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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • 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