Hujun Yin

Peter Tino

Ke Tang

Paulo Novais

David Camacho

Sung-Bae Cho

Antonio J. Tallón-Ballesteros

Richard Allmendinger

Susana Nascimento

This book constitutes the refereed proceedings of the 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021, which took place during November 25-27, 2021. The conference was originally planned to take place in Manchester, UK, but was held virtually due to the COVID-19 pandemic. The 61 full papers included in this book were carefully reviewed and selected from 85 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.

22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings

Intelligent Data Engineering and Automated Learning – IDEAL 2021

Ronaldo Menezes

This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.

20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part II

Intelligent Data Engineering and Automated Learning – IDEAL 2019

Richard Allmendinger

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