| 研究生: |
蘇俊瑋 Su, Chun-Wei |
|---|---|
| 論文名稱: |
應用統計學習於網路聲量及空氣品質之研究---以台南地區為例 Application of Statistical Learning to Internet Opinions and Air Quality --- Taking Tainan Area as an Example |
| 指導教授: |
李坤洲
Lee, Kun-Chou |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 系統及船舶機電工程學系 Department of Systems and Naval Mechatronic Engineering |
| 論文出版年: | 2024 |
| 畢業學年度: | 112 |
| 語文別: | 中文 |
| 論文頁數: | 98 |
| 中文關鍵詞: | 統計學習 、Google Trends 、網路聲量 、空氣品質指標 、動態時間扭曲 |
| 外文關鍵詞: | Statistical Learning, Google Trends, Internet Opinions, Air Quality index, Dynamic Time Warping |
| ORCID: | 0009-0003-4846-8497 |
| 相關次數: | 點閱:63 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
[1] D. Sanjeev, “Implementation of machine learning algorithms for analysis and prediction of air quality,” International Journal of Engineering Research & Technology, vol. 10, no. 3, pp. 533-538, March 2021.
[2] 蒙彥超,季風及區外污染源對工業區空氣品質監測站網優選之影響分析,國立交通大學環境工程系所碩士論文,95年
[3] 楊智翰,境外不同區域長程傳輸對台灣空氣品質影響之模擬研究,國立雲林科技大學環境與安全衛生工程系所碩士論文,102年。
[4] 張立農、江孟玲、林昭遠,「台灣交通空氣品質監測站 PM10 變異影響因素之研究」,水土保持學報,第47卷,1235-1246頁,2015年3月
[5] R. Nurazi, P. Kananlua and B. Usman, “The effect of google trend as determinant of return and liquidity in Indonesia Stock Exchange,” Jurnal Pengurusan, vol. 45, pp. 131-142, 2015.
[6] 王武吉,關鍵字搜尋量應用於郵輪艙位訂購預測之研究,國立交通大學運輸與物流管理學系碩士論文,106年。
[7] 張昭憲、周書任,「以模型融合配合社群網路資料進行流感趨勢預測」,資訊、科技與社會學報,第18期,1-20頁,2018年12月。
[8] M. Y. Huang, R. R. Rojas, and P. D. Convery, “Forecasting stock market movements using Google Trend searches,” Empirical Economics, vol. 59, pp. 2821-2839, 2020.
[9] W. Anggraeni and L. Aristiani, “Using Google Trend data in forecasting number of dengue fever cases with ARIMAX method case study: Surabaya, Indonesia,” 2016 International Conference on Information & Communication Technology and Systems, Surabaya, Indonesia, October 12-12, 2016.
[10] 盧芓云,運用網路聲量於台北捷運運量分析之研究,元智大學資訊管理學系所碩士學位論文,109年。
[11] Z. Pan, H. L. Nguyen, H. Abu-Gellban, and Y. Zhang, “Google trends analysis of covid-19 pandemic,” IEEE International Conference on Big Data, Atlanta, United States, Decenber 10-13, 2020.
[12] T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh, “Searching and mining trillions of time series subsequences under dynamic time warping,” 2012 International Conference on Knowledge Discovery and Data Mining, Beijing, China, August 12-16, 2012
[13] 張昱維、蔡易昌、楊惠春、樊聖,「Google Trends 搜尋關鍵字熱度與 COVID-19 疫情趨勢的相關性-以臺灣為例的網路行為觀察性研究」,醫學與健康期刊,第10卷,17-31頁,2021年11月。
[14] R. J. Kate, “Using dynamic time warping distances as features for improved time series classification,” Data mining and knowledge discovery, vol. 30, pp. 283-312, May, 2016.
[15] S. Abhishek and S. Suresh, “A Novel Online Signature Verification System Based on GMM Features in a DTW Framework,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 3, pp. 705-718, March 2017.
[16] B. K. Iwana, M. Mori, A. Kimura, and S. Uchida, “Introducing local distance-based features to temporal convolutional neural networks,” 2018 16th International Conference on Frontiers in Handwriting Recognition, Niagara Falls, United States, August 05-08, 2018.
[17] J. Trelinski and B. Kwolek, “CNN-based and DTW features for human activity recognition on depth maps,” Neural Computing and Applications, vol. 33, no. 21, pp. 14551-14563, November 2021.
[18] J. Rech, “Discovering trends in software engineering with google trend,” ACM SIGSOFT software engineering notes, August 5-8, vol.68, pp.1-2, 2007.
[19] J. Li, L. Xu, L. Tang, S. Wang, and L. Li, “Big data in tourism research: A literature review.” Tourism management,” vol.68, pp.301-323, October, 2020.
[20] 政府資料開放平台空氣品質資料, https://data.gov.tw/(Retrieved on April 24, 2024).
[21] Uyanık, G. K., and N. Güler, “A study on multiple linear regression analysis,” Procedia-Social and Behavioral Sciences, vol. 106, pp. 234-240, December 2013.
[22] M. J. Hayat and M. Higgins, “Understanding poisson regression,” Journal of Nursing Education, vol. 53, no. 4, pp. 207-215, March 2014.
[23] S. Mittal, “A survey on modeling and improving reliability of DNN algorithms and accelerators,” Journal of Systems Architecture, vol. 104, p. 101689, March 2020.
[24] L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie, and L. Farhan, “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, pp. 1-74, March 2021.
[25] P. Senin, Dynamic time warping algorithm review, Information and Computer Science Department, University of Hawaii at Manoa, Honolulu, USA, vol. 855, no. 1-23, p. 40, December 2008.
[26] 李坤洲,應用統計學習及公共物聯網於空氣污染防制之研究,環境部112年度補助空氣污染防制基金科技研究計畫結案報告,2024年1月。
[27] Z. Zhang, P. Tang and C. Thomas, “Time adaptive optimal transport: A framework of time series similarity measure.” IEEE Access, vol.8, pp. 149764–149774, August. 2020.
[28] S. V. Nuti, B. Wayda, I. Ranasinghe, S. Wang, R. P. Dreyer, S. I. Chen, and K. Murugiah, “The use of google trends in health care research: a systematic review,” PloS One, vol. 9, no. 10, p. e109583, October 2014.
[29] H. Choi and H. Varian, “Predicting the present with Google Trends,” Economic Record, vol. 88, pp. 2-9, June 2012.
[30] A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN features off-the-shelf: an astounding baseline for recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 806-813, September 2014.
校內:2029-08-29公開