Górriz, J. M., Álvarez-Illán, I., Álvarez-Marquina, A., Arco, J. E., Atzmueller, M., Ballarini, F., Barakova, E., Bologna, G., Bonomini, P., Castellanos-Dominguez, G., Castillo-Barnes, D., Cho, S. B., Contreras, R., Cuadra, J. M., Domínguez, E., Domínguez-Mateos, F., Duro, R. J., Elizondo, D., Fernández-Caballero, A., ... Ferrández-Vicente, J. M. (2023). Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends. Information Fusion, 100, Article 101945. https://doi.org/10.1016/j.inffus.2023.101945
@article{a694ba6a1e31419aaa61cbaa23e5a317,
title = "Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends",
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 = "Biomedical applications, Computational approaches, Computer-aided diagnosis systems, Data science, Deep learning, Explainable Artificial Intelligence, Machine learning, Neuroscience, Robotics",
author = "G{\'o}rriz, {J. M.} and I. {\'A}lvarez-Ill{\'a}n and A. {\'A}lvarez-Marquina and Arco, {J. E.} and M. Atzmueller and F. Ballarini and E. Barakova and G. Bologna and P. Bonomini and G. Castellanos-Dominguez and D. Castillo-Barnes and Cho, {S. B.} and R. Contreras and Cuadra, {J. M.} and E. Dom{\'i}nguez and F. Dom{\'i}nguez-Mateos and Duro, {R. J.} and D. Elizondo and A. Fern{\'a}ndez-Caballero and E. Fernandez-Jover and Formoso, {M. A.} and Gallego-Molina, {N. J.} and J. Gamazo and Gonz{\'a}lez, {J. Garc{\'i}a} and J. Garcia-Rodriguez and C. Garre and J. Garrig{\'o}s and A. G{\'o}mez-Rodellar and P. G{\'o}mez-Vilda and M. Gra{\~n}a and B. Guerrero-Rodriguez and Hendrikse, {S. C.F.} and C. Jimenez-Mesa and M. Jodra-Chuan and V. Julian and G. Kotz and K. Kutt and M. Leming and {de Lope}, J. and B. Macas and V. Marrero-Aguiar and Martinez, {J. J.} and Martinez-Murcia, {F. J.} and R. Mart{\'i}nez-Tom{\'a}s and J. Mekyska and Nalepa, {G. J.} and P. Novais and D. Orellana and A. Ortiz and D. Palacios-Alonso and J. Palma and A. Pereira and P. Pinacho-Davidson and Pinninghoff, {M. A.} and M. Ponticorvo and A. Psarrou and J. Ram{\'i}rez and M. Rinc{\'o}n and V. Rodellar-Biarge and I. Rodr{\'i}guez-Rodr{\'i}guez and Roelofsma, {P. H.M.P.} and J. Santos and D. Salas-Gonzalez and P. Salcedo-Lagos and F. Segovia and A. Shoeibi and M. Silva and D. Simic and J. Suckling and J. Treur and A. Tsanas and R. Varela and Wang, {S. H.} and W. Wang and Zhang, {Y. D.} and H. Zhu and Z. Zhu and Ferr{\'a}ndez-Vicente, {J. M.}",
note = "Publisher Copyright: {\textcopyright} 2023 The Author(s)",
year = "2023",
month = dec,
doi = "10.1016/j.inffus.2023.101945",
language = "English",
volume = "100",
journal = "Information Fusion",
issn = "1566-2535",
}
Górriz, JM, Álvarez-Illán, I, Álvarez-Marquina, A, Arco, JE, Atzmueller, M, Ballarini, F, Barakova, E, Bologna, G, Bonomini, P, Castellanos-Dominguez, G, Castillo-Barnes, D, Cho, SB, Contreras, R, Cuadra, JM, Domínguez, E, Domínguez-Mateos, F, Duro, RJ, Elizondo, D, Fernández-Caballero, A, Fernandez-Jover, E, Formoso, MA, Gallego-Molina, NJ, Gamazo, J, González, JG, Garcia-Rodriguez, J, Garre, C, Garrigós, J, Gómez-Rodellar, A, Gómez-Vilda, P, Graña, M, Guerrero-Rodriguez, B, Hendrikse, SCF, Jimenez-Mesa, C, Jodra-Chuan, M, Julian, V, Kotz, G, Kutt, K, Leming, M, de Lope, J, Macas, B, Marrero-Aguiar, V, Martinez, JJ, Martinez-Murcia, FJ, Martínez-Tomás, R, Mekyska, J, Nalepa, GJ, Novais, P, Orellana, D, Ortiz, A, Palacios-Alonso, D, Palma, J, Pereira, A, Pinacho-Davidson, P, Pinninghoff, MA, Ponticorvo, M, Psarrou, A, Ramírez, J, Rincón, M, Rodellar-Biarge, V, Rodríguez-Rodríguez, I, Roelofsma, PHMP, Santos, J, Salas-Gonzalez, D, Salcedo-Lagos, P, Segovia, F, Shoeibi, A, Silva, M, Simic, D, Suckling, J, Treur, J, Tsanas, A, Varela, R, Wang, SH, Wang, W, Zhang, YD, Zhu, H, Zhu, Z & Ferrández-Vicente, JM 2023, 'Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends', Information Fusion, vol. 100, 101945. https://doi.org/10.1016/j.inffus.2023.101945
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. M.
AU - Domínguez, E.
AU - Domínguez-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 - González, J. García
AU - Garcia-Rodriguez, J.
AU - Garre, C.
AU - Garrigós, J.
AU - Gómez-Rodellar, A.
AU - Gómez-Vilda, P.
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, V.
AU - Martinez, J. J.
AU - Martinez-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 - Ramírez, 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, A.
AU - Varela, R.
AU - Wang, S. H.
AU - Wang, W.
AU - Zhang, Y. D.
AU - Zhu, H.
AU - Zhu, Z.
AU - Ferrández-Vicente, J. M.
N1 - Publisher Copyright:
© 2023 The Author(s)
PY - 2023/12
Y1 - 2023/12
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 - Biomedical applications
KW - Computational approaches
KW - Computer-aided diagnosis systems
KW - Data science
KW - Deep learning
KW - Explainable Artificial Intelligence
KW - Machine learning
KW - Neuroscience
KW - Robotics
UR - http://www.scopus.com/inward/record.url?scp=85166914338&partnerID=8YFLogxK
U2 - 10.1016/j.inffus.2023.101945
DO - 10.1016/j.inffus.2023.101945
M3 - Article
AN - SCOPUS:85166914338
SN - 1566-2535
VL - 100
JO - Information Fusion
JF - Information Fusion
M1 - 101945
ER -
