Results
eNauka >
Results >
DWFH: An improved data-driven deep weather forecasting hybrid model using Transductive Long Short Term Memory (T-LSTM)
| Title: | DWFH: An improved data-driven deep weather forecasting hybrid model using Transductive Long Short Term Memory (T-LSTM) | Authors: | Venkatachalam, K.; Trojovský, Pavel; Pamučar, Dragan   ; Bačanin Džakula, Nebojša ; Simić, Vladimir |
Issue Date: | 2023 | Publication: | Expert systems with applications | ISSN: | 0957-4174 Expert Systems with Applications Search Idenfier |
Publisher: | OxfordNew York : Pergamon Press | Type: | Article | Collation: | vol. 213 str. 119270-119270 | DOI: | 10.1016/j.eswa.2022.119270 | WoS-ID: | 000890656300005 | Scopus-ID: | 2-s2.0-85145616827 | VBS COBISS: | 85561353 | URI: | https://rfos.fon.bg.ac.rs/handle/123456789/2486 https://enauka.gov.rs/handle/123456789/753308 https://plus.cobiss.net/cobiss/sr/sr/bib/85561353#izum.si http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/9462 |
URL: | https://www.sciencedirect.com/science/article/abs/pii/S0957417422022886?via%3Dihub | M-category: | 21aM21a |
40
SCOPUSTM
24
WEB OF SCIENCETM
Altmetric
Dimensions
Unpaywall
Items in eNauka are protected by copyright, with all rights reserved, unless otherwise indicated.