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

2023; Elsevier BV; Volume: 100; Linguagem: Inglês

10.1016/j.inffus.2023.101945

1872-6305

J. M. Górriz, I. Álvarez, Agustín Álvarez-Marquina, Juan E. Arco, Martin Atzmueller, Fabricio Ballarini, Emilia Barakova, Guido Bologna, P. Bonomini, G. Castellanos-Domínguez, Diego Castillo-Barnés, S.B. Cho, Ricardo Contreras, Jorge Cuadra, Enrique Domínguez, Francisco Domínguez, Richard J. Duro, David Elizondo, Antonio Fernández‐Caballero, Eduardo Fernández, Marco A. Formoso, Nicolás J. Gallego-Molina, J. Gamazo, Javier García González, José García‐Rodríguez, Carlos Garre, Javier Garrigós, Andrés Gómez-Rodellar, Pedro Gómez‐Vilda, Manuel Graña, Byron Guerrero-Rodríguez, Sophie C. F. Hendrikse, C. Jiménez-Mesa, Marina Jodra, Vicente Julián, Gabriela Kotz, Krzysztof Kutt, Matthew Leming, Javier de Lope, Beatriz Ordoñez, Victoria Marrero Aguiar, J. Javier Martínez, Francisco J. Martínez-Murcia, Rafael Martínez‐Tomás, Jiří Mekyska, Grzegorz J. Nalepa, Paulo Nováis, Diego Vinicio Orellana Villavicencio, Andrés Ortíz, Daniel Palacios‐Alonso, José Palma, Ántónio Pereira, Pedro Pinacho-Davidson, M. Angélica Pinninghoff, Michela Ponticorvo, Αλεξάνδρα Ψαρρού, Javier Ramı́rez, M. Rincón, V. Rodellar, Ignacio Rodríguez‐Rodríguez, Peter H. M. P. Roelofsma, José Sántos, D. Salas‐Gonzalez, Pedro Salcedo, F. Segovia, Ali Shoeibi, Marco Silva, Dragan Simić, John Suckling, Jan Treur, Athanasios Tsanas, Ramiro Varela, Shuang Wang, Wei Wang, Yudong Zhang, Hengde Zhu, Zhiyuan Zhu, José Manuel Ferrández Vicente,

Advanced Neural Network Applications

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.