Abstract This article aims to conduct empirical testing on 6043 students from 7 high schools to explore the data background and potential risks that third-party physical health measurements can reveal. Through Apriori Analysis of algorithms, we have concluded that there is a certain degree of similarity between the results and reference values of most indicators when measured by a third party. Therefore, this paper... This article aims to conduct empirical testing on 6043 students from 7 high schools to explore the data background and potential risks that third-party physical health measurements can reveal. Through Apriori Analysis of algorithms, we have concluded that there is a certain degree of similarity between the results and reference values of most indicators when measured by a third party. Therefore, this paper can effectively reveal the potential risks to effectively control and reduce the possible risks when preventing and treating diseases. Through the Apriori algorithm, we found that six rules with more than 10% support showed relatively small interaction between the Standing long jump and other sports. Therefore, we concluded that the athletes from seven middle schools had relatively good physical conditions, muscle elasticity, strength, and durability. In addition, data from third parties can more accurately reflect athletes’ exercise status. Read More Read Less

Abstract In this study, we employed a novel technique, data mining, to accurately evaluate the failure rate of secure computers, providing valuable data information for our decision-making layer. This technique is beneficial not only for our decision-making but also for the long-term operation of our systems. Through in-depth analysis, we discovered inherent connections among various failure events and their mutual impacts. These findings contribute to... In this study, we employed a novel technique, data mining, to accurately evaluate the failure rate of secure computers, providing valuable data information for our decision-making layer. This technique is beneficial not only for our decision-making but also for the long-term operation of our systems. Through in-depth analysis, we discovered inherent connections among various failure events and their mutual impacts. These findings contribute to a deeper understanding of common cause failures in secure computer systems and prepare for enhancing their security. Establishing a robust information system is essential to meet the increasing demands, especially in the complex internet environment where secure digital computer systems face ever-growing challenges. Read More Read Less

Abstract College Chinese,” as a core course in literary majors, faces challenges in achieving its intended goals due to the limitations of traditional teacher-centred teaching methods and students’ lack of enthusiasm. This study adopts genetic algorithms, particle swarm algorithms, hybrid frog-leaping methods, and swarm intelligence optimization algorithms to improve this situation. Through practical application, it is found that these methods outperform traditional manual and composite... College Chinese,” as a core course in literary majors, faces challenges in achieving its intended goals due to the limitations of traditional teacher-centred teaching methods and students’ lack of enthusiasm. This study adopts genetic algorithms, particle swarm algorithms, hybrid frog-leaping methods, and swarm intelligence optimization algorithms to improve this situation. Through practical application, it is found that these methods outperform traditional manual and composite methods. However, in-depth research reveals that swarm intelligence algorithms have lower solving efficiency. To better meet practical application needs, a series of convergence and precision adjustments are applied to the results obtained by the swarm intelligence algorithms. Furthermore, improvements are made to the hybrid frog-leaping algorithm to cater to various engineering improvement requirements. Using artificial intelligence algorithms to guide classroom activities expands students’ thinking and inspires their minds to better cope with multiple complex engineering challenges. Read More Read Less

Abstract This study aims to develop a secure education solution that meets the individual needs of different college students, helping them grasp knowledge better, enhance their learning enthusiasm, and stimulate their motivation. The research adopts experimental design and analysis methods, setting up experimental and control groups, collecting experimental data, and conducting data analysis. The matrix factorization algorithm is used to obtain a suitable system, and... This study aims to develop a secure education solution that meets the individual needs of different college students, helping them grasp knowledge better, enhance their learning enthusiasm, and stimulate their motivation. The research adopts experimental design and analysis methods, setting up experimental and control groups, collecting experimental data, and conducting data analysis. The matrix factorization algorithm is used to obtain a suitable system, and the experimental results show that personalized recommendation systems can recommend secure education resources that meet the users’ needs based on their personal characteristics and interests, thereby increasing their attention and interest in learning and enhancing learning effectiveness. The college students’ secure education system based on personalized recommendation has potential and room for development, and its performance and user experience can be improved through continuous optimization of recommendation strategies and algorithms. This research provides valuable references for the design and study of personalized recommendation systems in college students’ secure education. Read More Read Less