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Computational approaches to Explainable Artificial Intelligence: : Advances in theory, applications and trends

Published: 01 December 2023 Publication History

Abstract

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.

Highlights

The most groundbreaking advances in theoretical and applied Artificial Intelligence.
Deep Learning in real-world tasks, such as clinical diagnostics or robotics.
Several applications are presented, reviewed and discussed.
State-of-the-art in AI methods, models and applications.
New scientific discoveries successfully transferred to real-life applications.

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cover image Information Fusion
Information Fusion  Volume 100, Issue C
Dec 2023
963 pages

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Elsevier Science Publishers B. V.

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Published: 01 December 2023

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  1. Explainable Artificial Intelligence
  2. Data science
  3. Computational approaches
  4. Machine learning
  5. Deep learning
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