Intelligent employment rate prediction model based on a neural computing framework and human–computer interaction platform
An intelligent employment rate prediction model based on a neural computing framework and human–computer interaction platform is demonstrated in this manuscript. Predictive analytics is the future of things, and its significance is manifested in two main aspects: understanding the future so that people can prepare for its arrival, and predicting the current decision so that people can understand the possible consequences, and by the consequences of the analysis to determine the current decision, and strive to make the current decision. However, there are lots of challenges for the prediction tasks. The novelty of this research is mainly concentrated on two major aspects: (1) the neural network model is optimized and enhanced. The proposed nerve tree network model is essentially based on a tree-structured code for a multi-layered feed-forward sparse neural network; with the tree-structured code, the nerve tree network model does not require interconversion between its genotype and phenotype in the coding and decoding operations, and also effectively reduces the computing time. (2) The human–computer interaction is integrated to construct a user-friendly system. In interactive technology, the interactive contact surface and the model, interactive methods and social acceptance have also given rise to many questions that must be solved and problems that require further research and technological innovation. Through numerical verification, the performance of the proposed framework is validated, and the simulation proves the overall performance of the proposed model. Compared with other models, the proposed algorithms can achieve higher prediction accuracy.
KeywordsHuman–computer interaction Neural computing framework Intelligent employment rate prediction Data mining
The research is supported by the following projects: (1) National Natural Science Foundation of China “Research on the high quality Employment of College Graduates in China Based on the Data Mining: Evaluation System, Influencing Factors and Realization Path” (71874205); (2) Beijing Social Science Fund Project “Research on the Effectiveness Evaluation of Beijing Promoting Employment and Entrepreneurship Policy” (17YJB008); (3) China University of Political Science and Law School-level Scientific Research Project “Research on the Influence of Human capital and Social capital on the Employment Quality of College Graduates: A Multilevel Data Mining Approach Based on Machine Learning” (16ZFG79002); (4) China University of Political Science and Law “Excellent Young and Middle-aged Teacher Development Support Program” (01146511).
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Conflict of interest
All authors declare that they have no conflict of interest.
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