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

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.; Fernandez-Jover, E.; Formoso, M. A.; Gallego-Molina, N. J.; Gamazo, J.; González, J. García; Garcia-Rodriguez, J.; Garre, C.; Garrigós, J.; Gómez-Rodellar, A.; Gómez-Vilda, P.; Graña, M.; Guerrero-Rodriguez, B.; Hendrikse, S. C. F.; Jimenez-Mesa, C.; Jodra-Chuan, M.; Julian, V.; Kotz, G.; Kutt, K.; Leming, M.; Lope, J. de; Macas, B.; Marrero-Aguiar, V.; Martinez, J. J.; Martinez-Murcia, F. J.; Martínez-Tomás, R.; Mekyska, J.; Nalepa, G. J.; Novais, P.; Orellana, D.; Ortiz, A.; Palacios-Alonso, D.; Palma, J.; Pereira, A.; Pinacho-Davidson, P.; Pinninghoff, M. A.; Ponticorvo, M.; Psarrou, A.; Ramírez, J.; Rincón, M.; Rodellar-Biarge, V.; Rodríguez-Rodríguez, I.; Roelofsma, P. H. M. P.; 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, S. H.; Wang, W.; Zhang, Y. D.; Zhu, H.; Zhu, Z.; Ferrández-Vicente, J. M.

http://hdl.handle.net/10400.8/16990

Utilize este identificador para referenciar este registo.

Nome:	Descrição:	Tamanho:	Formato:
Computational approaches to Explainable Artificial Intelligence Advances in theory, applications and trends.pdf	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.	6.71 MB	Adobe PDF	Ver/Abrir

Contacte-nos

Autores

Castellanos-Dominguez, G.

Resumo(s)

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.

Palavras-chave

Explainable Artificial Intelligence Data science Computational approaches Machine learning Deep learning Neuroscience Robotics Biomedical applications Computer-aided diagnosis systems

URI

http://hdl.handle.net/10400.8/16990

Citação

J.M. Górriz, I. Álvarez-Illán, A. Álvarez-Marquina, J.E. Arco, M. Atzmueller, F. Ballarini, E. Barakova, G. Bologna, P. Bonomini, G. Castellanos-Dominguez, D. Castillo-Barnes, S.B. Cho, R. Contreras, J.M. Cuadra, E. Domínguez, F. Domínguez-Mateos, R.J. Duro, D. Elizondo, A. Fernández-Caballero, E. Fernandez-Jover, M.A. Formoso, N.J. Gallego-Molina, J. Gamazo, J. García González, J. Garcia-Rodriguez, C. Garre, J. Garrigós, A. Gómez-Rodellar, P. Gómez-Vilda, M. Graña, B. Guerrero-Rodriguez, S.C.F. Hendrikse, C. Jimenez-Mesa, M. Jodra-Chuan, V. Julian, G. Kotz, K. Kutt, M. Leming, J. de Lope, B. Macas, V. Marrero-Aguiar, J.J. Martinez, F.J. Martinez-Murcia, R. Martínez-Tomás, J. Mekyska, G.J. Nalepa, P. Novais, D. Orellana, A. Ortiz, D. Palacios-Alonso, J. Palma, A. Pereira, P. Pinacho-Davidson, M.A. Pinninghoff, M. Ponticorvo, A. Psarrou, J. Ramírez, M. Rincón, V. Rodellar-Biarge, I. Rodríguez-Rodríguez, P.H.M.P. Roelofsma, J. Santos, D. Salas-Gonzalez, P. Salcedo-Lagos, F. Segovia, A. Shoeibi, M. Silva, D. Simic, J. Suckling, J. Treur, A. Tsanas, R. Varela, S.H. Wang, W. Wang, Y.D. Zhang, H. Zhu, Z. Zhu, J.M. Ferrández-Vicente, Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends, Information Fusion, Volume 100, 2023, 101945, ISSN 1566-2535, https://doi.org/10.1016/j.inffus.2023.101945.

Editora

Elsevier

DOI

10.1016/j.inffus.2023.101945

Coleções

CIIC - Artigos em Revistas com Peer Review
ESTG - Artigos em revistas internacionais

Licença CC

cclicense-by-nc

Métricas Alternativas

Ver registo completo