Yap Bee Wah

3.5k total citations · 1 hit paper
81 papers, 2.1k citations indexed

About

Yap Bee Wah is a scholar working on Artificial Intelligence, Accounting and Statistics and Probability. According to data from OpenAlex, Yap Bee Wah has authored 81 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 12 papers in Accounting and 12 papers in Statistics and Probability. Recurrent topics in Yap Bee Wah's work include Imbalanced Data Classification Techniques (16 papers), Advanced Statistical Methods and Models (8 papers) and Machine Learning and Data Classification (6 papers). Yap Bee Wah is often cited by papers focused on Imbalanced Data Classification Techniques (16 papers), Advanced Statistical Methods and Models (8 papers) and Machine Learning and Data Classification (6 papers). Yap Bee Wah collaborates with scholars based in Malaysia, United States and Macao. Yap Bee Wah's co-authors include C. H. Sim, Azlinah Mohamed, Michael W. Berry, S. H. Ong, T. Ramayah, Maryam Khanian Najafabadi, Ruhaila Maskat, Wan Fairos Wan Yaacob, Ruhaya Atan and Mohamed Azmi Hassali and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and Quality of Life Research.

In The Last Decade

Yap Bee Wah

73 papers receiving 2.0k citations

Hit Papers

Comparisons of various ty... 2011 2026 2016 2021 2011 100 200 300 400 500
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.

  1. Comparisons of various types of normality tests
    2011 591 citations Yap Bee Wah, C. H. Sim Journal of Statistical Computation and Simulation profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yap Bee Wah Malaysia 18 375 273 247 183 181 81 2.1k
Fibi Eko Putra Indonesia 4 250 0.7× 248 0.9× 75 0.3× 250 1.4× 88 0.5× 7 3.0k
Steven Walczak United States 26 462 1.2× 359 1.3× 65 0.3× 228 1.2× 239 1.3× 106 2.2k
Michael Gusenbauer Austria 11 157 0.4× 325 1.2× 68 0.3× 225 1.2× 118 0.7× 15 2.1k
Ankur Joshi India 10 156 0.4× 326 1.2× 48 0.2× 228 1.2× 187 1.0× 86 2.3k
Weishu Liu China 20 184 0.5× 242 0.9× 55 0.2× 237 1.3× 98 0.5× 44 2.3k
Xiao‐Guang Yue China 32 253 0.7× 235 0.9× 129 0.5× 245 1.3× 222 1.2× 188 3.6k
Abir Chakravorty India 5 207 0.6× 208 0.8× 63 0.3× 204 1.1× 76 0.4× 9 2.6k
Satish Chandel India 2 153 0.4× 318 1.2× 43 0.2× 222 1.2× 186 1.0× 6 2.0k
M. Dachyar Indonesia 13 155 0.4× 227 0.8× 49 0.2× 218 1.2× 157 0.9× 120 2.0k
S. C. Sharma India 26 603 1.6× 379 1.4× 163 0.7× 960 5.2× 260 1.4× 143 5.4k

Countries citing papers authored by Yap Bee Wah

Since Specialization
Citations

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

Fields of papers citing papers by Yap Bee Wah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yap Bee Wah. 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 Yap Bee Wah. The network helps show where Yap Bee Wah may publish in the future.

Co-authorship network of co-authors of Yap Bee Wah

This figure shows the co-authorship network connecting the top 25 collaborators of Yap Bee Wah. A scholar is included among the top collaborators of Yap Bee Wah 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 Yap Bee Wah. Yap Bee Wah 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.
Wah, Yap Bee, et al.. (2025). Prediction of crowdfunding success in Malaysia using quality signaling theory and machine learning. Business Process Management Journal. 1–30.
2.
Yaacob, Wan Fairos Wan, et al.. (2024). Visualising Current Research Trends in Class Imbalance using Clustering Approach: A Bibliometrics Analysis. Journal of Advanced Research in Applied Sciences and Engineering Technology. 38(2). 95–111. 1 indexed citations
3.
Aslam, Sarfraz, et al.. (2024). Mapping the global landscape of STEM education: a bibliometric analysis using Scopus database. International Journal of Evaluation and Research in Education (IJERE). 13(6). 4225–4225. 3 indexed citations
4.
5.
Aslam, Sarfraz, et al.. (2024). Transitioning towards Tomorrow’s Workforce: Education 5.0 in the Landscape of Society 5.0: A Systematic Literature Review. Education Sciences. 14(10). 1041–1041. 11 indexed citations
6.
Wah, Yap Bee, et al.. (2023). An evaluation of nature-inspired optimization algorithms and machine learning classifiers for electricity fraud prediction. Indonesian Journal of Electrical Engineering and Computer Science. 32(1). 468–468. 1 indexed citations
7.
Wah, Yap Bee, Azlan Ismail, Jafreezal Jaafar, et al.. (2023). Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction. Computers, materials & continua/Computers, materials & continua (Print). 75(3). 4821–4841. 9 indexed citations
8.
Wah, Yap Bee, et al.. (2023). Predicting automobile insurance fraud using classical and machine learning models. International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering. 14(1). 911–911. 5 indexed citations
9.
Wah, Yap Bee, Michael W. Berry, Azlinah Mohamed, & Dhiya Al‐Jumeily. (2023). Data Science and Emerging Technologies. Lecture notes on data engineering and communications technologies.
10.
Abdullah, Mohammad, et al.. (2023). Comparative Evaluation of Machine Learning Algorithms for Alzheimer’s Disease Classification using Synthetic Transcriptomics Dataset. Trends in Sciences. 20(11). 6881–6881. 1 indexed citations
11.
Wah, Yap Bee, et al.. (2019). Assessing Diabetes Distress Among Type 2 Diabetes Mellitus in Malaysia Using the Problem Areas in Diabetes Scale. Value in Health Regional Issues. 18. 159–164. 5 indexed citations
12.
Wah, Yap Bee, et al.. (2018). Feature selection methods: case of filter and wrapper approaches for maximising classification accuracy. Pertanika journal of science & technology. 26(1). 329–340. 92 indexed citations
13.
14.
Wah, Yap Bee, et al.. (2017). Infopreneurship: New Career for University Graduates. 9(4). 1 indexed citations
15.
16.
Wah, Yap Bee & C. H. Sim. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation. 81(12). 2141–2155. 591 indexed citations breakdown →
17.
18.
Wah, Yap Bee, et al.. (2010). Using data mining predictive models to classify credit card applicants. 394–398. 17 indexed citations
19.
20.
Wah, Yap Bee, et al.. (2007). Moving Holiday Effects Adjustment for Malaysian Economic Time Series. 6 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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