The knowledge tracing (KT) model is an effective means to realize the personalization of online education using artificial intelligence methods. It can accurately evaluate the learning state of students and conduct personalized instruction according to the characteristics of different students. However, the current knowledge tracing models still have problems of inaccurate prediction results and poor features utilization. The study applies XGBoost algorithm to knowledge tracing model to improve the prediction performance. In addition, the model also effectively handles the multi-skill problem in the knowledge tracing model by adding the features of problem and knowledge skills. Experimental results show that the best AUC value of the XGBoost-based knowledge tracing model can reach 0.9855 using multiple features. Furthermore, compared with previous knowledge tracing models used deep learning, the model saves more training time.

The complexity of clouds, particularly in terms of texture detail at high resolutions, has not been well explored by most existing cloud detection networks. We introduce the high-resolution cloud detection network (HR-cloud-Net), which utilizes a hierarchical high-resolution integration approach. HR-cloud-Net integrates a high-resolution representation module, layer-wise cascaded feature fusion module, and multiresolution pyramid pooling module to effectively capture complex cloud features. This architecture preserves detailed cloud texture information while facilitating feature exchange across different resolutions, thereby enhancing the overall performance in cloud detection. Additionally, an approach is introduced wherein a student view, trained on noisy augmented images, is supervised by a teacher view processing normal images. This setup enables the student to learn from cleaner supervisions provided by the teacher, leading to an improved performance. Extensive evaluations on three optical satellite image cloud detection datasets validate the superior performance of HR-cloud-Net compared with existing methods.

Epilepsy, a chronic disease, whose symptoms are sudden onset of brain neurons by the abnormal discharge, leads to transient cerebral dysfunction. In recent years, the observation of EEG signals plays an important role in the diagnosis of epilepsy, as EEG signals contain much useful detailed information which helps doctors to diagnose epilepsy. However, artificial analyses rely heavily on doctors' experience and may produce erroneous and missed diagnosis. Thus many researchers focus on methods that can automate the process of EEG detection. According to recent studies, the mainstream approaches mainly consist of two steps: signal pre-processing and classification. In this paper, our method uses a deep convolutional network called Temporal Convolutional Neural Networks (TCN) to classify the EEG signals. The proposed approach can automatically learn feature representation from the raw EEG signals without any pre-processing. To evaluate the effectiveness of our approach, we design and train a classifier base on TCN to classify EEG data. The best performance of the 14 different combinations of two-class epilepsy detection gives an accuracy of 100.00%. Our results show that TCN is able to accurately distinguish epileptic data from non epileptic data. In terms of computation cost and accuracy, this novel approach proposed in this paper is more competitive than others, such as wavelet transform, Naïve Bayesian and KNN etc.

With the increasing complexity of maritime traffic, vessel trajectory prediction becomes a vital part of the maritime security. Although many methods working on this problem have been proposed including machine learning methods, physical model methods and deep learning methods. But all these methods can not take long-time prediction and multidimensional prediction in to account at the same time. Besides, even some methods avoided the curved trajectory prediction deliberately. In this paper we investigated a novel approach based on Variational LSTM to predict the vessel trajectory using Automatic Identification System (AIS) data. The predicted time scope in ranges from about 5 minutes to about 20 minutes and the predicted features including time, longitude, latitude, speed over ground (SOG) and course over ground (COG). To avoid damaging the structure of the raw AIS data, we have not used any interpolation method when processed it. Instead, we calculated the time increment between two continuous data points. Then we used a novel method based on time increment to select out those trajectory segments with preferable time continuity to make dataset. Lastly, in long-time and multidimensional prediction experiments, variational LSTM achieved the better results and showed the potential in the long-time trajectory prediction comparing with the LSTM. In 20-time-steps position prediction, the MSE of variational LSTM was only 6.0303e-4 but that value of LSTM reached to 6.0014e-3.