Innovative Alignment-Based Method for Antiviral Peptide Prediction

Autor: Daniela de Llano García, Yovani Marrero-Ponce, Guillermin Agüero-Chapin, Francesc J. Ferri, Agostinho Antunes, Felix Martinez-Rios, Hortensia Rodríguez

Publikováno v: Antibiotics, Vol 13, Iss 8, p 768 (2024)

Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for ef

Externí odkaz: https://doaj.org/article/0f4aa876d1fd4518997ba17432479e44

A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification

Autor: Javier Naranjo-Alcazar, Sergi Perez-Castanos, Irene Martin-Morato, Pedro Zuccarello, Francesc J. Ferri, Maximo Cobos

Publikováno v: IEEE Access, Vol 8, Pp 188875-188882 (2020)

Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of cr

Externí odkaz: https://doaj.org/article/01c09393e2544aefb8bf50668de4e118

A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification

Autor: Irene Martin-Morato, Javier Naranjo-Alcazar, Maximo Cobos, Francesc J. Ferri, Sergi Perez-Castanos, Pedro Zuccarello

Publikováno v: IEEE Access, Vol 8, Pp 188875-188882 (2020)

Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of cr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c1e1f5d703d41afb4d414089f9b801f
https://doi.org/10.1109/access.2020.3031685

An efficient method for clustered multi-metric learning

Autor: Francesc J. Ferri, Carlos Morell, Bac Nguyen, Bernard De Baets

Publikováno v: Information Sciences. 471:149-163

Distance metric learning, which aims at finding a distance metric that separates examples of one class from examples of the other classes, is the key to the success of many machine learning tasks. Although there has been an increasing interest in thi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ed80a98b08e6e70f6ea9a0ea1ac8ba10
https://doi.org/10.1016/j.ins.2018.08.055

An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments

Autor: Javier Naranjo-Alcazar, Sergi Perez-Castanos, Pedro Zuccarello, Ana M. Torres, Jose J. Lopez, Francesc J. Ferri, Maximo Cobos

Publikováno v: Naranjo-Alcazar, J., Perez-Castanos, S., Zuccarello, P., Torres, A. M., Lopez, J. J., Ferri, F. J., & Cobos, M. (2022). An open-set recognition and few-shot learning dataset for audio event classification in domestic environments. Pattern Recognition Letters, 164, 40-45.

The problem of training with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show very good performance when trained with large datasets. However, in many

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7c4d35e36b3d35119490fd0d7e36a9e