World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
13451
World Ranking
5260
National Ranking
39

Ching-Hsue Cheng 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 Ching-Hsue Cheng 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: 220 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Ching-Hsue Cheng 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 Ching-Hsue Cheng 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: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 1999 - Fellow of the American Statistical Association (ASA)

Overview

Ching-Hsue Cheng is affiliated with the National Yunlin University of Science and Technology in Taiwan. Their research primarily spans the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Management Science and Operations Research, Statistics and Probability, Computational Theory and Mathematics, and Information Systems.

Their work addresses a range of topics, particularly in techniques for handling imbalanced data classification, rough sets and fuzzy logic, and applications of artificial intelligence in healthcare. Additional research interests include stock market forecasting methods, financial distress and bankruptcy prediction, forecasting techniques, and energy load and power forecasting.

Cheng has published extensively across various venues, with frequent contributions to Symmetry, Computers in Biology and Medicine, Soft Computing, Multimedia Tools and Applications, and PLoS ONE.

  • A multiple combined method for rebalancing medical data with class imbalances, 2021, Computers in Biology and Medicine
  • A financial statement fraud model based on synthesized attribute selection and a dataset with missing values and imbalanced classes, 2021, Applied Soft Computing
  • A novel weighted distance threshold method for handling medical missing values, 2020, Computers in Biology and Medicine
  • A Time Series Model Based on Deep Learning and Integrated Indicator Selection Method for Forecasting Stock Prices and Evaluating Trading Profits, 2022, Systems
  • A novel clustering-based purity and distance imputation for handling medical data with missing values, 2021, Soft Computing

Collaborations have occurred frequently with several scholars, including Ming-Chi Tsai, Shu-Fen Huang, Jun-He Yang, Yunchun Wang, and Jing-Rong Chang.

In recognition of professional contributions, Cheng was awarded the title of Fellow of the American Statistical Association (ASA) in 1999.

Best Publications

  • A new approach for ranking fuzzy numbers by distance method

    Ching-Hsue Cheng

  • EVALUATING THE BEST MAIN BATTLE TANK USING FUZZY DECISION THEORY WITH LINGUISTIC CRITERIA EVALUATION

    Ching-Hsue Cheng;Yin Lin

  • Fuzzy hierarchical TOPSIS for supplier selection

    Jia-Wen Wang;Ching-Hsue Cheng;Kun-Cheng Huang

  • Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function

    Ching-Hsue Cheng

  • Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly

    Ming-Hung Shu;Ching-Hsue Cheng;Jing-Rong Chang

  • Classifying the segmentation of customer value via RFM model and RS theory

    Ching-Hsue Cheng;You-Shyang Chen

  • Evaluating attack helicopters by AHP based on linguistic variable weight

    Ching-Hsue Cheng;Kuo-Lung Yang;Chia-Lung Hwang

  • Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight

    Don-Lin Mon;Ching-Hsue Cheng;Jiann-Chern Lin

  • Fuzzy time-series based on adaptive expectation model for TAIEX forecasting

    Ching-Hsue Cheng;Tai-Liang Chen;Hia Jong Teoh;Chen-Han Chiang

  • Evaluating weapon systems using ranking fuzzy numbers

    Ching-Hsue Cheng

  • Fuzzy system reliability analysis by interval of confidence

    Ching-Hsue Cheng;Don-Lin Mon

  • A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting

    Ching-Hsue Cheng;Tai-Liang Chen;Liang-Ying Wei

  • Multi-attribute fuzzy time series method based on fuzzy clustering

    Ching-Hsue Cheng;Guang-Wei Cheng;Jia-Wen Wang

  • Evaluating the risk of failure using the fuzzy OWA and DEMATEL method

    Kuei-Hu Chang;Ching-Hsue Cheng

  • Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost

    Ching-Hsue Cheng;Jing-Rong Chang;Che-An Yeh

  • A risk assessment methodology using intuitionistic fuzzy set in FMEA

    Kuei-Hu Chang;Ching-Hsue Cheng

  • Fuzzy system reliability analysis for components with different membership functions

    Don-Lin Mon;Ching-Hsue Cheng

  • Selecting IS personnel use fuzzy GDSS based on metric distance method

    Ling-Show Chen;Ling-Show Chen;Ching-Hsue Cheng

  • Fuzzy time-series based on Fibonacci sequence for stock price forecasting

    Tai-Liang Chen;Ching-Hsue Cheng;Hia Jong Teoh;Hia Jong Teoh

  • Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets

    Hia Jong Teoh;Ching-Hsue Cheng;Hsing-Hui Chu;Jr-Shian Chen

Frequent Co-Authors

Arun Kumar Sangaiah
Arun Kumar Sangaiah National Yunlin University of Science and Technology
Shu-Hsien Liao
Shu-Hsien Liao Tamkang University

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Related Online Degrees & Career Pathways

If you’re considering studying Computer Science in the USA, there are several related fields and flexible online degree options that can complement or expand your expertise. Many students now opt for specialized programs that merge Computer Science with areas like engineering, physics, or data analytics to enhance their career prospects.

For example, pursuing an online environmental engineering degree or an online degree in mechanical engineering can be attractive pathways for those interested in applying computational skills to solve engineering challenges. These online programs offer flexibility and can make it easier to balance studies with work or other commitments.

Additionally, a physics degree online teaches foundational analytical skills that are directly relevant to problem-solving in tech roles. For students specifically drawn to data and analytics, an affordable data science degree can open doors to high-demand roles in industries like finance, healthcare, and technology.

Exploring these online degree options can diversify your skill set and boost your employability in the fast-evolving field of Computer Science.

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