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
Enabling Big Data and Machine Learning Applications in High..:
, In:
NOMS 2024-2024 IEEE Network Operations and Management Symposium
,
Dahdal, Simon
;
Tortonesi, Mauro
- p. 1-4 , 2024
Link:
https://doi.org/10.1109/NOMS59830.2024.10574906
RT T1
NOMS 2024-2024 IEEE Network Operations and Management Symposium
: T1
Enabling Big Data and Machine Learning Applications in High-Stakes Environments
UL https://suche.suub.uni-bremen.de/peid=ieee-10574906&Exemplar=1&LAN=DE A1 Dahdal, Simon A1 Tortonesi, Mauro YR 2024 SN 2374-9709 K1 Training K1 Machine learning algorithms K1 Disasters K1 Decision making K1 Machine learning K1 Organizations K1 Real-time systems K1 High-Stakes Environments K1 Humanitarian Assistance and Disaster Relief (HADR) K1 Industry 5.0 K1 Machine Learning K1 Big Data K1 Machine Learning Operations (MLOps) K1 Data Gravity SP 1 OP 4 LK http://dx.doi.org/https://doi.org/10.1109/NOMS59830.2024.10574906 DO https://doi.org/10.1109/NOMS59830.2024.10574906 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)