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.

Recent researches on image super-resolution (SR) have achieved great progressing with the great development of convolutional neural networks (CNNs). However, existing CNNs usually adopt fixed filter structures and the convolutions just rely on the local information contained in the fixed receptive field. Above phenomena prevent high-level convolution layers from encoding semantics over spatial locations and largely limits the learning capacity of CNNs. What's more, many methods simply used a single-size feature map and failed to consider the spatial information, thereby these results also are unsatisfactory. To address these problems, in this paper, a network with multi-scale space features and deformable convolutional (MulSSD) is presented to further improve the reconstruction accuracy. Specifically, a multi-scale space features compressed block containing the deformable convolutional layer is proposed, which can augment the spatial sampling locations and incorporate the multi-scale space compression features and adaptively adjust the sampling grid and receptive fields. In addition, the design of symmetrical combinations make the information can be smoothly propagated through multiple channels during the training, which effectively improves the training efficiency. Extensive experiments on benchmark datasets validate that the proposed method achieves outperforming quantitative and qualitative performance. And the experimental results also proved that our proposed MulSSD can reconstruct high-quality high-resolution (HR) images at a relatively fast speed and outperform other methods by a large margin.

A synthetic aperture radar (SAR) target recognition method combining multiple features and multiple classifiers is proposed. The Zernike moments, kernel principal component analysis (KPCA), and monographic signals are used to describe SAR image features. The three types of features describe SAR target geometric shape features, projection features, and image decomposition features. Their combined use can effectively enhance the description of the target. In the classification stage, the support vector machine (SVM), sparse representation-based classification (SRC), and joint sparse representation (JSR) are used as the classifiers for the three types of features, respectively, and the corresponding decision variables are obtained. For the decision variables of the three types of features, multiple sets of weight vectors are used for weighted fusion to determine the target label of the test sample. In the experiment, based on the MSTAR dataset, experiments are performed under standard operating condition (SOC) and extended operating conditions (EOCs). The experimental results verify the effectiveness, robustness, and adaptability of the proposed method.

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.