About: Study on Prediction Model of HIV Incidence Based on GRU Neural Network Optimized by MHPSO

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Study on Prediction Model of HIV Incidence Based on GRU Neural Network Optimized by MHPSO Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Study on Prediction Model of HIV Incidence Based on GRU Neural Network Optimized by MHPSO
Creator	Li, Jing Wang, Jie Li, Xiaoming Sheng Qin, Xu, Xianghui Yuan, Juxiang
Source	PMC
abstract	Acquired Immune Deficiency Syndrome (AIDS) is still one of the most life-threatening diseases in the world. Moreover, new infections are still potentially increasing. This difficult problem must be solved. Early warning is the most effective way to solve this problem. Here, we aim to determine the best performing model to track the epidemic of AIDS, which will provide a methodological basis for testing the time characteristics of the disease. From January 2004 to January 2018, we built four computing methods based on AIDS dataset: BPNN model, RNN model, LSTM model and MHPSO-GRU model. Compare the final estimated performance to determine the preferred method. Result. Considering the root mean square error (RMSE), mean absolute error (MAE), mean error rate (MER) and mean absolute percentage error (MAPE) in the simulation and prediction subsets, the MHPSO-GRU model is determined as the best performance technology. Estimates for the period from May 2018 to December 2020 suggest that the event appears to continue to increase and remain high.
has issue date	2020-03-10(xsd:dateTime)
bibo:doi	10.1109/access.2020.2979859
bibo:pmid	32391239
has license	cc-by
sha1sum (hex)	9ed0cf8e65aef68e990b1c46c6ded2315c53982e
schema:url	https://doi.org/10.1109/access.2020.2979859
resource representing a document's title	Study on Prediction Model of HIV Incidence Based on GRU Neural Network Optimized by MHPSO
has PubMed Central identifier	PMC7176027
has PubMed identifier	32391239
schema:publication	IEEE Access
resource representing a document's body	covid:9ed0cf8e65aef68e990b1c46c6ded2315c53982e#body_text
is schema:about of	named entity 'event' named entity 'root' named entity 'ABSOLUTE' named entity 'DISEASES' named entity 'LSTM' named entity 'RATE' named entity 'TO INCREASE' named entity 'PERFORMING' named entity 'HIGH' named entity 'INCREASING' named entity 'problem' named entity 'period' named entity 'AIDS' named entity 'LSTM' named entity 'continue' named entity 'December' named entity 'model' named entity 'January' named entity 'dataset' named entity 'RNN' named entity 'MAPE' named entity 'percentage error' named entity 'RMSE' named entity 'LSTM' named entity 'DEEP LEARNING ALGORITHMS' named entity 'time' named entity 'AIDS' named entity 'inversely proportional' named entity 'iatrogenic' named entity 'LSTM' named entity 'NAE' named entity 'ANN' named entity 'LSTM' named entity 'time series' named entity 'systematic review' named entity 'ANN' named entity 'disease incidence' named entity 'loss function' named entity 'China' named entity 'short-term' named entity 'GRU' named entity 'incidence rate' named entity 'time series data' named entity 'perinatal transmission' named entity 'global minimum' named entity 'China' named entity 'fitness function' named entity 'AIDS' named entity 'RMSE' named entity 'GRU' named entity 'time series data' named entity 'model training' named entity 'time series' named entity 'neural network' named entity 'HIV' named entity 'neural network' named entity 'AIDS epidemic' named entity 'infectious diseases' named entity 'AIDS' named entity 'activation function' named entity 'AIDS' named entity 'AIDS' named entity 'AIDS' named entity 'gradient descent' named entity 'mortality rate' named entity 'artificial neural network' named entity 'time series' named entity 'AIDS' named entity 'Shallow learning' named entity 'LSTM' named entity 'incidence rate' named entity 'machine learning' named entity 'prediction interval' named entity 'algorithm' named entity 'large deviation' named entity 'data points'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software