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A new classroom expression classification has also been concluded with considering the concentration. Moreover, activation function is modified to reduce the number of parameters and computations, at the same time, dropout regularization is added after the pool layer to prevent overfitting of the model. The experiments show that the accuracy of our method named Ixception has an maximize improvement of 5.25% than other algorithms. It can well meet the requirements of the analysis of classroom concentration.<\/jats:p>","DOI":"10.3233\/jifs-235541","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T12:15:29Z","timestamp":1697199329000},"page":"11873-11882","source":"Crossref","is-referenced-by-count":0,"title":["A classroom facial expression recognition method based on attention mechanism"],"prefix":"10.1177","volume":"45","author":[{"given":"Huilong","family":"Jin","sequence":"first","affiliation":[{"name":"College of Engineering, Hebei Normal University, Shijiazhuang, China"},{"name":"Vocational and Technical College of Hebei Normal University, Shijiazhuang, China"}]},{"given":"Ruiyan","family":"Du","sequence":"additional","affiliation":[{"name":"College of Engineering, Hebei Normal University, Shijiazhuang, China"}]},{"given":"Tian","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Engineering, Hebei Normal University, Shijiazhuang, 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