Shingo Mabu

3.0k citations
310 papers · 2.1k · h-index 22

Impact in

Papers in

    • Evolutionary Algorithms and Applications 142
    • Metaheuristic Optimization Algorithms Research 114
    • Reinforcement Learning in Robotics 49
    • Neural Networks and Applications 25
    • Elevator Systems and Control 51
    • Traffic control and management 36

Shingo Mabu

274 papers receiving 1.9k citations

Peers

Shingo Mabu
Comparison fields: 5 of 116
  • Artificial Intelligence 1.2k
  • Management Science and Operations Research 450
  • Signal Processing 270
  • Control and Systems Engineering 343
  • Building and Construction 150
Replace Kotaro Hirasawa with:
Kotaro Hirasawa Japan
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Shingo Mabu relative to Kotaro Hirasawa Japan Kotaro Hirasawa's profile →
Citations per field
00.5×2.8×
Kotaro Hirasawa · 1×
Citations per year

Countries citing papers authored by Shingo Mabu

Since Specialization
Citations

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

Fields of papers citing papers by Shingo Mabu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Shingo Mabu, 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 Shingo Mabu Line = papers co-authored together Shingo Mabu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 310 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007227
2 2010135
3 200940
4 201938
5 201537
6 200834
7 202233
8 201333
9 201329
10 200627
11 200927
12 201027
13 200826
14 202226
15 201526
16 200725
17 202125
18 201824
19 200723
20 201423

About Shingo Mabu

Shingo Mabu is a scholar working on Artificial Intelligence, Control and Systems Engineering, Management Science and Operations Research, Information Systems and Signal Processing, having authored 310 papers that have together received 2.1k indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (142 papers), Metaheuristic Optimization Algorithms Research (114 papers), Elevator Systems and Control (51 papers), Reinforcement Learning in Robotics (49 papers), Data Mining Algorithms and Applications (45 papers), Stock Market Forecasting Methods (43 papers), Traffic control and management (36 papers) and Neural Networks and Applications (25 papers). The work is most often cited by research in Artificial Intelligence (1.2k citations), Management Science and Operations Research (450 citations), Signal Processing (270 citations), Control and Systems Engineering (343 citations) and Building and Construction (150 citations). Shingo Mabu has collaborated with scholars based in Japan, China and Indonesia. Frequent co-authors include Kotaro Hirasawa, Kaoru Shimada, Jinglu Hu, Takashi Kuremoto, Masanao Obayashi, Kotaro Hirasawa, Xianneng Li, Ci Chen, Yan Chen and Nannan Lu. Their work appears in journals such as Expert Systems with Applications, Applied Soft Computing, International Journal of Computer Assisted Radiology and Surgery, Computers & Electrical Engineering 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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