Open Access
Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
|
|
---|---|---|
Article Number | 01056 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/e3sconf/202339101056 | |
Published online | 05 June 2023 |
- J. Qiu, Q. Wu, G. Ding, Y. Xu, S. Feng, “A survey of machine learning for big data processing,” EURASIP J. Adv. Signal Process. vol. 2016, pp. 1–16, (2016) [CrossRef] [Google Scholar]
- S. Suthanharan, “Big data classification: Problems and challenges in network intrusion prediction with machine learning,” ACM SIGMETRICS Perf. Eval. Rev. vol. 41, pp. 70–73 (2014) [CrossRef] [Google Scholar]
- O. Jarrah, P. Yoo, S. Muhaidat, G. Karagiannidis, K. Taha, “Efficient Machine Learning for Big Data: A Review,” Big Data Res. Vol. 2, pp. 87–93 (2015) [CrossRef] [Google Scholar]
- E. Xing, Q. Ho, W. Dai, J. Kim, Y. Yu, “Petuum: A New Platform for Distributed Machine Learning on Big Data,” IEEE Trans. Big Data, vol. 1, pp. 49–67 (2015). [CrossRef] [Google Scholar]
- M. Chen, Y. Hao, K. Hwang, L. Wang, L. Wang, “Disease Prediction by Machine Learning Over Big Data From Healthcare Communities,” IEEE Access, vol. 5, pp. 8869–8879, (2017) [CrossRef] [Google Scholar]
- M. Gunasekaran, V. Vijayakumar, R. Varatharajan, K. Priyan S. Revathi, H. Ching-Hsien, “Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering,” Wireless Personal Communications, vol. 102, pp. 2099–2116 (2018) [CrossRef] [Google Scholar]
- W. Xiaofei, Z. Yuhua, L. Victor, G. Nadra, J. Tianpeng, “D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks,” IEEE Wireless Communications. vol. 25, pp. 32–38 (2018) [Google Scholar]
- Z. Zhenhua, H. Qing, G. Jing, N. Ming, “A deep learning approach for detecting traffic accidents from social media data,” Transportation Research Part C: Emerging Technologies, vol. 86, pp. 580–596 (2017) [Google Scholar]
- S. Ou; J. Lee, “Implementation of a Spam Message Filtering System using Sentence Similarity Measurements,” KIISE Trans. Comput. Pract. (KTCP), vol. 23, pp. 57–64 (2017) [CrossRef] [Google Scholar]
- D. Cho; K. Lim; S. Cho; S. Han; Y. Hwang, “Classifying Windows Executables using API-based Information and Machine Learning,” J. KIISE, vol. 43, pp. 1325–1333 (2016) [CrossRef] [Google Scholar]
- H. Choi, J. Park, “Security tendency analysis techniques through machine learning algorithms applications in big data environments,” J. Digit. Converg. vol. 13, pp. 269–267 (2015) [CrossRef] [Google Scholar]
- M. Yang, M. Kiang, W. Shang, “Filtering big data from social media - Building an early warning system for adverse drug reactions,” J. Biomed. Inf. vol. 54, pp. 230–240, (2015) [CrossRef] [Google Scholar]
- R. Hu, W. Dou, J. Liu, “ClubCF: A Clustering- Based Collaborative Filtering Approach for Big Data Application,” IEEE Trans. Emerg. Topics Comput. vol. 2, pp. 302–313 (2013) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.