Ming Tan

2.3k total citations
13 papers, 241 citations indexed

About

Ming Tan is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Networks and Communications. According to data from OpenAlex, Ming Tan has authored 13 papers receiving a total of 241 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 4 papers in Control and Systems Engineering and 2 papers in Computer Networks and Communications. Recurrent topics in Ming Tan's work include Machine Learning and Algorithms (4 papers), Machine Learning and Data Classification (4 papers) and Neural Networks and Applications (3 papers). Ming Tan is often cited by papers focused on Machine Learning and Algorithms (4 papers), Machine Learning and Data Classification (4 papers) and Neural Networks and Applications (3 papers). Ming Tan collaborates with scholars based in United States, China and Indonesia. Ming Tan's co-authors include Jeffrey C. Schlimmer, Ji Li, Yanfeng Li, Muhammad Ilham Aldika Akbar, Erry Gumilar Dachlan and Chien‐Hsing Lu and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Machine Learning.

In The Last Decade

Ming Tan

12 papers receiving 215 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ming Tan United States	6	216	69	32	32	28	13	241
Karl Pfleger United States	5	117 0.5×	58 0.8×	27 0.8×	23 0.7×	14 0.5×	7	166
Hiroshi Tsukimoto Japan	5	157 0.7×	35 0.5×	46 1.4×	20 0.6×	25 0.9×	12	213
Peter Geibel Germany	8	119 0.6×	33 0.5×	18 0.6×	23 0.7×	15 0.5×	20	146
Jieun Eom South Korea	4	201 0.9×	65 0.9×	19 0.6×	48 1.5×	6 0.2×	5	235
HyungChul Kang South Korea	3	170 0.8×	41 0.6×	14 0.4×	49 1.5×	6 0.2×	5	196
Khalid M. Salama United Kingdom	12	254 1.2×	88 1.3×	43 1.3×	11 0.3×	6 0.2×	29	330
Bakh Khoussainov New Zealand	10	87 0.4×	64 0.9×	96 3.0×	37 1.2×	14 0.5×	31	227
Nicholas Holden United Kingdom	5	178 0.8×	48 0.7×	58 1.8×	16 0.5×	20 0.7×	5	229
Elena Botoeva United Kingdom	8	135 0.6×	27 0.4×	10 0.3×	18 0.6×	15 0.5×	21	157
David Evans United Kingdom	6	133 0.6×	41 0.6×	12 0.4×	18 0.6×	6 0.2×	25	199

Countries citing papers authored by Ming Tan

Since Specialization

Citations

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

Fields of papers citing papers by Ming Tan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Tan. A scholar is included among the top collaborators of Ming Tan 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 Ming Tan. Ming Tan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

1.

Akbar, Muhammad Ilham Aldika, et al.. (2025). Artificial intelligence-large language models (AI-LLMs) for reliable and accurate cardiotocography (CTG) interpretation in obstetric practice. Computational and Structural Biotechnology Journal. 27. 1140–1147. 3 indexed citations

2.

Tan, Ming, et al.. (2024). Research on Road Extraction From High-Resolution Remote Sensing Images Based on Improved UNet++. IEEE Access. 12. 50300–50309. 4 indexed citations

3.

Tan, Ming, et al.. (2023). SPT: Learning to Selectively Insert Prompts for Better Prompt Tuning. 11862–11878. 3 indexed citations

4.

Tan, Ming, et al.. (2003). Modeling cooperative agents for customer network control using planning and agent-oriented programming. ii. 537–543. 2 indexed citations

5.

Tan, Ming. (2002). CSL: a cost-sensitive learning system for sensing and grasping objects. 858–863. 10 indexed citations

6.

Tan, Ming, et al.. (1996). PERFEX: A cellular performance support expert. Expert Systems with Applications. 11(4). 449–454. 1 indexed citations

7.

Tan, Ming & Jeffrey C. Schlimmer. (1993). A cost-sensitive machine learning method for the approach and recognize task. MIT Press eBooks. 31–45. 2 indexed citations

8.

Tan, Ming. (1993). Cost-sensitive learning of classification knowledge and its applications in robotics. Machine Learning. 13(1). 7–33. 60 indexed citations

9.

Tan, Ming. (1993). Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics. Machine Learning. 13(1). 7–33. 87 indexed citations

10.

Tan, Ming. (1991). Cost-sensitive reinforcement learning for adaptive classification and control. National Conference on Artificial Intelligence. 774–780. 15 indexed citations

11.

Tan, Ming & Jeffrey C. Schlimmer. (1991). A cost-sensitive machine learning method for the approach and recognize task. Robotics and Autonomous Systems. 8(1-2). 31–45. 3 indexed citations

12.

Tan, Ming. (1991). Cost-sensitive robot learning. 15 indexed citations

13.

Tan, Ming & Jeffrey C. Schlimmer. (1990). Two case studies in cost-sensitive concept acquisition. National Conference on Artificial Intelligence. 854–860. 36 indexed citations

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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