Under global climate change, the frequency of typhoons and their strong wind, heavy rain, and storm surge increase, seriously threatening the life and property of human society. However, traditional tropical cyclone track prediction methods have difficulties in processing large amounts of complex data in terms of prediction efficiency and accuracy. Recently, deep learning methods have shown a potential capability to process complex data efficiently and accurately. In this paper, we propose a novel data-driven approach based on auto-encoder (AE) and gated recurrent unit (GRU) models to forecast tropical cyclone landing locations using the historical tropical cyclone tracks and various meteorological attributes. This approach fuses a data preprocessing layer, an AE layer, and a GRU layer with a customized batch process. The model is trained on a real-world tropical cyclone dataset from the years 1945–2017. Through a comparison with existing forecasting methods, the results verified that our proposed model performed around 15%, 42%, and 56% better than the Numerical Weather Prediction model (NWP) in 24, 48, and 72 h forecasts, and 27%, 13%, 17%, and 17% better than RNN, AE-RNN, GRU, and LSTM, respectively, in 24 h forecasts, using the absolute position error. In addition, a comparison of the meteorological variables indicated that the variable maximum sustained wind speed had the most significant effect on tropical cyclone track prediction.

Human Activity Recognition (HAR) is nowadays widely used in intelligent perception and medical detection, and the use of traditional neural networks and deep learning methods has made great progress in this field in recent years. However, most of the existing methods assume that the data has independent identical distribution (I.I.D.) and ignore the data variability of different individual volunteers. In addition, most deep learning models are characterized by many parameters and high resources consumption, making it difficult to run in real time on embedded devices. To address these problems, this paper proposes a Gate Recurrent Units (GRU) network fusing the channel attention and the temporal attention for human activity recognition method without I.I.D. By using channel attention to mitigate sensor data bias, GRU and the temporal attention are used to capture important motion moments and aggregate temporal features to reduce model parameters. Experimental results show that our model outperforms existing methods in terms of classification accuracy on datasets without I.I.D., and reduces the number of model parameters and resources consumption, which can be easily used in low-resource embedded devices.

A multi-step and multi-stage high-throughput materials search via ensemble machine learning is developed to carefully and comprehensively screen nearly 12 million all-inorganic perovskites with potential high photovoltaic performance.

Knowledge tracing is a significant research area in educational data mining, aiming to predict future performance based on students’ historical learning data. In the field of programming, several challenges are faced in knowledge tracing, including inaccurate exercise representation and limited student information. These issues can lead to biased models and inaccurate predictions of students’ knowledge states. To effectively address these issues, we propose a novel programming knowledge tracing model named GPPKT (Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing), which enhances performance by using knowledge graphs and personalized answer sequences. Specifically, we establish the associations between well-defined knowledge concepts and exercises, incorporating student learning abilities and latent representations generated from personalized answer sequences using Variational Autoencoders (VAE) in the model. This deep knowledge tracing model employs Long Short-Term Memory (LSTM) networks and attention mechanisms to integrate the embedding vectors, such as exercises and student information. Extensive experiments are conducted on two real-world programming datasets. The results indicate that GPPKT outperforms state-of-the-art methods, achieving an AUC of 0.8840 and an accuracy of 0.8472 on the Luogu dataset, and an AUC of 0.7770 and an accuracy of 0.8799 on the Codeforces dataset. This demonstrates the superiority of the proposed model, with an average improvement of 9.03% in AUC and 2.02% in accuracy across both datasets.