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Most of the traditional athletic performance dynamic change prediction platforms use gray correlation and neural networks to establish prediction methods, and the prediction results are inaccurate and time-consuming. Therefore, the construction of a mobile prediction platform for dynamic changes in athletic performance based on screening factors is proposed to improve the accuracy of its prediction results and shorten the prediction time. On the basis of hardware, the main body of students is determined, the athletic performance data of students is collected by the physical education management system of colleges and universities, and the missing data is filled by the criterion function of K-average algorithm. The KNN algorithm is used to make decisions to obtain the most representative predictors of athletic performance data, and the characteristics of students' athletic performance are extracted. The effective factors are selected according to the tenfold cross-validation fold MSE minimum principle of SVM, and the mandatory screening based on SVM is used to assign weights to each reserved factor. According to the weighted result of the obtained factors, the influencing factors of athletic performance are analyzed, and the weighting process is completed for the factors, and the athletic performance prediction is realized by using support vector machine. In the platform established in this paper, the students' 1000-m running results are predicted. The simulation results show that, compared with the traditional platform, the constructed platform has higher prediction accuracy and takes less time. [ABSTRACT FROM AUTHOR]

To solve the problem of low recognition accuracy and long recognition time in conventional methods for digital moving image feature recognition, a method for digital image feature recognition on mobile platforms based on machine learning is proposed. The support vector machine method is used to cluster the dataset, and then the weight reconstruction method is used to reduce the dimension of digital moving image data. The reduced dimension of the digital moving image is embedded in the coordinates to obtain the reduced dimension of the image data information and complete the feature recognition of the digital moving image. The simulation results show that the recognition accuracy of this method in recognizing features of moving digital images is basically stable at more than 95% and the running time is 0.42s. The recognition effect of the proposed method is good, the recognition efficiency is high, and it has some practical application value. [ABSTRACT FROM AUTHOR]

In the process of learning English, the status of spoken language is particularly important, and it is also the most concerned aspect of most English learners. However, the current situation is that due to the limited resources of traditional teachers and the lack of oral practice environment, it is difficult for many learners to effectively improve their English level. Based on this, this study builds a smart English recognition system based on support vector machine. Moreover, this paper introduces a support vector machine to characterize speech signals. In addition, this paper uses feature fusion to map complex nonlinear relationships between features based on support vector machines and establishes a smart English recognition system based on support vector machine. The model can accurately identify the syllables and pronunciations in the words. Moreover, the use of a large-scale corpus based on non-specific people in this article can represent the generality of spoken learner. [ABSTRACT FROM AUTHOR]

Humanfall detection has attracted broad attentions as sensors and mobile devices are increasingly adopted in real-life scenarios such as smart homes. The complexity of activities in home environments pose severe challenges to the fall detection research with respect to the detection accuracy. We propose a collaborative detection platform that combines two subsystems: a threshold-based fall detection subsystem using mobile phones and a support vector machine (SVM)-based fall detection subsystem using Kinects. Both subsystems have their respective confidence models and the platform detects falls by fusing the data of both subsystems using two methods: the logical rules-based and D-S evidence fusion theory-based methods. We have validated the two confidence models based on mobile phone and Kinect, which achieve the accuracy of 84.17% and 97.08%, respectively. Our collaborative fall detection approach achieves the best accuracy of 100%. [ABSTRACT FROM AUTHOR]