In recent years, the research on personalized learning under the background of “Internet +” mainly focuses on the theory, design, and application and there is less research on learning evaluation. As an important means to measure the learning process and results, learning assessment plays an important role in supporting the effectiveness of personalized learning. From the perspective of educational services, how to realize learning evaluation that meets the needs of personalized learning is an important issue to be studied in the field of personalized learning. In this paper, the big data generated by learners on the online learning platform are used as the research target, and according to the level of learners’ learning ability, a deep neural network is established to cluster and group them according to the cognitive thinking method. In order to reduce data redundancy and improve processing efficiency, a deep neural network with five hidden layers is used to extract typical features, so as to obtain more accurate evaluation results. Finally, the neural network model is used to obtain the clustering results of different groups of learning behaviors and the evaluation curves of the five-course knowledge points of learners at different levels. From the experimental results, the proposed personalized evaluation method can effectively analyze the learning differences between learners with different ability levels, and it is basically consistent with the evaluation standards of artificial experts.
For Internet information services, it is very important to closely monitor a large number of key time series data generated by core business for anomaly detection. Although there have been many anomaly detection models in recent years, its practical application is still a big challenge. The model usually needs repeated iteration and parameter adjustment; and for different types of time series data, we need to select different models. Therefore, this paper proposes an anomaly detection model based on time series. The model first designs the statistical features, fitting features, and time-frequency domain features for the time series, and then uses the random forest integration model to automatically select the appropriate features for anomaly classification. In addition, this paper presents an anomaly evaluation index ADC score with timeliness window, which adds the time delay factor of anomaly detection on the basis of F1-score. We use the KPI time series, a representative key performance index in the industry, as the experimental data. It is found that the ADC score of the anomaly detection model in this paper reaches the level of 0.7–0.8, which can meet the needs of practical application.
Sports performance prediction has gradually become a research hotspot in various colleges and universities, and colleges and universities pay more and more attention to the development of college students’ comprehensive quality. Aiming at the problems of low accuracy and slow convergence of the existing college students’ sports performance prediction models, a method of college students’ sports performance prediction based on improved BP neural network is proposed. First, preprocess the student’s sports performance data, then use the BP neural network to train the data samples, optimize the selection of weights and thresholds in the neural network through the DE algorithm, and establish an optimal college student’s sports performance prediction model, and then based on cloud computing, the platform implements and runs the sports performance prediction model, which speeds up the prediction of sports performance. The results show that the model can improve the accuracy of college students’ sports performance prediction, provide more reliable prediction results, and provide valuable information for sports training.
In recent years, many EEG-based emotion recognition methods have been proposed, which can achieve good performance on single-subject data. However, when the models are applied to crosssubject scenarios, due to the existence of subject differences, these models are often difficult to accurately identify the emotions of new subjects, which is not conducive to the practical application of the models. Many transfer learning methods have been applied to cross-subject EEG emotion recognition tasks to reduce the effect of subject differences. Most of them need to be trained with source data of many subjects and calibrated with more data of target subjects to obtain better classification performance on target subjects. However, this process relies on a large amount of training data to guarantee the final effect. This paper proposed a meta-transfer learning model for emotion recognition. The model can reduce the amount of data required by the meta-learning optimization algorithm. Even if only a small amount of data is used for training, it can achieve good performance, thereby reducing the cost of EEG acquisition and labeling, and it is also conducive to the model for new subjects. Finally, this paper conducts cross-subject emotion recognition experiments based on two public datasets SEED and SEED-IV. The experimental results show that the performance of the proposed meta-transfer learning method is better than the baseline method, and can rapid adaptation to unknown subjects while reducing training data.
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