Bay Vo

31.2k total citations
98 papers, 1.5k citations indexed

About

Bay Vo is a scholar working on Information Systems, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Bay Vo has authored 98 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Information Systems, 57 papers in Artificial Intelligence and 52 papers in Computational Theory and Mathematics. Recurrent topics in Bay Vo's work include Data Mining Algorithms and Applications (65 papers), Rough Sets and Fuzzy Logic (48 papers) and Imbalanced Data Classification Techniques (25 papers). Bay Vo is often cited by papers focused on Data Mining Algorithms and Applications (65 papers), Rough Sets and Fuzzy Logic (48 papers) and Imbalanced Data Classification Techniques (25 papers). Bay Vo collaborates with scholars based in Vietnam, South Korea and Poland. Bay Vo's co-authors include Bac Le, Loan T. T. Nguyen, Jerry Chun‐Wei Lin, Unil Yun, Tzung‐Pei Hong, Tuong Le, Witold Pedrycz, Zhongcui Li, Jimmy Ming‐Tai Wu and Norbert Herencsár and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Information Sciences.

In The Last Decade

Bay Vo

93 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bay Vo Vietnam 23 950 740 627 294 245 98 1.5k
Bay Vo Vietnam 29 1.8k 1.9× 1.4k 1.9× 1.2k 2.0× 560 1.9× 120 0.5× 114 2.5k
Bart Goethals Belgium 23 1.4k 1.5× 847 1.1× 632 1.0× 575 2.0× 169 0.7× 95 2.1k
Daniel Sánchez Spain 21 775 0.8× 1.0k 1.4× 702 1.1× 429 1.5× 237 1.0× 123 1.8k
Tsau Young Lin United States 17 428 0.5× 570 0.8× 821 1.3× 154 0.5× 475 1.9× 74 1.4k
Mohammad Karim Sohrabi Iran 22 671 0.7× 666 0.9× 181 0.3× 181 0.6× 145 0.6× 70 1.5k
Howard J. Hamilton Canada 17 1.6k 1.7× 986 1.3× 1.1k 1.7× 664 2.3× 147 0.6× 86 2.1k
Chowdhury Farhan Ahmed Bangladesh 21 1.4k 1.5× 894 1.2× 886 1.4× 635 2.2× 65 0.3× 64 1.7k
Claudio Lucchese Italy 24 984 1.0× 978 1.3× 377 0.6× 381 1.3× 163 0.7× 122 1.8k
Salvatore Orlando Italy 26 1.1k 1.2× 945 1.3× 382 0.6× 554 1.9× 121 0.5× 151 2.1k
Wentao Li China 20 417 0.4× 663 0.9× 1.0k 1.6× 79 0.3× 330 1.3× 93 1.6k

Countries citing papers authored by Bay Vo

Since Specialization
Citations

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

Fields of papers citing papers by Bay Vo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bay Vo

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

All Works

20 of 20 papers shown
1.
Kim, Hanju, et al.. (2024). Advanced incremental erasable pattern mining from the time-sensitive data stream. Knowledge-Based Systems. 299. 112001–112001. 13 indexed citations
2.
Nguyen, Loan T. T., et al.. (2024). An efficient strategy for mining high-efficiency itemsets in quantitative databases. Knowledge-Based Systems. 299. 112035–112035. 3 indexed citations
3.
Nguyên, Ngoc Thanh, et al.. (2023). An efficient pruning method for mining inter-sequence patterns based on pseudo-IDList. Expert Systems with Applications. 238. 121738–121738. 2 indexed citations
4.
Kim, Heonho, Sinyoung Kim, Hanju Kim, et al.. (2023). An advanced approach for incremental flexible periodic pattern mining on time-series data. Expert Systems with Applications. 230. 120697–120697. 13 indexed citations
5.
Snåšel, Václav, et al.. (2023). Information measures based on similarity under neutrosophic fuzzy environment and multi-criteria decision problems. Engineering Applications of Artificial Intelligence. 122. 106026–106026. 11 indexed citations
6.
Yun, Unil, et al.. (2023). FTKHUIM: A Fast and Efficient Method for Mining Top-K High-Utility Itemsets. IEEE Access. 11. 104789–104805. 13 indexed citations
7.
Vo, Bay, et al.. (2023). An Approach for Incremental Mining of Clickstream Patterns as a Service Application. IEEE Transactions on Services Computing. 16(6). 3892–3905. 3 indexed citations
8.
Nguyen, Loan T. T., et al.. (2023). An efficient method for mining High-Utility itemsets from unstable negative profit databases. Expert Systems with Applications. 237. 121489–121489. 17 indexed citations
9.
Nguyen, Loan T. T., et al.. (2023). An efficient method for mining high occupancy itemsets based on equivalence class and early pruning. Knowledge-Based Systems. 267. 110441–110441. 5 indexed citations
10.
Pham, Phu, Witold Pedrycz, & Bay Vo. (2022). Dual attention-based sequential auto-encoder for Covid-19 outbreak forecasting: A case study in Vietnam. Expert Systems with Applications. 203. 117514–117514. 8 indexed citations
11.
Fujita, Hamido, et al.. (2021). Multiple-objective optimization applied in extracting multiple-choice tests. Engineering Applications of Artificial Intelligence. 105. 104439–104439. 8 indexed citations
12.
Vo, Bay, et al.. (2021). A Sliding Window-Based Approach for Mining Frequent Weighted Patterns Over Data Streams. IEEE Access. 9. 56318–56329. 15 indexed citations
13.
Nguyen, Dang, Wei Luo, Bay Vo, Loan T. T. Nguyen, & Witold Pedrycz. (2021). Con2Vec: Learning embedding representations for contrast sets. Knowledge-Based Systems. 229. 107382–107382. 4 indexed citations
14.
Nguyen, Loan T. T., et al.. (2021). An efficient method for mining sequential patterns with indices. Knowledge-Based Systems. 239. 107946–107946. 10 indexed citations
15.
Yun, Unil, et al.. (2020). Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure. IEEE Access. 8. 50958–50968. 34 indexed citations
16.
Kim, Heonho, et al.. (2020). Efficient list based mining of high average utility patterns with maximum average pruning strategies. Information Sciences. 543. 85–105. 48 indexed citations
17.
Nguyen, Dang, Wei Luo, Bay Vo, & Witold Pedrycz. (2020). Succinct contrast sets via false positive controlling with an application in clinical process redesign. Expert Systems with Applications. 161. 113670–113670. 2 indexed citations
18.
Vo, Bay, et al.. (2018). Application of particle swarm optimization to create multiple-choice tests. Journal of information science and engineering. 34. 1405–1423. 7 indexed citations
19.
Nguyen, Loan T. T., et al.. (2016). A method for mining top-rank- k frequent closed itemsets. Journal of Intelligent & Fuzzy Systems. 32(2). 1297–1305. 8 indexed citations
20.
Vo, Bay, et al.. (2011). Dynamic bit vectors: An efficient approach for mining frequent itemsets. Scientific Research and Essays. 6(25). 5358–5368. 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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