Abstract
When the traditional method is used to warn the students’ mentality transition data, the weight of the sample set is ignored, which leads to large warning errors. An intelligent early warning algorithm for student mentality transformation in psychological database is proposed. Obtain the sample set of the training sample of the student’s mentality transformation data data, obtain the weight of the initial sample set, construct a strong classifier to classify the data samples of the student’s mentality transition, and establish an early warning optimization algorithm for the student’s mental state transition data. The experimental simulation proves that the proposed method has high early warning efficiency and can provide strong support for ensuring students’ health psychology.
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Huang, H. (2020). Intelligent Early Warning Algorithm for Students’ Mentality Transformation in Psychological Database. In: Xu, Z., Parizi, R., Hammoudeh, M., Loyola-González, O. (eds) Cyber Security Intelligence and Analytics. CSIA 2020. Advances in Intelligent Systems and Computing, vol 1147. Springer, Cham. https://doi.org/10.1007/978-3-030-43309-3_104
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DOI: https://doi.org/10.1007/978-3-030-43309-3_104
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