Yee Ling Boo

797 total citations · 1 hit paper
22 papers, 522 citations indexed

About

Yee Ling Boo is a scholar working on Artificial Intelligence, Information Systems and Computer Science Applications. According to data from OpenAlex, Yee Ling Boo has authored 22 papers receiving a total of 522 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Information Systems and 5 papers in Computer Science Applications. Recurrent topics in Yee Ling Boo's work include Mobile Crowdsensing and Crowdsourcing (4 papers), Anomaly Detection Techniques and Applications (3 papers) and Expert finding and Q&A systems (2 papers). Yee Ling Boo is often cited by papers focused on Mobile Crowdsensing and Crowdsourcing (4 papers), Anomaly Detection Techniques and Applications (3 papers) and Expert finding and Q&A systems (2 papers). Yee Ling Boo collaborates with scholars based in Australia, Malaysia and India. Yee Ling Boo's co-authors include Kok‐Leong Ong, Booi Kam, William Yeoh, Alireza Moayedikia, Richard Jensen, Mong Shan Ee, Bob T. Li, Mamunur Rashid, Damminda Alahakoon and Cindy Chen and has published in prestigious journals such as IEEE Access, Decision Support Systems and Future Generation Computer Systems.

In The Last Decade

Yee Ling Boo

19 papers receiving 495 citations

Hit Papers

align trajectories sort by overperformance

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.

Fraud detection: A systematic literature review of graph-based anomaly detection approaches

2020 244 citations Kok‐Leong Ong, Booi Kam et al. Decision Support Systems profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yee Ling Boo Australia	7	306	136	73	73	58	22	522
Dženana Đonko Bosnia and Herzegovina	11	244 0.8×	181 1.3×	79 1.1×	32 0.4×	34 0.6×	50	532
Juan A. Recio-García Spain	14	341 1.1×	387 2.8×	83 1.1×	35 0.5×	79 1.4×	40	740
Yuanzhi Li United States	14	397 1.3×	81 0.6×	54 0.7×	117 1.6×	116 2.0×	49	756
Chihli Hung Taiwan	11	450 1.5×	197 1.4×	32 0.4×	111 1.5×	129 2.2×	39	720
Franco Maria Nardini Italy	17	533 1.7×	367 2.7×	100 1.4×	56 0.8×	225 3.9×	94	965
Yoon‐Joo Park South Korea	9	263 0.9×	313 2.3×	41 0.6×	18 0.2×	70 1.2×	22	554
Shan Huang United States	14	247 0.8×	146 1.1×	116 1.6×	17 0.2×	40 0.7×	40	557
Gabriel Pui Cheong Fung Hong Kong	13	343 1.1×	121 0.9×	47 0.6×	9 0.1×	77 1.3×	31	602
Hanjoon Kim South Korea	14	264 0.9×	143 1.1×	218 3.0×	93 1.3×	56 1.0×	95	669

Countries citing papers authored by Yee Ling Boo

Since Specialization

Citations

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

Fields of papers citing papers by Yee Ling Boo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yee Ling Boo. 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 Yee Ling Boo. The network helps show where Yee Ling Boo may publish in the future.

Co-authorship network of co-authors of Yee Ling Boo

This figure shows the co-authorship network connecting the top 25 collaborators of Yee Ling Boo. A scholar is included among the top collaborators of Yee Ling Boo 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 Yee Ling Boo. Yee Ling Boo 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

Hasan, Md Jahid, et al.. (2025). Vehicle Damage Detection Using Artificial Intelligence: A Systematic Literature Review. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 15(2).

Boo, Yee Ling, Manik Gupta, Weijia Zhang, & Philippe Fournier‐Viger. (2024). Special Issue Editorial on “The Innovative Use of Data Science to Transform How We Work and Live”. Data Science and Engineering. 9(1). 3–4.

Hossain, Mohammad Alamgir, et al.. (2024). A Configurational Approach to CSP Selection and Rejection. Journal of Computer Information Systems. 1–16. 2 indexed citations

Hasan, Md Jahid, et al.. (2024). GroundingCarDD: Text-Guided Multimodal Phrase Grounding for Car Damage Detection. IEEE Access. 12. 179464–179477. 1 indexed citations

Erfani, Sarah, et al.. (2023). Data Science and Machine Learning. Communications in computer and information science.

Ong, Kok‐Leong, et al.. (2021). Detecting covert communities in multi-layer networks: A network embedding approach. Future Generation Computer Systems. 124. 467–479. 3 indexed citations

Ong, Kok‐Leong, et al.. (2021). DarkNetExplorer (DNE): Exploring dark multi-layer networks beyond the resolution limit. Decision Support Systems. 146. 113537–113537. 5 indexed citations

Xu, Yue, X. Rosalind Wang, Anton Lord, et al.. (2021). Data Mining. Communications in computer and information science. 1 indexed citations

Ong, Kok‐Leong, et al.. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems. 133. 113303–113303. 244 indexed citations breakdown →

10.

Moayedikia, Alireza, William Yeoh, Kok‐Leong Ong, & Yee Ling Boo. (2019). Improving accuracy and lowering cost in crowdsourcing through an unsupervised expertise estimation approach. Decision Support Systems. 122. 113065–113065. 21 indexed citations

11.

Boo, Yee Ling, David Stirling, Lianhua Chi, et al.. (2018). Data Mining. Communications in computer and information science. 2 indexed citations

12.

Moayedikia, Alireza, Kok‐Leong Ong, Yee Ling Boo, & William Yeoh. (2018). Task assignment in microtask crowdsourcing platforms using learning automata. Engineering Applications of Artificial Intelligence. 74. 212–225. 17 indexed citations

13.

Moayedikia, Alireza, William Yeoh, Kok‐Leong Ong, & Yee Ling Boo. (2017). Framework and Literature Analysis for Crowdsourcing’s Answer Aggregation. Journal of Computer Information Systems. 60(1). 49–60. 15 indexed citations

14.

Moayedikia, Alireza, Kok‐Leong Ong, Yee Ling Boo, & William Yeoh. (2016). Bee Colony Based Worker Reliability Estimation Algorithm in Microtask Crowdsourcing. RMIT Research Repository (RMIT University Library). 713–717. 6 indexed citations

15.

Moayedikia, Alireza, Kok‐Leong Ong, Yee Ling Boo, William Yeoh, & Richard Jensen. (2016). Feature selection for high dimensional imbalanced class data using harmony search. Engineering Applications of Artificial Intelligence. 57. 38–49. 120 indexed citations

16.

Ee, Mong Shan, et al.. (2016). Examining the effect of time constraint on the online mastery learning approach towards improving postgraduate students' achievement. Studies in Higher Education. 43(2). 217–233. 6 indexed citations

17.

Boo, Yee Ling, Mong Shan Ee, Bob T. Li, & Mamunur Rashid. (2016). Islamic or conventional mutual funds: Who has the upper hand? Evidence from Malaysia. Pacific-Basin Finance Journal. 42. 183–192. 54 indexed citations

18.

Yeoh, William, et al.. (2015). A Comprehensive Diagnostic Framework for Evaluating Business Intelligence and Analytics Effectiveness. AJIS. Australasian journal of information systems/AJIS. Australian journal of information systems/Australian journal of information systems. 19. 6 indexed citations

19.

Yim, Hoi Yin Bonnie, Yee Ling Boo, & Marjory Ebbeck. (2014). A Study of Children’s Musical Preference: A Data Mining Approach. The Australian journal of teacher education. 39(2). 2 indexed citations

20.

Yeoh, William, et al.. (2013). THE IMPACT OF FEATURE SELECTION: A DATA‐MINING APPLICATION IN DIRECT MARKETING. Intelligent systems in accounting, finance and management. 20(1). 23–38. 3 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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