Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148691" target="_blank" >RIV/00216305:26220/23:PU148691 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.inffus.2023.101945" target="_blank" >https://doi.org/10.1016/j.inffus.2023.101945</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.inffus.2023.101945" target="_blank" >10.1016/j.inffus.2023.101945</a>
Alternative languages
Result language
angličtina
Original language name
Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information Fusion
ISSN
1566-2535
e-ISSN
1872-6305
Volume of the periodical
100
Issue of the periodical within the volume
December 2023
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
37
Pages from-to
1-37
UT code for WoS article
001055273000001
EID of the result in the Scopus database
2-s2.0-85166914338