World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
5197
World Ranking
13503
National Ranking
215

Mikhail Kanevski 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 Mikhail Kanevski 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: 222 publications — 54th percentile

54% of scientists in this discipline score the same or lower.

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

Mikhail Kanevski 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 Mikhail Kanevski 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: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Overview

Mikhail Kanevski is affiliated with the University of Lausanne in Switzerland. Their research spans multiple fields including Computer Science and Engineering, with a particular focus on subfields such as Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing, Economics and Econometrics, and Aerospace Engineering.

Their work addresses several main topics including:

  • Time Series Analysis and Forecasting
  • Complex Systems and Time Series Analysis
  • Energy Load and Power Forecasting
  • Wind Energy Research and Development
  • Electric Power System Optimization
  • Machine Learning and Extreme Learning Machines (ELM)
  • Machine Learning and Algorithms

Kanevski has collaborated frequently with several coauthors, including Federico Amato, Fabian Guignard, Mohamed Laib, Alina Walch, and Nahid Mohajeri.

Their recent publications demonstrate a range of research outputs across various scientific journals:

  • "Analysis of air pollution time series using complexity-invariant distance and information measures," 2020, Physica A Statistical Mechanics and its Applications
  • "Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential," 2022, Stochastic Environmental Research and Risk Assessment
  • "Advanced Analysis of Temporal Data Using Fisher-Shannon Information: Theoretical Development and Application in Geosciences," 2020, Frontiers in Earth Science
  • "Uncertainty quantification in extreme learning machine: Analytical developments, variance estimates and confidence intervals," 2021, Neurocomputing
  • "Unsupervised learning of Swiss population spatial distribution," 2021, PLoS ONE

Kanevski's work has been published in several venues, with frequent contributions to arXiv (Cornell University), Frontiers in Earth Science, Stochastic Environmental Research and Risk Assessment, Physica A Statistical Mechanics and its Applications, and Neurocomputing.

Best Publications

  • A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

    Devis Tuia;Michele Volpi;Loris Copa;Mikhail Kanevski

  • Active Learning Methods for Remote Sensing Image Classification

    D. Tuia;F. Ratle;F. Pacifici;M.F. Kanevski

  • Supervised change detection in VHR images using contextual information and support vector machines

    Michele Volpi;Devis Tuia;Francesca Bovolo;Mikhail F. Kanevski

  • Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

    Natan Micheletti;Loris Foresti;Sylvain Robert;Michael Leuenberger

  • Machine Learning for Spatial Environmental Data: Theory, Applications, and Software

    Mikhail Kanevski;Alexei Pozdnoukhov;Vadim Timonin

  • Learning Relevant Image Features With Multiple-Kernel Classification

    Devis Tuia;Gustavo Camps-Valls;Giona Matasci;Mikhail Kanevski

  • Semisupervised Transfer Component Analysis for Domain Adaptation in Remote Sensing Image Classification

    Giona Matasci;Michele Volpi;Mikhail Kanevski;Lorenzo Bruzzone

  • Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines

    D. Tuia;F. Pacifici;M. Kanevski;W.J. Emery

  • Environmental data mining and modeling based on machine learning algorithms and geostatistics

    Mikhail F. Kanevski;Roman Parkin;Aleksey Pozdnukhov;Aleksey Pozdnukhov;Vadim Timonin

  • Wildfire susceptibility mapping: deterministic vs. stochastic approaches

    Michael Leuenberger;Joana Parente;Marj Tonini;Mário Gonzalez Pereira;Mário Gonzalez Pereira

  • Unsupervised Change Detection With Kernels

    Michele Volpi;Devis Tuia;Gustavo Camps-Valls;Mikhail Kanevski

  • Data-driven mapping of the potential mountain permafrost distribution

    Nicola Deluigi;Christophe Lambiel;Mikhail Kanevski

  • SVM-Based Boosting of Active Learning Strategies for Efficient Domain Adaptation

    G. Matasci;D. Tuia;M. Kanevski

  • Scan statistics analysis of forest fire clusters

    Devis Tuia;Fréderic Ratle;Rosa Lasaponara;Luciano Telesca

  • Extreme Learning Machines for spatial environmental data

    Michael Leuenberger;Mikhail Kanevski

  • Fuzzy definition of Rural Urban Interface: An application based on land use change scenarios in Portugal

    Federico Amato;Marj Tonini;Beniamino Murgante;Mikhail F. Kanevski

  • Spatio-temporal avalanche forecasting with Support Vector Machines

    Alexei Pozdnoukhov;Giona Matasci;Mikhail Kanevski;Ross S Purves

  • Support-Based Implementation of Bayesian Data Fusion for Spatial Enhancement: Applications to ASTER Thermal Images

    D. Fasbender;D. Tuia;P. Bogaert;M. Kanevski

  • Applying machine learning methods to avalanche forecasting

    Alexei Pozdnoukhov;Ross S Purves;Mikhail Kanevski

  • Advanced Mapping of Environmental Data/Geostatistics, Machine Learning and Bayesian Maximum Entropy

    Mikhail Kanevski

  • Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network

    Mohamed Laib;Luciano Telesca;Mikhail Kanevski

  • A novel framework for spatio-temporal prediction of environmental data using deep learning

    Federico Amato;Fabian Guignard;Sylvain Robert;Mikhail Kanevski

Frequent Co-Authors

Devis Tuia
Devis Tuia École Polytechnique Fédérale de Lausanne
Luciano Telesca
Luciano Telesca National Research Council (CNR)
Marco Conedera
Marco Conedera Swiss Federal Institute for Forest, Snow and Landscape Research
Stéphane Canu
Stéphane Canu Institut National des Sciences Appliquées de Rouen
Lorenzo Bruzzone
Lorenzo Bruzzone University of Trento
William J. Emery
William J. Emery University of Colorado Boulder
Ross S. Purves
Ross S. Purves University of Zurich
Jordi Muñoz-Marí
Jordi Muñoz-Marí University of Valencia
Enrico Benetto
Enrico Benetto Luxembourg Institute of Science and Technology
Patrick C. M. Wong
Patrick C. M. Wong Chinese University of Hong Kong

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