Peerapon Vateekul

2.4k total citations
105 papers, 1.0k citations indexed

About

Peerapon Vateekul is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Peerapon Vateekul has authored 105 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Artificial Intelligence, 15 papers in Information Systems and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Peerapon Vateekul's work include Text and Document Classification Technologies (18 papers), Topic Modeling (14 papers) and Natural Language Processing Techniques (9 papers). Peerapon Vateekul is often cited by papers focused on Text and Document Classification Technologies (18 papers), Topic Modeling (14 papers) and Natural Language Processing Techniques (9 papers). Peerapon Vateekul collaborates with scholars based in Thailand, United States and Japan. Peerapon Vateekul's co-authors include Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern, Teerapong Panboonyuen, Miroslav Kubát, Kanoksri Sarinnapakorn, Roongroj Bhidayasiri, Rungsun Rerknimitr, Kornraphop Kawintiranon and Rapat Pittayanon and has published in prestigious journals such as Scientific Reports, IEEE Access and Sensors.

In The Last Decade

Peerapon Vateekul

99 papers receiving 980 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peerapon Vateekul Thailand 16 359 194 148 116 108 105 1.0k
Martin Längkvist Sweden 9 379 1.1× 234 1.2× 161 1.1× 140 1.2× 83 0.8× 26 1.5k
Shilpa Gite India 19 319 0.9× 322 1.7× 97 0.7× 59 0.5× 42 0.4× 92 1.2k
Yu Du China 16 245 0.7× 198 1.0× 229 1.5× 74 0.6× 50 0.5× 108 1.3k
Diego Andina Spain 19 489 1.4× 302 1.6× 52 0.4× 176 1.5× 55 0.5× 93 1.2k
Siyu Lu China 14 307 0.9× 222 1.1× 46 0.3× 147 1.3× 43 0.4× 20 1.3k
Eric Eaton United States 20 670 1.9× 279 1.4× 80 0.5× 48 0.4× 59 0.5× 64 1.2k
Qian Yang China 20 375 1.0× 171 0.9× 67 0.5× 56 0.5× 41 0.4× 102 1.2k
Yifan Shi China 12 771 2.1× 393 2.0× 60 0.4× 82 0.7× 43 0.4× 37 1.8k
Rafał Scherer Poland 20 518 1.4× 460 2.4× 80 0.5× 58 0.5× 30 0.3× 103 1.4k

Countries citing papers authored by Peerapon Vateekul

Since Specialization
Citations

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

Fields of papers citing papers by Peerapon Vateekul

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peerapon Vateekul. 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 Peerapon Vateekul. The network helps show where Peerapon Vateekul may publish in the future.

Co-authorship network of co-authors of Peerapon Vateekul

This figure shows the co-authorship network connecting the top 25 collaborators of Peerapon Vateekul. A scholar is included among the top collaborators of Peerapon Vateekul 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 Peerapon Vateekul. Peerapon Vateekul 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.
Pittayanon, Rapat, et al.. (2024). Real-time gastric intestinal metaplasia segmentation using a deep neural network designed for multiple imaging modes on high-resolution images. Knowledge-Based Systems. 300. 112213–112213. 1 indexed citations
2.
Lohwongwatana, Boonrat, et al.. (2024). CraNeXt: Automatic Reconstruction of Skull Implants With Skull Categorization Technique. IEEE Access. 12. 84907–84922. 3 indexed citations
3.
4.
Lertvittayakumjorn, Piyawat, et al.. (2024). Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages. 195–203. 1 indexed citations
5.
Vateekul, Peerapon, et al.. (2024). Optimization of Peer-to-Peer Energy Trading With a Model-Based Deep Reinforcement Learning in a Non-Sharing Information Scenario. IEEE Access. 12. 111021–111034. 1 indexed citations
6.
Painmanakul, Pisut, et al.. (2023). Spatio-Temporal PM2.5 Forecasting in Thailand Using Encoder-Decoder Networks. IEEE Access. 11. 69601–69613. 6 indexed citations
7.
Hemrungrojn, Solaphat, et al.. (2023). Explainable Depression Detection Based on Facial Expression Using LSTM on Attentional Intermediate Feature Fusion with Label Smoothing. Sensors. 23(23). 9402–9402. 7 indexed citations
8.
Panyakaew, Pattamon, et al.. (2022). Vibratory and plantar pressure stimulation: Steps to improve freezing of gait in Parkinson's disease. Parkinsonism & Related Disorders. 105. 43–51. 12 indexed citations
9.
Vateekul, Peerapon, et al.. (2022). Model-Based Approach on Multi-Agent Deep Reinforcement Learning With Multiple Clusters for Peer-To-Peer Energy Trading. IEEE Access. 10. 127882–127893. 17 indexed citations
10.
Pittayanon, Rapat, et al.. (2022). Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach. Clinical Endoscopy. 55(3). 390–400. 15 indexed citations
11.
12.
Phokaewvarangkul, Onanong, et al.. (2021). Using Machine Learning for Predicting the Best Outcomes With Electrical Muscle Stimulation for Tremors in Parkinson’s Disease. Frontiers in Aging Neuroscience. 13. 727654–727654. 6 indexed citations
13.
Panboonyuen, Teerapong, Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern, & Peerapon Vateekul. (2021). Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images. Remote Sensing. 13(24). 5100–5100. 61 indexed citations
14.
Vateekul, Peerapon, et al.. (2021). Model-based deep reinforcement learning for wind energy bidding. International Journal of Electrical Power & Energy Systems. 136. 107625–107625. 22 indexed citations
15.
Panboonyuen, Teerapong, Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern, & Peerapon Vateekul. (2020). Semantic Labeling in Remote Sensing Corpora Using Feature Fusion-Based Enhanced Global Convolutional Network with High-Resolution Representations and Depthwise Atrous Convolution. Remote Sensing. 12(8). 1233–1233. 9 indexed citations
16.
Chuangsuwanich, Ekapol, et al.. (2020). Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations. ACM Transactions on Asian and Low-Resource Language Information Processing. 2 indexed citations
17.
Jitkajornwanich, Kulsawasd, et al.. (2019). Combining Attentional CNN and GRU Networks for Ocean Current Prediction based on HF Radar Observations. 440–446. 8 indexed citations
18.
Vateekul, Peerapon, et al.. (2016). Enhancing accuracy of multi-label classification by applying one-vs-one support vector machine. 1–6. 11 indexed citations
19.
Vateekul, Peerapon, et al.. (2016). Combining deep convolutional networks and SVMs for mass detection on digital mammograms. 239–244. 24 indexed citations
20.
Lertvittayakumjorn, Piyawat, et al.. (2015). A system to analyze Twitter data for social science study. 581–586. 1 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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