Zobrazeno 1 - 3
of 3
pro vyhledávání: '"José R. Abreu-Pederzini"'
Autor:
Guillermo A. Martínez-Mascorro, José R. Abreu-Pederzini, José C. Ortiz-Bayliss, Angel Garcia-Collantes, Hugo Terashima-Marín
Publikováno v:
Computation, Vol 9, Iss 2, p 24 (2021)
Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks generate vast amounts of data, and the surveillance staff cannot process all t
Externí odkaz:
https://doaj.org/article/68cd56c4985c408cac71d5f63d53ee23
Autor:
Hugo Terashima-Marín, José Carlos Ortiz-Bayliss, Guillermo A. Martínez-Mascorro, Angel Garcia-Collantes, José R. Abreu-Pederzini
Publikováno v:
Computation, Vol 9, Iss 24, p 24 (2021)
udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
instname
Computation
Volume 9
Issue 2
udiMundus: Repositorio Institucional de la Universidad a Distancia de Madrid
Universidad a Distancia de Madrid (UDIMA)
udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
instname
Computation
Volume 9
Issue 2
udiMundus: Repositorio Institucional de la Universidad a Distancia de Madrid
Universidad a Distancia de Madrid (UDIMA)
Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks generate vast amounts of data, and the surveillance staff cannot process all t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0ddfb2f146b31a7c7f332558cc82abf
https://www.mdpi.com/2079-3197/9/2/24
https://www.mdpi.com/2079-3197/9/2/24
Autor:
Hugo Terashima-Marín, Guillermo A. Martínez-Mascorro, José R. Abreu-Pederzini, José Carlos Ortiz-Bayliss
Publikováno v:
Applied Sciences, Vol 11, Iss 9374, p 9374 (2021)
Applied Sciences
Volume 11
Issue 20
Applied Sciences
Volume 11
Issue 20
Artificial neural networks are efficient learning algorithms that are considered to be universal approximators for solving numerous real-world problems in areas such as computer vision, language processing, or reinforcement learning. To approximate a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b498b5773b26533c2a859d768bcaba69
https://doi.org/10.3390/app11209374
https://doi.org/10.3390/app11209374