Wan-Jui Lee

872 total citations · 1 hit paper
20 papers, 471 citations indexed

About

Wan-Jui Lee is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Wan-Jui Lee has authored 20 papers receiving a total of 471 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 6 papers in Control and Systems Engineering. Recurrent topics in Wan-Jui Lee's work include Neural Networks and Applications (5 papers), Face and Expression Recognition (5 papers) and Data Mining Algorithms and Applications (4 papers). Wan-Jui Lee is often cited by papers focused on Neural Networks and Applications (5 papers), Face and Expression Recognition (5 papers) and Data Mining Algorithms and Applications (4 papers). Wan-Jui Lee collaborates with scholars based in Taiwan, Netherlands and Switzerland. Wan-Jui Lee's co-authors include Mélanie Ducoffe, Olga Fink, Markus Svensén, Qin Wang, Pierre Dersin, Shie-Jue Lee, Jung-Yi Jiang, Robert P. W. Duin, Mark L. Grimes and Laurens van der Maaten and has published in prestigious journals such as PLoS ONE, Engineering Applications of Artificial Intelligence and Data & Knowledge Engineering.

In The Last Decade

Wan-Jui Lee

19 papers receiving 441 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Potential, challenges and future directions for deep learning in prognostics and health management applications

2020 357 citations Olga Fink, Qin Wang et al. Engineering Applications of Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wan-Jui Lee Taiwan	8	221	107	73	63	50	20	471
Stefan Byttner Sweden	11	194 0.9×	108 1.0×	64 0.9×	43 0.7×	17 0.3×	43	479
Shimeng Wu China	10	432 2.0×	136 1.3×	114 1.6×	89 1.4×	37 0.7×	23	656
Al Salour India	12	255 1.2×	72 0.7×	125 1.7×	20 0.3×	67 1.3×	38	413
Xiaoxi Hu China	12	182 0.8×	71 0.7×	105 1.4×	28 0.4×	46 0.9×	47	468
Patrice Aknin France	13	161 0.7×	186 1.7×	148 2.0×	32 0.5×	38 0.8×	42	603
Zhe Zhou China	11	261 1.2×	64 0.6×	121 1.7×	19 0.3×	76 1.5×	31	478
Zhenyu Yin China	12	107 0.5×	59 0.6×	74 1.0×	29 0.5×	80 1.6×	97	484
Hongpeng Yin China	15	263 1.2×	173 1.6×	90 1.2×	142 2.3×	140 2.8×	49	711
Ke Zhao China	10	114 0.5×	73 0.7×	50 0.7×	20 0.3×	31 0.6×	40	354

Countries citing papers authored by Wan-Jui Lee

Since Specialization

Citations

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

Fields of papers citing papers by Wan-Jui Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wan-Jui Lee. 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 Wan-Jui Lee. The network helps show where Wan-Jui Lee may publish in the future.

Co-authorship network of co-authors of Wan-Jui Lee

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

All Works

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20 of 20 papers shown

1.

Fink, Olga, Qin Wang, Markus Svensén, et al.. (2020). Potential, challenges and future directions for deep learning in prognostics and health management applications. Engineering Applications of Artificial Intelligence. 92. 103678–103678. 357 indexed citations breakdown →

2.

Lee, Wan-Jui. (2020). Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes. International Journal of Prognostics and Health Management. 8(3). 3 indexed citations

3.

Zhang, Yingqian, et al.. (2019). Determining Capacity of Shunting Yards by Combining Graph Classification with Local Search. TU/e Research Portal. 285–293. 1 indexed citations

4.

Menkovski, Vlado, et al.. (2018). Shunting Trains with Deep Reinforcement Learning. TU/e Research Portal. 3063–3068. 12 indexed citations

5.

Lee, Wan-Jui, David M. J. Tax, & Robert P. W. Duin. (2016). Beyond Condition-Monitoring: Comparing Diagnostic Events with Word Sequence Kernel for Train Delay Prediction. PHM Society European Conference. 3(1). 1 indexed citations

6.

Grimes, Mark L., Wan-Jui Lee, Laurens van der Maaten, & Paul Shannon. (2013). Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways. PLoS ONE. 8(1). e52884–e52884. 15 indexed citations

7.

Lee, Wan-Jui, Veronika Cheplygina, David M. J. Tax, Marco Loog, & Robert P. W. Duin. (2012). BRIDGING STRUCTURE AND FEATURE REPRESENTATIONS IN GRAPH MATCHING. International Journal of Pattern Recognition and Artificial Intelligence. 26(5). 1260005–1260005. 8 indexed citations

8.

Lee, Wan-Jui, et al.. (2010). An experimental study on combining Euclidean distances. Zenodo (CERN European Organization for Nuclear Research). 304–309. 3 indexed citations

9.

Duin, Robert P. W., et al.. (2010). A Study on Combining Sets of Differently Measured Dissimilarities. Zenodo (CERN European Organization for Nuclear Research). 5. 3360–3363. 6 indexed citations

10.

Lee, Wan-Jui, Jung-Yi Jiang, & Shie-Jue Lee. (2008). Mining fuzzy periodic association rules. Data & Knowledge Engineering. 65(3). 442–462. 20 indexed citations

11.

Lee, Wan-Jui, et al.. (2007). VECTOR QUANTIZATION OF IMAGES USING A FUZZY CLUSTERING METHOD. Cybernetics & Systems. 39(1). 45–60. 2 indexed citations

12.

Lee, Wan-Jui, et al.. (2006). A Kernel-Based Fuzzy Clustering Algorithm. 1. 550–553. 16 indexed citations

13.

Yang, Chih-Cheng, Wan-Jui Lee, & Shie-Jue Lee. (2006). Learning of Kernel Functions in Support Vector Machines. The 2006 IEEE International Joint Conference on Neural Network Proceedings. 1150–1155. 9 indexed citations

14.

Yang, Chih-Cheng, Wan-Jui Lee, & Shie-Jue Lee. (2006). Learning of Kernel Functions in Support Vector Machines. The 2006 IEEE International Joint Conference on Neural Network Proceedings. 1150–1155. 1 indexed citations

15.

Jiang, Jung-Yi, Wan-Jui Lee, & Shie-Jue Lee. (2005). Mining Calendar-Based Asynchronous Periodical Association Rules with Fuzzy Calendar Constraints. 773–778. 4 indexed citations

16.

Lee, Wan-Jui, Jung-Yi Jiang, & Shie-Jue Lee. (2005). An efficient algorithm to discover calendar-based temporal association rules. 3122–3127. 2 indexed citations

17.

Lee, Wan-Jui & Shie-Jue Lee. (2004). An Incremental Algorithm for Discovering Fuzzy Temporal Web Usage Patterns.. International Conference on Internet Computing. 90–96.

18.

Lee, Wan-Jui, et al.. (2004). Fuzzy calendar algebra and its applications to data mining. 71–78. 2 indexed citations

19.

Ouyang, Chen‐Sen, Wan-Jui Lee, & Shie-Jue Lee. (2003). An adaptive neuro-fuzzy approach for system modeling. 4. 1875–1880. 1 indexed citations

20.

Lee, Wan-Jui, Chen‐Sen Ouyang, & Shie-Jue Lee. (2003). Constructing neuro-fuzzy systems with TSK fuzzy rules and hybrid SVD-based learning. 2. 1174–1179. 8 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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