MATEC Web Conf.
Volume 355, 20222021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
|Number of page(s)||10|
|Section||Mathematical Science and Application|
|Published online||12 January 2022|
- Li Junyi, Guo Fangce, Sivakumar Aruna et al. Transferability improvement in short-term traffic prediction using stacked LSTM network[J]. Transportation Research Part C, 2021,124: 102977–102995 [Google Scholar]
- Yonghua Huo, Chunxiao Song, Sheng Gao et al. Network Traffic Prediction Method Based on Time Series Characteristics[C]//Proceedings of the 10th International Conference on Computer Engineering and Networks, 2020: 1547–1555. [Google Scholar]
- Hui Xia, Chenhao Zhao, Zhiguo Tang et al. Application of ARMA Model in Prediction of Development Trend of Partial Discharge[C]//Poceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering. 2019: 395–402. [Google Scholar]
- Jian Zhou, Qidong Yang, Xiaofei Zhang et al. Traffic Prediction Method for GEO Satellites Combining ARIMA Model and Grey Model[J]. Journal of Shanghai Jiaotong University,2020,25(8):65–69. [Google Scholar]
- Makuvaza Auther, Jat Dharm Singh, Gamundani Attlee M. Deep Neural Network (DNN) Solution for Real-time Detection of Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDNs)[J]. SN Computer Science,2021,2(2):107–117. [Google Scholar]
- Zhongda Tian. Network traffic prediction method based on wavelet transform and multiple models fusion[J]. International Journal of Communication Systems, 2020, 33(11):4415–4440. [Google Scholar]
- Lei Ming, Qin Rui, Mao Wentao, Lu Hongxia. Traffic data prediction of mobile communication base station based on wavelet neural network[J]. Journal of Physics: Conference Series, 2021, 1883(1):12065–12071. [Google Scholar]
- S. Sharma, L. Parashar and U. Singh. Network Traffic Prediction Using Long Short-Term Memory[C]//2020 International Conference on Electronics and Sustainable Communication Systems. 2020:338–343. [Google Scholar]
- R. Vinayakumar, K. P. Soman and P. Poornachandran. Applying deep learning approaches for network traffic prediction. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017: 2353–2358. [Google Scholar]
- Bai S , Kolter J Z , Koltun V. Trellis Networks for Sequence Modeling[J]. 2018. [Google Scholar]
- Luming Bai, Hui Wang, Zhibo Zhai. Research on Network Traffic Forecast Based on Improved LSTM Neural Network[J]. International Core Journal of Engineering, 2020,6(6):225–234. [Google Scholar]
- Hongzhi Liu, Yingpeng Du, Zhonghai Wu. AEM: Attentional Ensemble Model for personalized classifier weight learning[J]. Pattern Recognition,2019,96:106976–106984. [Google Scholar]
- Song Shuang, Li Shugang, Zhang Tianjun et al. Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN[J]. Energies, 2021,14(5):1384–1384. [Google Scholar]
- Sharafaldin, I., Lashkari, A. and Ghorbani, A. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffific Characterization[C]//Proceedings of the 4th International Conference on Information Systems Security and Privacy,2018:108–116. [Google Scholar]
- Gao Shuai, Huang Yuefei, Zhang Shuo et al. Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation[J]. Journal of Hydrology, 2020, 589:125188–125197. [Google Scholar]
- L. Zhang et al. LNTP: An End-to-End Online Prediction Model for Network Traffic[J]. IEEE Network, 2021: 226–233. [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.