World's Best Scientists 2026 revealed!
Friedhelm Schwenker

Friedhelm Schwenker

D-Index & Metrics

Computer Science

D-Index
43
Citations
7271
World Ranking
8021
National Ranking
393

Friedhelm Schwenker 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 Friedhelm Schwenker 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: 321 publications — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Friedhelm Schwenker 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 Friedhelm Schwenker 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

Friedhelm Schwenker is affiliated with the University of Ulm in Germany. Their research primarily spans the fields of Computer Science and Medicine, with a particular focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Experimental and Cognitive Psychology, and Signal Processing.

The scientist's work covers several specialized topics including:

  • AI in cancer detection
  • Emotion and Mood Recognition
  • EEG and Brain-Computer Interfaces
  • Anomaly Detection Techniques and Applications
  • Radiomics and Machine Learning in Medical Imaging
  • COVID-19 diagnosis using AI
  • Machine Learning and Data Classification

Frequent co-authors collaborating with Schwenker include:

  • Peter Bellmann
  • Patrick Thiam
  • Ram Sarkar
  • Taye Girma Debelee
  • Hans A. Kestler

Schwenker has published extensively in several venues. The most common publication outlets are:

  • IEEE Access
  • Zenodo (CERN European Organization for Nuclear Research)
  • Sensors
  • arXiv (Cornell University)
  • Applied Sciences

Recent notable publications include:

  • "A Survey of Brain Tumor Segmentation and Classification Algorithms," 2021, Journal of Imaging
  • "Enhanced Region Growing for Brain Tumor MR Image Segmentation," 2021, Journal of Imaging
  • "Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review," 2022, Diagnostics
  • "Deep Learning in Selected Cancers' Image Analysis-A Survey," 2020, Journal of Imaging
  • "Comparison of short-term electrical load forecasting methods for different building types," 2021, Energy Informatics

In addition to journal publications, Schwenker has contributed to book literature published by Springer Science+Business Media. One recent contribution is the 2024 volume titled "Pan-African Conference on Artificial Intelligence."

Best Publications

  • Three learning phases for radial-basis-function networks

    Friedhelm Schwenker;Hans A. Kestler;Günther Palm

  • Pattern classification and clustering: A review of partially supervised learning approaches

    Friedhelm Schwenker;Edmondo Trentin

  • Hierarchical support vector machines for multi-class pattern recognition

    F. Schwenker

  • A dataset of continuous affect annotations and physiological signals for emotion analysis.

    Karan Sharma;Karan Sharma;Claudio Castellini;Egon L. van den Broek;Alin Albu-Schaeffer

  • Multiple classifier systems for the classificatio of audio-visual emotional states

    Michael Glodek;Stephan Tschechne;Georg Layher;Martin Schels

  • A Survey of Brain Tumor Segmentation and Classification Algorithms.

    Erena Siyoum Biratu;Friedhelm Schwenker;Yehualashet Megersa Ayano;Taye Girma Debelee;Taye Girma Debelee

  • Survey of deep learning in breast cancer image analysis

    Taye Girma Debelee;Friedhelm Schwenker;Achim Ibenthal;Dereje Yohannes

  • Semi-supervised Learning

    Mohamed Farouk Abdel Hady;Friedhelm Schwenker

  • Iterative retrieval of sparsely coded associative memory patterns

    F. Schwenker;F. T. Sommer;G. Palm

  • Enhanced Region Growing for Brain Tumor MR Image Segmentation

    Erena Siyoum Biratu;Friedhelm Schwenker;Taye Girma Debelee;Taye Girma Debelee;Samuel Rahimeto Kebede

  • Ensemble Methods: Foundations and Algorithms [Book Review]

    Friedhelm Schwenker

  • Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

    Markus Kachele;Patrick Thiam;Mohammadreza Amirian;Friedhelm Schwenker

  • A study of the robustness of KNN classifiers trained using soft labels

    Neamat El Gayar;Friedhelm Schwenker;Günther Palm

  • Exploring Deep Physiological Models for Nociceptive Pain Recognition.

    Patrick Thiam;Peter Bellmann;Hans A. Kestler;Friedhelm Schwenker

  • Adaptive confidence learning for the personalization of pain intensity estimation systems

    Markus Kächele;Mohammadreza Amirian;Patrick Thiam;Philipp Werner

  • Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification

    Stefan Scherer;John Kane;Christer Gobl;Friedhelm Schwenker

  • Multimodal emotion classification in naturalistic user behavior

    Steffen Walter;Stefan Scherer;Martin Schels;Michael Glodek

  • Co-training by Committee: A New Semi-supervised Learning Framework

    M. Hady;F. Schwenker

  • De-noising of high-resolution ECG signals by combining the discrete wavelet transform with the Wiener filter

    H.A. Kestler;M. Haschka;W. Kratz;F. Schwenker

  • Combining committee-based semi-supervised learning and active learning

    Mohamed Farouk Abdel Hady;Friedhelm Schwenker

  • The SenseEmotion Database: A Multimodal Database for the Development and Systematic Validation of an Automatic Pain- and Emotion-Recognition System

    Maria Velana;Sascha Gruss;Georg Layher;Patrick Thiam

  • Emotion Recognition from Speech

    Andreas Wendemuth;Bogdan Vlasenko;Ingo Siegert;Ronald Böck

Frequent Co-Authors

Günther Palm
Günther Palm University of Ulm
Hans A. Kestler
Hans A. Kestler University of Ulm
Heiko Neumann
Heiko Neumann University of Ulm
Ram Sarkar
Ram Sarkar Jadavpur University
Claudio Castellini
Claudio Castellini University of Erlangen-Nuremberg
Louis-Philippe Morency
Louis-Philippe Morency Carnegie Mellon University
Klaus Dietmayer
Klaus Dietmayer University of Ulm
Nick Campbell
Nick Campbell Trinity College Dublin
Michael Weber
Michael Weber University of Ulm

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