Kang Leng Chiew

1.4k total citations · 1 hit paper
38 papers, 911 citations indexed

About

Kang Leng Chiew is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kang Leng Chiew has authored 38 papers receiving a total of 911 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Information Systems, 13 papers in Artificial Intelligence and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kang Leng Chiew's work include Spam and Phishing Detection (16 papers), Text and Document Classification Technologies (10 papers) and User Authentication and Security Systems (6 papers). Kang Leng Chiew is often cited by papers focused on Spam and Phishing Detection (16 papers), Text and Document Classification Technologies (10 papers) and User Authentication and Security Systems (6 papers). Kang Leng Chiew collaborates with scholars based in Malaysia, Australia and Saudi Arabia. Kang Leng Chiew's co-authors include Choon Lin Tan, Kelvin S. C. Yong, Wei King Tiong, San Nah Sze, KokSheik Wong, Josef Pieprzyk, Johari Abdullah, Yakub Sebastian, Kim Gaik Tay and Dayang Hanani Abang Ibrahim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Kang Leng Chiew

35 papers receiving 854 citations

Hit Papers

A new hybrid ensemble feature selection framework for mac... 2019 2026 2021 2023 2019 50 100 150 200

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kang Leng Chiew Malaysia 14 745 417 406 243 165 38 911
Choon Lin Tan Malaysia 10 573 0.8× 317 0.8× 345 0.8× 202 0.8× 116 0.7× 11 711
Scott E. Coull United States 15 266 0.4× 523 1.3× 1.1k 2.7× 794 3.3× 110 0.7× 25 1.2k
Wayne Jansen United States 13 300 0.4× 180 0.4× 226 0.6× 474 2.0× 94 0.6× 40 731
Ram Krishnan United States 17 421 0.6× 184 0.4× 403 1.0× 272 1.1× 353 2.1× 60 758
Weina Niu China 14 312 0.4× 203 0.5× 300 0.7× 260 1.1× 26 0.2× 52 629
Paulo B. Golgher Brazil 9 563 0.8× 172 0.4× 330 0.8× 211 0.9× 37 0.2× 15 727
Brett Stone-Gross United States 11 518 0.7× 514 1.2× 525 1.3× 682 2.8× 79 0.5× 11 949
Mehmet Altınel United States 11 288 0.4× 287 0.7× 328 0.8× 731 3.0× 37 0.2× 17 832
Johanna Amann United States 11 334 0.4× 337 0.8× 458 1.1× 403 1.7× 75 0.5× 18 778

Countries citing papers authored by Kang Leng Chiew

Since Specialization
Citations

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

Fields of papers citing papers by Kang Leng Chiew

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kang Leng Chiew. 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 Kang Leng Chiew. The network helps show where Kang Leng Chiew may publish in the future.

Co-authorship network of co-authors of Kang Leng Chiew

This figure shows the co-authorship network connecting the top 25 collaborators of Kang Leng Chiew. A scholar is included among the top collaborators of Kang Leng Chiew 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 Kang Leng Chiew. Kang Leng Chiew 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.
Chiew, Kang Leng, et al.. (2025). Key insights into recommended SMS spam detection datasets. Scientific Reports. 15(1). 8162–8162. 2 indexed citations
2.
Chiew, Kang Leng, et al.. (2024). An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA. Journal of Advanced Research in Applied Sciences and Engineering Technology. 44(1). 225–238. 7 indexed citations
3.
Tan, Choon Lin, et al.. (2023). Hybrid phishing detection using joint visual and textual identity. Expert Systems with Applications. 220. 119723–119723. 21 indexed citations
4.
Tiong, Wei King, et al.. (2020). Simulation of Internal Undular Bores of Depression Propagating over a Slowly Varying Region. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. 70(1). 13–27. 4 indexed citations
5.
Sze, San Nah, et al.. (2020). Two-stage Heuristic for Primary School Timetabling Problem with Combined Classes Consideration. International Journal on Advanced Science Engineering and Information Technology. 10(3). 1051–1057. 1 indexed citations
6.
Tan, Choon Lin, Kang Leng Chiew, Kelvin S. C. Yong, et al.. (2020). A graph-theoretic approach for the detection of phishing webpages. Computers & Security. 95. 101793–101793. 41 indexed citations
7.
Chiew, Kang Leng, et al.. (2019). Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory. Computational Intelligence and Neuroscience. 2019. 1–16. 5 indexed citations
8.
Yong, Kelvin S. C., Kang Leng Chiew, & Choon Lin Tan. (2019). A survey of the QR code phishing: the current attacks and countermeasures. 1–5. 10 indexed citations
9.
Chiew, Kang Leng, et al.. (2018). Building Standard Offline Anti-phishing Dataset for Benchmarking. International Journal of Engineering & Technology. 7(4.31). 7–14. 15 indexed citations
10.
Chiew, Kang Leng, et al.. (2018). Identifying the Most Effective Feature Category in Machine Learning-based Phishing Website Detection. 1 indexed citations
11.
Tan, Choon Lin, et al.. (2018). Identifying the Most Effective Feature Category in Machine Learning-based Phishing Website Detection. International Journal of Engineering & Technology. 7(4.31). 1–6. 4 indexed citations
12.
Ibrahim, Dayang Hanani Abang, et al.. (2017). Achieving Reproducibility Incorporating Service Versioning into Provenance Model. Journal of Telecommunication Electronic and Computer Engineering (JTEC). 9. 131–138. 1 indexed citations
13.
Tiong, Wei King, et al.. (2017). Simulation of undular bores evolution with damping. Unimas Institutional Repository (Universiti Malaysia Sarawak). 24(2). 113–126. 1 indexed citations
14.
Sze, San Nah, et al.. (2017). Case study: University lecture timetabling without pre-registration data. 732–735. 3 indexed citations
15.
Tan, Choon Lin, Kang Leng Chiew, KokSheik Wong, & San Nah Sze. (2016). PhishWHO: Phishing webpage detection via identity keywords extraction and target domain name finder. Decision Support Systems. 88. 18–27. 77 indexed citations
16.
Chiew, Kang Leng, et al.. (2015). Utilisation of website logo for phishing detection. Computers & Security. 54. 16–26. 102 indexed citations
17.
Chiew, Kang Leng, et al.. (2013). Phishing Detection via Identification of Website Identity. Unimas Institutional Repository (Universiti Malaysia Sarawak). 1–4. 44 indexed citations
18.
Sze, San Nah, et al.. (2012). Multi-trip vehicle routing and scheduling problem with time window in real life. AIP conference proceedings. 1151–1154. 1 indexed citations
19.
Chiew, Kang Leng & Josef Pieprzyk. (2008). JPEG Image Steganalysis Improvement Via Image-to-Image Variation Minimization. 3200. 223–227. 1 indexed citations
20.
Chiew, Kang Leng, et al.. (2003). Shape Feature Representation for Content Based Image Retrieval.. 162–167.

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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