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
Predicting Hydrogen Fuel Cell Capacity using Supervised Lea..:
, In:
2024 International Conference on Inventive Computation Technologies (ICICT)
,
Dasavandi Krishnamurthy, Vinay Nagarad
;
Degadwala, Sheshang
;
Vyas, Dhairya
- p. 1934-1938 , 2024
Link:
https://doi.org/10.1109/ICICT60155.2024.10544401
RT T1 <cTitle>2024 International Conference on Inventive Computation Technologies (ICICT)</cTitle>: T1 <Title>Predicting Hydrogen Fuel Cell Capacity using Supervised Learning Models</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10544401&Exemplar=1&LAN=DE A1 Dasavandi Krishnamurthy, Vinay Nagarad A1 Degadwala, Sheshang A1 Vyas, Dhairya YR 2024 SN 2767-7788 K1 Adaptation models K1 Computational modeling K1 Hydrogen K1 Fuel cells K1 Predictive models K1 Polynomials K1 Reliability K1 Hydrogen fuel cells K1 Capacity prediction K1 Supervised learning models K1 Non-linear relationships K1 Ensemble learning K1 Evaluation metrics SP 1934 OP 1938 LK http://dx.doi.org/https://doi.org/10.1109/ICICT60155.2024.10544401 DO https://doi.org/10.1109/ICICT60155.2024.10544401 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
