World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
10572
World Ranking
7447
National Ranking
444

Mohamed Medhat Gaber 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 Mohamed Medhat Gaber 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: 254 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

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

Mohamed Medhat Gaber 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 Mohamed Medhat Gaber 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Mohamed Medhat Gaber is affiliated with Birmingham City University in the United Kingdom. Their primary research interests span across the fields of Computer Science and Medicine, with a significant focus on Artificial Intelligence and its applications in medical imaging and diagnosis.

Their research contributions include work in the subfields of Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Signal Processing, and Oncology. Key topics addressed by their work include AI in cancer detection, COVID-19 diagnosis using AI, anomaly detection techniques and applications, radiomics and machine learning in medical imaging, data stream mining techniques, explainable artificial intelligence (XAI), and digital imaging for blood diseases.

Recent publications by Mohamed Medhat Gaber include the following:

  • Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network, 2020, Applied Intelligence
  • DeTrac: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks, 2020, IEEE Access
  • CHIRPS: Explaining random forest classification, 2020, Artificial Intelligence Review
  • Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences, 2020, BMC Medical Informatics and Decision Making
  • 4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection, 2021, PubMed Central

The scientist has frequently published in venues such as arXiv (Cornell University), IEEE Access, International Journal of Machine Learning and Cybernetics, Research Square, and Applied Intelligence.

Collaborative work includes frequent co-authorship with researchers like Mohammed M. Abdelsamea, Asmaa Abbas, Hansi Hettiarachchi, R. Muhammad Atif Azad, and Shadi Basurra.

Mohamed Medhat Gaber has contributed to book publications under Springer Nature, notably including titles such as "Federated Learning Systems" (2021) and proceedings for the "International Conference on Artificial Intelligence Science and Applications (CAISA)" (2023).

Best Publications

  • Mining data streams: a review

    Mohamed Medhat Gaber;Arkady Zaslavsky;Shonali Krishnaswamy

  • Imitation Learning: A Survey of Learning Methods

    Ahmed Hussein;Mohamed Medhat Gaber;Eyad Elyan;Chrisina Jayne

  • Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.

    Asmaa Abbas;Mohammed M. Abdelsamea;Mohammed M. Abdelsamea;Mohamed Medhat Gaber

  • Random forests: from early developments to recent advancements

    Khaled Fawagreh;Mohamed Medhat Gaber;Eyad Elyan

  • Knowledge discovery from data streams

    João Gama;Auroop Ganguly;Olufemi Omitaomu;Raju Vatsavai

  • Learning from Data Streams: Processing Techniques in Sensor Networks

    Joao Gama;Mohamed Medhat Gaber

  • A Survey of Data Mining Techniques for Social Media Analysis

    Mariam Adedoyin-Olowe;Mohamed Medhat Gaber;Frederic T. Stahl

  • SA-E: Sentiment Analysis for Education

    Nabeela Altrabsheh;M. Gaber;Mihaela Cocea

  • Advances in data stream mining

    Mohamed Medhat Gaber

  • Adaptive mobile activity recognition system with evolving data streams

    Zahraa Said Abdallah;Mohamed Medhat Gaber;Bala Srinivasan;Shonali Krishnaswamy

  • A survey of classification methods in data streams

    Mohamed Medhat Gaber;Arkady B. Zaslavsky;Shonali Krishnaswamy

  • Next challenges for adaptive learning systems

    Indre Zliobaite;Albert Bifet;Mohamed Gaber;Bogdan Gabrys

  • DeTrac: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks

    Asmaa Abbas;Mohammed M. Abdelsamea;Mohamed Medhat Gaber

  • Edge Machine Learning: Enabling Smart Internet of Things Applications

    Mahmut Taha Yazici;Shadi Basurra;Mohamed Medhat Gaber

  • A genetic algorithm approach to optimising random forests applied to class engineered data

    Eyad Elyan;Mohamed Medhat Gaber

  • Reasoning about Context in Uncertain Pervasive Computing Environments

    Pari Delir Haghighi;Shonali Krishnaswamy;Arkady Zaslavsky;Mohamed Medhat Gaber

  • CHIRPS: Explaining random forest classification

    Julian Hatwell;Mohamed Medhat Gaber;R. Muhammad Atif Azad

  • Activity Recognition with Evolving Data Streams: A Review

    Zahraa S. Abdallah;Mohamed Medhat Gaber;Bala Srinivasan;Shonali Krishnaswamy

  • A rule dynamics approach to event detection in Twitter with its application to sports and politics

    Mariam Adedoyin-Olowe;Mohamed Medhat Gaber;Carlos M. Dancausa;Frederic Stahl

  • A framework for resource-aware knowledge discovery in data streams: a holistic approach with its application to clustering

    Mohamed Medhat Gaber;Philip S. Yu

  • On-board Mining of Data Streams in Sensor Networks

    Mohamed Medhat Gaber;Shonali Krishnaswamy;Arkady Zaslavsky

  • Knowledge Discovery from Sensor Data

    Auroop R. Ganguly;Joao Gama;Olufemi A. Omitaomu;Mohamed Medhat Gaber

  • INTELLIGENT DATA ANALYSIS

    Joao Gama;Auroop R Ganguly;Olufemi A Omitaomu;Raju Vatsavai

Frequent Co-Authors

Shonali Krishnaswamy
Shonali Krishnaswamy Monash University
Arkady Zaslavsky
Arkady Zaslavsky Deakin University
João Gama
João Gama University of Porto
Alfredo Cuzzocrea
Alfredo Cuzzocrea University of Calabria
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Mykola Pechenizkiy
Mykola Pechenizkiy Eindhoven University of Technology
Bala Srinivasan
Bala Srinivasan Monash University
Seng Wai Loke
Seng Wai Loke Deakin University
Nitesh V. Chawla
Nitesh V. Chawla University of Notre Dame
Bogdan Gabrys
Bogdan Gabrys University of Technology Sydney

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