apriori algorithm. It is the most widely used distance education standard at present. In addition, the idea and method of making educational resources in the specification are also expounded. The educational resources made by using this method have higher sharing and reuse. The article analyzes the present situation of the education resource management system, for the insufficiency in its function. The solution for different shortcomings has been dealt with one by one and presented as a grid with an Apriori algorithm for technical support. Moreover, various heterogeneous education resources have been integrated to a unified standardization conforming to the Apriori algorithm. It has been scientifically designed to provide an application platform that can store and manage educational resources efficiently and query educational resources conveniently. The universities can not only effectively manage their own educational resources but also use the resources of other schools, so as to avoid the repeated construction of educational resources. This enables them to achieve the whole Apriori within the scope of resource sharing and reuse. On this basis, this paper gives the system architecture diagram and topological structure diagram of educational resource management system on the Apriori algorithm based, puts forward the design scheme of the system, and describes the system's technical architecture and functional architecture, respectively. [ABSTRACT FROM AUTHOR]

Ground subsidence is important during shield construction. How to select reasonable tunnelling parameters is critical to control ground subsidence. However, the selection of shield tunnelling parameters for ground subsidence control usually relies on human experience. It is critical to study how to make judgements of ground subsidence adaptively during shield construction and give recommended values of tunnelling parameters to mitigate subsidence. To meet the above requirements, this paper innovatively proposes a method for analysing the correlation between ground subsidence and tunnelling parameters based on the fusion method of qualitative and quantitative models. First, the a priori algorithm based on MAX–MIN Ant System is adopted to analyse the qualitative relationship between ground subsidence and tunnelling parameters and provides the tunnelling parameters intervals under different ground subsidence intervals. Second, the improved Beetle Antennae Search-Deep Neural Networks is constructed to calculate the occurrence probability of different discrete internals of ground subsidence to correct the rougher results obtained by qualitative analysis. Finally, a qualitative and quantitative model fusion formula is proposed. The occurrence probability is used to modify the discrete interval value range of the tunnelling parameters intervals under different ground subsidence intervals to obtain the optimised values' range of the key tunnelling parameters under different ground subsidence intervals. The proposed model is used in case analysis. The results of the model predict the probability values corresponding to each subsidence interval that occurred. The prediction accuracy of the quantitative model between ground subsidence and tunnelling parameters in different intervals is higher than 96%. Highlights: The maximum-minimum ant system is applied to the Apriori algorithm to construct the qualitative correlation model in the process of qualitative analysis. Deep Neural Networks quantitative model optimizes the initial weight of the Deep Neural Networks. A qualitative and quantitative model fusion formula is creatively proposed. [ABSTRACT FROM AUTHOR]

Modularization is a well-known approach that can potentially mitigate many of the current construction industry's challenges including, for example, poor productivity, shortages in skilled labors, and poor safety performance. Recent studies have shown that contractual risks and disputes are still considered one of the top factors affecting the performance of modular processes. Many research efforts have analyzed disputes and their causes in the construction industry, but none of them addressed the unique and complex sources of disputes that could be associated with the use of modularization. The goal of this paper is to investigate dispute causation in modular construction projects. To this end, this study adopted an integrated multistep analytical research methodology. First, the authors collected and analyzed a total of 39 publicly available law cases of modular construction disputes in the US. Second, social network analysis was performed to identify the most common causes leading to disputes in modular construction projects. Third, spectral clustering was conducted to group the dispute causes based on the strength of their interconnectivities. Fourth, association rule analysis and Apriori algorithms were used to identify key associations of causes that would most likely trigger modular construction disputes. The results indicated that payment holds and delays, delay in project completion, poor communication among the project stakeholder, and lack of collaboration between various trades are common causes of disputes in modular construction projects. Also, as the authors were able to cluster the 40 identified causes of disputes into four clusters, the most significant associations of dispute causes were determined and quantified within each identified cluster. Further, the findings reflected that modular construction disputes are mostly triggered by the occurrence of multiple causes rather than by just a single cause. This paper adds to the body of knowledge by helping practitioners manage and control sources of disputes in modular construction projects. This should help to minimize dispute occurrence during modular processes, and thus maximize capitalization on modularization benefits. Ultimately, the outcomes of this paper should direct researchers and industry practitioners toward the contractual aspects of modularization that require appropriate adjustments and revision so as to better account for the uniqueness of modular processes within today's complex construction industry. [ABSTRACT FROM AUTHOR]

This literature review focuses on research related to Port State Control (PSC) inspections carried out on board ships, with a particular focus on areas of interest and data analysis methods adopted during research. The five steps involved in the literature review process include: (1) determining the research questions, (2) gathering works of literature for review, (3) conducting selection and screening based on the inclusion and exclusion criteria, (4) analyzing the selected works of literature and ensuring the quality of the data, and (5) reporting the result of the literature review. Based on the comprehensive searches throughout various databases, the most highimpacted databases in sequence were Elsevier, Taylor & Francis Online, (Multidisciplinary Digital Publishing Institute) MDPI, Springer, and others such as Emerald Insight, Science Direct, and Google Scholar. There were 2,023 articles published between 2018 and 2022 gathered during the initial search process, and the 41 final papers were ultimately selected for in-depth analysis after a selection process. The four main research focuses found from this literature review were (1) the selection of ships for PSC inspection, (2) the New Inspection Regime (NIR), (3) the identification of findings during PSC inspection, and (4) detention of ships under PSC inspection. From the literature review, 37% of the final selected articles focused on vessel selection, 33% noted the findings, 23% focused on vessel detention, and only 7% explored the New Inspection Regime (NIR). From the review, most articles used the Bayesian networks (BN) method for data analysis, followed by traditional analysis, Ideal Solution Similarity Order Priority Technique (TOPSIS), Hierarchical Analytical Process (AHP), Apriori Algorithm, and Gray Relational Analysis (GRA). This result could provide valuable information to professionals in the maritime industry, and this literature review signifies the importance of Port State Control (PSC) inspections in fostering the better development of the global maritime transportation system especially to ensure maritime safety. [ABSTRACT FROM AUTHOR]

Data mining plays a crucial role in extracting meaningful knowledge fromlarge-scale data repositories, such as data warehouses and databases. Association rule mining, a fundamental process in data mining, involves discovering correlations, patterns, and causal structures within datasets. In the healthcare domain, association rules offer valuable opportunities for building knowledge bases, enabling intelligent diagnoses, and extracting invaluable information rapidly. This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The MLARMC-HDMS technique integrates classification and association rule mining (ARM) processes. Initially, the chimp optimization algorithm-based feature selection (COAFS) technique is employed within MLARMC-HDMS to select relevant attributes. Inspired by the foraging behavior of chimpanzees, the COA algorithm mimics their search strategy for food. Subsequently, the classification process utilizes stochastic gradient descent with a multilayer perceptron (SGD-MLP) model, while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques. This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set. We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers. Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods, achieving higher accuracy and precision rates in medical data classification tasks. The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features, thereby enhancing the diagnosis and treatment of various diseases. To provide further validation, we conduct detailed experiments on a benchmark medical dataset, revealing the superiority of the MLARMCHDMS model over other methods, with a maximum accuracy of 99.75%. Therefore, this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis. The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning. [ABSTRACT FROM AUTHOR]

Cloudburst is a transient phenomenon, where intense rainfall occurs in a localised area in a very short period, resulting in catastrophic effects like landslides and floods. The Western Himalayas region is found to be more vulnerable to cloudburst events. In this paper, the robustness of the alerts provided by the nowcasting model NETRA (Nowcasting of Extreme Orographic Rain) has been evaluated for the Uttarakhand Region. These alerts are routinely provided over Indian Space Research Organisation (ISRO) web portal www.mosdac.gov.in. It was found that the alerts generated from NETRA compare very well with actual events with district-level accuracy. The results also indicate that May month is more vulnerable to cloudburst events. From the perspective of location, the districts of Chamoli, Rudra Prayag, and Uttarkashi are found to be the most vulnerable. Among all districts, NETRA gave the best location accuracy for the district of Uttarkashi. Moreover, Apriori-based analysis is used to find the co-occurrence patterns in a cloudburst. The co-occurrence analysis brings out the spatial linkage between districts concerning cloudburst. We find out that Pauri and Uttarkashi exhibit the maximum co-occurrence probability. [ABSTRACT FROM AUTHOR]

Deep learning techniques for automatically detecting teeth in dental X-rays have gained popularity, providing valuable assistance to healthcare professionals. However, teeth detection in X-ray images is often hindered by alterations in tooth appearance caused by dental prostheses. To address this challenge, our paper proposes a novel method for teeth detection and numbering in dental panoramic X-rays, leveraging two separate CNN-based object detectors, namely YOLOv7, for detecting teeth and prostheses, alongside an optimization algorithm to refine the outcomes. The study utilizes a dataset of 3138 radiographs, of which 2553 images contain prostheses, to build a robust model. The tooth and prosthesis detection algorithms perform excellently, achieving mean average precisions of 0.982 and 0.983, respectively. Additionally, the trained tooth detection model is verified using an external dataset, and six-fold cross-validation is conducted to demonstrate the proposed method's feasibility and robustness. Moreover, the investigation of performance improvement resulting from the inclusion of prosthesis information in the teeth detection process reveals a marginal increase in the average F1-score, rising from 0.985 to 0.987 compared to the sole teeth detection method. The proposed method is unique in its approach to numbering teeth as it incorporates prosthesis information and considers complete restorations such as dental implants and dentures of fixed bridges during the teeth enumeration process, which follows the universal tooth numbering system. These advancements hold promise for automating dental charting processes. [ABSTRACT FROM AUTHOR]

Incidents are caused by a variety of factors, and there are correlations between incident causative factors. How to effectively clarify the importance of incidental injury-causing factors and their correlations is the current technical challenge in the field of incident causation analysis. This paper takes the study of injury-causing factors and their relationships between object-striking incidents in the process of construction as an example, and it statistically analyzes the incident investigation reports of 126 cases of object-striking incidents in construction projects in China from 2016 to 2022; it screens out 52 categories of incident-causing factors. The Apriori algorithm and FP-growth algorithm are used to data mine the influencing factors obtained from the 126 object-striking incidents: 28 main incident causative items of object-striking incidents and the respective correlation degree between each factor are obtained. By analyzing the support degree of the main incident causation items, as well as comparing and analyzing the results of the incident causation support degree and association rules with Bayesian inference, 9 key injury-causing factors of object-striking incidents are identified. The research results put forward a new research idea for the analysis of the injury factors of object-striking incidents in construction, which can provide theoretical reference for improving the pertinence and effectiveness of incident prevention measures. [ABSTRACT FROM AUTHOR]

Big data is a torrent of data streams that are complex such that traditional data-processing system software is inadequate to deal with this huge data. Dealing with big data consists of challenges such as capturing data, data processing, data storage, data analysis, search, sharing, transfer, visualization, querying and updating. Predictive modelling is used for analysing historical events or data to predict known or unknown facts. Neural networks is used to train models efficiently based on the input data sets consisting of parametric features provided. Educational institutions deal with voluminous student mark records that are utilized for understanding and interpreting the institutional academic competency with other institutions. Analysing the performance involves manual computations for report generation. In this paper we have introduced a student performance analyser and recommender that uses prediction algorithms and content based recommendation approach to predict the academic performances which is fully automated and reduces the manual calculations while enabling students to select from a range of recommended subjects based on multiple queries that are suitable to the caliber of the student. The prediction algorithm uses back propagation techniques that take multiple input parametric features to improve the performance in terms of reducing the error rate. Multiple parametric feature inputs are integrated and compared to the performance when single input features are provided. This research paper helps in analysing the data by automating the tedious calculations including prediction of students performance and recommending papers for the upcoming year. It has been observed that based on the analysis reports that are automatically generated with prediction, future performances of students are found to be accurate due to the multiple input features that gave a higher accuracy and low error rate when compared to other traditional models. [ABSTRACT FROM AUTHOR]

Data mining is an essential technique in knowledge discovery which is widely used for pattern extraction and information classification. Extracting useful rules and knowledge by considering the relationships and association of the data is as an important data mining technique used for data analysis, called association rule mining (ARM). Several scans of the dataset are necessary to extract frequent patterns and association rules during a time-consuming process. Discovery of frequent patterns within data is the major phase of the ARM process, which is very expensive in terms of execution times. Powerful parallel systems with multiple graphics processing units (GPUs) and multiple general-purpose graphics processing units (GPGPUs) are appropriate choices to reduce the execution time. Although GPU architectures can speed up the mining process, a single GPU is usually unable to use a large amount of data to extract frequent patterns. It is therefore necessary to use multiple GPU processors on a system or distribute them within a network to improve the efficiency of parallelization. In this paper, multiple GPUs are parallelized to propose a new framework, called GPApbmp, for parallelization of the Apriori algorithm, which is a well-known level-wise frequent pattern mining method, for faster extraction of association rules. The proposed framework uses multiple GPUs, on which the dataset is distributed to reduce the execution time and the number of database scans in the Apriori method using a vertical approach. The experimental results on standard datasets show that the proposed method reduces the execution time speeds up the mining process. The results obtained from two and four parallelized NVidia GeForce 710 processors evaluated in CUDA. [ABSTRACT FROM AUTHOR]

In order to improve the identification accuracy and shorten the analysis time of poor households in poverty alleviation, this paper studies a logistic regression analysis algorithm of targeted poverty alleviation based on mobile big data. Based on the theories related to big poverty alleviation data, Apriori algorithm is used to mine the basic information of households collected through mobile network based on Maslow's hierarchy of needs theory. A multi-dimensional item data of poverty detection is obtained by analyzing the frequent itemsets of association rules in poor areas, and the poverty characteristics of poor areas from different dimensions are analyzed. Taking the big data platform of targeted poverty alleviation in Jiangxi Province, China, as an example, the economic assistance data is selected and sent into the k-means algorithm to cluster by taking the village as the unit. Then, combined with the correlation of poverty characteristics, the abnormal phenomena in poverty alleviation are found, and the effectiveness of the targeted assistance to poverty alleviation target areas is analyzed. Based on nonlinear logistic regression, the identification model of poor households is built, and the Spark frame is used to extract, transform and read the characteristics of samples respectively. Finally, the poor households are identified with the logistic regression algorithm. Experimental results show that the average recognition accuracy of poor households reaches 92%, and the mining time of poverty feature analysis is only 18 s, which improves the efficiency of data analysis than current algorithms. [ABSTRACT FROM AUTHOR]

A supply chain management (SCM) method must include information sharing as a vital component in order to improve supply chain performance and boost an organisation's strategic advantage. Since, due to a lack of trust concern over information leakage, and security breaches by nefarious individuals or groups, several organisations are hesitant to share information with their supply chain partners. This work presents a new supply chain management-based secure data transmission method. By using blockchain-based data storage, it is assumed that the manufacturers, suppliers, and customers would transfer data that must be kept private during transmission. As a consequence, this paper aims to provide an improved association rule mining with a data sanitisation scheme with an improved Apriori algorithm used in the proposed data sanitisation process. In particular, the Long Short-Term Memory (LSTM) will generate keys by considering the objective relying on the value of the preservation ratio, false rule generation, hiding failure, and degree of modification. The weights are adjusted via a novel Minkowski distance-based Namib beetle optimisation (MDNBO) technique, which also improves the performance of the LSTM model. The reverse process of encryption occurs when encrypted data are restored at the receiving end. By contrasting it with the old methods with regard to security as well, the proposed protected data in SCM with blockchain technology will be proved to be efficient. [ABSTRACT FROM AUTHOR]

Although the Occupational Safety and Health Administration (OSHA) has played a key role in decreasing safety accidents on construction sites, such reduction is not well perceived among the different types of incidents. For instance, while the injuries resulting in days away from work were substantially decreased, construction fatalities rose in the previous years. While previous safety fatality research work concentrated on studying how different variables could influence the number of fatalities in the construction industry, there is a lack of research efforts that studied the associations, interdependencies, and critical combinations between the different fatality causes. As such, to solve the critical fatality problem that the construction industry is facing, more advanced management methods should be applied. That said, this paper aims to address this important knowledge gap through a methodology based on data-driven computational algorithms. The data analyzed in this paper is comprised of 100 OSHA national fatal accident case files that are prepared by OSHA compliance officers. First, a reference matrix of 100 case files was constructed based on 60 causes that have shown to be the most pertinent to construction fatalities based on a previous study. Second, the spectral clustering computational algorithm was used to cluster the fatality causes based on the strength of their interconnectivities. Third, the frequent pattern data mining and Apriori algorithms were used to determine the critical combinations and associations of causes that mostly contribute to fatal accidents on construction sites. The findings indicate that the 60 fatality causes could be categorized into five clusters. Thus, the most critical combination of fatality causes was determined within each one of the identified clusters. Also, the findings reflected that while safety accidents could happen due to factors or causes that are individually critical, fatalities on construction sites could also result due to a combination of factors that might not be perceived to be critical on the individual level but rather become critical when combined with other factors. This study adds to the body of knowledge by proposing a data-driven accident causation approach to analyze construction fatality accidents, which adds a different dimension to the traditional analysis of safety accidents by factoring possible associations and combinations between different causes. That said, this paper equips safety professionals with a proactive approach that allows them to take the needed preventive actions to avoid fatalities on construction sites by identifying, in hindsight, the critical combinations and associations of fatality causes. Ultimately, the outcomes of this paper would enhance the safety performance in the construction industry and prevent construction fatalities. [ABSTRACT FROM AUTHOR]

China's economy has grown significantly since the turn of the century, and its domestic situation has become more stable. The employment and employment issues that college graduates encounter across the nation, however, demonstrate the opposite tendency. The general public's attention has turned to "difficulty in getting employment," which has emerged as a crucial component of the overall social employment problem. Additionally, it examines the environment and elements that influence college students' employment, such as the social, economic, and policy elements as well as the university, employer, and individual elements. This study examines the development of college students' entrepreneurial service system by using colleges and universities as the research object and integrating theoretical analysis with empirical research. The M Apriori technique is then parallelized using a Spark and Credible neural network-based approach. This solution makes full use of Spark's benefits based on in-memory processing and data item RDD storage by adopting data parallelism and a local rather than global strategy. The M Apriori algorithm is parallelized and ported to the Spark platform for parallelization, enhancing Spark MLlib. The simulation test and analysis are completed in the end. The study's findings demonstrate the algorithm's excellent accuracy, which is 8.65% greater than that of the Apriori method. Finally, the experimental findings demonstrate the effectiveness of the method used in this paper. [ABSTRACT FROM AUTHOR]

Campus football has become a core content of school physical education. Through football education, we can cultivate students' sound personality and promote students' all-round physical and mental development. At the same time, through psychological skills training methods, we can enrich the educational methods of football skills and provide theoretical reference for promoting educational reform. On the basis of Gaussian features, this paper combines the mixed Gaussian feature model to further describe the relationship between football education and students' psychology. At the same time, Apriori association rule algorithm in data mining is introduced, and Apriori algorithm is improved in parallel with Hadoop data processing platform. Several parallel association rule algorithms are emphatically studied and analyzed to strengthen the analysis of the relationship between football education and students' psychology. The results show that the average recognition rate of the correlation between football education and students' psychology based on Gaussian features is 17.91% higher than that of ordinary results, which obviously improves the correlation recognition result and has a good descriptive ability. Therefore, it has become an important issue for today's physical education workers to analyze the correlation between football education and students' psychology in order to cultivate students' sports and mental health. [ABSTRACT FROM AUTHOR]

As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis. [ABSTRACT FROM AUTHOR]

In order to increase the effectiveness and teaching quality of numerous classroom activities in colleges and universities, this paper puts forward the effectiveness assessment technique of teaching activities in colleges, universities, and institutions of higher education based on the optimized Apriori algorithm. A mathematical model for assessing the efficacy and usefulness of classroom activities in colleges and universities is constructed. Teaching contents, teaching attitudes, teaching methods, teaching effects, test results, and students' performance are introduced as comprehensive evaluation factors, and a decision-making model for assessing the success and efficiency of classroom activities in colleges and universities is established by adopting scientific, reasonable, and systematic teaching methods. Through the grey correlation analysis of classroom teaching quality, the delay characteristic analysis and the adaptive parameter adjustment method are adopted. This paper constructs the optimal Apriori algorithm model of college teaching classroom activities, constructs the optimal evaluation function model of college teaching classroom activities effectiveness evaluation by the method of support-confidence joint estimation, extracts the optimal quality parameter set of college teaching classroom activities by the optimization detection method and association rule mining, and realizes the effectiveness evaluation and multi-dimensional parameter estimation of college teaching classroom activities by the optimized Apriori algorithm. The test outcomes confirm that this technique is reliable in evaluating the effectiveness of classroom activities in colleges and universities, and the directional distribution of association rules of classroom quality in colleges and universities is significant, which meaningfully advances the classroom teaching levels and quantitative evaluation abilities. [ABSTRACT FROM AUTHOR]

Rural tourism, as a vital component of tourism, is critical to the development of rural economies, farmers' income, rural civilization, new rural construction, and urban-rural interaction. Simultaneously, as the size and complexity of data sets grow larger, how to improve the efficiency of association rule algorithms for mining large data sets has become a hot topic in association rule mining. Rural tourism development that is cultural and creative not only contributes to rural revitalization, but also to the preservation and inheritance of rural culture. The Apriori algorithm is the most widely used and influential algorithm for mining Boolean association rules, and the majority of current algorithms are extensions of the Apriori algorithm. Demand, supply, marketing, and support forces of rural tourism, which are the core driving force of rural tourism development, are formed by the basic needs of each subsystem of rural tourism. One of the main methods is to promote the sustainable and healthy development of rural tourism in accordance with the nature, characteristics, and laws of rural tourism destination construction, in order to create a dynamic system for long-term development and establish a rural tourism development dynamic system. The study of rural tourism driving factors and their system optimization is proposed in this paper. The main tourism dynamic system is adopted by the association rule algorithm of Apriori, the driving factors of rural tourism development are analyzed in the paper, and the system optimization method is proposed, all based on the Apriori algorithm. In terms of support, the Apriori algorithm is 0.436 higher than the CD algorithm and 0.568 higher than the SVM algorithm, and the Apriori algorithm can greatly reduce database size and improve record reading speed. As a result, the findings of this paper can be used to improve the spatial layout of rural tourism and to develop urban-rural tourism. [ABSTRACT FROM AUTHOR]

This paper concerns the problem of recovering an unknown but structured signal ${x}\in \mathbb {R} ^{n}$ from $m$ -quadratic measurements of the form ${y}_{r}= \left |{\langle {a}_{r}, {x}\rangle }\right |^{{2}}$ for ${r}={1},{2},\ldots, {m}$. We focus on the under-determined setting where the number of measurements is significantly smaller than the dimension of the signal (${m} < < {n}$). We formulate the recovery problem as a nonconvex optimization problem where prior structural information about the signal is enforced through constrains on the optimization variables. We prove the projected gradient descent, when initialized in a neighborhood of the desired signal, converges to the unknown signal at a linear rate. These results hold for any closed constraint set (convex or non-convex) providing convergence guarantees to the global optimum even when the objective function and constraint set are nonconvex. Furthermore, these results hold with a number of measurements that are only a constant factor away from the minimal number of measurements required to uniquely identify the unknown signal. Our results provide the first provably tractable algorithm for this data-poor regime, breaking local sample complexity barriers that have emerged in this paper. In this paper, we utilize and further develop powerful tools for uniform convergence of empirical processes that may have broader implications for rigorous understanding of constrained nonconvex optimization heuristics. [ABSTRACT FROM AUTHOR]

In this paper, a data mining approach has been developed to analyze customer behavioral patterns and to get a further idea of the scale of the products purchased. Using the approach we found the best and least performing products, the average number of the products that the customer is interested in buying, And using the apriori algorithm we can generate the frequent items which can be bought together which will help us in suggesting products for the customer to buy. Each pattern is supported by the support and confidence generated using the apriori algorithm. [ABSTRACT FROM AUTHOR]

Summary: While location information sharing technology provides convenience for unmanned driving and journey navigation, user journey information sharing has also become a disaster for privacy information leakage. The traditional differential privacy method can only perturb the data entirely and cannot consider the design of data availability. In this paper, the difference privacy algorithm is improved by combining it with the Apriori algorithm, and the relevant perturbation is carried out after mining the associated data of the user's trip. In the face of possible data attacks, the privacy protection of the sensitive information of the user's actual data is ensured while the availability of the data is ensured. By testing 3000 trip data generated by experimental simulation, the results show that the correlation information between the original datasets is destroyed. However good availability is maintained after the Laplace data perturbation of the proposed algorithm for both simultaneous and multi‐person trips. [ABSTRACT FROM AUTHOR]

Coal mine gas accidents will cause great economic losses and casualties. It is of great significance to find out the essential causes of coal mine gas accidents and put forward measures to prevent them. In this paper, 110 coal mine gas accidents which occurred in China from 2001 to 2022 are selected to analyze the causes of the accidents by extracting the keywords of human factors, equipment factors, environment factors, and management factors from the accident investigation reports. Firstly, the accident statistical analysis is carried out from three dimensions of factor frequency, accident type, and accident grade. Secondly, the Apriori algorithm is used for data mining to obtain frequent item sets and association rules of coal mine gas accident factors. Finally, the coal mine gas accident cause chains which are obtained by using the association rule. The frequent terms of 9 factors, 23 association rules, and 3 coal mine gas accident cause chains are obtained. The results show that the production of coal mine enterprises by illegal organizations is an important reason for the occurrence of coal mine gas accidents. The lack of good management culture easily leads to habitual violations of personnel and decision-making errors, and then causes equipment problems, reflected in the ventilation system which is not perfect, resulting in gas accumulation. The occurrence of coal mine gas accidents can be prevented to a large extent by preventing the absence of good management culture in enterprise management and the occurrence of illegal production behaviors. [ABSTRACT FROM AUTHOR]

Wireless timeout has become one of the main problems affecting the safe and stable operation of CTCS-3 train control system. Traditionally, analysis of such timeout issues relies on manual processing, resulting in a heavy workload and low efficiency. Aiming at solving the problem of CTCS-3 train control system wireless timeout, this paper presents an intelligent analysis technology based on data mining analysis algorithm, and uses the Apriori algorithm to comprehensively analyze all kinds of monitoring data so that fault causes are located more efficiently and accurately. [ABSTRACT FROM AUTHOR]

Mining frequent item sets from transaction datasets is always being one of the most important problems of data mining. Apriori is the most popular and simplest algorithm for frequent itemset mining. Transaction data is a set of recording data resulting in connections with sales-purchase activities at a particular online store. In recent years transaction data has been used in research for discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. This paper focuses on the performance comparison of Apriori and FP-growth algorithms with respect to the execution time. [ABSTRACT FROM AUTHOR]

With the popularization of intelligent devices and the rapid development of information technology, many valuable data become easier to obtain. After obtaining massive data, the problem of how to make more efficient and scientific use of these data is in front of us. Data mining is a deeper processing of data. It is aimed at mining the internal associations hidden in complex data and making the data give full play to its maximum value. At present, college physical training is also in urgent need of more scientific and modern upgrading and reform. Data mining is introduced into college physical training so that physical training data is no longer just a simple collection, query, and storage. This paper uses and introduces the basic content of Apriori algorithm. The actual college sports training data are collected for preprocessing, and the discrete dynamic model is established by using Apriori algorithm. Using this model, the actual data are deeply analyzed. Using the strong correlation subintegration results, the physical training of college students is optimized and reformed. The scores of college students' physical quality are improved, and the goal of sustainable development of college physical training is achieved. Based on the data of 126 students in four classes of a major in 2020, this paper mines the frequent subsets with strong correlation in the data and finds out the relationship between the correlation and trust between subsets. In the 70-day experiment, the scores of college students' comprehensive physical quality have been significantly improved, which proves that the discrete dynamic model established by Apriori algorithm data mining has a significant role in college physical training. The questionnaire survey results show that 83% of the college students are very satisfied with the discrete dynamic model-assisted physical training, and 16% of the college students are basically satisfied with the system. Apriori algorithm has significant advantages in dealing with big data. Single Apriori algorithm also has some defects. The efficiency of data mining, data security, and the accuracy of model analysis results need to be tested in practice. [ABSTRACT FROM AUTHOR]

With the development of educational informatization, the educational data of each school is increasing day by day. How to rationally use the existing information to make scientific teaching decisions is a problem that every educator is closely concerned about. This paper proposes a correlation analysis method for college art courses based on data mining technology. Through the study of association rules in data mining, the Apriori algorithm based on three-dimensional matrix is used to quickly mine student performance data, so as to obtain some reasonable and reliable courses. The results show that the method in this paper can find valuable information for curriculum setting from the actual teaching data, so as to reasonably optimize the curriculum, provide a decision-making basis for the revision of the art curriculum and syllabus in colleges and universities, and further improve the teaching effect and the quality of personnel training. [ABSTRACT FROM AUTHOR]

The concept based on data mining has drawn considerable attention from various database professionals and research scholars. The progression of computer-based advancements, namely database management and data storage has facilitated the storage of large data and the data mining approaches are employed to gain valuable information from huge databases. Recently, several techniques to association rule mining (ARM) and frequent itemset mining (FIM) have been established; yet the efficiency based on execution time and scalability continues to be seen as a significant limitation that results in poor solution quality. Therefore, it is necessary to enhance the consistency that signifies the total number of frequently discovered frequent itemsets. This paper proposes three different phases namely the pre-processing phase, FIM phase and ARM phase. In the first pre-processing phase, the Twitter databases are pre-processed and converted into a suitable format for FIM. Here, the tweets are converted into related feature sets and items. In the second FIM phase, an improved Apriori algorithm is 1utilized in mining and extracting the frequent Then in the final phase, an adaptive billiard inspired optimization (ABIO) algorithm which is the integration of neural network (NN) optimization algorithm and billiard inspired optimization (BIO) algorithm is proposed for the optimal generation of association rules with minimum support and confidence from the huge itemsets. Finally, the recent tweets based on covidvaccine, BTSlivestreaming, KFC, McDonald's as well as lockdown achieved using the hashtag is evaluated for various performance measures, like precision, recall, f -measure, execution time and memory utilization. Also, comparative analyses are performed to evaluate the efficiency of the proposed technique. [ABSTRACT FROM AUTHOR]

Colleges and universities are in an important position to train builders and successors of the socialist cause whilst promoting quality education. Mental health education is an important foundation and condition for comprehensively improving students' overall quality. This research explores adjustment methods for college students' mental health based on virtual reality under the background of positive psychology. It discusses the importance of system requirements analysis in the software development process, analyzes the system's functional requirements, safety requirements, and software and hardware requirements, and uses the Apriori algorithm to explore the influencing factors of college students' mental health. Based on the system engineering method and using the data mining clustering method undertake detailed analysis and research on the mental health of college students, it then designs an anomaly mining algorithm based on clustering to quickly find anomalous data health problems. The interface design of the system is concise and the operation is simple. Users can conveniently input, query, and count information according to the various controls on the interface, which fully embodies human-oriented characteristics. Exploration of the characteristics of students' frequent Internet access ensures the efficiency, accuracy, and comprehensiveness of the evaluation and consultation work, facilitating psychological counseling for teachers and students, and saving paper. By establishing a data mining model, mining the database, and learning about different student groups and their respective characteristics, we discuss our research on student psychology and summarize the mental health status and gender, adaptation and anxiety, introversion, emotionality, and calmness of college students. We also consider the relationship between sex, negative, and courage. Using positive psychology theory, we examine the positive experiences of students and interconnected qualities, to build a mental health practice system. In the experiment, the happiness index evaluation of the virtual reality treatment system group was significant, P = 0.002 < 0.05. Mental health education plays an important role in cultivating the healthy psychology of college students, developing their psychological potential, enhancing their adaptability, and improving their personality. This analysis based on actual data provides a reliable basis for psychological educators to improve the efficiency and effectiveness of school psychological counseling and to facilitate schools in establishing new methods of early prevention and intervention for psychological disorders, enabling institutions to create a healthy atmosphere for college students. [ABSTRACT FROM AUTHOR]

Based on the data mining algorithm, this paper combines the background of the requirements for cultivating talents in the new era, the psychological characteristics, and learning features of contemporary college students and the realistic needs of the reform of the teaching of mental health education courses in colleges and universities. By analysing the characteristics, psychology, and causes of crime of contemporary university students, the study will explore the regular experience of guiding and preventing university students from committing crimes at the level of mental health education in universities, which will not only help to reduce the crime rate of university students but also extend to the prevention of crimes of other people in society through the work of mental health education in general, so as to reduce the crime rate in society and create a harmonious society. In this paper,the decision tree C4.5 algorithm and the association rule apriori algorithm are used to analyze the mental health data of college students, which can improve the teaching quality of college mental health education courses and enhance the psychological quality of college students. It is of great significance to improve the teaching quality of mental health education courses in colleges and universities and to enhance and improve the psychological quality and health literacy of college students. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing popularity of higher education and the increasing scale of students in schools, the educational administration management system of various colleges and universities has also accumulated a large amount of performance data. In the face of these massive data, many teaching managers still stay in the simple operation of adding, deleting, modifying, and checking the data and cannot effectively extract and analyze the useful knowledge and information hidden behind the data. Therefore, Markov model is proposed. The advantage of the Markov model is that it has better prediction effect on random series and data series with large volatility; that is, grey prediction model is used to reveal the overall trend of development and change of prediction data series. This paper studies the curriculum association classification model and student achievement prediction based on the Markov model. According to the student's historical achievement, the average missed detection rate of future grade point is 48.65%, the average missed detection rate of Apriori algorithm is 35.5%, the average missed detection rate of FP growth algorithm is 43.2%, the average missed detection rate of this algorithm is 37.5%, and only 17 of the 40 grade point interval predictions match the actual interval. Make full use of the association rules between courses to provide students with early warning and teacher guidance for specific courses, which can effectively reduce the failure rate while reducing the academic burden. The Markov model can mine students' data inside and outside the classroom, establish and improve college students' achievement index system, then deeply analyze and discuss the development of college students' achievement and ability, and finally give each student's achievement results. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of sports training decision support systems through data mining techniques, and designs a corresponding sports training decision support system for application in practical situations. It investigates the application of data mining techniques in solving the problem of optimal analysis of sports training indicators. The Apriori algorithm is used to find out the hidden correlations behind the data, while the correlation study applicable to college students' physical fitness indicators is proposed and practiced, and the potential hidden relationships between the indicator data are found. The analysis results are further verified by the improved AP algorithm, FP algorithm, which provides important opinions for college physical education curriculum reform and youth physical culture development. Then, the Shi-Tomasi algorithm is used for feature point tracking and the support vector machine approach for human motion posture recognition. Considering that the collected action data contain a large amount of stationary action data, a threshold-based action segmentation algorithm is designed to extract useful action segments; after extracting the action segments, common action features are extracted from both statistical features, such as mean, variance, skewness, kurtosis, and physical features, such as acceleration and plantar pressure, to give a specific description of human daily actions. Thus, the fatigue of different postures is judged according to their number of occurrences and a preliminary assessment result is obtained. Then, the surface EMG data collected by the sensors are pre-processed to reduce noise, and the surface EMG signals are fused at the feature level based on the wavelet change algorithm, and the feature vectors are trained with BP neural networks to evaluate the fatigue of human muscle movements and obtain a preliminary evaluation result. Finally, by studying the application of information fusion technology at the decision level, the assessment results of the comprehensive human movement analysis are obtained using D-S evidence theory. In addition, a web platform is built on this basis to display and manage human exercise data and assessment results, and to facilitate the storage and query of historical exercise records. [ABSTRACT FROM AUTHOR]

The fault data of the secondary system of smart substations hide some information that the association analysis algorithm can mine. The convergence speed of the Apriori algorithm and FP-growth algorithm is slow, and there is a lack of indicators to evaluate the correlation of association rules and the method to determine the parameter threshold. In this paper, the H-mine algorithm is used to realize the fast mining of fault data. The algorithm can traverse data faster by using the data structure of the H-struct. This paper also sets the lift and CF value to screen the association rules with good correlation. When setting the three key parameters of association analysis, namely, support threshold, confidence threshold, and lift threshold, an objective function composed of weighted average lift, CF value, and data coverage rate was selected, and the adaptive fireworks algorithm was used to optimize the parameters in the association analysis. In particular, the rule screening strategy is introduced in fault cause analysis in this paper. By eliminating rules with high similarity, derived signals in association rules are eliminated to the greatest extent to improve the readability of rules and ensure easy understanding of results. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is one of the typical structured, systematic and proactive approaches for product or system failure analysis. A critical step in FMEA is identifying potential failure modes for product sub-systems, components, and processes, for which component-failure mode (CF) knowledge is necessarily needed as an important source of knowledge. However, this knowledge is usually acquired manually based on historical documents such as bills of material and failure analysis reports, which is a labor-intensive and time-consuming task, incurring inefficiency and plenty of mistakes. Nevertheless, few existing studies have developed an effective and intelligent approach to acquiring accurate CF knowledge automatically. To fill the gap, this paper proposes a method to construct the CF matrix automatically by mining unstructured and short quality problem texts and mapping as well as representing them as CF knowledge. Starting with mining the frequent itemsets of failure modes through Apriori algorithm, the method uses the semantic dictionary WordNet to find synonyms in the set of failure modes, based on which the standard set of failure modes is finally built. Subsequently, upon the previous work and components set, we design the component-failure mode matrix mining (CFMM) algorithm and apply it to establish the CF matrix from unstructured quality problem texts. Lastly, we examine the quality data of the seat module of an automobile company as a case study in order to validate the proposed method. The result shows that the failure mode extraction method with standardized features can extract failure modes more effectively than the FP-growth and K-means clustering methods. Meanwhile, the devised CFMM algorithm can extract more combinations of CF than the FP-growth method and build a richer CF matrix. Although different industries have distinct domain characteristics, our proposed method can be applicable not only to manufacturing but also to other fields needing FMEA to enhance product and system reliability. [ABSTRACT FROM AUTHOR]

We have been coping with rough set-based rule generation and Apriori-based rule generation. We developed a software tool termed "NIS-Apriori." The software tool makes use of the characteristics of both rough sets and the Apriori algorithm. This NIS-Apriori generates certain rules and possible rules based on the possible world semantics, and the mathematical properties of "soundness and completeness" for defined rules are preserved. Some important propositions have been proved for the framework of the NIS-Apriori. We are now applying this environment to several different data sets, and we especially describe three cases of data sets in this paper. This paper newly considers the rule-based functionality for decision making with reasoning, which will recover the black-box problem in recent AI. [ABSTRACT FROM AUTHOR]

In specific judicial practice, the ODR mechanism has played an important role in the settlement of e-commerce disputes and network disputes. However, ODR is still in the exploratory stage in many aspects, and it is inevitable that there will be some problems. This article focuses on the A priori algorithm and frequent pattern growth algorithm in association with rule mining learning. The shortcomings of A priori algorithm and frequent pattern growth algorithm are described and researched accordingly, and an improved algorithm for learning A priori association rules based on matrix is proposed. By analyzing the international commercial dispute resolution mechanism in the Internet era, this article constructs an international commercial dispute resolution mechanism platform. By analyzing the process of disputes in foreign trade e-commerce transactions, this article proposes a method to automatically capture gateway dispute information through automated management system tools to improve the efficiency of the gateway. The algorithm in this paper scans the transaction database only once, and according to the matrix structure, it performs the logical AND operation of the row vector representing the data item in the matrix, and it learns frequent item sets quickly and accurately. The entire process effectively reduces the time consumption and space occupation required for frequent item set learning. According to the effective measurement method of the association rules, the effective association rules are extracted from the frequent item set. Through the realization of functions such as allocation, viewing, dispute handling, and statistics in the management tool, the progress and processing efficiency of the gateway dispute handling can be effectively managed. In addition, this article gives the dispute handling process of the website itself, adds the process of automatic completion of disputes by buyers and sellers through agreements, and improves the tools of the CS manual handling management system. The data results show that the method in this paper greatly improves the efficiency of handling disputes in foreign trade e-commerce transactions. [ABSTRACT FROM AUTHOR]

With the rapid development of data mining and dynamic data modeling, this technology has been applied in most groups. In recent years, with the continuous development of the data processing technology, society has entered the digital era. Big data technology is widely used in economics, medicine, computer, and other fields. Based on the development status of the big data technology, an association rule mining algorithm Apriori is proposed to study the characteristics of massive data in a university management system. Combined with the big data discrete dynamic modeling technology, the cloud storage problem in the management system is optimized and improved. Dynamic modeling technology is used to optimize the energy-saving and energy storage functions of the system. Secondly, the Apriori algorithm is used to mine the student achievement data, and the factors affecting the change of student achievement are analyzed. Finally, the running efficiency of the whole university management system is dynamically modeled and analyzed. The results show that the system performance of Apriori algorithm mining has high client compatibility. Association rule algorithm is mainly used in student personality analysis and personal information management, The algorithm used in this paper can optimize the overall performance of the university management system and has certain effectiveness and applicability. It can quickly query the required information under the background of big data and has strong applicability. Dynamic modeling and optimization of the storage system can also improve the utilization of the storage system. [ABSTRACT FROM AUTHOR]

For universities, HR is the first element and the first resource to build university resources. Systematic construction and management of HR and effective development and utilization are the key factors to promote the development of universities. In this paper, the Apriori correlation rule mining algorithm is studied, the optimal allocation model of university HR based on the Apriori algorithm is studied, and the strategy of optimal allocation of university HR is discussed. DM can discover the talent patterns and laws within the organization, so as to find the talent model within the organization. The results show that the method proposed in this paper can be used to mine the correlation rules of the university HR data set, and good results can be achieved. The system can realize the standardization, safety, and paperless work of university personnel, which has changed the original manual work mode and greatly improved the work efficiency. The method in this paper can be used to determine a reasonable HR structure according to the development goals and plans of our school, establish a scientific talent evaluation mechanism, clarify the age structure of teachers, provide an important scientific method, and provide objective decision support for formulating talent demand planning, talent recruitment, and training. [ABSTRACT FROM AUTHOR]