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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
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" }