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10.1140/epjp/s13360-021-01285-3 http://scihub22266oqcxt.onion/10.1140/epjp/s13360-021-01285-3 33758734!7970774!33758734     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+Phys+J+Plus 2021 ; 136 (3): 319 Nephropedia Template TP {{tp|p=33758734|t=2021. Recurrent neural network ensemble, a new instrument for the prediction of infectious diseases |pdf=|usr=}}{{33758734}} gab.com Text Recurrent neural network ensemble, a new instrument for the prediction of infectious diseases JO: Eur Phys J Plus http://www.kidney.de/pget.php?&tw=1&pmid=33758734 #FOAvip Infectious diseases afflict human beings since ancient times. We can classify the infectious disease in two principal types: the emerging diseases, that are caused by new pathogens, and the re-emerging diseases, due to a new spread of a known pathogen. Both types can then be subdivided in natural, accidental or intentional spreads. The risk associated to infectious diseases strongly increased in the last decades, especially because of the globalisation, which leads to a denser and more efficient link between nations, involving that a local infectious may easily spread worldwide, such as the SARS-CoV-2 in 2019-2020. The development of new methods to predict the spread of diseases is crucial. However, sometimes the variables are too many that classical algorithms fail in the prediction. Aim of this work is to investigate the use of an ensemble of recurrent neural networks for disease prediction, using real flu's data to train and develop an instrument with the capability to determine the future flues. Two different types of study have been conducted. The first study investigates the influence of the neural network architecture, and it has been performed using 12 seasons to train the model and 3 seasons to test it. The second test aims to investigate the number of seasons needed to have a good prediction for future ones. The results demonstrated that this approach could ensure very high performances also with simple architectures. The ensemble approach allows to have information about the uncertainty of the prediction, allowing also to take countermeasures as a function of that value. In the future, the use of this approach may be applied to many other types of disease. Twit Text FOAVip Recurrent neural network ensemble, a new instrument for the prediction of infectious diseases ????Eur Phys J Plus http://www.kidney.de/pget.php?&tw=1&pmid=33758734 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33758734 #FOAvip English Wikipedia <ref>{{Cite pmid|33758734}}</ref> Recurrent neural network ensemble, a new instrument for the prediction of infectious diseases #MMPMID33758734 Puleio A Eur Phys J Plus 2021[]; 136 (3): 319 PMID33758734show ga Infectious diseases afflict human beings since ancient times. We can classify the infectious disease in two principal types: the emerging diseases, that are caused by new pathogens, and the re-emerging diseases, due to a new spread of a known pathogen. Both types can then be subdivided in natural, accidental or intentional spreads. The risk associated to infectious diseases strongly increased in the last decades, especially because of the globalisation, which leads to a denser and more efficient link between nations, involving that a local infectious may easily spread worldwide, such as the SARS-CoV-2 in 2019-2020. The development of new methods to predict the spread of diseases is crucial. However, sometimes the variables are too many that classical algorithms fail in the prediction. Aim of this work is to investigate the use of an ensemble of recurrent neural networks for disease prediction, using real flu's data to train and develop an instrument with the capability to determine the future flues. Two different types of study have been conducted. The first study investigates the influence of the neural network architecture, and it has been performed using 12 seasons to train the model and 3 seasons to test it. The second test aims to investigate the number of seasons needed to have a good prediction for future ones. The results demonstrated that this approach could ensure very high performances also with simple architectures. The ensemble approach allows to have information about the uncertainty of the prediction, allowing also to take countermeasures as a function of that value. In the future, the use of this approach may be applied to many other types of disease. äRecurrent neural network ensemble, a new instrument for the prediction(epmc).pdf DeepDyve Pubget Overpricing