World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
8437
World Ranking
10050
National Ranking
25

Fadi Thabtah 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 Fadi Thabtah 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: 133 publications — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Fadi Thabtah 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 Fadi Thabtah 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Fadi Thabtah is affiliated with Manukau Institute of Technology in New Zealand. Their research spans the fields of computer science and medicine, with a focus on subfields including artificial intelligence, psychiatry and mental health, cognitive neuroscience, health information management, and education. The scientist's publication record features 32 works in computer science and 22 in medicine.

Their work addresses a range of topics, notably dementia and cognitive impairment research, autism spectrum disorder research, machine learning in healthcare, and artificial intelligence applied to healthcare. Other significant areas include child development and digital technology, imbalanced data classification techniques, and COVID-19 diagnosis using AI.

Frequent publication venues for their research include:

  • Health and Technology
  • Journal of Information & Knowledge Management
  • Intelligent Decision Technologies
  • Technology and Health Care
  • Health Information Science and Systems

Fadi Thabtah has frequently collaborated with researchers such as Firuz Kamalov, David Peebles, Robinson Spencer, Neda Abdelhamid, and Joan Lu. These collaborations have contributed to both the depth and breadth of their research output.

Some of the recent academic papers authored or co-authored by Fadi Thabtah include:

  • Least Loss: A simplified filter method for feature selection, 2020, Information Sciences
  • Dementia medical screening using mobile applications: A systematic review with a new mapping model, 2020, Journal of Biomedical Informatics

Other influential papers in related work by co-authors or contemporaries in the field include:

  • Exploring feature selection and classification methods for predicting heart disease, 2020, Digital Health
  • Autism AI: a New Autism Screening System Based on Artificial Intelligence, 2020, Cognitive Computation
  • Feature Selection in Imbalanced Data, 2022, Annals of Data Science

Best Publications

  • Data imbalance in classification: Experimental evaluation

    Fadi A. Thabtah;Suhel Hammoud;Firuz Kamalov;Amanda H. Gonsalves

  • Phishing detection based Associative Classification data mining

    Neda Abdelhamid;Aladdin Ayesh;Fadi A. Thabtah

  • Predicting phishing websites based on self-structuring neural network

    Rami M. Mohammad;Fadi Thabtah;Lee Mccluskey

  • A review of associative classification mining

    Fadi Thabtah

  • MMAC: a new multi-class, multi-label associative classification approach

    F.A. Thabtah;P. Cowling;Yonghong Peng

  • A machine learning framework for sport result prediction

    Rory P. Bunker;Fadi Thabtah

  • MCAR: multi-class classification based on association rule

    F. Thabtah;P. Cowling;Y. Peng

  • Machine learning in autistic spectrum disorder behavioral research: A review and ways forward

    Fadi Thabtah

  • Intelligent rule-based phishing websites classification

    Rami Mustafa A. Mohammad;Fadi A. Thabtah;Lee McCluskey

  • Intelligent phishing detection system for e-banking using fuzzy data mining

    Maher Aburrous;M. A. Hossain;Keshav Dahal;Fadi Thabtah

  • A new machine learning model based on induction of rules for autism detection.

    Fadi A. Thabtah;David Peebles

  • An assessment of features related to phishing websites using an automated technique

    R. M. Mohammad;F. Thabtah;L. McCluskey

  • Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment

    Fadi Thabtah

  • Exploring feature selection and classification methods for predicting heart disease.

    Robinson Spencer;Fadi Thabtah;Neda Abdelhamid;Michael Thompson

  • A new computational intelligence approach to detect autistic features for autism screening.

    Fadi A. Thabtah;Firuz Kamalov;Khairan Rajab

  • Tutorial and critical analysis of phishing websites methods

    Rami M. Mohammad;Fadi Thabtah;Lee McCluskey

  • An accessible and efficient autism screening method for behavioural data and predictive analyses.

    Fadi A. Thabtah

  • Predicting Phishing Websites Using Classification Mining Techniques with Experimental Case Studies

    Maher Aburrous;M. A. Hossain;Keshav Dahal;Fadi Thabtah

  • A machine learning autism classification based on logistic regression analysis

    Fadi A. Thabtah;Neda Abdelhamid;David Peebles

  • Prediction of Coronary Heart Disease using Machine Learning: An Experimental Analysis

    Amanda H. Gonsalves;Fadi Thabtah;Rami Mustafa A. Mohammad;Gurpreet Singh

  • A recent review of conventional vs. automated cybersecurity anti-phishing techniques

    Issa Qabajeh;Fadi A. Thabtah;Francisco Chiclana

  • Naïve Bayesian Based on Chi Square to Categorize Arabic Data

    Fadi Thabtah;Mohammad Ali

Frequent Co-Authors

Peter I. Cowling
Peter I. Cowling Queen Mary University of London
Francisco Chiclana
Francisco Chiclana De Montfort University
Keshav Dahal
Keshav Dahal University of the West of Scotland

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