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Intelligent Data Engineering and Automated Learning ¿ IDEAL 2023

Om Intelligent Data Engineering and Automated Learning ¿ IDEAL 2023

This book constitutes the proceedings of the 24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023, held in Évora, Portugal, during November 22¿24, 2023. The 45 full papers and 4 short papers presented in this book were carefully reviewed and selected from 77 submissions. IDEAL 2023 is focusing on big data challenges, machine learning, deep learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models, agents and hybrid intelligent systems, and real-world applications of intelligence techniques and AI. The papers are organized in the following topical sections: main track; special session on federated learning and (pre) aggregation in machine learning; special session on intelligent techniques for real-world applications of renewable energy and green transport; and special session on data selection in machine learning.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9783031482311
  • Bindende:
  • Paperback
  • Sider:
  • 568
  • Utgitt:
  • 15 november 2023
  • Utgave:
  • 23001
  • Dimensjoner:
  • 155x31x235 mm.
  • Vekt:
  • 850 g.
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 11 juni 2024

Beskrivelse av Intelligent Data Engineering and Automated Learning ¿ IDEAL 2023

This book constitutes the proceedings of the 24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023, held in Évora, Portugal, during November 22¿24, 2023.
The 45 full papers and 4 short papers presented in this book were carefully reviewed and selected from 77 submissions. IDEAL 2023 is focusing on big data challenges, machine learning, deep learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models, agents and hybrid intelligent systems, and real-world applications of intelligence techniques and AI.
The papers are organized in the following topical sections: main track; special session on federated learning and (pre) aggregation in machine learning; special session on intelligent techniques for real-world applications of renewable energy and green transport; and special session on data selection in machine learning.

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