@misc{10481/77987,
year = {2020},
month = {5},
url = {https://hdl.handle.net/10481/77987},
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
arti cial intelligence within the interplay between natural and arti cial computation. A review of recent
works published in the latter  eld and the state the art are summarized in a comprehensive and self-contained
way to provide a baseline framework for the international community in arti cial 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  elds covered in the essay, from theoretical models in arti cial intelligence and machine learning to the most prospective applications in robotics, neuroscience,
brain computer interfaces, medicine and society, in general.},
organization = {Ministry of Science and Innovation, Spain (MICINN)
Spanish Government	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},
organization = {Autonomous Government of Andalusia (Spain)	UMA18-FEDERJA-084},
organization = {Conselleria de Cultura, Educacion e Ordenacion Universitaria of Galicia	ED431C2017/12	
ED431G/08	
ED431C2018/29	
Y2018/EMT-5062	
ED431F2018/02},
organization = {Michael J. Fox Foundation for Parkinson's Research},
organization = {Abbott Laboratories},
organization = {Biogen},
organization = {F. Hoffman-La Roche Ltd.},
organization = {General Electric
GE Healthcare},
organization = {Roche Holding},
organization = {Genentech},
organization = {Pfizer},
organization = {United States Department of Health & Human Services
National Institutes of Health (NIH) - USA},
organization = {NIH National Institute of Neurological Disorders & Stroke (NINDS)	U01 AG024904},
organization = {DOD ADNI (Department of Defense)	W81XWH-12-2-0012},
organization = {United States Department of Health & Human Services
National Institutes of Health (NIH) - USA
NIH National Institute on Aging (NIA)		
United States Department of Health & Human Services
National Institutes of Health (NIH) - USA},
organization = {NIH National Institute of Biomedical Imaging & Bioengineering (NIBIB)},
organization = {AbbVie},
organization = {Alzheimer's Association		
Alzheimer's Drug Discovery Foundation},
organization = {Araclon Biotech},
organization = {BioClinica, Inc.		
Biogen},
organization = {Bristol-Myers Squibb},
organization = {CereSpir, Inc.},
organization = {Eisai Co Ltd},
organization = {Elan Pharmaceuticals, Inc.},
organization = {Eli Lilly},
organization = {EuroImmun},
organization = {F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.},
organization = {Fujirebio},
organization = {IXICO Ltd.},
organization = {Janssen Alzheimer Immunotherapy Research AMP; Development, LLC.},
organization = {Johnson AMP; Johnson Pharmaceutical Research AMP; Development LLC.},
organization = {Lumosity},
organization = {Lundbeck Corporation},
organization = {Merck & Company},
organization = {Meso Scale Diagnostics, LLC.},
organization = {NeuroRx Research		
Neurotrack Technologies},
organization = {Novartis},
organization = {Piramal Imaging},
organization = {Servier},
organization = {Takeda Pharmaceutical Company Ltd},
organization = {Transition Therapeutics},
organization = {Canadian Institutes of Health Research (CIHR)},
organization = {Spanish Government	FPU15/06512	
FPU17/04154	
FJCI-2017-33022},
publisher = {Elsevier},
keywords = {Arti cial intelligence (AI)},
keywords = {Machine learning},
keywords = {Deep learning},
keywords = {Reinforcement learning},
keywords = {Evolutionary computation},
keywords = {Ontologies},
keywords = {Arti cial neural networks (ANNs)},
keywords = {Big data},
keywords = {Data fusion},
keywords = {Robots},
keywords = {Neuroscience},
keywords = {Human-machine interaction},
keywords = {Virtual reality},
keywords = {Emotion recognition},
keywords = {Computational neuroethology},
keywords = {Electroencephalography (EEG)},
keywords = {Mobile EEG},
keywords = {Brain-computer interface (BCI)},
keywords = {Connectivity},
keywords = {Body pose and motion estimation},
keywords = {Heart-rate variability},
keywords = {Gait},
keywords = {Speaking},
keywords = {Gaming},
keywords = {Neuroacoustical stimulation},
keywords = {Instability phonation},
keywords = {Autism},
keywords = {Dyslexia},
keywords = {Alzheimer},
keywords = {Parkinson},
keywords = {Ischemia},
keywords = {Glaucoma},
keywords = {AI for social well-being},
keywords = {Education},
keywords = {Home care},
keywords = {Assistance},
title = {Artificial intelligence within the interplay between natural and arti cial Computation: advances in data science, trends and applications},
doi = {10.1016/j.neucom.2020.05.078},
author = {Gorriz Sáez, Juan Manuel and Ramírez Pérez De Inestrosa, Javier and Segovia Román, Fermín and Charte Luque, Francisco David and Herrera Triguero, Francisco},
}