Abstract This work studies the multi-venue basketball event scheduling plan based on a simulated annealing algorithm, aiming to improve the stability and fairness of event scheduling. By reviewing the application advantages and disadvantages of traditional intelligent algorithms in basketball event scheduling, this paper introduces the principle of simulated annealing algorithm and its application in basketball event scheduling. A basketball tournament scheduling plan based on a... This work studies the multi-venue basketball event scheduling plan based on a simulated annealing algorithm, aiming to improve the stability and fairness of event scheduling. By reviewing the application advantages and disadvantages of traditional intelligent algorithms in basketball event scheduling, this paper introduces the principle of simulated annealing algorithm and its application in basketball event scheduling. A basketball tournament scheduling plan based on a simulated annealing algorithm was designed and implemented for multiple competition venues, and its effectiveness was verified through experiments. The experimental results show that compared with traditional intelligent algorithms, the plan exhibits better performance in terms of stability and fairness. Read More Read Less

Abstract With the popularity of smartphones and mobile applications, the threat of Android malware is increasingly serious. The analysis and behaviour modelling of Android malware features is studied to realize the efficient and accurate detection of Android malware, and an Android malware detection method combining mean aggregator and long-term and short-term memory is proposed. The results show that the improved system detection time is relatively... With the popularity of smartphones and mobile applications, the threat of Android malware is increasingly serious. The analysis and behaviour modelling of Android malware features is studied to realize the efficient and accurate detection of Android malware, and an Android malware detection method combining mean aggregator and long-term and short-term memory is proposed. The results show that the improved system detection time is relatively stable regardless of the number of samples. The average detection time of the im- proved and unimproved systems is 0.274 s and 0.336 s, respectively, and the improved detection efficiency of the improved system is more prominent. The highest improvement rate of the enhanced system reached 18.2%. Compared with other models, the average absolute error and root mean square error were the smallest, with 3.84 and 6.26, respectively, indicating that the detection performance of the improved model is the best. With permission features and third-party library features, the accuracy of the enhanced model was 98.89% and 92.65%, and the recall rate was 99.24% and 99.09%, respectively. The improved model detection perfor- mance is good, and the robustness and stability are enhanced. Applied to actual Android devices, it can im- prove the security and privacy protection level of user data. This method ensures enhanced efficiency and stability and provides a certain reference direction for Android malware detection. Read More Read Less

Abstract The research aims to solve the problems of unstable performance parameters and insufficient coverage in software testing and proposes a big data platform software performance testing system based on the clock- controlled computation tree logic method. The particle swarm algorithm finds the optimal solution through the movement and cooperation of particles in the search space. The genetic algorithm evolves the population through selection, crossover,... The research aims to solve the problems of unstable performance parameters and insufficient coverage in software testing and proposes a big data platform software performance testing system based on the clock- controlled computation tree logic method. The particle swarm algorithm finds the optimal solution through the movement and cooperation of particles in the search space. The genetic algorithm evolves the population through selection, crossover, and mutation operations, ultimately finding the optimal solution. Secondly, long short-term memory networks and linear autoregressive models also have advantages in software testing, which can improve the effectiveness and efficiency of software testing through reasonable selection and combined use. The research uses the algorithmic logic of the particle swarm and genetic algorithms to confirm the software testing system's moment parameters and other information. At the same time, an algorithmic model researches the joint coverage and the use of the system's value, and finally, the big data platform is used to analyze the research system. The innovative combination of the CCTL method and optimization algorithm in the research has improved the accuracy and stability of software testing. The research results show that using the system to test software can achieve a coverage rate of 100% for its component use cases, while the functional coverage rates of the genetic algorithm and particle swarm algorithm reach 90.36% and 91.32%, respectively. The accuracy of software testing for researching usage methods is 5% and 6% higher than testing methods. When the moment range of the particle parameter position information of the model is [150 ms, 250 ms], the maximum value of the target parameter velocity is 80 m/s, and the minimum value is 0 m/s. The maximum value of the target azimuth velocity is 20 rad/s, and the minimum is 0 rad/s. The system can determine the various parameters of the software, and at the same time, if the software test results on the test results are typical, fault analysis can be completed typically; the performance of the use of algorithms is also better than other algorithms, and the study of the use of algorithms with a higher degree of stability. It canbe seen that the system and methods used in this research are better than traditional methods, and the test results in software testing have improved, providing a research direction for software testing after the research. Read More Read Less

Abstract This work studies the multi-venue basketball event scheduling plan based on a simulated annealing algorithm, aiming to improve the stability and fairness of event scheduling. By reviewing the application advantages and disadvantages of traditional intelligent algorithms in basketball event scheduling, this paper introduces the principle of simulated annealing algorithm and its application in basketball event scheduling. A basketball tournament scheduling plan based on a... This work studies the multi-venue basketball event scheduling plan based on a simulated annealing algorithm, aiming to improve the stability and fairness of event scheduling. By reviewing the application advantages and disadvantages of traditional intelligent algorithms in basketball event scheduling, this paper introduces the principle of simulated annealing algorithm and its application in basketball event scheduling. A basketball tournament scheduling plan based on a simulated annealing algorithm was designed and implemented for multiple competition venues, and its effectiveness was verified through experiments. The experimental results show that compared with traditional intelligent algorithms, the plan exhibits better performance in terms of stability and fairness. Read More Read Less