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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Information Fusion

ISSN

1566-2535

e-ISSN

1872-6305

Svazek periodika

100

Číslo periodika v rámci svazku

December 2023

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

37

Strana od-do

1-37

Kód UT WoS článku

001055273000001

EID výsledku v databázi Scopus

2-s2.0-85166914338