Wei‐Chiang Hong

11.5k total citations · 4 hit papers
165 papers, 8.7k citations indexed

About

Wei‐Chiang Hong is a scholar working on Electrical and Electronic Engineering, Management Science and Operations Research and Artificial Intelligence. According to data from OpenAlex, Wei‐Chiang Hong has authored 165 papers receiving a total of 8.7k indexed citations (citations by other indexed papers that have themselves been cited), including 96 papers in Electrical and Electronic Engineering, 75 papers in Management Science and Operations Research and 40 papers in Artificial Intelligence. Recurrent topics in Wei‐Chiang Hong's work include Energy Load and Power Forecasting (89 papers), Grey System Theory Applications (44 papers) and Stock Market Forecasting Methods (26 papers). Wei‐Chiang Hong is often cited by papers focused on Energy Load and Power Forecasting (89 papers), Grey System Theory Applications (44 papers) and Stock Market Forecasting Methods (26 papers). Wei‐Chiang Hong collaborates with scholars based in Taiwan, China and India. Wei‐Chiang Hong's co-authors include Ping‐Feng Pai, Yucheng Dong, Guo‐Feng Fan, Yinfeng Xu, Li‐Ling Peng, Guiqing Zhang, Jing Geng, Zichen Zhang, Yi Liang and Pradeep Kumar Singh and has published in prestigious journals such as Scientific Reports, Applied Energy and European Journal of Operational Research.

In The Last Decade

Wei‐Chiang Hong

158 papers receiving 8.4k citations

Hit Papers

Consensus models for AHP group decision making under row ... 2010 2026 2015 2020 2010 2019 2022 2021 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wei‐Chiang Hong Taiwan 52 3.7k 2.7k 2.1k 1.2k 1.1k 165 8.7k
Shanlin Yang China 57 3.8k 1.0× 2.2k 0.8× 2.5k 1.2× 1.5k 1.2× 850 0.8× 351 12.1k
Zhiwu Li China 71 1.9k 0.5× 1.6k 0.6× 2.3k 1.1× 1.9k 1.6× 634 0.6× 731 18.2k
Kin Keung Lai Hong Kong 58 1.4k 0.4× 3.7k 1.4× 1.9k 0.9× 1.3k 1.0× 519 0.5× 369 11.4k
Muhammet Deveci Türkiye 53 762 0.2× 4.3k 1.6× 1.6k 0.7× 1.3k 1.1× 807 0.7× 414 10.3k
Ali Diabat United States 48 777 0.2× 1.2k 0.4× 1.6k 0.8× 931 0.8× 878 0.8× 148 10.4k
Mitsuo Gen Japan 57 1.1k 0.3× 2.3k 0.8× 3.0k 1.4× 3.1k 2.5× 938 0.9× 400 16.3k
Amir F. Atiya Egypt 37 1.8k 0.5× 1.5k 0.6× 3.4k 1.6× 873 0.7× 293 0.3× 130 8.0k
Lean Yu China 50 2.0k 0.5× 2.9k 1.1× 1.8k 0.8× 727 0.6× 257 0.2× 215 7.9k
Deb Kalyanmoy India 4 1.8k 0.5× 942 0.3× 3.4k 1.6× 1.5k 1.2× 441 0.4× 7 11.0k
Yacine Rezgui United Kingdom 55 2.3k 0.6× 1.3k 0.5× 1.2k 0.5× 579 0.5× 5.0k 4.6× 290 10.4k

Countries citing papers authored by Wei‐Chiang Hong

Since Specialization
Citations

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

Fields of papers citing papers by Wei‐Chiang Hong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wei‐Chiang Hong. 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 Wei‐Chiang Hong. The network helps show where Wei‐Chiang Hong may publish in the future.

Co-authorship network of co-authors of Wei‐Chiang Hong

This figure shows the co-authorship network connecting the top 25 collaborators of Wei‐Chiang Hong. A scholar is included among the top collaborators of Wei‐Chiang Hong 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 Wei‐Chiang Hong. Wei‐Chiang Hong 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.
Fan, Guo‐Feng, et al.. (2025). Application of ensemble empirical mode decomposition with support vector regression and wavelet neural network in electric load forecasting. Energy Sources Part B Economics Planning and Policy. 20(1). 3 indexed citations
2.
3.
Zhang, Zichen, Chenglong Zhang, Yongquan Dong, & Wei‐Chiang Hong. (2024). Bi-directional gated recurrent unit enhanced twin support vector regression with seasonal mechanism for electric load forecasting. Knowledge-Based Systems. 310. 112943–112943. 3 indexed citations
4.
Li, Mingwei, et al.. (2024). Optimizing berth-crane allocation considering tidal effects using chaotic quantum whale optimization algorithm. Applied Soft Computing. 162. 111811–111811. 17 indexed citations
5.
Ma, He, et al.. (2024). SD-CSMOTE: Over-sampling method based on SNN-DPC and improved SMOTE. Neurocomputing. 620. 129233–129233.
6.
Zhang, Zichen, Wei‐Chiang Hong, & Yongquan Dong. (2024). Multi-hyperplane twin support vector regression guided with fuzzy clustering. Information Sciences. 666. 120435–120435. 10 indexed citations
7.
Fan, Guo‐Feng, et al.. (2024). The volatility mechanism and intelligent fusion forecast of new energy stock prices. Financial Innovation. 10(1). 6 indexed citations
8.
Rana, Bharti, Yashwant Singh, Pradeep Kumar Singh, & Wei‐Chiang Hong. (2024). A Priority Based Energy-Efficient Metaheuristic Routing Approach for Smart Healthcare System (SHS). IEEE Access. 12. 85694–85708. 6 indexed citations
9.
Li, Mingwei, Xiang‐Yang Li, Yutian Wang, Zhongyi Yang, & Wei‐Chiang Hong. (2024). Chaos crossover quantum attraction-repulsion optimization algorithm. Swarm and Evolutionary Computation. 92. 101811–101811. 2 indexed citations
10.
Fan, Guo‐Feng, et al.. (2024). Uncertainty analysis of photovoltaic power generation system and intelligent coupling prediction. Renewable Energy. 234. 121174–121174. 8 indexed citations
11.
Fan, Guo‐Feng, et al.. (2023). A new intelligent hybrid forecasting method for power load considering uncertainty. Knowledge-Based Systems. 280. 111034–111034. 20 indexed citations
12.
Singh, Dilbag, et al.. (2023). Adoption of Blockchain Technology in Healthcare: Challenges, Solutions, and Comparisons. Applied Sciences. 13(4). 2380–2380. 13 indexed citations
13.
Fan, Guo‐Feng, et al.. (2023). Use of weighted local constant method to short-term forecasting of electric load in cities at weekends. Electric Power Systems Research. 221. 109464–109464. 4 indexed citations
15.
Gill, Nasib Singh, et al.. (2023). Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment. Sustainability. 15(3). 2204–2204. 26 indexed citations
16.
Fan, Guo‐Feng, et al.. (2021). Application of COEMD-S-SVR model in tourism demand forecasting and economic behavior analysis: The case of Sanya City. Journal of the Operational Research Society. 73(7). 1474–1486. 5 indexed citations
17.
Fan, Guo‐Feng, et al.. (2020). Fault detection in switching process of a substation using the SARIMA–SPC model. Scientific Reports. 10(1). 11417–11417. 5 indexed citations
18.
Umbarkar, A. J., Madhuri S. Joshi, & Wei‐Chiang Hong. (2016). Comparative study of diversity based parallel dual population genetic algorithm for unconstrained function optimisations. International Journal of Bio-Inspired Computation. 8(4). 248–263. 3 indexed citations
19.
Hong, Wei‐Chiang, et al.. (2009). Application of SVR with improved ant colony optimization algorithms in exchange rate forecasting. Control and Cybernetics. 38(3). 863–891. 46 indexed citations
20.
Hong, Wei‐Chiang. (2008). Competitiveness in the tourism sector : a comprehensive approach from economic and management points. Physica-Verlag eBooks. 38 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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