Publication detail

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

GÓRRIZ, J.M. MEKYSKA, J. et al.

Original Title

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

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Explainable Artificial Intelligence; Data science; Computational approaches; Machine learning; Deep learning; Neuroscience; Robotics; Biomedical applications; Computer-aided diagnosis systems

Authors

GÓRRIZ, J.M.; MEKYSKA, J.; et al.

Released

29. 7. 2023

Publisher

Elsevier B.V.

ISBN

1872-6305

Periodical

Information Fusion

Year of study

100

Number

December 2023

State

Kingdom of the Netherlands

Pages from

1

Pages to

37

Pages count

37

URL

https://doi.org/10.1016/j.inffus.2023.101945

BibTex

@article{BUT184242,
  author="GÓRRIZ, J.M. and MEKYSKA, J. and et al.",
  title="Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends",
  journal="Information Fusion",
  year="2023",
  volume="100",
  number="December 2023",
  pages="1--37",
  doi="10.1016/j.inffus.2023.101945",
  issn="1872-6305",
  url="https://doi.org/10.1016/j.inffus.2023.101945"
}