Seong‐O Shim

813 total citations
48 papers, 591 citations indexed

About

Seong‐O Shim is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Seong‐O Shim has authored 48 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Media Technology, 24 papers in Computer Vision and Pattern Recognition and 13 papers in Artificial Intelligence. Recurrent topics in Seong‐O Shim's work include Image Processing Techniques and Applications (19 papers), Digital Holography and Microscopy (11 papers) and Cell Image Analysis Techniques (6 papers). Seong‐O Shim is often cited by papers focused on Image Processing Techniques and Applications (19 papers), Digital Holography and Microscopy (11 papers) and Cell Image Analysis Techniques (6 papers). Seong‐O Shim collaborates with scholars based in Saudi Arabia, South Korea and Pakistan. Seong‐O Shim's co-authors include Tae‐Sun Choi, Wajid Aziz, Aamir Saeed Malik, Ishtiaq Rasool Khan, Saleh Alshomrani, Malik Sajjad Ahmed Nadeem, Francisco Herrera, Lal Hussain, Abdulrahman Altalhi and Abdullah Bawakid and has published in prestigious journals such as Optics Letters, IEEE Access and Pattern Recognition.

In The Last Decade

Seong‐O Shim

48 papers receiving 562 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seong‐O Shim Saudi Arabia	16	243	188	169	100	85	48	591
Reecha Sharma India	13	188 0.8×	56 0.3×	98 0.6×	187 1.9×	18 0.2×	41	631
Hailiang Ye China	13	300 1.2×	145 0.8×	245 1.4×	23 0.2×	13 0.2×	51	686
Sharif Bhuiyan United States	13	387 1.6×	220 1.2×	83 0.5×	173 1.7×	15 0.2×	48	742
Jiun-Wei Liou Taiwan	5	120 0.5×	56 0.3×	131 0.8×	40 0.4×	19 0.2×	8	432
Xiangling Ding China	14	486 2.0×	74 0.4×	85 0.5×	54 0.5×	12 0.1×	56	653
Jun Xiao China	13	444 1.8×	101 0.5×	141 0.8×	37 0.4×	18 0.2×	76	658
Shangchen Zhou Singapore	15	1.1k 4.5×	257 1.4×	100 0.6×	32 0.3×	12 0.1×	36	1.2k
Mariusz Oszust Poland	16	351 1.4×	161 0.9×	125 0.7×	25 0.3×	17 0.2×	43	590
Zetao Jiang China	13	348 1.4×	114 0.6×	58 0.3×	51 0.5×	13 0.2×	93	542

Countries citing papers authored by Seong‐O Shim

Since Specialization

Citations

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

Fields of papers citing papers by Seong‐O Shim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seong‐O Shim. 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 Seong‐O Shim. The network helps show where Seong‐O Shim may publish in the future.

Co-authorship network of co-authors of Seong‐O Shim

This figure shows the co-authorship network connecting the top 25 collaborators of Seong‐O Shim. A scholar is included among the top collaborators of Seong‐O Shim 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 Seong‐O Shim. Seong‐O Shim 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

Shim, Seong‐O, Lal Hussain, Wajid Aziz, et al.. (2024). Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach. International Journal of Imaging Systems and Technology. 34(2). 4 indexed citations

Majid, Abdul, et al.. (2024). Intelligent Bayesian Inference for Multiclass Lung Infection Diagnosis: Network Analysis of Ranked Gray Level Co-occurrence (GLCM) Features. New Generation Computing. 42(5). 997–1048. 1 indexed citations

Khan, Ishtiaq Rasool, Wajid Aziz, & Seong‐O Shim. (2020). Tone-Mapping Using Perceptual-Quantizer and Image Histogram. IEEE Access. 8. 31350–31358. 21 indexed citations

Nadeem, Malik Sajjad Ahmed, et al.. (2020). Machine Learning Based Cost Effective Electricity Load Forecasting Model Using Correlated Meteorological Parameters. IEEE Access. 8. 146847–146864. 58 indexed citations

Khan, Asifullah, et al.. (2020). Mitosis detection in breast cancer histopathology images using hybrid feature space. Photodiagnosis and Photodynamic Therapy. 31. 101885–101885. 26 indexed citations

Shim, Seong‐O. (2020). Estimation of gamma-corrected exposure time ratio in multi-exposure images for removal of moving objects. Applied Optics. 59(13). 4076–4076. 3 indexed citations

Shim, Seong‐O, Wajid Aziz, Ameen Banjar, Abdullah Alamri, & Mohammed A. Alqarni. (2019). Improving Depth Computation From Robust Focus Approximation. IEEE Access. 7. 20144–20149. 8 indexed citations

Nadeem, Malik Sajjad Ahmed, et al.. (2019). Class Association and Attribute Relevancy Based Imputation Algorithm to Reduce Twitter Data for Optimal Sentiment Analysis. IEEE Access. 7. 136535–136544. 15 indexed citations

Nadeem, Malik Sajjad Ahmed, et al.. (2019). Accuracy Rejection Normalized-Cost Curves (ARNCCs): A Novel 3-Dimensional Framework for Robust Classification. IEEE Access. 7. 160125–160143. 6 indexed citations

10.

Aziz, Wajid, et al.. (2019). A Novel Phase Space Reconstruction- (PSR-) Based Predictive Algorithm to Forecast Atmospheric Particulate Matter Concentration. Scientific Programming. 2019. 1–12. 18 indexed citations

11.

Waseem, Muhammad, Malik Sajjad Ahmed Nadeem, Assad Abbas, et al.. (2019). On the Feature Selection Methods and Reject Option Classifiers for Robust Cancer Prediction. IEEE Access. 7. 141072–141082. 16 indexed citations

12.

Shim, Seong‐O & Ishtiaq Rasool Khan. (2017). Removal of ghosting artefacts in HDRI using intensity scaling cue. 1–4. 4 indexed citations

13.

Malik, Aamir Saeed, Hafeez Ullah Amin, Mark Llewellyn Smith, et al.. (2015). EEG based evaluation of stereoscopic 3D displays for viewer discomfort. BioMedical Engineering OnLine. 14(1). 21–21. 31 indexed citations

14.

Nisar, Humaira, Aamir Saeed Malik, Habib Ullah, et al.. (2014). Tracking of EEG Activity Using Motion Estimation to Understand Brain Wiring. Advances in experimental medicine and biology. 823. 159–174. 4 indexed citations

15.

Shim, Seong‐O & Tae‐Sun Choi. (2012). A fast and robust depth estimation method for 3D cameras. 321–322. 2 indexed citations

16.

Shim, Seong‐O, Muhammad Tariq Mahmood, & Tae‐Sun Choi. (2011). Searching surface orientation of microscopic objects for accurate 3D shape recovery. Microscopy Research and Technique. 75(5). 561–565. 1 indexed citations

17.

Shim, Seong‐O & Tae‐Sun Choi. (2010). Depth from focus based on combinatorial optimization. Optics Letters. 35(12). 1956–1956. 8 indexed citations

18.

Shim, Seong‐O, Aamir Saeed Malik, & Tae‐Sun Choi. (2010). Pre-processing for noise reduction in depth estimation. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7546. 754625–754625. 4 indexed citations

19.

Mahmood, Muhammad Tariq, Seong‐O Shim, Asifullah Khan, & Tae‐Sun Choi. (2009). Accurate depth approximation through Bezier-Bernstein polynomial for 3D cameras. 1–2. 1 indexed citations

20.

Shim, Seong‐O & Tae‐Sun Choi. (2009). Accurate 3D shape estimation based on combinatorial optimization. 3777–3780. 3 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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