To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introdu...To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.展开更多
The purpose of the present research was to investigate the predictors to Chinese college students seeking psychological help from professionals. By surveying 1,408 Chinese college students at five universities in Chin...The purpose of the present research was to investigate the predictors to Chinese college students seeking psychological help from professionals. By surveying 1,408 Chinese college students at five universities in China's Mainland, the results showed that among the factors examined, problem severity and help-seeking attitudes directly predicted help-seeking intention, while gender and subjective norms had a significant effect on college students＇ professional psychological help-seeking intention through help-seeking attitudes. Overall, the model explained 25% and 38.0% （for half-1 and half-2 data sets, respectively） of the variances of help-seeking intention. The results indicated that mainland Chinese college students rationally knew that they should choose to seek such professional help when the problem got severer enough, although they were emotionally reluctant to seek professional psychological help due to negative subjective norms around them. The implications and limitations were discussed.展开更多
基金Project(61403422)supported by the National Natural Science Foundation of ChinaProject(17C1084)supported by Hunan Education Department Science Foundation of ChinaProject(17ZD02)supported by Hunan University of Arts and Science,China
文摘To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.
文摘The purpose of the present research was to investigate the predictors to Chinese college students seeking psychological help from professionals. By surveying 1,408 Chinese college students at five universities in China's Mainland, the results showed that among the factors examined, problem severity and help-seeking attitudes directly predicted help-seeking intention, while gender and subjective norms had a significant effect on college students＇ professional psychological help-seeking intention through help-seeking attitudes. Overall, the model explained 25% and 38.0% （for half-1 and half-2 data sets, respectively） of the variances of help-seeking intention. The results indicated that mainland Chinese college students rationally knew that they should choose to seek such professional help when the problem got severer enough, although they were emotionally reluctant to seek professional psychological help due to negative subjective norms around them. The implications and limitations were discussed.
