Abstract This paper mainly studies the application of computer neural network algorithms in the training and testing of upper and lower limb coordination in aerobics. By analyzing deep learning algorithms, we can accurately assess and predict the coordination ability of aerobics athletes, thereby improving their competitiveness. We introduce the application of computer neural network algorithms in sports training and testing, especially for testing upper and... This paper mainly studies the application of computer neural network algorithms in the training and testing of upper and lower limb coordination in aerobics. By analyzing deep learning algorithms, we can accurately assess and predict the coordination ability of aerobics athletes, thereby improving their competitiveness. We introduce the application of computer neural network algorithms in sports training and testing, especially for testing upper and lower limb coordination in aerobics. We use deep learning algorithms, such as convolutional neural networks (CNN), to evaluate and predict the coordination of athletes’ upper and lower limbs. Our research results show that computer neural network algorithms have broad application prospects in the training and testing upper and lower limb coordination in aerobics. Read More Read Less

Abstract To improve football players’ training efficiency and performance, a training simulation system based on computer simulation AI intelligence technology is designed. The system includes a perception interaction subsystem and a model calculation subsystem. The perception interaction subsystem builds a three-dimensional training scene through a virtual environment generator, then renders the scene in the model calculation subsystem’s view module while the logic module calculates the athlete’s physical... To improve football players’ training efficiency and performance, a training simulation system based on computer simulation AI intelligence technology is designed. The system includes a perception interaction subsystem and a model calculation subsystem. The perception interaction subsystem builds a three-dimensional training scene through a virtual environment generator, then renders the scene in the model calculation subsystem’s view module while the logic module calculates the athlete’s physical fitness decline. The calculation results are then passed back to the perception interaction subsystem’s effect generator to present the interactive effects of the athlete’s training. Tests show that the system can effectively perform athletes’ physical fitness decay calculations, has good load-bearing capacity, enhances the physical training effect of football players, and improves simulation efficiency. The application of this system can improve athletes’ competition performance. Read More Read Less

Abstract Academic honesty in higher education can be affected by personal or organizational factors. Engaging in dishonest practices diminishes the integrity of the educational setting and can lead to adverse outcomes for individual learners and the academic community. To investigate the elements that contribute to students’ cheating behaviors, a quantitative research study was undertaken, concentrating on the types of assessments and tasks that are most... Academic honesty in higher education can be affected by personal or organizational factors. Engaging in dishonest practices diminishes the integrity of the educational setting and can lead to adverse outcomes for individual learners and the academic community. To investigate the elements that contribute to students’ cheating behaviors, a quantitative research study was undertaken, concentrating on the types of assessments and tasks that are most vulnerable to cheating. The data gathered has been analyzed using Machine Learning techniques, and the findings have been presented visually. This research is part of a dissertation project, and the survey outcomes will be utilized for a study involving eye-tracking to assess students’ cheating behaviors. The long-term objective is to create online examination methods resistant to specific cheating tactics. Read More Read Less