World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
12154
World Ranking
3663
National Ranking
25

Wei-Chiang Hong publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Wei-Chiang Hong sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 183 publications — 40th percentile

40% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Wei-Chiang Hong D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Wei-Chiang Hong sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 58 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Wei-Chiang Hong is affiliated with Asia Eastern University of Science and Technology in Taiwan. Their research activity is concentrated in the fields of Engineering and Computer Science, with a substantial body of work in Electrical and Electronic Engineering, Artificial Intelligence, Management Science and Operations Research, Control and Systems Engineering, and Computer Vision and Pattern Recognition.

Their main research topics encompass Energy Load and Power Forecasting, Grey System Theory Applications, Machine Fault Diagnosis Techniques, Image and Signal Denoising Methods, Advanced Algorithms and Applications, Maritime Ports and Logistics, as well as IoT and Edge/Fog Computing.

Frequently collaborating co-authors include Guo-Feng Fan, Li-Ling Peng, Pradeep Kumar Singh, Hsin-Pou Huang, and Yi-Hsuan Yeh.

Wei-Chiang Hong has published extensively in venues such as IEEE Access, Applied Soft Computing, Sustainability, Nonlinear Dynamics, and Sensors.

Selected recent papers include:

  • Internet of Things: Evolution, Concerns and Security Challenges, 2021, Sensors
  • Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads, 2021, Knowledge-Based Systems
  • Applications of random forest in multivariable response surface for short-term load forecasting, 2022, International Journal of Electrical Power & Energy Systems
  • A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems, 2020, IEEE Access
  • A hybrid approach for forecasting ship motion using CNN-GRU-AM and GCWOA, 2021, Applied Soft Computing

Best Publications

  • Consensus models for AHP group decision making under row geometric mean prioritization method

    Yucheng Dong;Guiqing Zhang;Wei-Chiang Hong;Yinfeng Xu

  • Support vector machines with simulated annealing algorithms in electricity load forecasting

    Ping-Feng Pai;Wei-Chiang Hong

  • Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms

    Ping-Feng Pai;Wei-Chiang Hong

  • Electric load forecasting by support vector model

    Wei-Chiang Hong

  • Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

    Sudeep Tanwar;Qasim Bhatia;Pruthvi Patel;Aparna Kumari

  • Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model

    Wei-Chiang Hong

  • Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm

    Wei-Chiang Hong

  • SVR with hybrid chaotic genetic algorithms for tourism demand forecasting

    Wei-Chiang Hong;Yucheng Dong;Li-Yueh Chen;Shih-Yung Wei

  • Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm

    Rong Chen;Chang-Yong Liang;Wei-Chiang Hong;Dong-Xiao Gu

  • Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm

    Wei-Chiang Hong

  • Rainfall forecasting by technological machine learning models

    Wei-Chiang Hong

  • Short term load forecasting based on feature extraction and improved general regression neural network model

    Yi Liang;Dongxiao Niu;Wei-Chiang Hong

  • Software reliability forecasting by support vector machines with simulated annealing algorithms

    Ping-Feng Pai;Wei-Chiang Hong

  • Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression

    Guo-Feng Fan;Li-Ling Peng;Wei-Chiang Hong;Fan Sun

  • Internet of Things: Evolution, Concerns and Security Challenges

    Parushi Malhotra;Yashwant Singh;Pooja Anand;Deep Kumar Bangotra

  • Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm

    Wei-Chiang Hong;Yucheng Dong;Wen Yu Zhang;Li-Yueh Chen

  • Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm

    Zichen Zhang;Wei-Chiang Hong

  • Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads

    Zichen Zhang;Wei-Chiang Hong

  • Forecasting urban traffic flow by SVR with continuous ACO

    Wei-Chiang Hong;Yucheng Dong;Feifeng Zheng;Chien-Yuan Lai

  • Linguistic Computational Model Based on 2-Tuples and Intervals

    Yucheng Dong;Guiqing Zhang;Wei-Chiang Hong;Shui Yu

  • Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model

    Guo-Feng Fan;Li-Ling Peng;Wei-Chiang Hong

  • Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting

    Wei-Chiang Hong;Wei-Chiang Hong

Frequent Co-Authors

Yucheng Dong
Yucheng Dong Sichuan University
Shui Yu
Shui Yu University of Technology Sydney
Bijaya Ketan Panigrahi
Bijaya Ketan Panigrahi Indian Institute of Technology Delhi
Sudeep Tanwar
Sudeep Tanwar Marwadi University
Bharat Bhargava
Bharat Bhargava Purdue University West Lafayette

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science doesn't always mean pursuing a traditional four-year degree. If you're seeking a faster track, the shortest online masters degree programs can help you accelerate your education and enter the workforce more quickly. These programs are designed for those who want to enhance their credentials without spending years in school.

If value and career impact are your priorities, identifying graduate degrees that are worth it can help guide you toward high-demand fields like data science, artificial intelligence, and cybersecurity. Many of these degrees are now offered entirely online, making them more accessible than ever before.

For those just starting their academic journey, an associate degree online in computer science or IT can be an affordable and flexible way to gain foundational skills. These programs offer a pathway to entry-level jobs or further education.

Additionally, aspiring students often look for cheap online degrees fast to minimize debt and start their careers sooner. With so many accredited online programs available, it’s easier than ever to find flexible and budget-friendly options that suit your goals.

Best Scientists Citing Wei-Chiang Hong

Trending Scientists

Recently Published Articles