Keem Siah Yap

2.1k citations
66 papers · 1.6k indexed · h-index 17

Keem Siah Yap

65 papers receiving 1.5k citations

Peers

Keem Siah Yap
Comparison fields: 5 of 98
  • Control and Systems Engineering 472
  • Artificial Intelligence 633
  • Electrical and Electronic Engineering 1.0k
  • Civil and Structural Engineering 351
  • Environmental Engineering 151
Replace Muzaffar Eusuff with:
Muzaffar Eusuff United States
Haixiang Zang China
Francesco Grimaccia Italy
Wenlong Liao China
Zhihuan Chen China
Hui Jiang China
Abdollah Kavousi‐Fard Iran
Junzhao Yu China
Mojtaba Ghasemi Iran
Jianchun Peng China
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Citations per year

Countries citing papers authored by Keem Siah Yap

Since Specialization
Citations

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

Fields of papers citing papers by Keem Siah Yap

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Keem Siah Yap, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Keem Siah Yap Line = papers co-authored together Keem Siah Yap links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20222
2 20193
3 20181
4 20180
5 20181
6 20162
7 20155
8 2015210
9 20146
10 20135
11
Comparison of Supervised Learning Techniques for Non-Technical Loss Detection in Power Utility
201211
12 20121
13 201129
14 201177
15 201039
16
Short term power load forecasting using a modified generalized regression neural network
20081
17 200833
18 20084
19 2008105
20
A hybrid neural network for time series prediction
20061

About Keem Siah Yap

Keem Siah Yap is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 66 papers that have together received 1.6k indexed citations. Recurring topics across this work include Machine Learning and ELM (23 papers), Neural Networks and Applications (22 papers), Energy Load and Power Forecasting (17 papers), Fuzzy Logic and Control Systems (9 papers), Electric Power System Optimization (8 papers), Electricity Theft Detection Techniques (7 papers), Image and Signal Denoising Methods (6 papers) and Water Systems and Optimization (6 papers). The work is most often cited by research in Control and Systems Engineering (472 citations), Artificial Intelligence (633 citations) and Electrical and Electronic Engineering (1.0k citations). Keem Siah Yap has collaborated with scholars based in Malaysia, United States and China. Frequent co-authors include Tiong Sieh Kiong, Jawad Nagi, Syed Khaleel Ahmed, Hwa Jen Yap, Farrukh Nagi, Shen Yuong Wong, Syed Khaleel Ahmed, M. A. Malek, Özgür Kişi and Chee Peng Lim. Their work appears in journals such as Journal of Hydrology, IEEE Access and IEEE Transactions on Power Delivery.

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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