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Computational Sciences and Artificial Intelligence in Industry

New Digital Technologies for Solving Future Societal and Economical Challenges

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  • © 2022

Overview

Editors:
  1. Tero Tuovinen

    1. Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

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  2. Jacques Periaux

    1. CIMNE, International Center for Numerical Methods in Engineering, Barcelona, Spain

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  3. Pekka Neittaanmäki

    1. Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

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  • Reports support vector machines in clusterwise linear regression
  • Presents similarity solutions for condensation on a non-isothermal vertical plate
  • Provides a second-order method with enriched hessian information for composite sparse optimization problems

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About this book

This book is addressed to young researchers and engineers in the fields of Computational Science and Artificial Intelligence, ranging from innovative computational methods to digital machine learning tools and their coupling used for solving challenging industrial and societal problems.This book provides the latest knowledge from jointly academic and industries experts in Computational Science and Artificial Intelligence fields for exploring possibilities and identifying challenges of applying Computational Sciences and AI methods and tools in industrial and societal sectors.

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xv
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  2. Overview

    1. Front Matter

      Pages 1-1
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    2. Co-development of Methodology, Applications, and Hardware in Computational Science and Artificial Intelligence

      • Pekka Neittaanmäki, Matti Savonen, Jacques Periaux, Tero Tuovinen
      Pages 3-8

  3. Methodology

    1. Front Matter

      Pages 9-9
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    2. Novel Strategies for Data-Driven Evolutionary Optimization

      • Swagata Roy, Nirupam Chakraborti
      Pages 11-25

    3. Artificial Intelligence and Computational Science

      • Pekka Neittaanmäki, Sergey Repin
      Pages 27-35

    4. Supervised Learning and Applied Mathematics

      • Olivier Pironneau
      Pages 37-46

    5. Application of the Topological Gradient to Parsimonious Neural Networks

      • Kateryna Bashtova, Mathieu Causse, Cameron James, Florent Masmoudi, Mohamed Masmoudi, Houcine Turki et al.
      Pages 47-61

    6. Generation of Error Indicators for Partial Differential Equations by Machine Learning Methods

      • Alexey Muzalevskiy, Pekka Neittaanmäki, Sergey Repin
      Pages 63-96

    7. Newton Method for Minimal Learning Machine

      • Joonas Hämäläinen, Tommi Kärkkäinen
      Pages 97-108

    8. Limited Memory Bundle Method for Clusterwise Linear Regression

      • Napsu Karmitsa, Adil M. Bagirov, Sona Taheri, Kaisa Joki
      Pages 109-122

    9. Improving Clustering and Cluster Validation with Missing Data Using Distance Estimation Methods

      • Marko Niemelä, Tommi Kärkkäinen
      Pages 123-133

  4. Medical Applications

    1. Front Matter

      Pages 135-135
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    2. Kubelka–Munk Model and Stochastic Model Comparison in Skin Physical Parameter Retrieval

      • Leevi Annala, Ilkka Pölönen
      Pages 137-151

    3. Unsupervised Numerical Characterization in Determining the Borders of Malignant Skin Tumors from Spectral Imagery

      • Ilkka Pölönen, Tero Tuovinen, Hannu-Heikki Puupponen, Mari Salmivuori, Mari Grönroos, Noora Neittaanmäki
      Pages 153-176

    4. Validation of Knee KL-classifying Deep Neural Network with Finnish Patient Data

      • Esko Niinimäki, Juha Paloneva, Ilkka Pölönen, Ari Heinonen, Sami Äyrämö
      Pages 177-188

    5. Predicting Future Overweight and Obesity from Childhood Growth Data: A Case Study

      • Ilkka Rautiainen, Jukka-Pekka Kauppi, Toni Ruohonen, Eero Karhu, Keijo Lukkarinen, Sami Äyrämö
      Pages 189-201

    6. Predicting Overweight and Obesity in Later Life from Childhood Data: A Review of Predictive Modeling Approaches

      • Ilkka Rautiainen, Sami Äyrämö
      Pages 203-220

  5. Industrial and Technological Applications

    1. Front Matter

      Pages 221-221
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    2. Applications of Industrial IoT and WSNs in O&M Programmes for Offshore Wind Farms

      • Sean Loughney, Ariel J. Edesess
      Pages 223-245
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Editors and Affiliations

  • Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland

    Tero Tuovinen, Pekka Neittaanmäki

  • CIMNE, International Center for Numerical Methods in Engineering, Barcelona, Spain

    Jacques Periaux

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