比較線性模型、多層感知器和卷積神經網絡在回歸分析應用中的性能;Comparing the performance of linear model, multilayer perceptron, and convolutional neural network in regression analysis application

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/92403

Title:	比較線性模型、多層感知器和卷積神經網絡在回歸分析應用中的性能;Comparing the performance of linear model, multilayer perceptron, and convolutional neural network in regression analysis application
Authors:	歐奕;OU-YI
Contributors:	系統生物與生物資訊研究所
Keywords:	深度學習;卷積神經網路;線性回歸;預測成績;Deep learning;Convolutional Neural Network;Linear regression;Predict Value
Date:	2023-02-02
Issue Date:	2024-09-19 15:50:20 (UTC+8)
Publisher:	國立中央大學
Abstract:	近年來人工智慧越加蓬勃發展，加上網路的進步，5G也正在被廣泛的使用而元宇宙正步入我們的生活中，其基礎離不開深度學習，而TensorFlow是重要的深度學習框架，用於語言理解任務的機器學習，TensorFlow為電腦科學家重新簡化重建DistBelief的代碼庫，使其變成更快更好的代碼庫，使用者可以簡單的使用裡面的代碼庫裡的深度學習運算法來建構模型。現今我們廣泛使用深度學習來分析大數據，其中Convolutional Neural Network(CNN)在影像辨識方面有良好的表現，本文將介紹如何使用Keras建構CNN模型來分析學校的學生成績資料，我們將建立一個模型，當我們有此學生的學期成績資料，就能使用此模型來預測此學生下一學期的成績。當我們取得學生成績資料後，裡面有每位學生的成績與多項欄位，這些欄位代表著不同的意義，其中我們找出每位學生每個學期的各科成績，用來加以分析學生成績。我們使用R語言來處理資料，選取大學部學生的資料並將學生的每一學期成績資料轉換成圖片，用學生的前幾個學期成績做線性回歸來預測下一學期的成績。本文用Keras在R語言中建立CNN模型，我們發現模型的參數設定並沒有一定的規律，其中epoch不是越多越好，learning rate也不是越慢越好，需透過不斷的訓練模型與調整模型參數，才能找出最適合處理學生成績資料的模型。早期預測數值普遍使用線性回歸，如今人工智慧越發純熟，本文將探討使用深度學習建立的MLP與CNN模型是否能比傳統的線性回歸模型有更好的效果，並且對機器的運算不會付出太大的代價。 ;In recent years, artificial intelligence has been rapidly developing, and with the progress of the internet, 5G is also widely used, and the metaverse is entering our lives. The foundation of this cannot be separated from deep learning, and TensorFlow is an important deep learning framework for language understanding tasks of machine learning. TensorFlow simplifies and improves the code library of DistBelief for computer scientists, making it faster and better, so that users can easily use the deep learning algorithms inside the code library to construct models. Nowadays, we widely use deep learning to analyze big data, among which, the Convolutional Neural Network (CNN) has good performance in image recognition. This article will introduce how to use Keras to construct a CNN model to analyze school student performance data. We will establish a model, when we have this student′s semester performance data, we can use this model to predict the student′s performance in the next semester. After we acquire student performance data, it contains the grades and multiple fields of each student, these fields represent different meanings. Among them, we find out each student′s grades for each subject in each semester to analyze student performance. We use R language to process data, select college students′ data, and convert students′ performance data in each semester into images. We use students′ previous few semester grades to do linear regression to predict the grade for the next semester. This article uses Keras to create a CNN model in R language, we find that the model′s parameter settings do not have a certain rule, the number of epochs is not the more the better and the learning rate is not the slower the better. We need to continuously train the model and adjust the model parameters to find the best model for processing student performance data. In the early days, linear regression was commonly used for predicting values. Now that artificial intelligence is becoming more and more proficient, this article will explore whether the MLP and CNN models established using deep learning can have better results than traditional linear regression models, and the calculation of the machine will not pay too much the price.
Appears in Collections:	[Institute of Systems Biology and Bioinformatics] Electronic Thesis & Dissertation

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