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Deep learning based method for determining students from low-income families

A technology of deep learning and impoverished students, applied in neural learning methods, biological neural network models, data processing applications, etc., can solve problems such as insufficient accuracy and insufficient depth of hidden layers, and achieve the effect of improving accuracy

Inactive Publication Date: 2018-12-07
HUAIYIN INSTITUTE OF TECHNOLOGY
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AI Technical Summary

Problems solved by technology

However, the accuracy of this method is not high enough, and the hidden layer depth is not enough

Method used

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  • Deep learning based method for determining students from low-income families
  • Deep learning based method for determining students from low-income families
  • Deep learning based method for determining students from low-income families

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Embodiment Construction

[0060] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0061] Such as Figure 1-5 Shown, the present invention comprises the steps:

[0062] Step 1: As attached figure 2 , extract student characteristics from student card consumption data, grade data and library data Step 101 From step 201 to step 205:

[0063] Step 201: Set the student card consumption data set as X={X1n, X2n,...,Xmn}, where m represents the consumption category, n represents the student number, and Xmn is a matrix composed of the total consumption amount and the total consumption times;

[0064] Step 202: Set the student achievement data set as Y={Y1, Y2,...,Yn}, n represents the student number, and Yn represents the school ranking of the student's weighted average score;

[0065] Step 203: Set the data set of the student library as Z={Z1, Z2,...,Zn}, n represents the student number, and Zn represents the total number of borr...

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Abstract

The invention discloses a deep learning based method for determining students from low-income families. Features of each student are extracted from consumption data of an all-purpose card, score dataand library borrowing data of the student, students are divided into four poverty levels and coded by one-hot, and a coding result serves as a type label of the student. The extracted features and thetype labels are used to train an established neural network model. When it is required to determine the student from the low-income family, the consumption data of the all-purpose card, score data and library borrowing data of the student are extracted, and the type of the student is obtained after prediction by the neural network model. The result helps determine the student from the low-incomefamily. The method is characterized by high accuracy and sufficient hidden layer depth.

Description

technical field [0001] The invention belongs to the technical field of feature extraction and classification algorithms, in particular to a method for identifying poor students based on deep learning. Background technique [0002] After decades of development, China has formed a subsidy policy for impoverished students based on grants, national encouragement scholarships, and student loans. However, compared with western developed countries, it is difficult for China to achieve precise funding for poor students due to the lack of a complete personal taxation system. At the same time, due to the large population base, it is impossible to conduct individual visits and surveys, so that it is difficult to achieve precise funding for poor students. In recent years, in response to this problem, researchers have proposed corresponding identification programs for poor students, such as using K-means, SVM, and decision trees. [0003] The existing research foundations of Zhu Quanyi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08G06Q50/20
CPCG06N3/08G06Q50/205G06F18/2415G06F18/214
Inventor 朱全银李翔胡荣林蔡兵刘权周泓吴思凯倪金霆潘舒新其他发明人请求不公开姓名
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
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