Xin Feng

1.3k citations
67 papers · 895 indexed · h-index 17
Topics
Time Series Analysis and Forecasting (11 papers)Gene expression and cancer classification (10 papers)Neural Networks and Applications (10 papers)

In The Last Decade

Xin Feng

61 papers receiving 824 citations

Peers

Xin Feng
Comparison fields: 5 of 139
  • Artificial Intelligence 229
  • Molecular Biology 204
  • Rehabilitation 127
  • Signal Processing 114
  • Cognitive Neuroscience 107
Replace Ruichen Liu with:
Ruichen Liu China
Abdul Quaiyum Ansari India
Steve Warren United States
Hansung Lee South Korea
Juan Manuel Gálvez Spain
Ivan Chorbev North Macedonia
Van-Huy Pham Vietnam
Alejandro Baldominos Spain
Fang Shen China
Xin Feng relative to Ruichen Liu China Ruichen Liu's profile →
Citations per field
00.5×4.2×
Ruichen Liu · 1×
Citations per year

Countries citing papers authored by Xin Feng

Since Specialization
Citations

This map shows the geographic impact of Xin Feng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xin Feng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xin Feng more than expected).

Fields of papers citing papers by Xin Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xin Feng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xin Feng. The network helps show where Xin Feng may publish in the future.

Co-authorship network of co-authors of Xin Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Xin Feng. A scholar is included among the top collaborators of Xin Feng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xin Feng. Xin Feng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 1
3 0
4 2
5 1
6 4
7 4
8 6
9 20
10 55
11 2
12 5
13 80
14 17
15 10
16 7
17
A New Methodology For Emergent System Identification Using Particle Swarm Optimization (PSO) And The Group Mehtod Data Handling (GMDH)
14
18 21
19
TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS
22
20 8

About Xin Feng

Xin Feng is a scholar working on Signal Processing, Rehabilitation and Artificial Intelligence, having authored 67 papers that have together received 895 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (11 papers), Gene expression and cancer classification (10 papers) and Neural Networks and Applications (10 papers). The work is most often cited by research in Rehabilitation (127 citations), Signal Processing (114 citations) and Artificial Intelligence (229 citations). Xin Feng has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Richard J. Povinelli, Jack M. Winters, Fengfeng Zhou, Laura Johnson, Michelle J. Johnson, Lan Huang, Meiyu Duan, Liancheng Lei, Chencheng Gao and Yifan Zhang. Their work appears in journals such as Bioinformatics, PLoS ONE and Scientific Reports.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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