| 摘要(英) |
Modern people, using computers or mobile devices to surf the Internet is a habit
to do every day. The websites browsed are from general content sites, community sites,
video sites to e-commerce sites. In some cases, when we visit a website, some webpage
layout is incorrect or the content of the presented webpage is broken. There may be a
missing picture, image and non-matched text, or a content block has moved to a place
where it should not appear. This situation is called "broken layout" within the industry.
It is true that the development team that built the website is very reluctant to show
the "broken layout" to end users, so that not only may the website traffic be lost, but
also the degradation for the quality of the website. Users would have doubt about the
quality and hurt brand.
This research is mainly to explore the method of using image identification to
identify whether the image converted from the website page has a "broken layout"
problem; deep learning is used in the experiment since it performs well in the field of
image identification. The neural network algorithm is trained with different factors such
as the number of training pictures, the size of the pictures, the number of training
iterations, and the number of convolutional layers. According to the results of this
research, if the various factors are adjusted appropriately, the accuracy rate obtained
and the confusion matrix classification accuracy will be improved. |
| 參考文獻 |
【英文文獻】
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【中文文獻】
郭柏宏 (2015) 。基於深度學習之靜態影像超解析度技術。國立成功大學電機工程研究所博碩士論文。
【書籍與網站】
Brandon (2016) . End to End Machine Learning - How do Convolutional Neural Networks work? Retrieved June, 2020 from https://e2eml.school/how_convolutional_neural_networks_work.html
CH.Tseng (2017, September 12) 。初探卷積神經網路。Retrieved June, 2020 from: https://chtseng.wordpress.com/2017/09/12/初探卷積神經網路/
Cinnamon AI Taiwan (2019, June 5) 。深度學習：CNN原理。Retrieved June, 2020 from: https://medium.com/@CinnamonAITaiwan/深度學習-cnn原理-keras實現-432fd9ea4935
Machine Learning Notebook (2017, April 07) . Convolutional Neural Networks - Basics. Retrieved June, 2020 from: https://mlnotebook.github.io/post/CNN1/
Steven Shen (2018, January 2) 。入門深度學習—2。Retrieved June, 2020 from: https://medium.com/@syshen/入門深度學習-2-d694cad7d1e5
三津村直貴 (2018) 。圖解AI人工智慧大未來：關於人工智慧一定要懂的96件事。臺灣：旗標出版社。
全球資訊網協會 (World Wide Web Consortium，W3C) 簡介 (2020.06) 取自：https://www.w3.org/
林大貴 (2017) 。TensorFlow+Keras 深度學習人工智慧實務應用。臺灣：博碩出版社。 |
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