With the advancement of deep learning and the growth of AI, a new concept for classifying music score images has been introduced. This paper presents a classification method for guitar tabs and numbered musical notation based on the ResNet50 network. The ResNet50 residual network model is used to extract features from music score images, and then the classification is performed based on the recognition probability. Results of the experiments indicate that the recognition accuracy of this model can reach 99.154%, suggesting that the ResNet50 network-based classification method holds good research potential in music score image classification.
Research on dangerous driving behavior recognition is beneficial to regulate the driving behavior of drivers. As the existing algorithms are sensitive to noise, and abnormal data often affects the process of identifying dangerous driving behaviors. This paper proposes a novel driving behavior research method. Such method establishes a driving behavior recognition model based on Support Vector Machine (SVM) and oversampling. The experimental results show that the proposed model demonstrates a higher recognition rate.
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