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Traffic safety is an important part of the roadway in sustainable development. Freeway traffic crashes typically cause serious casualties and property losses, being a serious threat to public safety. Figuring out the potential correlation between various risk factors and revealing their coupling mechanisms are of effective ways to explore and identity freeway crash causes. However, the existing association rule mining algorithms still have some limitations in both efficiency and accuracy. Based on this consideration, using the freeway traffic crash data obtained from WDOT (Washington Department of Transportation), this research constructed a multi-dimensional multilevel system for traffic crash analysis. Considering the load balancing, the FP-Growth (Frequent Pattern- Growth) algorithm was optimized parallelly based on Hadoop platform, to achieve an efficient and accurate association rule mining calculation for massive amounts of traffic crash data; then, according to the results of the coupling mechanism among the crash precursors, the causes of freeway traffic crashes were identified and revealed. The results show that the parallel FPgrowth algorithm with load balancing constraints has a better operating speed than both the conventional FP-growth algorithm and parallel FP-growth algorithm towards processing big data. This improved algorithm makes full use of Hadoop cluster resources and is more suitable for large traffic crash data sets mining while retaining the original advantages of conventional association rule mining algorithm. In addition, the mining association rules model with the improvement of multi-dimensional interaction proposed in this research can catch the occurrence mechanism of freeway traffic crash with serious consequences (lower support degree probably) accurately and efficiently.

The integration of Civic-Political elements into physical education teaching is an innovative strategy for teaching reform and Civic-Political construction in colleges and universities, which has a certain impact on physical education teaching. The article mainly proposes four strategies for integrating Civic-Political elements into physical education teaching, and uses the SOM clustering algorithm and FP-growth rule association algorithm respectively to analyze the physical education teaching data generated in the process of realizing the strategy in a big data analysis. The results of the analysis pointed out that the strength quality and cardiorespiratory fitness of male and female college students need to be strengthened at specific stages of physical education and sports. In addition, the results of the teaching experiment showed that the level of the quality of the students who received the integrated education of the Civics and Physical Education programs had been significantly improved (p<0.05).

Association rule analysis algorithm is widely used in Web log analysis, but the existing association rule analysis algorithm will significantly reduce the analysis and mining performance when the amount of Web log is relatively large. This paper proposes an improved clustering algorithm, which first clusters users with the same interests and hobbies, and then mines association rules for users in the same category, thereby reducing data dispersion. Based on Django’s MVC framework, it optimizes the storage and storage of Web logs. In the analysis part, users can configure the support and confidence of association rule mining through the front-end, and at the same time query the results of mining through Hive, and use encryption algorithms in the data transmission process to ensure data security.

In this paper, an Apriori association rule optimization model based on the combination of transaction compression and Hash technology is proposed for the relationship between core strength exercises and the explosive strength quality of throwing track and field athletes. In order to improve the efficiency of mining, the Hash function is set on the basis of the FP-growth algorithm, and its grouping strategy is improved by the load optimization algorithm of the greedy strategy, and it is fused with the improved Apriori algorithm. Finally, we set up an athletic training comparison experiment with or without core strength exercises to explore the effect of core strength exercises on the improvement of explosive strength quality with the help of the fusion algorithm. After core strength exercises, the grip strength and vertical jump events of throwing athletes increased from 30.94 and 36.03 to 33.52 and 38.28, respectively, and the quality of explosive force was significantly improved. While comparing with the conventional strength exercises, the athletes who performed core strength exercises had a P-value of less than 0.05 in all the other five test items except static squat jump; the effect of the exercises was significantly better than that of the conventional strength exercises, and the lower limbs, trunk and waist and abdominal muscle groups, and the upper limbs explosive strength were also effectively improved.

Technological innovation is vital for the survival and development of enterprises. In the era of intelligent information interconnection and knowledge-driven economy, there is a growing interest in how to manage high-volume data, unlock its potential value, and provide intelligent analysis and decision-making support for enterprise’s technological innovation. This paper proposes an improved knowledge association analysis method based on the semantic concept model. This approach enables the discovery of potential correlations and interaction modes between the influencing factors of enterprise’s technological innovation, and provides a useful reference for decision-making by combining the analysis with the enterprise’s own situation.

Globally, China is among the countries most severely affected by natural disasters. Understanding the spatial and temporal distribution characteristics of natural hazard events on a spatial and temporal scale can help understand natural hazard risks more comprehensively. However, there remains a lack of research on spatiotemporal clustering relationship analysis of multi-hazard natural disasters and the co-occurrence relationship between different natural hazard events and spatial locations. In this study, we obtained the geographic text of natural disaster events in China from 2008 to 2021 mined from news, extracted the spatiotemporal information of natural disaster types and situations, and introduced the theory of spatiotemporal scanning statistics and co-occurrence network relationships. The results showed that (1) information on the location, time, and intensity of attention to disaster events contained in news data is highly correlated with the actual occurrence of disasters. The spatial and temporal characteristics of disasters differ among regions, and 15 natural disasters have high-risk clustering areas with log-likelihood ratio values up to 1016.77 and relative risk values up to 46.95. Seasonal differences exist in the occurrences of different natural disasters, with most occurring frequently from May to August. (2) The association rules mining disaster events show that distinct co-occurrence relationships between several hazards are present, and the confidence level of the most frequent item number sets was above 95%. Regardless of meteorological, hydrological, and geological hazards, these interconnected regions were geographically close, and most regions in the spatial association centers of natural hazards were closely connected, showing a pattern of multiple low-frequency regions linked to one high-frequency region, with the strongest connection in southern China, with a frequency of 360, and the weakest connection in Northeast and Northwest China, with a frequency of only single digits. This study can provide a reference for relevant departments to identify natural disaster risks in different regions, formulate disaster risk zoning, and improve their disaster prevention and control capabilities.

As global energy networks expand and smart grid technology evolves rapidly, the volume of historical power accident data has increased dramatically, containing valuable risk information that is essential for building efficient public safety early warning systems. This paper introduces an innovative text analysis method, the Sparse Coefficient Optimized Weighted FP-Growth Algorithm (SCO-WFP), which is designed to optimize the processing of power accident-related textual data and more effectively uncover hidden patterns behind accidents. The method enhances the evaluation of sparse risk factors by preprocessing, clustering analysis, and calculating piecewise weights of power accident data. The SCO-WFP algorithm is then applied to extract frequent itemsets, revealing deep associations between accident severity and risk factors. Experimental results show that, compared to traditional methods, the SCO-WFP algorithm significantly improves both accuracy and execution speed. The findings demonstrate the method’s effectiveness in mining frequent itemsets from text semantics, facilitating a deeper understanding of the relationship between risk factors and accident severity.

The imperative to meticulously assess and manage the legal and ethical risks associated with artificial intelligence (AI) in public safety management is increasingly recognized. This study employs the Apriori algorithm to identify frequent itemsets in public safety risk management, further refining these findings using the FP-growth algorithm’s Gini coefficient to pinpoint optimal features representing legal-ethical risk factors. Cloud modeling techniques are also applied to examine the nuances of AI’s legal ethics. Our analysis reveals a significant growth in AI patent applications within the public safety sector, showing an increase in the relative growth rate from 1.1679 to 1.4810 over eight years, equating to an 88.66% rise. Based on highest membership values in the risk prevention and control system, risk categorization identified social ethics risk and public security threat risk with indices of 0.461, 0.721, and 0.499, respectively, classifying them into class II and III risks. This investigation into AI’s legal ethics forms a critical foundation for developing a risk regulation framework and offers strategic recommendations for legal reform, ensuring AI’s positive trajectory in public safety.

The present work is a part of the ESTenLigne project which is the result of several years of experience for developing e-learning in Sidi Mohamed Ben Abdellah University through the implementation of open, online and adaptive learning environment. However, this platform faces many challenges, such as the increasing amount of data, the diversity of pedagogical resources and a large number of learners that makes harder to find what the learners are really looking for. Furthermore, most of the students in this platform are new graduates who have just come to integrate higher education and who need a system to help them to take the relevant courses that take into account the requirements and needs of each learner. In this article, we develop a distributed courses recommender system for the e-learning platform. It aims to discover relationships between student’s activities using association rules method in order to help the student to choose the most appropriate learning materials. We also focus on the analysis of past historical data of the courses enrollments or log data. The article discusses particularly the frequent itemsets concept to determine the interesting rules in the transaction database. Then, we use the extracted rules to find the catalog of more suitable courses according to the learner’s behaviors and preferences. Next, we deploy our recommender system using big data technologies and techniques. Especially, we implement parallel FP-growth algorithm provided by Spark Framework and Hadoop ecosystem. The experimental results show the effectiveness and scalability of the proposed system. Finally, we evaluate the performance of Spark MLlib library compared to traditional machine learning tools including Weka and R.

The work of party branch in colleges and universities is an important part of our party’s construction and an important part of building a harmonious campus. The purpose of this article is to research and analyze the role of computer technology in the work of college party branches in the information age. This paper proposes to use the classic algorithm FP-growth algorithm for digital technology problems commonly used in computer technology. The FP-growth algorithm has the widest application range. It compresses the transaction database into an FP tree for processing. It also uses the Apriori algorithm, which eliminates the need to generate candidate frequent itemsets, which improves the efficiency of use [1]. In addition, this article uses the two core technologies of computer technology, data mining technology and data fusion technology, and their use strengthens the research of computer technology in the work of college party branches in the information age. With the use of high-precision algorithm processing of computer technology, problems can be accurately found and solved in the work of the party branch. Through the high precision of the FP-growth algorithm and the Apriori algorithm, the computer can simulate the problems that will occur in the construction problem in the construction of college party branches and give solutions in advance [2]. The experimental results show that the application of computer technology in the work of party branches in colleges and universities in the information age explored in this article is more recognized and liked by party branch workers. The efficiency of party branch workers has increased by about 90%, which is helpful for improving party building efficiency. Played a significant role.