ABSTRACT
The positive impact of mind-body movement therapy on mental health has been confirmed, but the current effect of various mind-body movement-specific therapies on improving the negative psychology of college students is controversial. This study compared the effects of six mind-body exercise (MBE) therapies on improving negative psychological symptoms in college students. The study found that Tai Chi (standardized mean difference [SMD] = -0.87, 95% confidence interval [CI] (-1.59, -0.15), p < 0.05), yoga (SMD = -0.95, 95% CI (-1.74, -0.15), p < 0.05), Yi Jin Jing (SMD = -1.15, 95% CI (-2.36, -0.05), p=<0.05), Five Animal Play (SMD = -1.1, 95% CI (-2.09, -0.02), p < 0.05), and Qigong Meditation (SMD = -1.31, 95% CI (-2.2, -0.4), p < 0.05) improved depressive symptoms in college students (p < 0.05). Tai Chi (SMD = -7.18, 95% CI (-13.18, -1.17), p = 0.019), yoga (SMD = -6.8, 95% CI (-11.79, -1.81), p = 0.008), and Yi Jin Jing (SMD = -9.21, 95% CI (-17.55, -0.87), p = 0.03) improved college students' anxiety symptoms. It shows that the six MBE therapies are effective in improving anxiety and depression in college students.
ABSTRACT
Recent discretization-based feature selection methods show great advantages by introducing the entropy-based cut-points for features to integrate discretization and feature selection into one stage for high-dimensional data. However, current methods usually consider the individual features independently, ignoring the interaction between features with cut-points and those without cut-points, which results in information loss. In this paper, we propose a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO), which searches for the feature subsets with and without entropy-based cut-points simultaneously. For the features with cut-points, a ranking mechanism is used to control the probability of mutation and crossover in GA. In addition, a binary-coded PSO is applied to update the indices of the selected features without cut-points. Experimental results on 10 real datasets verify the effectiveness of our algorithm in classification accuracy compared with several state-of-the-art competitors.
