Machine learning and digital signal processing methods are used in various industries, including in the analysis and classification of seismic signals from surface sources. The developed wave type analysis algorithm makes it possible to automatically identify and, accordingly, separate incoming seismic waves based on their characteristics. To distinguish the types of waves, a seismic measuring complex is used that determines the characteristics of the boundary waves of surface sources using special molecular electronic sensors of angular and linear oscillations. The results of the algorithm for processing data obtained by the method of seismic observations using spectral analysis based on the Morlet wavelet are presented. The paper also describes an algorithm for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves, considers the use of statistical characteristics and MFCC (Mel-frequency cepstral coefficients) parameters, as well as their joint application. At the same time, the following were used as statistical characteristics of the signal: variance, kurtosis coefficient, entropy and average value, and gradient boosting was chosen as a machine learning method; a machine learning method based on gradient boosting using statistical and MFCC parameters was used as a method for determining the distance to the signal source. The training was conducted on test data based on the selected special parameters of signals from sources of seismic excitation of surface waves. From a practical point of view, new methods of seismic observations and analysis of boundary waves make it possible to solve the problem of ensuring a dense arrangement of sensors in hard-to-reach places, eliminate the lack of knowledge in algorithms for processing data from seismic sensors of angular movements, classify and systematize sources, improve prediction accuracy, implement algorithms for locating and tracking sources. The aim of the work was to create algorithms for processing seismic data for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves.
On the Internet, "fake news" is a common phenomenon that frequently disturbs society because it contains intentionally false information. The issue has been actively researched using supervised learning for automatic fake news detection. Although accuracy is increasing, it is still limited to identifying fake information through channels on social platforms. This study aims to improve the reliability of fake news detection on social networking platforms by examining news from unknown domains. Especially, information on social networks in Vietnam is difficult to detect and prevent because everyone has equal rights to use the Internet for different purposes. These individuals have access to several social media platforms. Any user can post or spread the news through online platforms. These platforms do not attempt to verify users or the content of their locations. As a result, some users try to spread fake news through these platforms to propagate against an individual, a society, an organization, or a political party. In this paper, we proposed analyzing and designing a model for fake news recognition using Deep learning (called AAFNDL). The method to do the work is: 1) First, we analyze the existing techniques such as Bidirectional Encoder Representation from Transformer (BERT); 2) We proceed to build the model for evaluation; and finally, 3) We approach some Modern techniques to apply to the model, such as the Deep Learning technique, classifier technique and so on to classify fake information. Experiments show that our method can improve by up to 8.72% compared to other methods.
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