Rezultati

eNauka >  Results >  Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme
Title: Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme
Authors: V. Kandasamy; P. Trojovský; F. Machot; K. Kyamakya; Nebojsa Bacanin  ; S. Askar; M. Abouhawwash
Issue Date: 2021
Publication: SENSORS
ISSN: 1424-8220 Sensors Search Idenfier
Type: Article
Collation: vol. 21 br. 22 str. 7582-7582
DOI: 10.3390/s21227582
WoS-ID: 000724454900001
Scopus-ID: 2-s2.0-85118926540
URI: https://enauka.gov.rs/handle/123456789/708329
http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/8917
URL: https://www.mdpi.com/1424-8220/21/22/7582
M-category: 
21M21

29
SCOPUSTM
5
OpenCitations
17
WEB OF SCIENCETM
Alt metrika
Dimensions
Unpaywall

Rezultati na eNauka su zaštićeni autorskim pravima i sva prava su zadržana, osim ako nije drugačije naznačeno.