The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning.

There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition.

In recent years, with the advent of the era of big data and the rise of data mining technology, people used to waste a lot of time looking for advantageous information, which was very inefficient. Music is an indispensable part of everyone’s daily life, and major music platforms are gradually emerging. Finding music that users like is the key to attracting users. At present, there are relatively few studies on personalized music recommendation using data mining algorithms. In this paper, the random forest algorithm is used to pre-process the collected data, and then the data is modeled, the model parameters and fitting coefficients are adjusted, the appropriate index is selected as the learning parameter, and the learning curve is obtained at the same time. Finally, the prediction is carried out through examples, and it is found that the prediction accuracy is high, and the WeChat applet is used for development and application. The approach in this article is simple and constructive, and it is dedicated to recommending personalized and exclusive music for particular users, so that you can spend every day happily.