World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
57
Citations
14698
World Ranking
2606
National Ranking
42

Noureddine Zerhouni publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Noureddine Zerhouni sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 356 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Noureddine Zerhouni D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Noureddine Zerhouni sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Noureddine Zerhouni is affiliated with the Centre national de la recherche scientifique (CNRS) in France. Their research spans multiple fields with a primary focus on engineering and computer science. They have contributed extensively to subfields including artificial intelligence, control and systems engineering, industrial and manufacturing engineering, electrical and electronic engineering, and mechanical engineering.

The scientist's work covers several main topics such as:

  • Machine Fault Diagnosis Techniques
  • Fuel Cells and Related Materials
  • Fault Detection and Control Systems
  • Advanced Battery Technologies Research
  • AI in Cancer Detection
  • Reliability and Maintenance Optimization
  • Industrial Vision Systems and Defect Detection

Notable recent papers authored or co-authored by Zerhouni include:

  • Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation, 2020, International Journal of Prognostics and Health Management
  • A CNN-based methodology for breast cancer diagnosis using thermal images, 2020, Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization
  • A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction, 2020, International Journal of Hydrogen Energy
  • A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell, 2022, Applied Energy
  • Hybrid fuel cell system degradation modeling methods: A comprehensive review, 2021, Journal of Power Sources

Zerhouni frequently publishes in venues such as:

  • International Journal of Prognostics and Health Management
  • PHM Society European Conference
  • arXiv (Cornell University)
  • International Journal of Hydrogen Energy
  • Applied Energy

Collaborations have been an important part of Zerhouni's research. Frequent co-authors include Zeina Al Masry, Labib Sadek Terrissa, Christophe Varnier, Christine Devalland, and Safa Meraghni, reflecting a network of joint work spanning multiple publications.

Best Publications

  • PRONOSTIA : An experimental platform for bearings accelerated degradation tests.

    Patrick Nectoux;Rafael Gouriveau;Kamal Medjaher;Emmanuel Ramasso

  • Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression

    Abdenour Soualhi;Kamal Medjaher;Noureddine Zerhouni

  • A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

    D. A. Tobon-Mejia;K. Medjaher;N. Zerhouni;G. Tripot

  • Health assessment and life prediction of cutting tools based on support vector regression

    T. Benkedjouh;K. Medjaher;N. Zerhouni;S. Rechak

  • Direct Remaining Useful Life Estimation Based on Support Vector Regression

    Racha Khelif;Brigitte Chebel-Morello;Simon Malinowski;Emna Laajili

  • Remaining useful life estimation based on nonlinear feature reduction and support vector regression

    Tarak Benkedjouh;Kamal Medjaher;Noureddine Zerhouni;Saïd Rechak

  • Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction

    A. Mosallam;K. Medjaher;N. Zerhouni

  • Particle filter-based prognostics: Review, discussion and perspectives

    Marine Jouin;Rafael Gouriveau;Daniel Hissel;Marie-Cécile Péra

  • Remaining Useful Life Estimation of Critical Components With Application to Bearings

    K. Medjaher;D. A. Tobon-Mejia;N. Zerhouni

  • Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics.

    Kamran Javed;Rafael Gouriveau;Noureddine Zerhouni;Patrick Nectoux

  • Degradations analysis and aging modeling for health assessment and prognostics of PEMFC

    Marine Jouin;Rafael Gouriveau;Daniel Hissel;Marie-Cécile Péra

  • Prognostics of PEM fuel cell in a particle filtering framework

    Marine Jouin;Rafael Gouriveau;Daniel Hissel;Marie-Cécile Péra

  • CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

    Diego Tobon-Mejia;Diego Tobon-Mejia;Kamal Medjaher;Noureddine Zerhouni

  • Review on health-conscious energy management strategies for fuel cell hybrid electric vehicles: Degradation models and strategies

    Meiling Yue;Samir Jemei;Rafael Gouriveau;Noureddine Zerhouni

  • Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation

    Vepa Atamuradov;Kamal Medjaher;Pierre Dersin;Benjamin Lamoureux

  • Prognostics and Health Management of PEMFC – State of the art and remaining challenges

    Marine Jouin;Rafael Gouriveau;Daniel Hissel;Marie-Cécile Péra

  • State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels

    Kamran Javed;Rafael Gouriveau;Noureddine Zerhouni

  • A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering

    Kamran Javed;Rafael Gouriveau;Noureddine Zerhouni

  • A CNN-based methodology for breast cancer diagnosis using thermal images

    Juan Zuluaga-Gomez;Zeina Al Masry;Khaled Benaggoune;Safa Meraghni

  • Deep Learning in the Biomedical Applications: Recent and Future Status

    Ryad Zemouri;Noureddine Zerhouni;Daniel Racoceanu

  • Review of prognostic problem in condition-based maintenance

    Otilia Elena Dragomir;Rafael Gouriveau;Florin Dragomir;Eugenia Minca

  • Recurrent radial basis function network for time-series prediction

    Ryad Zemouri;Daniel Racoceanu;Noureddine Zerhouni

Frequent Co-Authors

Rafael Gouriveau
Rafael Gouriveau Centre national de la recherche scientifique, CNRS
Kamal Medjaher
Kamal Medjaher Federal University of Toulouse Midi-Pyrénées
Daniel Hissel
Daniel Hissel University of Franche-Comté
Marie-Cécile Péra
Marie-Cécile Péra Centre national de la recherche scientifique, CNRS
Farhat Fnaiech
Farhat Fnaiech Tunis University

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