我在Applied Sciences（综合性、交叉性期刊，CiteScore=3.70；IF=2.84）组织了一个Special Issue，大题目是“大数据分析进展”，比较宽泛。该专栏的推出主要是为了回应因为可获取数据和数据分析的平台、工具的快速增长给自然科学和社会科学带来的重大影响。我们特别欢迎（但不限于）下面四类稿件：（1）数据分析中的基础理论分析，例如一个系统的可预测性（比如时间序列的可预测性）、分类问题的最小误差分析、各种数据挖掘结果的稳定性和可信度分析；（2）数据分析的新方法，例如挖掘因果关系的新方法（这和Topic 1也是相关的）、多模态分析的新方法、隐私计算的新方法等等；（3）推出新的、高价值的数据集、数据分析平台、数据分析工具等等；（4）把大数据分析的方法用到自然科学和社会科学的各个分支（并获得洞见），我们特别喜欢用到那些原来定量化程度不高的学科。
投稿链接：https://www.mdpi.com/journal/applsci/special_issues/75Y7F7607U
投稿截止时期为2023年6月30日，我们处理稿件非常快，欢迎大家投稿支持。
其中第十四篇论文已经正式发表：
In the context of global climate change, air quality prediction work has a substantial impact on humans’ daily lives. The current extensive usage of machine learning models for air quality forecasting has resulted in significant improvements to the sector. The long short-term memory network is a deep learning prediction model, which adds a forgetting layer to a recurrent neural network and has several applications in air quality prediction. The experimental data presented in this research include air pollution data (SO2, NO2, PM10, PM2.5, O3, and CO) and meteorological data (temperature, barometric pressure, humidity, and wind speed). Initially, using air pollution data to calculate the air pollution index (AQI) and the wavelet transform with the adaptive Stein risk estimation threshold is utilized to enhance the quality of meteorological data. Using detrended cross-correlation analysis (DCCA), the mutual association between pollution elements and meteorological elements is then quantified. On short, medium, and long scales, the prediction model’s accuracy increases by 1%, 1.6%, 2%, and 5% for window sizes (h) of 24, 48, 168, and 5000, and the efficiency increases by 5.72%, 8.64%, 8.29%, and 3.42%, respectively. The model developed in this paper has a substantial improvement effect, and its application to the forecast of air quality is of immense practical significance.
https://www.mdpi.com/2076-3417/13/5/2796
