A Method of Determining Needy Students Based on Deep Learning

For a long time, stipend has been send inaccurately due to China's national conditions. Machine learning has been used to find impoverished students and achieved some results, but it was unsupervised and the accuracy was difficult to measure. In order to ensure the stipend being distributed fairly, a method based on deep learning is presented in this paper. During the experiment, accuracy rate was reached to 96%, through use the data came from school one-card and school library. The results show that this method can effectively improve the accuracy of grants, can save labor cost.

[1]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[2]  Song Han,et al.  EIE: Efficient Inference Engine on Compressed Deep Neural Network , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

[3]  Q. Zha,et al.  Case Study: The Chinese Government Scholarship Program—the Brain Development Scheme That Illuminates a Vision Across 30 Years , 2018 .

[4]  Sergio Gomez Colmenarejo,et al.  Hybrid computing using a neural network with dynamic external memory , 2016, Nature.

[5]  Juan A. Botía Blaya,et al.  Generation of human computational models with machine learning , 2015, Inf. Sci..

[6]  Kagan Tumer,et al.  Error Correlation and Error Reduction in Ensemble Classifiers , 1996, Connect. Sci..

[7]  Zeshui Xu,et al.  Hesitant fuzzy linguistic entropy and cross-entropy measures and alternative queuing method for multiple criteria decision making , 2017, Inf. Sci..

[8]  Charlene Tan,et al.  Education policy borrowing in China: has the West wind overpowered the East wind? , 2015 .

[9]  Yoshua Bengio,et al.  BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 , 2016, ArXiv.

[10]  Matthias Hein,et al.  Variants of RMSProp and Adagrad with Logarithmic Regret Bounds , 2017, ICML.

[11]  Yoshifusa Ito,et al.  Approximation Capability of Layered Neural Networks with Sigmoid Units on Two Layers , 1994, Neural Computation.

[12]  Araceli Sanchis,et al.  Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble , 2013, Neurocomputing.