Ghim-Eng Yap

429 total citations
13 papers, 189 citations indexed

About

Ghim-Eng Yap is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Ghim-Eng Yap has authored 13 papers receiving a total of 189 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 4 papers in Information Systems. Recurrent topics in Ghim-Eng Yap's work include Context-Aware Activity Recognition Systems (4 papers), Recommender Systems and Techniques (3 papers) and Human Mobility and Location-Based Analysis (3 papers). Ghim-Eng Yap is often cited by papers focused on Context-Aware Activity Recognition Systems (4 papers), Recommender Systems and Techniques (3 papers) and Human Mobility and Location-Based Analysis (3 papers). Ghim-Eng Yap collaborates with scholars based in Singapore, India and United Kingdom. Ghim-Eng Yap's co-authors include HweeHwa Pang, Ah‐Hwee Tan, Song Huat Yeo, Kelvin Sim, Clifton Phua, Vivekanand Gopalkrishnan, Dhaval Patel, David Lo, Zeyar Aung and Daniel Berrar and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, Journal of Machine Learning Research and The International Journal of Advanced Manufacturing Technology.

In The Last Decade

Ghim-Eng Yap

13 papers receiving 172 citations

Peers

Ghim-Eng Yap

AI

IS

CVPR

CNC

EEE

Depeng Dang China

Wolfgang Trumler Germany

Rosario Culmone Italy

Najwa Aaraj United States

Yunfei Chu China

A. Damodaram India

Luis G. Martínez Mexico

Diletta Cacciagrano Italy

Jihong Ding China

Depeng Dang China

Ghim-Eng Yap

119 ×1.5

AI

60 ×0.8

IS

53 ×1.0

CVPR

23 ×0.8

CNC

16 ×0.6

EEE

Citations per year, relative to Ghim-Eng Yap Ghim-Eng Yap (= 1×) peers Depeng Dang

Countries citing papers authored by Ghim-Eng Yap

Since Specialization

Citations

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

Fields of papers citing papers by Ghim-Eng Yap

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ghim-Eng Yap. 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 Ghim-Eng Yap. The network helps show where Ghim-Eng Yap may publish in the future.

Co-authorship network of co-authors of Ghim-Eng Yap

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

All Works

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13 of 13 papers shown

1.

Dash, Manoranjan, et al.. (2015). CDR-To-MoVis: Developing a Mobility Visualization System from CDR data. 54. 1452–1455. 5 indexed citations

2.

Dash, Manoranjan, et al.. (2015). From Mobile Phone Data to Transport Network -- Gaining Insight about Human Mobility. 243–250. 4 indexed citations

3.

Kanagasabai, Rajaraman, et al.. (2014). Using Semantic Technologies to Mine Customer Insights in Telecom Industry.. International Semantic Web Conference. 1 indexed citations

4.

Oentaryo, Richard J., Ee‐Peng Lim, David Lo, et al.. (2014). Detecting click fraud in online advertising: a data mining approach. Journal of Machine Learning Research. 15(1). 99–140. 45 indexed citations

5.

Sim, Kelvin, Ghim-Eng Yap, David R. Hardoon, et al.. (2012). Centroid-Based Actionable 3D Subspace Clustering. IEEE Transactions on Knowledge and Data Engineering. 25(6). 1213–1226. 15 indexed citations

6.

Sim, Kelvin, Ghim-Eng Yap, Clifton Phua, et al.. (2010). Improving the accuracy of erroneous-plan recognition system for Activities of Daily Living. 9. 28–35. 14 indexed citations

7.

Yap, Ghim-Eng, et al.. (2009). A Bayesian approach integrating regional and global features for image semantic learning. 4384. 546–549. 1 indexed citations

8.

Yap, Ghim-Eng, Ah‐Hwee Tan, & HweeHwa Pang. (2007). Learning causal models for noisy biological data mining: an application to ovarian cancer detection. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 354–359. 2 indexed citations

9.

Yap, Ghim-Eng, Ah‐Hwee Tan, & HweeHwa Pang. (2007). Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders. IEEE Transactions on Knowledge and Data Engineering. 19(7). 977–992. 29 indexed citations

10.

Yap, Ghim-Eng, Ah‐Hwee Tan, & HweeHwa Pang. (2007). Explaining inferences in Bayesian networks. Applied Intelligence. 29(3). 263–278. 29 indexed citations

11.

Yap, Ghim-Eng, Ah‐Hwee Tan, & HweeHwa Pang. (2006). Discovering Causal Dependencies in Mobile Context-Aware Recommenders. 4–4. 5 indexed citations

12.

Yap, Ghim-Eng, Ah‐Hwee Tan, & HweeHwa Pang. (2005). Dynamically-optimized context in recommender systems. 265–272. 18 indexed citations

13.

Yeo, Song Huat & Ghim-Eng Yap. (2001). A Feasibility Study on the Micro Eletro-Discharge Machining Process for Photomask Fabrication. The International Journal of Advanced Manufacturing Technology. 18(1). 7–11. 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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