Jaehak Yu

925 total citations
37 papers, 617 citations indexed

About

Jaehak Yu is a scholar working on Computer Networks and Communications, Artificial Intelligence and Health Information Management. According to data from OpenAlex, Jaehak Yu has authored 37 papers receiving a total of 617 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Networks and Communications, 10 papers in Artificial Intelligence and 6 papers in Health Information Management. Recurrent topics in Jaehak Yu's work include Network Security and Intrusion Detection (7 papers), Internet Traffic Analysis and Secure E-voting (6 papers) and Acute Ischemic Stroke Management (6 papers). Jaehak Yu is often cited by papers focused on Network Security and Intrusion Detection (7 papers), Internet Traffic Analysis and Secure E-voting (6 papers) and Acute Ischemic Stroke Management (6 papers). Jaehak Yu collaborates with scholars based in South Korea, United States and Canada. Jaehak Yu's co-authors include Hansung Lee, Se Jin Park, Cheol‐Sig Pyo, Daihee Park, Soonhyun Kwon, Kang Hee Cho, Hyo‐Chan Bang, Myung‐Sup Kim, Chee Meng Benjamin Ho and Yang Sun Lee and has published in prestigious journals such as IEEE Access, Sensors and SAE technical papers on CD-ROM/SAE technical paper series.

In The Last Decade

Jaehak Yu

32 papers receiving 567 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jaehak Yu South Korea	13	227	180	119	116	93	37	617
Sidra Abbas Pakistan	14	233 1.0×	303 1.7×	59 0.5×	53 0.5×	82 0.9×	29	714
Zeyad Ghaleb Al-Mekhlafi Saudi Arabia	19	352 1.6×	347 1.9×	33 0.3×	45 0.4×	77 0.8×	53	1.0k
Badiea Abdulkarem Mohammed Saudi Arabia	19	308 1.4×	364 2.0×	33 0.3×	47 0.4×	81 0.9×	56	1.0k
Nasmin Jiwani United States	14	89 0.4×	100 0.6×	90 0.8×	27 0.2×	27 0.3×	37	446
Ajmeera Kiran India	13	125 0.6×	185 1.0×	65 0.5×	12 0.1×	44 0.5×	111	638
Soonhyun Kwon South Korea	8	136 0.6×	51 0.3×	50 0.4×	48 0.4×	26 0.3×	29	352
S. Phani Praveen India	14	108 0.5×	203 1.1×	80 0.7×	14 0.1×	48 0.5×	53	685
Ketan Gupta United States	12	79 0.3×	99 0.6×	88 0.7×	23 0.2×	26 0.3×	44	417
Arnisha Akhter Bangladesh	12	102 0.4×	416 2.3×	121 1.0×	14 0.1×	73 0.8×	19	776
Muhammad Asim Saleem China	14	141 0.6×	154 0.9×	39 0.3×	30 0.3×	15 0.2×	39	620

Countries citing papers authored by Jaehak Yu

Since Specialization

Citations

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

Fields of papers citing papers by Jaehak Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jaehak Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Jaehak Yu. A scholar is included among the top collaborators of Jaehak Yu 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 Jaehak Yu. Jaehak Yu 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

Yu, Jaehak, et al.. (2024). A novel approach for application classification with encrypted traffic using BERT and packet headers. Computer Networks. 254. 110747–110747. 1 indexed citations

Yu, Jaehak, Se Jin Park, Soonhyun Kwon, Kang Hee Cho, & Hansung Lee. (2022). AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals. IEEE Access. 10. 43623–43638. 51 indexed citations

Kwon, Soonhyun, et al.. (2021). Design and Implementation of Medical Knowledge Convergence System for Disease Prediction Services. KIISE Transactions on Computing Practices. 27(7). 338–352. 1 indexed citations

Kwon, Soonhyun, et al.. (2021). Stroke Medical Ontology for Supporting AI-based Stroke Prediction System using Bio-Signals. 53–59. 4 indexed citations

Yu, Jaehak, Se Jin Park, Soonhyun Kwon, et al.. (2020). AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals. Applied Sciences. 10(19). 6791–6791. 69 indexed citations

Yu, Jaehak, Se Jin Park, Hansung Lee, Cheol‐Sig Pyo, & Yang Sun Lee. (2020). An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale. Mathematics. 8(7). 1115–1115. 31 indexed citations

Ryu, Seung‐Ho, et al.. (2017). Neuropsychiatric Symptoms and Increased Risks of Progression from Amnestic Mild Cognitive Impairment to Alzheimer’s Dementia. 21(1). 29–34. 1 indexed citations

Bae, Ji‐Hoon, et al.. (2017). Performance Analysis of Hint-KD Training Approach for the Teacher-Student Framework Using Deep Residual Networks. Journal of the Institute of Electronics Engineers of Korea. 54(5). 35–41. 1 indexed citations

Subramaniyam, Murali, et al.. (2017). Wake-Up Stroke Prediction through IoT and Its Possibilities. 21. 1–5. 2 indexed citations

10.

Park, Se Jin, et al.. (2017). Conceptual Design of the Elderly Healthcare Services In-Vehicle using IoT. SAE technical papers on CD-ROM/SAE technical paper series. 6 indexed citations

11.

Yu, Jaehak, Nam-Kyung Lee, Cheol‐Sig Pyo, & Yang Sun Lee. (2016). WISE: web of object architecture on IoT environment for smart home and building energy management. The Journal of Supercomputing. 74(9). 4403–4418. 17 indexed citations

12.

Yu, Jaehak, et al.. (2013). Real-time cooling load forecasting using a hierarchical multi-class SVDD. Multimedia Tools and Applications. 71(1). 293–307. 8 indexed citations

13.

Yu, Jaehak, et al.. (2013). An in-depth analysis on traffic flooding attacks detection and system using data mining techniques. Journal of Systems Architecture. 59(10). 1005–1012. 35 indexed citations

14.

Park, Daihee, Jaehak Yu, Jun‐Sang Park, & Myung‐Sup Kim. (2012). NetCube: a comprehensive network traffic analysis model based on multidimensional OLAP data cube. International Journal of Network Management. 23(2). 101–118. 9 indexed citations

15.

Ryu, Seung‐Ho, et al.. (2011). Characteristics of Cognitive Faculties in Elderly Depressive Patientscomplaining of Memory Decline and Patients with Amnestic Mild Cognitive Impairment. 15(1). 38–44. 1 indexed citations

16.

Lee, Hansung, et al.. (2010). A News Video Mining based on Multi-modal Approach and Text Mining. 37(3). 127–136. 1 indexed citations

17.

Yu, Jaehak. (2010). Real-time Classification of Internet Application Traffic using a Hierarchical Multi-class SVM. KSII Transactions on Internet and Information Systems. 42 indexed citations

18.

Lee, Hansung, et al.. (2010). A unified scheme of shot boundary detection and anchor shot detection in news video story parsing. Multimedia Tools and Applications. 51(3). 1127–1145. 29 indexed citations

19.

Yu, Jaehak, et al.. (2010). Hierarchical Internet Application Traffic Classification using a Multi-class SVM. Journal of Korean institute of intelligent systems. 20(1). 7–14. 2 indexed citations

20.

Yu, Jaehak, et al.. (2006). A comparison Study of Positive and Negative Childhood Maltreatment Groups in Adult Substance Abuse Disorder Patients. Psychiatry Investigation. 3(2). 81–94. 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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