World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
5921
World Ranking
12976
National Ranking
389

Kate A. Smith 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 Kate A. Smith 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: 143 publications — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Kate A. Smith 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 Kate A. Smith 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Kate A. Smith is affiliated with Monash University in Australia. Their research primarily spans the fields of Computer Science and Engineering, with notable focus on Artificial Intelligence, Industrial and Manufacturing Engineering, Computational Theory and Mathematics, Civil and Structural Engineering, and Management Science and Operations Research.

Their body of work covers several specific topics including:

  • Machine Learning and Data Classification
  • Water Systems and Optimization
  • Advanced Multi-Objective Optimization Algorithms
  • Anomaly Detection Techniques and Applications
  • Data Stream Mining Techniques
  • Urban Stormwater Management Solutions
  • Machine Learning and Algorithms

Frequent co-authors collaborating with Kate A. Smith include:

  • Mario Andrés Muñoz
  • Shuming Liu
  • Ana Carolina Lorena
  • Tim D. Fletcher
  • Nicolau Andrés-Thió

Smith has published extensively in several academic venues, with repeated contributions to:

  • arXiv (Cornell University)
  • Computers & Operations Research
  • INFORMS Journal on Computing
  • European Journal of Operational Research
  • Journal of Water Resources Planning and Management

Among recent papers, Smith's research includes:

  • A transformation technique for the clustered generalized traveling salesman problem with applications to logistics (2020, European Journal of Operational Research)
  • Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management (2020, Computers & Operations Research)
  • Leakage Detection in Water Distribution Systems Based on Time-Frequency Convolutional Neural Network (2020, Journal of Water Resources Planning and Management)
  • Burst Detection in District Metering Areas Using Deep Learning Method (2020, Journal of Water Resources Planning and Management)
  • China's enhanced urban wastewater treatment increases greenhouse gas emissions and regional inequality (2022, Water Research)

Best Publications

  • Characteristic-Based Clustering for Time Series Data

    Xiaozhe Wang;Kate Smith;Rob Hyndman

  • Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research

    Kate A. Smith

  • On learning algorithm selection for classification

    Shawkat Ali;Kate A. Smith

  • Neural networks in business: techniques and applications for the operations researcher

    Kate A. Smith;Jatinder N.D. Gupta

  • On chaotic simulated annealing

    L. Wang;K. Smith

  • Static and dynamic channel assignment using neural networks

    K. Smith;M. Palaniswami

  • Web page clustering using a self-organizing map of user navigation patterns

    Kate A. Smith;Alan Ng

  • An Analysis of Customer Retention and Insurance Claim Patterns using Data Mining: A Case Study

    Kate A Smith;Robert J Willis;Malcolm Brooks

  • Neural techniques for combinatorial optimization with applications

    K. Smith;M. Palaniswami;M. Krishnamoorthy

  • Neural Networks in Business: Techniques and Applications

    Kate A. Smith;Jatinder N. D. Gupta

  • ODAM: An optimized distributed association rule mining algorithm

    M.Z. Ashrafi;D. Taniar;K. Smith

  • Parallel Fuzzy c-Means Clustering for Large Data Sets

    Terence Kwok;Kate A. Smith;Sebastián Lozano;David Taniar

  • Intelligent web traffic mining and analysis

    Xiaozhe Wang;Ajith Abraham;Kate A. Smith

  • Experimental analysis of chaotic neural network models for combinatorial optimization under a unifying framework

    T. Kwok;K. A. Smith

  • A unified framework for chaotic neural-network approaches to combinatorial optimization

    T. Kwok;K.A. Smith

  • Hopfield neural networks for timetabling: formulations, methods, and comparative results

    Kate A. Smith;David Abramson;David Duke

  • Neural versus traditional approaches to the location of interacting hub facilities

    Kate Smith;M Krishnamoorthy;Marimuthu Palaniswami

  • Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry

    Ai Cheo Yeo;Kate A. Smith;Robert J. Willis;Malcolm Brooks

  • Manufacturing cell formation using a new self-organizing neural network

    Fernando Guerrero;Sebastian Lozano;Kate A. Smith;David Canca

  • Traditional heuristic versus Hopfield neural network approaches to a car sequencing problem

    Kate Smith;Kate Smith;M. Palaniswami;M. Krishnamoorthy

  • Network and information security: a computational intelligence approach special issue of journal of network and computer applications

    Ajith Abraham;Kate Smith;Ravi Jain;Lakhmi Jain

Frequent Co-Authors

David Taniar
David Taniar Monash University
Sebastián Lozano
Sebastián Lozano University of Seville
Marimuthu Palaniswami
Marimuthu Palaniswami University of Melbourne
Chung-Hsing Yeh
Chung-Hsing Yeh Monash University
Ajith Abraham
Ajith Abraham Sai University
Glen B. Deacon
Glen B. Deacon Monash University
Mohan Krishnamoorthy
Mohan Krishnamoorthy University of Queensland
Lipo Wang
Lipo Wang Nanyang Technological University
Jatinder N. D. Gupta
Jatinder N. D. Gupta University of Alabama in Huntsville
David Abramson
David Abramson University of Queensland

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