An Overview of Machine Learning and 5G for People with Disabilities
dc.contributor.author | Domingo, M. C. | |
dc.date.accessioned | 2021-12-21T20:10:11Z | |
dc.date.available | 2021-12-21T20:10:11Z | |
dc.date.issued | 2021 | |
dc.description.abstract | Currently, over a billion people, including children (or about 15% of the world's population), are estimated to be living with disability, and this figure is going to increase to beyond two billion by 2050. People with disabilities generally experience poorer levels of health, fewer achievements in education, fewer economic opportunities, and higher rates of poverty. Artificial intelligence and 5G can make major contributions towards the assistance of people with disabilities, so they can achieve a good quality of life. In this paper, an overview of machine learning and 5G for people with disabilities is provided. For this purpose, the proposed 5G network slicing architecture for disabled people is introduced. Different application scenarios and their main benefits are considered to illustrate the interaction of machine learning and 5G. Critical challenges have been identified and addressed. | en_ES |
dc.identifier.citation | Domingo, M. C. (2021). An Overview of Machine Learning and 5G for People with Disabilities. En Sensors (Basel) (Vol. 21, Número 22). https://doi.org/10.3390/s21227572 | en_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://repositoriobibliotecas.uv.cl/handle/uvscl/3120 | |
dc.language.iso | en_US | en_ES |
dc.publisher | Sensors (Basel) | en_ES |
dc.subject | 5G | en_ES |
dc.subject | APPLICATIONS | en_ES |
dc.subject | MACHINE LEARNING | en_ES |
dc.subject | NETWORK | en_ES |
dc.subject | SLICING ARCHITECTURE | en_ES |
dc.subject | PEOPLE WITH DISABILITIES | en_ES |
dc.subject | RESEARCH | en_ES |
dc.subject | CHALLENGES | en_ES |
dc.subject | WIRELESS | en_ES |
dc.subject | COMMUNICATION | en_ES |
dc.title | An Overview of Machine Learning and 5G for People with Disabilities | en_ES |
dc.type | Articulo | en_ES |
dc.ubicacion | https://doi.org/10.3390/s21227572 | en_ES |
uv.catalogador | SGG | en_ES |
uv.colection | Bibliografía 5G | en_ES |