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| 研究生： |
Karina Dita Mandasari Karina Dita Mandasari |
|---|---|
| 論文名稱： |
利用基於卷積神經網路的生理訊號預測網站複雜度 Predicting Website Complexity Based on Physiological Signals Using Convolutional Neural Network-Based Method |
| 指導教授： |
林久翔
Chiuhsiang Joe Lin |
| 口試委員: |
曹譽鐘
Yu-Chung Tsao 林希偉 Shi-Woei Lin |
| 學位類別： |
碩士 Master |
| 系所名稱： |
管理學院 - 工業管理系 Department of Industrial Management |
| 論文出版年： | 2020 |
| 畢業學年度： | 108 |
| 語文別： | 英文 |
| 論文頁數： | 92 |
| 中文關鍵詞： | 網站複雜度 、網站可用性 、人類生理信號 、卷積神經網絡 、時間序列分類 、統計分析 |
| 外文關鍵詞： | Website Complexity, Website Usability, Human Physiological Signals, Convolutional Neural Network, Time Series Classification, Statistical Analysis |
| 相關次數： | 點閱：300 下載：0 |
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由於互聯網使用的增加，電子商務的增長已顯示出更快的速度，人們更喜歡網路資訊而不是紙本，這成為公司開發和改進其網頁的機會。影響用戶滿意度的因素之一是有關網頁的視覺複雜性，網站複雜性的評估變得越來越重要，尤其是關於用戶的感知。為了通過使用生理信號進行客觀評估，本研究比較了一些演算法的性能指標，此方法使用了最先進的卷積神經網絡，因為它在特徵提取方面非常強大。
卷積神經網路在時間序列領域的各種應用包括語音識別、金融交易預測和人類活動識別。 它的成功應用，激發了本研究使用來自人類生理信號的時間序列數據，來改進基於卷積神經網路算法對網站複雜性的評估。本研究將三種不同複雜性級別（低，中和高）的網站所獲取的人類生理信號以給卷積神經網路學習，藉調整卷積神經網路演算法中的幾個參數以獲得更好的性能，例如內核大小，卷積層數，濾波器數和時間序列長度。最後以性能指標（例如準確性，準確性，召回率，F1得分，損失和AUC）評估卷積神經網路算法的性能。
由於只有一個可用數據集，因此本研究使用基於用戶的重複k倍交叉驗證來複製時間序列數據。為了比較幾種卷積神經網路演算法，本研究使用單向方差分析和Kruskal-Wallis H檢驗進行統計分析。這項研究的結果表明，卷積神經網路可以用於預測網站的複雜性。在四種演算法 (擴張型，深度可分離，LeNet和ResNet) 之間的訓練時間和丟失概率在統計上有顯著差異LeNet作為最簡單的算法，在損失方面產生了較好的性能。LeNet具有最小的參數，因此在其他算法中的訓練時間也最短。此外，較小的內核大小能更好地用於卷積神經網路算法中，以便從時間序列數據中提取重要信息。調查結果為網站管理員或設計人員提供了預測模型，以便根據其視覺複雜性來評估網站，從而獲得較高的用戶滿意度。
The growth of e-commerce has been shown faster due to increasing the use of internet. Taiwanese people prefer to webrooming than showrooming. It becomes an opportunity for company to develop and improve their web page. One of the aspects affecting user satisfaction is about the visual complexity of web page. The evaluation of website complexity become more important, especially regarding user perception. To assess objectively by using physiological signal, some algorithms are compared in terms of performance measures. The state-of-art method, Convolutional Neural Network, is used in this study, as it is very powerful for feature extraction.
Various applications of CNN in time series domain can be found in speech recognition, financial trading prediction, and human activity recognition. Its successful applications motivate this study to use the time series data from human physiological signals to improve the subjective evaluation toward website complexity using CNN-based algorithm. The algorithms are used to predict the level of website complexity in three different levels (low, medium, and high) based on human physiological signals. Several parameters in CNN algorithm can be adjusted to obtain better performance, such as kernel size, the number of convolution layers, the number of filters, and time series length. The performance metrics, such as accuracy, precision, recall, F1-score, loss, and AUC, are used to evaluate the algorithms’ performance.
This study uses a user-based repeated k-folds cross validation to replicate the time series data since there is only one available dataset. In order to compare several algorithms, one-way ANOVA and Kruskal-Wallis H test are used to do a statistical analysis. The results of this study show that CNN can be used to predict website complexity. Moreover, there are statistically significantly differences in training time and loss probability between four algorithms: Dilated CNN, Depthwise Separable CNN, LeNet, and ResNet. LeNet, as the simplest algorithm, yields better performance in terms of loss. LeNet also has lowest training time among other algorithms since it has smallest parameters. In addition, the small kernel size is better to use in CNN algorithm in order to extract important information from time series data. The research results provide prediction models for website managers or designers to evaluate website in term of its visual complexity in order to obtain high user satisfaction.
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