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
Performance Analysis of a VGG based Deep Learning Model for..:
, In:
2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)
,
Ravindaran, Ramyea
;
N, Kasthuri
;
S, Preethi
... - p. 1-5 , 2023
Link:
https://doi.org/10.1109/ICCCNT56998.2023.10307169
RT T1 <cTitle>2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)</cTitle>: T1 <Title>Performance Analysis of a VGG based Deep Learning Model for Classification of Weeds and Crops</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10307169&Exemplar=1&LAN=DE A1 Ravindaran, Ramyea A1 N, Kasthuri A1 S, Preethi A1 B, Adithya A1 Sp, Gunaranjan A1 K, Dharanidharan A1 S, Aravinth YR 2023 SN 2473-7674 K1 Training K1 Analytical models K1 Computational modeling K1 Neurons K1 Crops K1 Computer architecture K1 Data models K1 classification K1 crop K1 weed K1 VGG19 K1 dropout K1 dense layer K1 accuracy SP 1 OP 5 LK http://dx.doi.org/https://doi.org/10.1109/ICCCNT56998.2023.10307169 DO https://doi.org/10.1109/ICCCNT56998.2023.10307169 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
