I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Assessing Breast Cancer Risk with 5G-Enabled Data-Sensorize..:
, In:
2023 South American Conference On Visible Light Communications (SACVLC)
,
Galeas-Cutino, Ignacio
;
Soto, Ismael
;
Zamorano-Illanes, Raul
.. - p. 29-34 , 2023
Link:
https://doi.org/10.1109/SACVLC59022.2023.10347801
RT T1 <cTitle>2023 South American Conference On Visible Light Communications (SACVLC)</cTitle>: T1 <Title>Assessing Breast Cancer Risk with 5G-Enabled Data-Sensorized Sample using Random Forest</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10347801&Exemplar=1&LAN=DE A1 Galeas-Cutino, Ignacio A1 Soto, Ismael A1 Zamorano-Illanes, Raul A1 Adasme, Pablo A1 Gutierrez, Sebastian YR 2023 K1 Performance evaluation K1 Machine learning algorithms K1 5G mobile communication K1 Prediction algorithms K1 Boosting K1 Breast cancer K1 Classification algorithms K1 Breast Cancer K1 Health K1 Random Forest K1 5G-enabled Devices K1 Gradient Boosting Method SP 29 OP 34 LK http://dx.doi.org/https://doi.org/10.1109/SACVLC59022.2023.10347801 DO https://doi.org/10.1109/SACVLC59022.2023.10347801 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
