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Characteristics of the data sets used to evaluate Machine L..:

Sebastião Rogério da Silva Neto (11944944) ; Thomás Tabosa Oliveira (11944947) ; Igor Vitor Teixeira (11944950)...
https://figshare.com/articles/dataset/Characteristics_of_the_data_sets_used_to_evaluate_Machine_Learning_and_Deep_Learning_models_for_arboviral_diseases_classification_/18340602. , 2022

Link: https://doi.org/10.1371/journal.pntd.0010061.t002

RT Journal
T1 <Title>Characteristics of the data sets used to evaluate Machine Learning and Deep Learning models for arboviral diseases classification</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_18340602&Exemplar=1&LAN=DE
				
A1 Sebastião Rogério da Silva Neto (11944944)
A1 Thomás Tabosa Oliveira (11944947)
A1 Igor Vitor Teixeira (11944950)
A1 Samuel Benjamin Aguiar de Oliveira (11944953)
A1 Vanderson Souza Sampaio (3167037)
A1 Theo Lynn (8499566)
A1 Patricia Takako Endo (10373252)
YR 2022
K1 Medicine
K1 Biotechnology
K1 Cancer
K1 Infectious Diseases
K1 systematic literature review
K1 patient &# 8217
K1 initial 963 records
K1 difficult task due
K1 primarily using tree
K1 present similar symptoms
K1 identify key papers
K1 15 relevant papers
K1 entire clinical process
K1 deep learning techniques
K1 support clinical diagnosis
K1 based ml algorithms
K1 identified using dl
K1 deep learning
K1 clinical diagnosis
K1 present evidence
K1 making process
K1 based search
K1 machine learning
K1 xlink ">
K1 urban slums
K1 thus increasing
K1 studies investigating
K1 south america
K1 rural areas
K1 primarily affect
K1 poorest populations
K1 often living
K1 large proportion
K1 investigated models
K1 help physicians
K1 google scholar
K1 current research
K1 conflict zones
K1 concurrent circulation
K1 class problems
K1 binary classification
K1 automatic classification
K1 associated treatment
K1 arboviral diseases
JF https://figshare.com/articles/dataset/Characteristics_of_the_data_sets_used_to_evaluate_Machine_Learning_and_Deep_Learning_models_for_arboviral_diseases_classification_/18340602
LK http://dx.doi.org/https://doi.org/10.1371/journal.pntd.0010061.t002
DO https://doi.org/10.1371/journal.pntd.0010061.t002
SF ELIB - SuUB Bremen

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