Yunlong Mi

447 citations
13 papers · 295 · h-index 8

Impact in

Papers in

    • Neural Networks and Applications 4
    • Data Stream Mining Techniques 4
    • Cognitive Computing and Networks 2
    • Anomaly Detection Techniques and Applications 2
    • Machine Learning and Algorithms 2
    • Imbalanced Data Classification Techniques 1
    • Rough Sets and Fuzzy Logic 7

Yunlong Mi

13 papers receiving 292 citations

Peers

Yunlong Mi
Comparison fields: 5 of 47
  • Computational Theory and Mathematics 179
  • Artificial Intelligence 209
  • Computer Vision and Pattern Recognition 74
  • Signal Processing 27
  • Management Science and Operations Research 26
Replace Jason Catlett with:
Jason Catlett Australia
Ma Xiao China
Sébastien Ferré France
Mario Alviano Italy
Larisa Ismailova Russia
Amir Kafshdar Goharshady Hong Kong
Franz J. Brandenburg Germany
María I. Sessa Italy
Nicole Schweikardt Germany
Simona Perri Italy
Yunlong Mi relative to Jason Catlett Australia Jason Catlett's profile →
Citations per field
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Citations per year

Countries citing papers authored by Yunlong Mi

Since Specialization
Citations

This map shows the geographic impact of Yunlong Mi'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 Yunlong Mi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yunlong Mi more than expected).

Fields of papers citing papers by Yunlong Mi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yunlong Mi. 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 Yunlong Mi. The network helps show where Yunlong Mi may publish in the future.

Co-authors

The 15 scholars most cited alongside Yunlong Mi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yunlong Mi Line = papers co-authored together Yunlong Mi links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 202074
2 201864
3 202045
4 201945
5 202321
6 202114
7 201810
8 20229
9 20204
10 20243
11 20242
12 20252
13 20222

About Yunlong Mi

Yunlong Mi is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Computer Networks and Communications and Information Systems, having authored 13 papers that have together received 295 indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (7 papers), Neural Networks and Applications (4 papers), Data Stream Mining Techniques (4 papers), Image Retrieval and Classification Techniques (3 papers), Cognitive Computing and Networks (2 papers), Anomaly Detection Techniques and Applications (2 papers), Machine Learning and Algorithms (2 papers) and Imbalanced Data Classification Techniques (1 paper). The work is most often cited by research in Computational Theory and Mathematics (179 citations), Artificial Intelligence (209 citations), Computer Vision and Pattern Recognition (74 citations), Signal Processing (27 citations) and Management Science and Operations Research (26 citations). Yunlong Mi has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Yong Shi, Wenqi Liu, Jinhai Li, Zongrun Wang, Yi Qu, Zhensong Chen, Pei Quan, Jinhai Li, Jing Lin and Hui Liu. Their work appears in journals such as European Journal of Operational Research, IEEE Transactions on Systems Man and Cybernetics Systems, Journal of Economic Dynamics and Control, IEEE Transactions on Knowledge and Data Engineering and Information Sciences.

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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