@article {784,
	title = {Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications},
	journal = {Neurocomputing},
	volume = {410},
	year = {2020},
	note = {TIN2017-85827-P; RTI2018-098913-B-I00; PSI2015-65848-R; PGC2018-098813-B-C31; PGC2018-098813-B-C32; RTI2018-101114-B-I; TIN2017-90135-R; RTI2018-098743-B-I00; RTI2018-094645-B-I00; FPU15/06512; FPU17/04154; FJCI-2017{\textendash}33022; UMA18-FEDERJA-084; ED431C2017/12; ED431G/08; ED431C2018/29; Y2018/EMT-5062; ED431F2018/02; U01 AG024904; W81XWH-12-2-0012},
	pages = {237-270},
	abstract = {Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.},
	keywords = {AI for social well-being, Alzheimer, Artificial intelligence (AI), Artificial neural networks (ANNs), Autism, Big Data, Computational neuroethology, Deep learning, Dyslexia, Emotion recognition, evolutionary computation, Glaucoma, Human{\textendash}machine interaction, machine learning, Neuroscience, Ontologies, Parkinson, Reinforcement learning, Robotics, Virtual reality},
	doi = {https://doi.org/10.1016/j.neucom.2020.05.078},
	author = {Juan M.G{\'o}rriz and Javier Ram{\'\i}rez and Andr{\'e}s Ort{\'\i}z and Francisco J. Mart{\'\i}nez-Murcia and Ferm{\'\i}n Segovia and John Suckling and Matthew Leming and Yu-Dong Zhang and Jos{\'e} Ram{\'o}n {\'A}lvarez-S{\'a}nchez and Guido Bologna and Paula Bonomini and Fernando E. Casado and David Charte and Francisco Charte and Ricardo Contreras and Alfredo Cuesta Infante and Richard J. Duro and Antonio Fern{\'a}ndez Caballero and Eduardo Fern{\'a}ndez Jover and Pedro G{\'o}mez Vilda and Manuel Gra{\~n}a and F. Herrera and Roberto Iglesias and Anna Lekova and Javier de Lope and Ezequiel L{\'o}pez Rubio and Rafael Mart{\'\i}nez Tom{\'a}s and Miguel A. Molina-Cabello and Antonio S. Montemayor and Paulo Novais and Daniel Palacios-Alonso and Juan J. Pantrigo and Bryson R. Payne and F{\'e}lix de la Paz L{\'o}pez and Mar{\'\i}a Ang{\'e}lica Pinninghoff and Mariano Rinc{\'o}n and Jos{\'e} Santos and Karl Thurnhofer-Hemsi and Athanasios Tsanas and Ramiro Varela and Jose M. Ferr{\'a}ndez}
}
@article {PALACIOS201660,
	title = {An extension of the FURIA classification algorithm to low quality data through fuzzy rankings and its application to the early diagnosis of dyslexia},
	journal = {Neurocomputing},
	volume = {176},
	year = {2016},
	note = {Recent Advancements in Hybrid Artificial Intelligence Systems and its Application to Real-World Problems},
	pages = {60 - 71},
	abstract = {An early detection and reeducation of dyslexic children is critical for their integration in the classroom. Parents and instructors can help the psychologist to detect potential cases of dyslexia before the children׳s writing age. Artificial intelligence tools can also assist in this task. Dyslexia symptoms are detected with tests whose results may be vague or ambiguous, thus machine learning techniques for low quality data are advised. In particular, in this paper it is suggested that a new extension to vague datasets of the classification algorithm FURIA (Fuzzy Unordered Rule Induction Algorithm) has advantages over other approaches in both the computational effort during the learning stage and the linguistic quality of the induced classification rules. The new approach is benchmarked with different test problems and compared to other artificial intelligence tools for dyslexia diagnosis in the literature.},
	keywords = {Dyslexia, FURIA, Fuzzy rule based classifiers, Low Quality Data},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2014.11.088},
	url = {http://www.sciencedirect.com/science/article/pii/S0925231215005706},
	author = {Ana Palacios and Luciano S{\'a}nchez and In{\'e}s Couso and S{\'e}bastien Destercke}
}
