Yeong Hyeon Gu

1.2k total citations · 1 hit paper
73 papers, 690 citations indexed

About

Yeong Hyeon Gu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Yeong Hyeon Gu has authored 73 papers receiving a total of 690 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 17 papers in Computer Vision and Pattern Recognition and 12 papers in Information Systems. Recurrent topics in Yeong Hyeon Gu's work include Smart Agriculture and AI (11 papers), Advanced Text Analysis Techniques (8 papers) and Sentiment Analysis and Opinion Mining (5 papers). Yeong Hyeon Gu is often cited by papers focused on Smart Agriculture and AI (11 papers), Advanced Text Analysis Techniques (8 papers) and Sentiment Analysis and Opinion Mining (5 papers). Yeong Hyeon Gu collaborates with scholars based in South Korea, China and Pakistan. Yeong Hyeon Gu's co-authors include Seong Joon Yoo, Jin Dong, Mugahed A. Al–antari, J. Park, Zaid Al‐Huda, Mohammed A. Al‐masni, Chang‐Jin Park, Muhammad Mateen, Shaukat Hayat and Dildar Hussain and has published in prestigious journals such as PLoS ONE, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Yeong Hyeon Gu

56 papers receiving 656 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

District heater load forecasting based on machine learning and parallel CNN-LSTM attention

2022 149 citations Yeong Hyeon Gu, Seong Joon Yoo et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yeong Hyeon Gu South Korea	13	220	155	108	97	96	73	690
Seong Joon Yoo South Korea	16	179 0.8×	179 1.2×	314 2.9×	77 0.8×	156 1.6×	54	926
Ling Yang China	15	88 0.4×	88 0.6×	129 1.2×	104 1.1×	98 1.0×	50	818
Muhammad Azam Canada	9	290 1.3×	51 0.3×	88 0.8×	142 1.5×	16 0.2×	31	583
Adnan Zeb China	17	513 2.3×	57 0.4×	189 1.8×	204 2.1×	81 0.8×	34	972
Athanasios Anagnostis Greece	14	140 0.6×	75 0.5×	133 1.2×	26 0.3×	18 0.2×	24	642
Ali Mustafa Qamar Saudi Arabia	18	134 0.6×	93 0.6×	307 2.8×	49 0.5×	25 0.3×	64	1.0k
S. P. Raja India	13	197 0.9×	110 0.7×	184 1.7×	50 0.5×	15 0.2×	152	833
Sandy Lauguico Philippines	18	303 1.4×	75 0.5×	39 0.4×	66 0.7×	18 0.2×	41	656
L V Narasimha Prasad India	12	220 1.0×	67 0.4×	137 1.3×	103 1.1×	7 0.1×	54	665

Countries citing papers authored by Yeong Hyeon Gu

Since Specialization

Citations

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

Fields of papers citing papers by Yeong Hyeon Gu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yeong Hyeon Gu. 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 Yeong Hyeon Gu. The network helps show where Yeong Hyeon Gu may publish in the future.

Co-authorship network of co-authors of Yeong Hyeon Gu

This figure shows the co-authorship network connecting the top 25 collaborators of Yeong Hyeon Gu. A scholar is included among the top collaborators of Yeong Hyeon Gu 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 Yeong Hyeon Gu. Yeong Hyeon Gu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Kwon, Hyunwook, et al.. (2025). Multimodal Knowledge-Infused VLM for Respiratory Disease Prediction and Clinical Report Generation. IEEE Journal of Biomedical and Health Informatics. PP. 1–14.

Fatima, Noor, Nadhem Nemri, Hadeel Alsolai, et al.. (2025). Time series AQI forecasting using Kalman-integrated Bi-GRU and Chi-square divergence optimization. Scientific Reports. 15(1). 29157–29157.

Razaq, Abdul, Shabana Ramzan, Ali Raza, et al.. (2025). Novel dual-input stream-based hybrid approach for wheat leaf disease classification using edge-aware features. Scientific Reports. 15(1). 42091–42091.

Zhang, Xiaoling, Happy Nkanta Monday, Grace Ugochi Nneji, et al.. (2025). Advancements, Challenges, and Future Directions in Scene-Graph-Based Image Generation: A Comprehensive Review. Electronics. 14(6). 1158–1158.

Nasir, Inzamam Mashood, et al.. (2025). Multi-Stage Neural Network-Based Ensemble Learning Approach for Wheat Leaf Disease Classification. IEEE Access. 13. 30101–30116. 7 indexed citations

Zhi-guang, Qin, et al.. (2025). A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract vulnerabilities. Scientific Reports. 15(1). 29810–29810.

Al–antari, Mugahed A., et al.. (2025). Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review. Artificial Intelligence Review. 58(8).

Dong, Jin, et al.. (2024). Shuffle-PG: Lightweight feature extraction model for retrieving images of plant diseases and pests with deep metric learning. Alexandria Engineering Journal. 113. 138–149. 2 indexed citations

Gu, Yeong Hyeon, et al.. (2024). Scalable deep learning framework for sentiment analysis prediction for online movie reviews. Heliyon. 10(10). e30756–e30756. 6 indexed citations

10.

Al‐masni, Mohammed A., et al.. (2024). Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model. Scientific Reports. 14(1). 14771–14771. 9 indexed citations

11.

Qin, Zhiguang, et al.. (2024). CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images. Bioengineering. 11(9). 887–887. 3 indexed citations

12.

Lee, Sungon, et al.. (2024). Cross-project software defect prediction based on the reduction and hybridization of software metrics. Alexandria Engineering Journal. 112. 161–176.

13.

Yoo, Seong Joon, et al.. (2024). Robust sensitive-information de-identification framework based on relative-position estimation of objects in closed-circuit television videos. Alexandria Engineering Journal. 89. 172–183. 1 indexed citations

14.

Raza, Ali, et al.. (2024). BdSentiLLM: A Novel LLM Approach to Sentiment Analysis of Product Reviews. IEEE Access. 12. 189330–189343. 3 indexed citations

15.

Mahdin, Hairulnizam, Mazni Omar, Nur Haryani Zakaria, et al.. (2023). A refactoring categorization model for software quality improvement. PLoS ONE. 18(11). e0293742–e0293742. 2 indexed citations

16.

Gu, Yeong Hyeon, et al.. (2023). Intracerebral hemorrhage CT scan image segmentation with HarDNet based transformer. Scientific Reports. 13(1). 7208–7208. 10 indexed citations

17.

Saif, Yazid, Anika Zafiah M. Rus, Yusri Yusof, et al.. (2023). Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques. Applied Sciences. 13(20). 11419–11419. 1 indexed citations

18.

Gu, Yeong Hyeon, et al.. (2022). Domain-Specific Language Model Pre-Training for Korean Tax Law Classification. IEEE Access. 10. 46342–46353. 3 indexed citations

19.

Gu, Yeong Hyeon, et al.. (2018). City Gas Pipeline Pressure Prediction Model. The e-Business Studies. 23(2). 33–47. 2 indexed citations

20.

Gu, Yeong Hyeon & Seong Joon Yoo. (2010). Searching a Best Product Based on Mining Comparison Sentences. 2010. 929–933. 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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