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Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery

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  • © 2022

Overview

Editors:
  1. Boris Kovalerchuk

    1. Department of Computer Science, Central Washington University, Ellensburg, USA

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  2. Kawa Nazemi

    1. Department of Media, Darmstadt University of Applied Sciences, Darmstadt, Germany

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  3. Răzvan Andonie

    1. Department of Computer Science, Central Washington University, Ellensburg, USA

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  4. Nuno Datia

    1. Department of Electronics, Telecommunications and Computers Engineering, Lisbon School of Engineering, Lisbon, Portugal

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  5. Ebad Banissi

    1. Department of Informatics, London South Bank University, London, UK

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  • Presents the current state of the art on combining artificial intelligence and machine learning with visual analytics
  • Presents research work on computational intelligence, machine learning, visual analytics, and knowledge discovery
  • Covers integrated systems, supervised learning, and unsupervised learning

Part of the book series: Studies in Computational Intelligence (SCI, volume 1014)

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Table of contents (26 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions

    • Boris Kovalerchuk, Răzvan Andonie, Nuno Datia, Kawa Nazemi, Ebad Banissi
    Pages 1-27

  3. Machine Learning and Visualization

    1. Front Matter

      Pages 29-29
      Download chapter PDF

    2. Visual Analytics for Strategic Decision Making in Technology Management

      • Kawa Nazemi, Tim Feiter, Lennart B. Sina, Dirk Burkhardt, Alexander Kock
      Pages 31-61

    3. Deep Learning Image Recognition for Non-images

      • Boris Kovalerchuk, Divya Chandrika Kalla, Bedant Agarwal
      Pages 63-100

    4. Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning

      • Sridevi Narayana Wagle, Boris Kovalerchuk
      Pages 101-139

    5. Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates

      • Rose McDonald, Boris Kovalerchuk
      Pages 141-172

    6. Convolutional Neural Networks Analysis Using Concentric-Rings Interactive Visualization

      • João Alves, Tiago Araújo, Bianchi Serique Meiguins, Beatriz Sousa Santos
      Pages 173-196

    7. “Negative” Results—When the Measured Quantity Is Outside the Sensor’s Range—Can Help Data Processing

      • Jonatan Contreras, Francisco Zapata, Olga Kosheleva, Vladik Kreinovich, Martine Ceberio
      Pages 197-211

    8. Visualizing and Explaining Language Models

      • Adrian M. P. Braşoveanu, Răzvan Andonie
      Pages 213-237

    9. Transparent Clustering with Cyclic Probabilistic Causal Models

      • Evgenii E. Vityaev, Bayar Pak
      Pages 239-253

    10. Visualization and Self-Organising Maps for the Characterisation of Bank Clients

      • Catarina Maçãs, Evgheni Polisciuc, Penousal Machado
      Pages 255-287

    11. Augmented Classical Self-organizing Map for Visualization of Discrete Data with Density Scaling

      • Phillip C. S. R. Kilgore, Marjan Trutschl, Hyung W. Nam, Angela P. Cornelius, Urška Cvek
      Pages 289-310

    12. Gragnostics: Evaluating Fast, Interpretable Structural Graph Features for Classification and Visual Analytics

      • Robert Gove
      Pages 311-336

    13. VisIRML: Visualization with an Interactive Information Retrieval and Machine Learning Classifier

      • Craig Hagerman, Richard Brath, Scott Langevin
      Pages 337-357

    14. Visual Analytics of Hierarchical and Network Timeseries Models

      • David Jonker, Richard Brath, Scott Langevin
      Pages 359-376

  4. Integrated Systems and Case Studies

    1. Front Matter

      Pages 377-377
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    2. ML Approach to Predict Air Quality Using Sensor and Road Traffic Data

      • Nuno Datia, M. P. M. Pato, Ruben Taborda, João Moura Pires
      Pages 379-401

    3. Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model

      • Lukas Kaupp, Kawa Nazemi, Bernhard Humm
      Pages 403-436

    4. Visual Discovery of Malware Patterns in Android Apps

      • Paolo Buono, Fabrizio Balducci
      Pages 437-457
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Keywords

About this book

This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. 

This book is a collection of 25 extended works of over 70 scholarspresented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level.  The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. 

The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.






Editors and Affiliations

  • Department of Computer Science, Central Washington University, Ellensburg, USA

    Boris Kovalerchuk, Răzvan Andonie

  • Department of Media, Darmstadt University of Applied Sciences, Darmstadt, Germany

    Kawa Nazemi

  • Department of Electronics, Telecommunications and Computers Engineering, Lisbon School of Engineering, Lisbon, Portugal

    Nuno Datia

  • Department of Informatics, London South Bank University, London, UK

    Ebad Banissi

Bibliographic Information

  • Book Title: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery

  • Editors: Boris Kovalerchuk, Kawa Nazemi, Răzvan Andonie, Nuno Datia, Ebad Banissi

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-030-93119-3

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-93118-6Published: 05 June 2022

  • Softcover ISBN: 978-3-030-93121-6Published: 06 June 2023

  • eBook ISBN: 978-3-030-93119-3Published: 04 June 2022

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XV, 674

  • Number of Illustrations: 46 b/w illustrations, 288 illustrations in colour

  • Topics: Computational Intelligence, Machine Learning

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