Seok-Beom Roh

725 total citations
44 papers, 534 citations indexed

About

Seok-Beom Roh is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Seok-Beom Roh has authored 44 papers receiving a total of 534 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 15 papers in Computer Vision and Pattern Recognition and 5 papers in Computational Theory and Mathematics. Recurrent topics in Seok-Beom Roh's work include Neural Networks and Applications (25 papers), Fuzzy Logic and Control Systems (21 papers) and Face and Expression Recognition (12 papers). Seok-Beom Roh is often cited by papers focused on Neural Networks and Applications (25 papers), Fuzzy Logic and Control Systems (21 papers) and Face and Expression Recognition (12 papers). Seok-Beom Roh collaborates with scholars based in South Korea, Canada and Poland. Seok-Beom Roh's co-authors include Sung‐Kwun Oh, Witold Pedrycz, Kyung‐Won Jang, Kisung Seo, Zunwei Fu, Witold Pedrycz, Seung Ja Oh, Yong Soo Kim, Jin Hee Yoon and Jihong Wang and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, Expert Systems with Applications and Sensors.

In The Last Decade

Seok-Beom Roh

37 papers receiving 491 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Seok-Beom Roh South Korea 14 256 97 79 77 64 44 534
G.A. Vijayalakshmi Pai India 12 153 0.6× 159 1.6× 44 0.6× 84 1.1× 48 0.8× 34 543
Nakaji Honda Japan 9 230 0.9× 120 1.2× 54 0.7× 67 0.9× 101 1.6× 46 504
Zhizhong Mao China 17 459 1.8× 46 0.5× 29 0.4× 49 0.6× 334 5.2× 58 853
Qiang Hua China 8 236 0.9× 64 0.7× 77 1.0× 113 1.5× 69 1.1× 37 465
S. Askari Iran 11 254 1.0× 121 1.2× 14 0.2× 114 1.5× 57 0.9× 22 589
Mohammad Anwar Hosen Australia 10 220 0.9× 44 0.5× 21 0.3× 35 0.5× 208 3.3× 29 609
Jesús Soto Mexico 7 301 1.2× 108 1.1× 53 0.7× 42 0.5× 79 1.2× 17 508
Hongfang Zhou China 8 241 0.9× 18 0.2× 41 0.5× 108 1.4× 33 0.5× 21 471
Kai-Bo Duan Singapore 3 184 0.7× 37 0.4× 15 0.2× 151 2.0× 73 1.1× 4 513
Najmeh Sadat Jaddi Malaysia 11 303 1.2× 61 0.6× 91 1.2× 39 0.5× 51 0.8× 24 545

Countries citing papers authored by Seok-Beom Roh

Since Specialization
Citations

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

Fields of papers citing papers by Seok-Beom Roh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seok-Beom Roh. 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 Seok-Beom Roh. The network helps show where Seok-Beom Roh may publish in the future.

Co-authorship network of co-authors of Seok-Beom Roh

This figure shows the co-authorship network connecting the top 25 collaborators of Seok-Beom Roh. A scholar is included among the top collaborators of Seok-Beom Roh 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 Seok-Beom Roh. Seok-Beom Roh 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.
Roh, Seok-Beom, et al.. (2025). Adaptive spatial–temporal graph attention network for real-time traffic forecasting. Engineering Applications of Artificial Intelligence. 163. 112883–112883.
2.
Oh, Sung‐Kwun, et al.. (2025). Dynamical multiple polynomial-based neural networks classifier realized with the aid of dropfilter and dual statistical selection. Engineering Applications of Artificial Intelligence. 157. 111164–111164.
3.
4.
Oh, Sung‐Kwun, et al.. (2024). Self-Organizing Hybrid Fuzzy Polynomial Neural Network Classifier Driven Through Dynamically Adaptive Structure and Compound Regularization Technique. IEEE Transactions on Fuzzy Systems. 32(9). 5385–5399. 3 indexed citations
5.
Roh, Seok-Beom, et al.. (2022). Design of Iterative Fuzzy Radial Basis Function Neural Networks Based on Iterative Weighted Fuzzy C-Means Clustering and Weighted LSE Estimation. IEEE Transactions on Fuzzy Systems. 30(10). 4273–4285. 19 indexed citations
6.
Roh, Seok-Beom, et al.. (2020). Lazy Learning for Nonparametric Locally Weighted Regression. International Journal of Fuzzy Logic and Intelligent Systems. 20(2). 145–155. 5 indexed citations
7.
Roh, Seok-Beom, Sung‐Kwun Oh, Witold Pedrycz, Kisung Seo, & Zunwei Fu. (2019). Design methodology for Radial Basis Function Neural Networks classifier based on locally linear reconstruction and Conditional Fuzzy C-Means clustering. International Journal of Approximate Reasoning. 106. 228–243. 20 indexed citations
8.
Roh, Seok-Beom, Sung‐Kwun Oh, Witold Pedrycz, & Zunwei Fu. (2019). Design of Fuzzy Ensemble Architecture Realized With the Aid of FCM-Based Fuzzy Partition and NN With Weighted LSE Estimation. IEEE Transactions on Fuzzy Systems. 29(3). 569–583. 9 indexed citations
9.
10.
Roh, Seok-Beom, et al.. (2018). Design of face recognition system based on fuzzy transform and radial basis function neural networks. Soft Computing. 23(13). 4969–4985. 13 indexed citations
11.
Roh, Seok-Beom & Sung‐Kwun Oh. (2016). Identification of Plastic Wastes by Using Fuzzy Radial Basis Function Neural Networks Classifier with Conditional Fuzzy C-Means Clustering. Journal of Electrical Engineering and Technology. 11(6). 1872–1879. 17 indexed citations
12.
Roh, Seok-Beom, et al.. (2014). Feature Selection of Fuzzy Pattern Classifier by using Fuzzy Mapping. Journal of Korean institute of intelligent systems. 24(6). 646–650.
13.
Roh, Seok-Beom, et al.. (2011). Fuzzy Learning Vector Quantization based on Fuzzy k-Nearest Neighbor Prototypes. International Journal of Fuzzy Logic and Intelligent Systems. 11(2). 84–88. 2 indexed citations
14.
Roh, Seok-Beom, et al.. (2010). Design of Lazy Classifier based on Fuzzy k-Nearest Neighbors and Reconstruction Error. Journal of Korean institute of intelligent systems. 20(1). 101–108. 1 indexed citations
15.
Roh, Seok-Beom, et al.. (2010). The development of fuzzy radial basis function neural networks based on the concept of information ambiguity. Neurocomputing. 73(13-15). 2464–2477. 10 indexed citations
16.
Roh, Seok-Beom, et al.. (2009). Feature Selection for Fuzzy Radial Basis Neural Networks based on Locally Linear Reconstruction. 한국지능시스템학회 국제학술대회 발표논문집. 288–291. 5 indexed citations
17.
Roh, Seok-Beom, et al.. (2009). The design methodology of radial basis function neural networks based on fuzzy K-nearest neighbors approach. Fuzzy Sets and Systems. 161(13). 1803–1822. 19 indexed citations
18.
Oh, Sung‐Kwun, Witold Pedrycz, & Seok-Beom Roh. (2009). Hybrid fuzzy set-based polynomial neural networks and their development with the aid of genetic optimization and information granulation. Applied Soft Computing. 9(3). 1068–1089. 28 indexed citations
19.
Roh, Seok-Beom, et al.. (2006). Design of Fuzzy Set-based Polynomial Neural Networks involving Information Granules with the aid of multi-population Genetic Algorithms. 2006 SICE-ICASE International Joint Conference. 2. 349–352. 9 indexed citations
20.
Oh, Seung Ja, Witold Pedrycz, & Seok-Beom Roh. (2005). Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons. Information Sciences. 176(23). 3490–3519. 31 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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