Binbin Yong

1.1k total citations · 1 hit paper
38 papers, 782 citations indexed

About

Binbin Yong is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Binbin Yong has authored 38 papers receiving a total of 782 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Electrical and Electronic Engineering, 14 papers in Artificial Intelligence and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Binbin Yong's work include Energy Load and Power Forecasting (13 papers), Neural Networks and Applications (6 papers) and Solar Radiation and Photovoltaics (5 papers). Binbin Yong is often cited by papers focused on Energy Load and Power Forecasting (13 papers), Neural Networks and Applications (6 papers) and Solar Radiation and Photovoltaics (5 papers). Binbin Yong collaborates with scholars based in China, Australia and United States. Binbin Yong's co-authors include Qingguo Zhou, Jun Shen, Vigna K. Ramachandaramurthy, Rajparthiban Kumar Rajkumar, Huaming Chen, Fucun Li, Xin Liu, Qingguo Zhou, Lei Shu and Yonghui Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Energy.

In The Last Decade

Binbin Yong

36 papers receiving 752 citations

Hit Papers

An intelligent blockchain-based system for safe vaccine s... 2019 2026 2021 2023 2019 50 100 150

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Binbin Yong China 14 269 196 175 140 110 38 782
Mehedi Hasan Bangladesh 21 739 2.7× 113 0.6× 96 0.5× 33 0.2× 50 0.5× 67 1.2k
Syed Muhammad Mohsin Pakistan 15 535 2.0× 305 1.6× 163 0.9× 36 0.3× 165 1.5× 40 1.0k
Faiza Qayyum South Korea 15 317 1.2× 99 0.5× 114 0.7× 30 0.2× 130 1.2× 33 615
Mohamed A. Ahmed South Korea 23 893 3.3× 96 0.5× 105 0.6× 65 0.5× 338 3.1× 69 1.3k
P. Venkatesh India 22 1.4k 5.1× 145 0.7× 111 0.6× 126 0.9× 488 4.4× 78 1.8k
Hossein Shahinzadeh Iran 23 1.0k 3.9× 114 0.6× 216 1.2× 180 1.3× 695 6.3× 156 1.6k
Xiong Wang China 10 599 2.2× 116 0.6× 70 0.4× 24 0.2× 189 1.7× 24 971
Ahmed Saeed AlGhamdi Saudi Arabia 16 290 1.1× 154 0.8× 127 0.7× 15 0.1× 61 0.6× 49 888
Liviu Miclea Romania 11 157 0.6× 115 0.6× 101 0.6× 32 0.2× 153 1.4× 129 703
Leong Hai Koh Singapore 17 1.2k 4.6× 117 0.6× 272 1.6× 163 1.2× 762 6.9× 62 1.7k

Countries citing papers authored by Binbin Yong

Since Specialization
Citations

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

Fields of papers citing papers by Binbin Yong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Binbin Yong

This figure shows the co-authorship network connecting the top 25 collaborators of Binbin Yong. A scholar is included among the top collaborators of Binbin Yong 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 Binbin Yong. Binbin Yong 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.
Li, Yuelei, Lipin Li, Haoran Zhang, et al.. (2025). Machine learning enhancing biochar abatement predictions: Advancing China climate goals for food production and promoting application. Resources Conservation and Recycling. 218. 108248–108248. 2 indexed citations
2.
Song, Lili, et al.. (2025). Physics-informed continuous-time reinforcement learning with data-driven approach for robotic arm manipulation. Journal of Industrial Information Integration. 49. 101008–101008.
3.
Yong, Binbin, et al.. (2025). ConvODE-Mixer: A multimodal deep learning model for ultra-short-term PV power forecasting. Solar Energy. 300. 113777–113777. 2 indexed citations
4.
Shen, Jun, et al.. (2025). Hierarchical gated pooling and progressive feature fusion for short-term PV power forecasting. Renewable Energy. 247. 122929–122929. 2 indexed citations
5.
Shen, Jun, et al.. (2024). Deep Spatio-Temporal Fuzzy Model for NDVI Forecasting. IEEE Transactions on Fuzzy Systems. 33(1). 290–301. 5 indexed citations
6.
Huang, Songtao, Qingguo Zhou, Jun Shen, Heng Zhou, & Binbin Yong. (2024). Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting. Energy. 290. 130308–130308. 28 indexed citations
7.
Shen, Jun, et al.. (2024). FoodFlavorNet: A Multimodal Deep Learning Model for Food Flavor Recognition. IEEE Transactions on Consumer Electronics. 71(2). 6829–6838. 3 indexed citations
8.
Zhou, Heng, et al.. (2024). Electrical load forecasting based on the fusion of multi-scale features extracted by using neural ordinary differential equation. The Journal of Supercomputing. 81(1). 1 indexed citations
9.
Zhou, Qingguo, et al.. (2023). Ensemble Machine Learning Method for Photovoltaic Power Forecasting. Research Online (University of Wollongong). 332–337. 1 indexed citations
10.
Huang, Songtao, Jun Shen, Qingquan Lv, Qingguo Zhou, & Binbin Yong. (2022). A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting. Future Internet. 15(1). 22–22. 7 indexed citations
11.
Jiang, Xuetao, et al.. (2021). Crop and weed classification based on AutoML. arXiv (Cornell University). 1(1). 46–60. 2 indexed citations
12.
Zhou, Qingguo, et al.. (2020). Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection. IEEE Access. 8. 45156–45166. 17 indexed citations
13.
Zhou, Rui, Xue Li, Binbin Yong, et al.. (2019). Arrhythmia recognition and classification through deep learning-based approach. International Journal of Computational Science and Engineering. 19(4). 506–506. 14 indexed citations
14.
Shen, Zebang, Binbin Yong, Gaofeng Zhang, Rui Zhou, & Qingguo Zhou. (2019). A deep learning method for Chinese singer identification. Tsinghua Science & Technology. 24(4). 371–378. 18 indexed citations
15.
Yong, Binbin, et al.. (2019). Malicious Web traffic detection for Internet of Things environments. Computers & Electrical Engineering. 77. 260–272. 18 indexed citations
16.
Yong, Binbin, et al.. (2017). Parallel GPU-based collision detection of irregular vessel wall for massive particles. Cluster Computing. 20(3). 2591–2603. 2 indexed citations
17.
Yong, Binbin, Fucun Li, Qingquan Lv, Jun Shen, & Qingguo Zhou. (2017). Derivative-based acceleration of general vector machine. Soft Computing. 23(3). 987–995. 4 indexed citations
18.
Yong, Binbin, et al.. (2016). Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland. Proceedings of the Australasian Computer Science Week Multiconference. 1–7. 19 indexed citations
19.
Yong, Binbin, et al.. (2010). Design of a PV/wind hybrid system for telecommunication load in Borneo region. 73–76. 3 indexed citations
20.

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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