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
Hybrid CNN & Random Forest Model for Effective Bitter Orang..:
, In:
2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM)
,
Banerjee, Deepak
;
Sharma, Neha
;
Chauhan, Rahul
.. - p. 1-6 , 2024
Link:
https://doi.org/10.1109/ICIPTM59628.2024.10563873
RT T1
2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM)
: T1
Hybrid CNN & Random Forest Model for Effective Bitter Orange Leaf Disease Diagnosis
UL https://suche.suub.uni-bremen.de/peid=ieee-10563873&Exemplar=1&LAN=DE A1 Banerjee, Deepak A1 Sharma, Neha A1 Chauhan, Rahul A1 Singh, Mukesh A1 Kumar, Bura Vijay YR 2024 K1 Productivity K1 Accuracy K1 Predictive models K1 Distance measurement K1 Convolutional neural networks K1 Medical diagnosis K1 Random forests K1 Precision K1 Security K1 Research K1 Production SP 1 OP 6 LK http://dx.doi.org/https://doi.org/10.1109/ICIPTM59628.2024.10563873 DO https://doi.org/10.1109/ICIPTM59628.2024.10563873 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)