Geok See Ng

1.1k total citations
56 papers, 792 citations indexed

About

Geok See Ng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Geok See Ng has authored 56 papers receiving a total of 792 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Artificial Intelligence, 16 papers in Computer Vision and Pattern Recognition and 10 papers in Signal Processing. Recurrent topics in Geok See Ng's work include Neural Networks and Applications (28 papers), Fuzzy Logic and Control Systems (18 papers) and Evolutionary Algorithms and Applications (7 papers). Geok See Ng is often cited by papers focused on Neural Networks and Applications (28 papers), Fuzzy Logic and Control Systems (18 papers) and Evolutionary Algorithms and Applications (7 papers). Geok See Ng collaborates with scholars based in Singapore, United States and United Kingdom. Geok See Ng's co-authors include Chai Quek, Tuan Zea Tan, E. Y. K. Ng, Theresa May Chin Tan, Feng Liu, Haiyong Jiang, Di Wang, Khalil Razvi, Giedre Sabaliauskaite and Daming Shi and has published in prestigious journals such as Expert Systems with Applications, Neural Computation and Neurocomputing.

In The Last Decade

Geok See Ng

55 papers receiving 744 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Geok See Ng Singapore	15	398	165	148	93	76	56	792
K. Thangavel India	14	529 1.3×	101 0.6×	267 1.8×	44 0.5×	105 1.4×	101	1.1k
Pei-Yi Hao Taiwan	12	404 1.0×	132 0.8×	262 1.8×	126 1.4×	54 0.7×	30	793
Kalyani Mali India	19	391 1.0×	101 0.6×	401 2.7×	18 0.2×	82 1.1×	84	947
Parimala Thulasiraman Canada	16	297 0.7×	117 0.7×	116 0.8×	29 0.3×	25 0.3×	101	1.2k
Boris Kovalerchuk United States	13	343 0.9×	124 0.8×	133 0.9×	40 0.4×	46 0.6×	98	761
Yevgeniy Bodyanskiy Ukraine	17	534 1.3×	101 0.6×	127 0.9×	231 2.5×	102 1.3×	157	988
Kuhu Pal India	7	696 1.7×	75 0.5×	438 3.0×	77 0.8×	125 1.6×	11	1.1k
Roman W. Świniarski United States	11	740 1.9×	188 1.1×	206 1.4×	71 0.8×	173 2.3×	29	1.5k
Julio López Chile	17	708 1.8×	73 0.4×	351 2.4×	108 1.2×	67 0.9×	54	1.2k
Helmi Md Rais Malaysia	11	620 1.6×	57 0.3×	199 1.3×	97 1.0×	191 2.5×	32	1.2k

Countries citing papers authored by Geok See Ng

Since Specialization

Citations

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

Fields of papers citing papers by Geok See Ng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Geok See Ng. 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 Geok See Ng. The network helps show where Geok See Ng may publish in the future.

Co-authorship network of co-authors of Geok See Ng

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

All Works

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20 of 20 papers shown

Wang, Di, Chai Quek, Ah‐Hwee Tan, et al.. (2017). Leveraging the trade-off between accuracy and interpretability in a hybrid intelligent system. DR-NTU (Nanyang Technological University). 9. 55–60. 2 indexed citations

Sabaliauskaite, Giedre, Geok See Ng, Justin Ruths, & Aditya P. Mathur. (2017). A comprehensive approach, and a case study, for conducting attack detection experiments in Cyber–Physical Systems. Robotics and Autonomous Systems. 98. 174–191. 21 indexed citations

Sabaliauskaite, Giedre, Geok See Ng, Justin Ruths, & Aditya P. Mathur. (2017). Comparison of Corrupted Sensor Data Detection Methods in Detecting Stealthy Attacks on Cyber-Physical Systems. 98. 235–244. 1 indexed citations

Wang, Di, Chai Quek, & Geok See Ng. (2016). Bank failure prediction using an accurate and interpretable neural fuzzy inference system. AI Communications. 29(4). 477–495. 10 indexed citations

Tan, Tuan Zea, Geok See Ng, & Chai Quek. (2008). Improving tractability of Clinical Decision Support system. 39. 1997–2002. 1 indexed citations

Ma, Lianyang, et al.. (2008). An experimental study of the extended NRBF regression model and its enhancement for classification problem. Neurocomputing. 72(1-3). 458–470. 3 indexed citations

Tan, Tuan Zea, Chai Quek, Geok See Ng, & Khalil Razvi. (2008). Ovarian cancer diagnosis with complementary learning fuzzy neural network. Artificial Intelligence in Medicine. 43(3). 207–222. 41 indexed citations

Ng, Geok See, et al.. (2008). Dynamically reconfigurable FPGA for robotics control. 2277–2282. 3 indexed citations

Ng, Geok See, et al.. (2008). Fault Tolerant Hardware for High Performance Signal Processing. 3. 408–412. 1 indexed citations

10.

Tang, Zhe, Meng Joo Er, & Geok See Ng. (2007). Humanoid Robotics Modeling by Dynamic Fuzzy Neural Network. IEEE International Conference on Neural Networks. 26. 2653–2657. 3 indexed citations

11.

Ng, Geok See, et al.. (2006). INSIGHT OF FUZZY NEURAL SYSTEMS IN THE APPLICATION OF HANDWRITTEN DIGITS CLASSIFICATION. International Journal of Image and Graphics. 6(4). 511–532. 2 indexed citations

12.

Ng, Geok See, et al.. (2006). RLDDE: A novel reinforcement learning-based dimension and delay estimator for neural networks in time series prediction. Neurocomputing. 70(7-9). 1331–1341. 19 indexed citations

13.

Wang, Dongdong, Geok See Ng, & Chai Quek. (2006). Ovarian Cancer Diagnosis Using Fuzzy Neural Networks Empowered By Evolutionary Clustering Technique. 15. 2764–2770. 1 indexed citations

14.

Nguyen, Minh Nhut, Daming Shi, Chai Quek, & Geok See Ng. (2006). Traffic Prediction Using Ying-Yang Fuzzy Cerebellar Model Articulation Controller. 2. 258–261. 4 indexed citations

15.

Ng, Geok See, Chai Quek, & Haiyong Jiang. (2006). FCMAC-EWS: A bank failure early warning system based on a novel localized pattern learning and semantically associative fuzzy neural network. Expert Systems with Applications. 34(2). 989–1003. 41 indexed citations

16.

Tan, Tuan Zea, Chai Quek, & Geok See Ng. (2005). Brain-inspired Genetic Complementary Learning for Stock Market Prediction. 3. 2653–2660. 40 indexed citations

17.

Tan, Tuan Zea, Chai Quek, & Geok See Ng. (2005). Ovarian cancer diagnosis by hippocampus and neocortex-inspired learning memory structures. Neural Networks. 18(5-6). 818–825. 24 indexed citations

18.

Quek, Chai, et al.. (2005). GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms. Expert Systems with Applications. 29(4). 769–781. 91 indexed citations

19.

Shi, Daming, Geok See Ng, R.I. Damper, & S.R. Gunn. (2005). Radical recognition of handwritten Chinese characters using GA-based kernel active shape modelling. IEE Proceedings - Vision Image and Signal Processing. 152(5). 634–634. 5 indexed citations

20.

Ng, Geok See, et al.. (2004). APPLICATION OF EFUNN FOR THE CLASSIFICATION OF HANDWRITTEN DIGITS. 5. 27–35. 5 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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