TY - JOUR
T1 - Computational Approaches to Explainable Artificial Intelligence
T2 - Advances in Theory, Applications and Trends
AU - Górriz, J. M.
AU - Álvarez-Illán, I.
AU - Álvarez-Marquina, A.
AU - Arco, J. E.
AU - Atzmueller, M.
AU - Ballarini, F.
AU - Barakova, E.
AU - Bologna, G.
AU - Bonomini, P.
AU - Castellanos-Dominguez, G.
AU - Castillo-Barnes, D.
AU - Cho, S. B.
AU - Contreras, R.
AU - Cuadra, J.
AU - Dominguez, E.
AU - Dominguez-Mateos, F.
AU - Duro, R. J.
AU - Elizondo, D.
AU - Fernández-Caballero, A.
AU - Fernandez-Jover, E.
AU - Formoso, M. A.
AU - Gallego-Molina, N. J.
AU - Gamazo, J.
AU - García González, J.
AU - Garcia-Rodriguez, J.
AU - Garre, C.
AU - Garrigós, J.
AU - Gómez Rodellar, Andrés
AU - Gómez-Vilda, M.
AU - Graña, M.
AU - Guerrero-Rodriguez, B.
AU - Hendrikse, S. C. F.
AU - Jimenez-Mesa, C.
AU - Jodra-Chuan, M.
AU - Julian, V.
AU - Kotz, G.
AU - Kutt, K.
AU - Leming, M.
AU - De Lope, J.
AU - Macas, B.
AU - Marrero-Aguiar, G. J.
AU - Martinez, J. J.
AU - Martínez-Murcia, F. J.
AU - Martínez-Tomás, R.
AU - Mekyska, J.
AU - Nalepa, G. J.
AU - Novais, P.
AU - Orellana, D.
AU - Ortiz, A.
AU - Palacios-Alonso, D.
AU - Palma, J.
AU - Pereira, A.
AU - Pinacho-Davidson, P.
AU - Pinninghoff, M. A.
AU - Ponticorvo, M.
AU - Psarrou, A.
AU - Ramirez, J.
AU - Rincón, M.
AU - Rodellar-Biarge, V.
AU - Rodríguez-Rodríguez, I.
AU - Roelofsma, P.H.M.P.
AU - Santos, J.
AU - Salas-Gonzalez, D.
AU - Salcedo-Lagos, P.
AU - Segovia, F.
AU - Shoeibi, A.
AU - Silva, M.
AU - Simic, D.
AU - Suckling, J.
AU - Treur, J.
AU - Tsanas, Thanasis
AU - Varela, R.
AU - Wang, S. H.
AU - Wang, W.
AU - Zhang, YD
AU - Zhu, H.
AU - Zhu, Z.
AU - Ferrández-Vicente, J. M.
N1 - Funding Information:
Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00 .
Funding Information:
This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 ( Spanish “Ministerio de Ciencia, Innovacón y Universidades” ), P18-RT-1624 , UMA20-FEDERJA-086 , CV20-45250 , A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF) . M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”.
Funding Information:
José Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC ( ED431G 2019/01 ), GPC ED431B 2022/33 , and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00 ). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID).
Funding Information:
The work reported here has been partially funded by Project Fondecyt 1201572 (ANID).
Funding Information:
The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084 , project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb.
Funding Information:
In [247] , the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain) . In [248] , the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4 , 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies” BG05M2OP001-1.002-0023 .
Funding Information:
This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746 ). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058 .
Funding Information:
Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250 , A-TIC-080-UGR18 , B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes.
Funding Information:
The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III ; by MinCiencias project 1222-852-69927 , contract 495-2020 .
Funding Information:
The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Fundaça̋o para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019 .
Funding Information:
S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361 , Artificial Intelligence Graduate School Program (Yonsei University)).
Funding Information:
The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe” , by the “European Union” or by the “European Union NextGenerationEU/PRTR” .
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
KW - Explainable Artificial Intelligence
KW - data science
KW - computational approaches
KW - Machine Learning
KW - Deep Learning
KW - neuroscience
KW - robotics
KW - biomedical applications
KW - computer-aided diagnosis systems
U2 - 10.1016/j.inffus.2023.101945
DO - 10.1016/j.inffus.2023.101945
M3 - Article
SN - 1566-2535
VL - 100
JO - Information Fusion
JF - Information Fusion
M1 - 101945
ER -