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Feiyi Liu - IEEE Xplore Author Profile
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Feiyi Liu

Publications
3
Citations
25
Publications by Year
20192023
Co-Authors:
Show All Co-Authors (13)
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Feiyi Liu

Affiliation

Qingdao Branch. China Mobile Group Shandong Company Limited, Qingdao, China

Publication Topics

Biography

Feiyi Liu was born in Foshan, Guangdong, China, in 1997. He is currently pursuing the B.S. degree in computer science and technology with Chongqing University. He has published an article in the conference UIC. His research interests include machine learning, natural language processing, and knowledge graph. He was rewarded many scholarships when pursuing the B.S. degree.(Based on document published on 10 January 2020).
Publications
3
Citations
25
Publications by Year
20192023
Co-Authors:
Show All Co-Authors (13)
Author's Published Works

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5G VoNR Traffic Real-time Prediction Method based on LSTM

Fangkai Lu;Fangzheng Yuan;Yongqiang Li;Xiaofeng Song;Cheng Liu;Feiyi Liu

2023 5th International Conference on Frontiers Technology of Information and Computer (ICFTIC)
Year: 2023 | Conference Paper |
With the advent of 5G technology, a growing number of users have started to embrace 5G-based voice calls, commonly referred to as VoNR (Voice over New Radio). Accurate prediction of 5G VoNR traffic holds significant value for telecommunication operators, enabling them to more efficiently manage network resources, conserve energy, and offer enhanced services to their users. Nonetheless, despite the...Show More

5G VoNR Traffic Real-time Prediction Method based on LSTM

Fangkai Lu;Fangzheng Yuan;Yongqiang Li;Xiaofeng Song;Cheng Liu;Feiyi Liu

Year: 2023 | Conference Paper |

DBGE: Employee Turnover Prediction Based on Dynamic Bipartite Graph Embedding

Xinjun Cai;Jiaxing Shang;Ziwei Jin;Feiyi Liu;Baohua Qiang;Wu Xie;Liang Zhao

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (19)
The issue of employee turnover is always critical for companies, and accurate predictions can help them prepare in time. Most past studies on employee turnover have focused on analyzing impact factors or using simple network centrality measures. In this paper, we study the problem from a completely new perspective by modeling users' historical job records as a dynamic bipartite graph. Specifically...Show More

DBGE: Employee Turnover Prediction Based on Dynamic Bipartite Graph Embedding

Xinjun Cai;Jiaxing Shang;Ziwei Jin;Feiyi Liu;Baohua Qiang;Wu Xie;Liang Zhao

Year: 2020 | Volume: 8 | Journal Article |

CoxRF: Employee Turnover Prediction Based on Survival Analysis

Qianwen Zhu;Jiaxing Shang;Xinjun Cai;Linli Jiang;Feiyi Liu;Baohua Qiang

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Year: 2019 | Conference Paper |
Cited by: Papers (6)
In human resource management, employee turnover prediction is very important for company operation since the leave of key employees can bring great loss to companies. However, most existing researches focused on employee-centered turnover prediction, while ignored the historical events of turnover behaviors as well as the longitudinal data of each work. Therefore, in this paper we propose a turnov...Show More

CoxRF: Employee Turnover Prediction Based on Survival Analysis

Qianwen Zhu;Jiaxing Shang;Xinjun Cai;Linli Jiang;Feiyi Liu;Baohua Qiang

Year: 2019 | Conference Paper |

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