The accurate prediction for the tropical cyclone (TC) intensity is a recognised challenge. Researchers usually develop dynamical models to address this task. However, since the TC intensity is highly influenced by various factors such as ocean and atmosphere conditions, it is difficult to build the very model which can explicitly describe the mechanism of TC. A new idea is developed, utilising the massive historical observation data by a deep learning approach, to conduct a completely data‐driven TC intensity prediction model. All the TC intensity and track data which have been observed in Western North Pacific since 1949 are collected, and recurrent neural network for TC intensity prediction is constructed. In general, their motivation as well as novelty is to develop a data‐driven approach instead of empirical models. There are very few researches similar to their exploratory work. The proposed method has presented 5.1 m⋅s−1 error in 24 h prediction, which is better than some widely used dynamical models and is close to subjective prediction.

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With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

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