School of Music, Shanxi Datong University, Datong Shanxi 037009, China.
J Environ Public Health. 2022 Sep 14;2022:3830522. doi: 10.1155/2022/3830522. eCollection 2022.
One of the public fundamental disciplines that is typically put up among the professional teaching units in universities is dance. In order for this sports project with fitness, mental health, and aesthetic functions to be widely developed in universities, the use of reasonable, scientific, and targeted teaching methods can effectively improve the instructional effect. At the same time, it has further promoted the quality of education in universities and implemented the guiding ideology of "health first." In order to avoid the classifier's performance-degrading effects brought on by the high dimension, this research suggests combining the classifier's psychological stress identification algorithm with a particle swarm optimization (PSO) approach. The experimental findings reveal that the PSO-SVM algorithm, PSO-BP algorithm, improved PSO-SVM algorithm, and improved PSO-BP algorithm, respectively, have recognition rates for psychological stress of 82.50%, 84.50%, 90.17%, and 94.83%. Additionally, the recognition rates of the improved PSO classifier are significantly higher than those of the basic PSO algorithm, demonstrating the improved PSO algorithm's strong generalization ability in optimization.
舞蹈是高校专业教学单位普遍开设的公共基础课之一。为使具有健身、健心、审美功能的体育项目在高校得到广泛开展，运用合理、科学、有针对性的教学方法，能有效提高教学效果，同时进一步促进高校素质教育，落实“健康第一”的指导思想。为避免分类器的高维带来的性能下降的影响，本研究提出将分类器的心理应激识别算法与粒子群优化（PSO）方法相结合。实验结果表明，PSO-SVM 算法、PSO-BP 算法、改进的 PSO-SVM 算法和改进的 PSO-BP 算法对心理应激的识别率分别为 82.50%、84.50%、90.17%和 94.83%。此外，改进的 PSO 分类器的识别率明显高于基本 PSO 算法，表明改进的 PSO 算法在优化中的泛化能力较强。
