C2 Edited work
Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (2022)
Tuovinen, T. T., Periaux, J., & Neittaanmäki, P. (Eds.). (2022). Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges. Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3
JYU authors or editors
Publication details
All authors or editors: Tuovinen, Tero T.; Periaux, Jacques; Neittaanmäki, Pekka
ISBN: 978-3-030-70786-6
eISBN: 978-3-030-70787-3
Journal or series: Intelligent Systems, Control and Automation: Science and Engineering
ISSN: 2213-8986
eISSN: 2213-8994
Publication year: 2022
Number in series: 76
Number of pages in the book: 250
Publisher: Springer
Place of Publication: Cham
Publication country: Switzerland
Publication language: English
DOI: https://doi.org/10.1007/978-3-030-70787-3
Publication open access: Not open
Publication channel open access:
Additional information: The CSAI 2019 Conference (Computational Science and AI in Industry: New Digital Technologies for Solving Future Societal and Economical Challenges) took place at Jyväskylä, Finland, on June 12–14, 2019.
Abstract
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.
Keywords: artificial intelligence; machine learning; applied research; industry; computational science; technology
Contributing organizations
Related projects
- Computational Sciences and AI in Industry (CSAI): new digital technologies for solving future societal and economical challenges conference
- Tuovinen, Tero
- Participants (conferences, training)
Ministry reporting: Yes
Reporting Year: 2022
Preliminary JUFO rating: 2
This publication includes articles with JYU authors:
- Neittaanmäki, P., & Repin, S. (2022). Artificial Intelligence and Computational Science. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 27-35). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_3
- Neittaanmäki, P., Savonen, M., Periaux, J., & Tuovinen, T. (2022). Co-development of Methodology, Applications, and Hardware in Computational Science and Artificial Intelligence. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 3-8). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_1
- Muzalevskiy, A., Neittaanmäki, P., & Repin, S. (2022). Generation of Error Indicators for Partial Differential Equations by Machine Learning Methods. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 63-96). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_6
- Niemelä, M., & Kärkkäinen, T. (2022). Improving Clustering and Cluster Validation with Missing Data Using Distance Estimation Methods. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 123-133). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_9
- Annala, L., & Pölönen, I. (2022). Kubelka-Munk Model and Stochastic Model Comparison in Skin Physical Parameter Retrieval. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 137-151). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_10
- Hämäläinen, J., & Kärkkäinen, T. (2022). Newton Method for Minimal Learning Machine. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 97-108). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_7
- Rautiainen, I., Kauppi, J.-P., Ruohonen, T., Karhu, E., Lukkarinen, K., & Äyrämö, S. (2022). Predicting Future Overweight and Obesity from Childhood Growth Data : A Case Study. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 189-201). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_13
- Rautiainen, I., & Äyrämö, S. (2022). Predicting Overweight and Obesity in Later Life from Childhood Data : A Review of Predictive Modeling Approaches. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 203-220). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_14
- Pölönen, I., Tuovinen, T., Puupponen, H.-H., Salmivuori, M., Grönroos, M., & Neittaanmäki, N. (2022). Unsupervised Numerical Characterization in Determining the Borders of Malignant Skin Tumors from Spectral Imagery. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 153-176). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_11
- Erkkilä, Anna-Leena, Räbinä, Jukka, Pölönen, Ilkka, Sajavaara, Timo, Alakoski, Esa, Tuovinen, Tero. (2022). Using Wave Propagation Simulations and Convolutional Neural Networks to Retrieve Thin Film Thickness from Hyperspectral Images. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 261-275). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_17
