Analysis on the Application of Multimedia-Assisted Music Teaching Based on AI Technology

In order to improve the effect of modern music teaching, this paper combines AI technology to construct a multimedia-assisted music teaching system, combines music teaching data processing requirements to improve the algorithm, proposes appropriate music data filtering algorithms, and performs appropriate data compression processing. Moreover, the functional structure analysis of the intelligent music teaching system is carried out with the support of the improved algorithm, and the three-tier framework technology that is currently more widely used is used in the music multimedia teaching system. Finally, in order to realize the complex functions of the system, the system adopts a layered approach. From the experimental research results, it can be seen that the multimedia-assisted music teaching system based on AI technology proposed in this paper can effectively improve the effect of modern music teaching.

Application of mathematical probabilistic statistical model of base – FFCA financial data processing

In order to improve the detection and filtering ability for financial data, a data-filtering method based on mathematical probability statistical model, a descriptive statistical analysis model of big data filtering, probability density characteristic statistical design data filtering analysis combined with fuzzy mathematical reasoning, regression analysis according to probability density of financial data distribution, and threshold test and threshold judgment are conducted to realize data filtering. The test results show that the big data filtering and the reliability and convergence of the mathematical model are optimal.

Analysis PM10 and Visibility During Forest Fire in Palangka Raya

Forest fires have an impact on air quality and visibility. Visibility can be associated with a highly visual indicator of air pollution. This research aims to analyze the relationship between the PM10 concentration and visibility during the forest firest events and normal conditions in Palangkaraya from 2000 to 2014 by using a regression method. The relative humidity data was used to filter the PM10 and visibility. Furthermore, the equation resulted from the regression analysis was used to predict PM10 concentration in Palangka Raya. The result showed that the regression pattern tends to form a logarithmic function. Specifically, without filtering data, the coefficient correlation (r-value) during the forest fire events and normal conditions are 0.69 and 0.5, respectively. Meanwhile, a data filtering method gives a higher relationship between PM10 and visibility, with the r-value of 0.7 for the forest fire events and 0.68 for the normal condition. On the other hand, the prediction of PM10 concentration indicates a high bias value due to the other influenced factors that have not been included in this study.

Application of convolutional neural networks in optical text recognition to junk data filtering

In this paper, the problem of constructing a model for detecting and filtering unwanted spam messages is solved. A fully connected convolutional neural network (FCNN) was chosen as the model of the classifier of unwanted emails in email. It allows you to divide emails into two categories: spam and not spam. The main result of the research is a software application in the C++ language, which has a micro-service architecture and solves the problem of image classification. The app can handle more than 106 requests per minute in real-time.

Application of Keras neural network in the era of big data

The experiment uses crawler tools to obtain data, and the data is preprocessed to find missing values and eliminate invalid data, meanwhile, the model is constructed by information entropy and ID3 algorithm so as to select the desired amount of features, and then basic modeling and data filtering is performed to train and evaluate the model for the first time, finally, in order to get a more ideal model, this experiment The optimal model is obtained by changing the number of hidden layers and neurons of the neural network to build a high-level neural network API neural network model written by pure python - Keras neural network model. The results show that when the model defines a 2-layer neural network and the number of neurons in the hidden layer is fourteen, the accuracy of the model is the highest, and the accuracy of the test set is as high as ninety-one percent.