開發CNN模型預測學生是否退學— 練習如何建立AI模型以從NGS短序列片段數據中偵測SNP

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题名:	開發CNN模型預測學生是否退學— 練習如何建立AI模型以從NGS短序列片段數據中偵測SNP
作者:	蔡婷安;Tsai, Ting-An
贡献者:	系統生物與生物資訊研究所
关键词:	卷積神經網路;退學預測;深度學習;Dropout prediction;Deep Learning;Convolutional Neural Network;CNN
日期:	2020-07-30
上传时间:	2020-09-02 15:42:51 (UTC+8)
出版者:	國立中央大學
摘要:	近年來人工智慧的發展迅速，TensorFlow為重要的深度學習框架之一，使用者可以輕鬆運用軟體庫裡的深度學習運算法，簡單開始架設模型，使得深度學習變得容易上手。如今運用深度學習分析大數據已成趨勢，而在影像辨識方面Convolution Neural Network(CNN)的表現也非常的優秀。我們認為，這項技術應用在學校系統上也一定能夠有實質的幫助。本文主要介紹如何將CNN技術應用於分析學生成績分析資料上，建立一個透過學期修課成績就可以預測下個學期是否會被退學的模型。取得「學生成績分析資料」後，我們觀察到「QUIT REASON」這個欄位裡「累積兩次1/2學分不及格」與「累積兩次2/3學分不及格」這兩項與成績有直接關係的退學原因。本文使用R語言，針對所有大學部學生的成績資料，將這些大學生每一學期的成績資料一一列出轉換成圖片，以他們下一學期是否有被退學作為圖片的Label。利用Keras 在R語言內建立CNN模型，調整模型內的參數，一一嘗試後找出最適合處理「學生成績分析資料」的模型。嘗試過各種不同參數的模型，我們發現參數的設定並沒有一定的趨勢，像是filter或是epoch，參數值增加但模型的表現不一定比較優秀。CNN模型需要透過經驗去做各種嘗試，從中挑選出最好的模型。 ;Artificial intelligence has developed rapidly in recent years. TensorFlow is one of the important deep learning frameworks, users can easily use the deep learning algorithms in TensorFlow and start up to build model easily. It make deep learning easy to get started. Nowadays, use deep learning to analyze big data has become a trend. Moreover, Convolution Neural Network’s performance in image recognition is excellent. Nowadays, deep learning has been widely used. The application of this technology in the school system will certainly be very helpful. This article mainly introduces how to apply CNN technology to the analysis of student score analysis data and to build a model that can predict whether student will be dropped out in the next semester. After we obtain student score analysis data, we observed two reasons for the dropouts in the "QUIT_REASON" column, "two cumulative 1/2 credit failures" and "two cumulative 2/3 credit failures", which are directly related to grades. This article use R language to analysis all required data. Focused on all college students’ data, list the results of each semester of these college students and convert them into pictures. Every picture has a label to show whether they will be dropped out in next semester. First of all, using Keras to build CNN model in R language. Second, adjust the parameters in the model. Third, trying one by one to find the most suitable model for processing "student score analysis data. After trying various models with different parameters, we found that there is no certain trend in parameter setting. Such as filters or epochs, the parameter value increases but the performance of the model is not necessarily better. However, CNN model needs to make various attempts through experience to find the best model.
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