Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.

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Nearly every industry is increasingly reliant on patent protections, and the future of entire industries and even countries can be analyzed and predicted based on patent development. Countries are responding to the rapid development of RFID applications by aggressively protecting pursuing and protecting RFID innovations. This study examines a number of patent indicators to predict and explain future technological developments in the RFID industry, thus providing insights for economic planning at the national scale. Patent indicators such as the number of patent applications and Patent Cooperation Treaties (PCTs) are used to analyze technological development trends in the context of overall development for Europe, Asia, and America, and to identify valuable technological development directions. Distributions of RFID patents are then used with the Revealed Patent Advantage (RPA) to conduct cluster analysis of K-means, thus providing insight into relative regional technological advantages in RFID development. Finally we discuss the future creative value of the Taiwanese market and its development direction.