The purpose of this study is to provide basic data for management and human resource management in the beauty industry by confirming the mediating effect on self-efficacy in the relationship between character strengths and job performance beauty industry workers. This study recruited 449 subjects who are engaged in the beauty field in Busan and Gyeongsang province. The survey using a self-questionnaire was conducted between 15 April and 10 May 2021. In conclusion, self-efficacy was found to have a direct and indirect mediation effect on character strengths and job performance, and among character strengths, wisdom and knowledge were found to affect organizational commitment and job satisfaction only through self-efficacy. Since it has a direct impact, it can be seen that self-efficacy improvement is essential in order to link the abilities of cosmetologists to job performance. Although the personality strengths of beauty industry workers directly and indirectly affect job performance, it can be seen that job performance can be further enhanced if self-regulation and self-esteem are improved and self-efficacy is improved.

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There have been much difficulties to construct an optimized neural network in complex nonlinear regression problems such as selecting the networks structure and avoiding overtraining problem generated by noise. In this paper, we propose a stepwise constructive method for neural networks using a flexible incremental algorithm. When the hidden nodes are added, the flexible incremental algorithm adaptively controls the number of hidden nodes by a validation dataset for minimizing the prediction residual error. Here, the ELM (Extreme Learning Machine) was used for fast training. The proposed neural network can be an universal approximator without user intervene in the training process, but also it has faster training and smaller number of hidden nodes. From the experimental results with various benchmark datasets, the proposed method shows better performance for real-world regression problems than previous methods.

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