Bay Vo

3.7k total citations
114 papers, 2.5k citations indexed

About

Bay Vo is a scholar working on Information Systems, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Bay Vo has authored 114 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 97 papers in Information Systems, 80 papers in Computational Theory and Mathematics and 74 papers in Artificial Intelligence. Recurrent topics in Bay Vo's work include Data Mining Algorithms and Applications (93 papers), Rough Sets and Fuzzy Logic (78 papers) and Imbalanced Data Classification Techniques (36 papers). Bay Vo is often cited by papers focused on Data Mining Algorithms and Applications (93 papers), Rough Sets and Fuzzy Logic (78 papers) and Imbalanced Data Classification Techniques (36 papers). Bay Vo collaborates with scholars based in Vietnam, South Korea and Taiwan. Bay Vo's co-authors include Tuong Le, Bac Le, Loan T. T. Nguyen, Tzung‐Pei Hong, Sung Wook Baik, Philippe Fournier‐Viger, Frans Coenen, Jerry Chun‐Wei Lin, Unil Yun and Minh Thanh Vo and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and IEEE Access.

In The Last Decade

Bay Vo

109 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bay Vo Vietnam	29	1.8k	1.4k	1.2k	560	254	114	2.5k
Yiming Ma China	16	2.1k 1.1×	1.8k 1.3×	1.1k 0.9×	769 1.4×	322 1.3×	53	3.6k
Unil Yun South Korea	39	3.3k 1.8×	2.4k 1.7×	2.3k 1.8×	1.1k 2.0×	438 1.7×	171	4.2k
Edward Omiecinski United States	19	1.5k 0.8×	1.1k 0.8×	1.1k 0.9×	778 1.4×	780 3.1×	61	2.6k
Bay Vo Vietnam	23	950 0.5×	740 0.5×	627 0.5×	294 0.5×	211 0.8×	98	1.5k
Elena Baralis Italy	25	866 0.5×	1.1k 0.8×	315 0.3×	452 0.8×	676 2.7×	193	2.3k
Dominik Ślȩzak Poland	22	743 0.4×	814 0.6×	999 0.8×	259 0.5×	171 0.7×	136	1.8k
Jong Soo Park South Korea	15	2.3k 1.2×	1.7k 1.2×	1.3k 1.1×	1.1k 2.0×	393 1.5×	24	3.3k
Daniel Sánchez Spain	21	775 0.4×	1.0k 0.7×	702 0.6×	429 0.8×	182 0.7×	123	1.8k
Vangipuram Radhakrishna India	30	928 0.5×	1.0k 0.7×	241 0.2×	849 1.5×	670 2.6×	77	2.0k
Sadok Ben Yahia Tunisia	19	618 0.3×	612 0.4×	326 0.3×	238 0.4×	336 1.3×	188	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

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

20 of 20 papers shown

Nguyen, Hung Son, et al.. (2025). A novel framework for handling uncertainty: Intuitionistic fuzzy rough soft sets. Information Sciences. 722. 122592–122592.

Nguyen, Loan T. T., et al.. (2024). NS-IDBSCAN: An efficient incremental clustering method for geospatial data in network space. Information Sciences. 690. 121526–121526. 5 indexed citations

Pham, Phu, et al.. (2023). Enhancing Anchor Link Prediction in Information Networks through Integrated Embedding Techniques. Information Sciences. 645. 119331–119331. 7 indexed citations

Nguyên, Ngoc Thanh, et al.. (2023). Mining inter-sequence patterns with Itemset constraints. Applied Intelligence. 53(17). 19827–19842. 3 indexed citations

Kim, Hanju, et al.. (2023). Efficient approach of high average utility pattern mining with indexed list-based structure in dynamic environments. Information Sciences. 657. 119924–119924. 23 indexed citations

Nguyen, Loan T. T., et al.. (2019). An efficient method for mining high utility closed itemsets. Information Sciences. 495. 78–99. 56 indexed citations

Le, Tuong, Bay Vo, Hamido Fujita, Ngoc Thanh Nguyên, & Sung Wook Baik. (2019). A fast and accurate approach for bankruptcy forecasting using squared logistics loss with GPU-based extreme gradient boosting. Information Sciences. 494. 294–310. 60 indexed citations

Vo, Bay, et al.. (2019). Mining weighted subgraphs in a single large graph. Information Sciences. 514. 149–165. 37 indexed citations

Vo, Bay, et al.. (2018). The predictive modeling for learning student results based on sequential rules. International journal of innovative computing, information & control. 14(6). 2129–2140. 6 indexed citations

10.

Vo, Bay. (2017). An Efficient Method for Mining Frequent Weighted Closed Itemsets from Weighted Item Transaction Databases.. Journal of information science and engineering. 33. 199–216. 13 indexed citations

11.

Vo, Bay, et al.. (2017). A weighted N-list-based method for mining frequent weighted itemsets. Expert Systems with Applications. 96. 388–405. 35 indexed citations

12.

Vo, Bay, et al.. (2017). An Efficient Parallel Method for Mining Frequent Closed Sequential Patterns. IEEE Access. 5. 17392–17402. 14 indexed citations

13.

Fournier‐Viger, Philippe, Jerry Chun‐Wei Lin, Bay Vo, et al.. (2017). A survey of itemset mining. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 7(4). 142 indexed citations

14.

Le, Tuong, Bay Vo, & Sung Wook Baik. (2017). Efficient algorithms for mining top-rank- k erasable patterns using pruning strategies and the subsume concept. Engineering Applications of Artificial Intelligence. 68. 1–9. 28 indexed citations

15.

Nguyen, Dang, et al.. (2016). Parallel frequent subgraph mining on multi-core processor systems. Own your potential (DEAKIN). 10(9). 2105–2113. 2 indexed citations

16.

Vo, Bay, et al.. (2016). An Efficient Approach for Mining Frequent Item sets with Transaction Deletion Operation. The International Arab Journal of Information Technology. 13. 595–602. 1 indexed citations

17.

Luo, Jiawei, et al.. (2016). Time Series Trend Analysis Based on K-Means and Support Vector Machine. Computing and Informatics / Computers and Artificial Intelligence. 35(1). 111–127. 8 indexed citations

18.

Nguyen, Loan T. T., Bay Vo, & Tzung‐Pei Hong. (2015). CARIM: An Efficient Algorithm for Mining Class- Association Rules with Interestingness Measures. The International Arab Journal of Information Technology. 12. 627–634. 1 indexed citations

19.

Nguyen, Dang, Bay Vo, & Bac Le. (2014). Efficient strategies for parallel mining class association rules. Expert Systems with Applications. 41(10). 4716–4729. 24 indexed citations

20.

Nguyen, Dang, Bay Vo, & Bac Le. (2014). CCAR: An efficient method for mining class association rules with itemset constraints. Engineering Applications of Artificial Intelligence. 37. 115–124. 21 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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