Abstract
School bullying is a common social problem around the world which affects teenagers, and physical violence is considered to be the most harmful while verbal bullying is the most frequent. This paper proposes an automatic physical and verbal bullying detecting method in the field of artificial intelligence. Dozens of features were extracted from acceleration and gyro data to train the physical bullying recognition while the mean value of each frame of samples was calculated for verbal bullying detection. The authors used the k-NN algorithm as the classifier. The final test accuracies of physical and verbal bullying detecting were 70.4% and 78.0%, respectively, indicating that activity recognition and speech emotion recognition can be used for detecting bullying behaviors as an artificial intelligence technique, and speech emotion recognition appeared to be better than activity recognition.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (61602127). The authors would like to thank the pupils from the second and the sixth grades who acted in the school bullying experiments.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Gao, S., Ye, L. (2019). A Physical and Verbal Bullying Detecting Algorithm Based on K-NN for School Bullying Prevention. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-22971-9_13
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DOI: https://doi.org/10.1007/978-3-030-22971-9_13
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