@article{1484203,
      recid = {1484203},
      author = {Quaresma, Paulo, and Camacho, David, and Yin, Hujun, and  Goncalves, Teresa, and Julian, Vicente, and  Tallón-Ballesteros, Antonio J.,},
      title = {Intelligent Data Engineering and Automated Learning --  IDEAL 2023 : 24th International Conference, Évora,  Portugal, November 22-24, 2023, Proceedings /. IDEAL  (Conference)},
      pages = {1 online resource (xvii, 549 pages) :},
      note = {Includes author index.},
      abstract = {This book constitutes the proceedings of the 24th  International Conference on Intelligent Data Engineering  and Automated Learning, IDEAL 2023, held in Elvora,  Portugal, during November 22⁰́b324, 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.},
      url = {http://library.usi.edu/record/1484203},
      doi = {https://doi.org/10.1007/978-3-031-48232-8},
}