It is difficult for the intelligent teaching system in colleges to effectively predict student grade, which makes it difficult to formulate follow-up teaching strategies. In order to improve the effect of student grade prediction, this study improves the neural network algorithm, combines support vector machines to build a student grade prediction model, and uses PCA to reduce the dimensionality of the sample data. The specific operation is realized by SPSS software. Moreover, this study removes redundant information inside the input vector and compresses multiple features into a few typical features as much as possible. In addition, the research set a control experiment to analyze the performance of the research model and compare the advantages and disadvantages of the classification prediction effect of traditional machine learning algorithms and neural network algorithms. Through experimental comparison, we can see that the model constructed in this paper has certain advantages in all aspects of parameter performance, and the prediction model proposed in this study has certain effects.
In a decentralized organization such as a university, recruitment is critical for the development of departments. With the limited resources for recruitment, how to allocate the recruitment quotas to different departments is an essential problem for the human resource management center of the organization. Specifically, for a recruitment process that includes multiple phases, a proper number of quotas should be allocated to different departments at each recruitment phase to guarantee the recruitment performance of the whole organization. However, it is difficult since the performance of the whole recruitment process is prior unknown at each recruitment phase. Traditionally, a recruitment quota allocation scheme based on requirements raised by departments is generally adopted, which is somewhat subjective and may not achieve satisfying recruitment performance. To address the above problem, a recruitment quota allocation scheme is proposed in this paper, which is based on the theoretical modeling and optimization of the recruitment process, aiming to provide a more objective and theoretical method that may achieve optimal performance. We first define the recruitment utility as the performance metric for the recruitment process. The recruitment quota allocation problem is formulated using the Markov decision process approach, where the effect of the information of potential applicants is innovatively taken into account. Then, the recruitment quota allocation is optimized by maximizing the recruitment utility to achieve optimal recruitment performance. In addition, a recruitment quota allocation scheme based on the requirements of departments, which is traditionally adopted in the recruitment of a university, is used for comparison with the optimized scheme of the paper. Simulation results show that the proposed scheme can improve the efficiency of recruitment quota allocation significantly.INDEX TERMS Recruitment, quota allocation, organizational network, Markov decision process.
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