Detail publikace

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

GÓRRIZ, J.M. MEKYSKA, J. et al.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

GÓRRIZ, J.M.; MEKYSKA, J.; et al.

Vydáno

29. 7. 2023

Nakladatel

Elsevier B.V.

ISSN

1872-6305

Periodikum

Information Fusion

Ročník

100

Číslo

December 2023

Stát

Nizozemsko

Strany od

1

Strany do

37

Strany počet

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"
}