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Computational Science – ICCS 2021

21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part III

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Maciej Paszynski

    1. AGH University of Science and Technology, Krakow, Poland

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  2. Dieter Kranzlmüller

    1. Ludwig-Maximilians-Universität München, Munich, Germany

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  3. Valeria V. Krzhizhanovskaya

    1. University of Amsterdam, Amsterdam, The Netherlands

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  4. Jack J. Dongarra

    1. University of Tennessee at Knoxville, Knoxville, USA

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  5. Peter M.A. Sloot

    1. University of Amsterdam, Amsterdam, The Netherlands

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12744)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: ICCS 2021.

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Table of contents (62 papers)

  1. Front Matter

    Pages i-xx
    Download chapter PDF

  2. Classifier Learning from Difficult Data

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Soft Confusion Matrix Classifier for Stream Classification

      • Pawel Trajdos, Marek Kurzynski
      Pages 3-17

    3. Some Proposal of the High Dimensional PU Learning Classification Procedure

      • Konrad Furmańczyk, Marcin Dudziński, Diana Dziewa-Dawidczyk
      Pages 18-25

    4. Classifying Functional Data from Orthogonal Projections – Model, Properties and Fast Implementation

      • Ewa Skubalska-Rafajłowicz, Ewaryst Rafajłowicz
      Pages 26-39

    5. Clustering and Weighted Scoring Algorithm Based on Estimating the Number of Clusters

      • Jakub Klikowski, Robert Burduk
      Pages 40-49

    6. Exact Searching for the Smallest Deterministic Automaton

      • Wojciech Wieczorek, Łukasz Strąk, Arkadiusz Nowakowski, Olgierd Unold
      Pages 50-63

    7. Learning Invariance in Deep Neural Networks

      • Han Zhang, Tomasz Arodz
      Pages 64-74

    8. Mimicking Learning for 1-NN Classifiers

      • Przemysław Śliwiński, Paweł Wachel, Jerzy W. Rozenblit
      Pages 75-80

    9. Application of Multi-objective Optimization to Feature Selection for a Difficult Data Classification Task

      • Joanna Grzyb, Mariusz Topolski, Michał Woźniak
      Pages 81-94

    10. Deep Embedding Features for Action Recognition on Raw Depth Maps

      • Jacek Trelinski, Bogdan Kwolek
      Pages 95-108

    11. Analysis of Variance Application in the Construction of Classifier Ensemble Based on Optimal Feature Subset for the Task of Supporting Glaucoma Diagnosis

      • Dominika Sułot, Paweł Zyblewski, Paweł Ksieniewicz
      Pages 109-117

    12. Multi-objective Evolutionary Undersampling Algorithm for Imbalanced Data Classification

      • Szymon Wojciechowski
      Pages 118-127

    13. Missing Value Imputation Method Using Separate Features Nearest Neighbors Algorithm

      • Tomasz Orczyk, Rafał Doroz, Piotr Porwik
      Pages 128-141

    14. On Validity of Extreme Value Theory-Based Parametric Models for Out-of-Distribution Detection

      • Tomasz Walkowiak, Kamil Szyc, Henryk Maciejewski
      Pages 142-155

    15. Clustering-Based Ensemble Pruning in the Imbalanced Data Classification

      • Paweł Zyblewski
      Pages 156-171

    16. Improvement of Random Undersampling to Avoid Excessive Removal of Points from a Given Area of the Majority Class

      • Małgorzata Bach, Aleksandra Werner
      Pages 172-186

    17. Predictability Classes for Forecasting Clients Behavior by Transactional Data

      • Elizaveta Stavinova, Klavdiya Bochenina, Petr Chunaev
      Pages 187-199

    18. A Non-intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones

      • Sai Vishwanath Venkatesh, D. Prasannakumaran, Joish J. Bosco, R. Pravin Kumaar, Vineeth Vijayaraghavan
      Pages 200-213

    19. Analysis of Semestral Progress in Higher Technical Education with HMM Models

      • Ewa Lach, Damian Grzechca, Andrzej Polański, Jerzy Rutkowski, Michał Staniszewski
      Pages 214-228
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Other volumes

  1. Computational Science – ICCS 2021

  2. Computational Science – ICCS 2021

  3. Computational Science – ICCS 2021

  4. Computational Science – ICCS 2021

  5. Computational Science – ICCS 2021

  6. Computational Science – ICCS 2021

Keywords

About this book

The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.*

The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named:

Part I: ICCS Main Track

Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science

Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health

Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems

Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation

Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models

*The conference was held virtually.


Editors and Affiliations

  • AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  • Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  • University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya, Peter M.A. Sloot

  • University of Tennessee at Knoxville, Knoxville, USA

    Jack J. Dongarra

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