scholarly journals Use of biplane quantitative angiographic imaging with ensemble neural networks to assess reperfusion status during mechanical thrombectomy

2021 ◽  
Author(s):  
Mohammad Mahdi Shiraz Bhurwani ◽  
Kenneth V. Snyder ◽  
Muhammad Waqas ◽  
Maxim Mokin ◽  
Ryan A. Rava ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mechanical Thrombectomy ◽  
Ensemble Neural Networks

scholarly journals Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Patricia Melin ◽  
Julio Cesar Monica ◽  
Daniela Sanchez ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Network Models ◽  
Neural Network Models ◽  
Validation Data ◽  
Data Set ◽  
Ensemble Neural Networks ◽  
Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

Regularized ensemble neural networks models in the Extreme Learning Machine framework

2019 ◽  
Vol 361 ◽  
pp. 196-211 ◽  
Author(s):  
Carlos Perales-González ◽  
Mariano Carbonero-Ruz ◽  
David Becerra-Alonso ◽  
Javier Pérez-Rodríguez ◽  
Francisco Fernández-Navarro
Keyword(s):  
Neural Networks ◽  
Extreme Learning Machine ◽  
Ensemble Neural Networks ◽  
Learning Machine
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