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10.1007/s11042-021-11065-x http://scihub22266oqcxt.onion/10.1007/s11042-021-11065-x 34093070!8170457!34093070     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Multimed+Tools+Appl 2022 ; 81 (14): 19341-19349 Nephropedia Template TP {{tp|p=34093070|t=2022. A novel self-learning semi-supervised deep learning network to detect fake news on social media |pdf=|usr=}}{{34093070}} gab.com Text A novel self-learning semi-supervised deep learning network to detect fake news on social media JO: Multimed Tools Appl http://www.kidney.de/pget.php?&tw=1&pmid=34093070 #FOAvip Social media has become a popular means for people to consume and share news. However, it also enables the extensive spread of fake news, that is, news that deliberately provides false information, which has a significant negative impact on society. Especially recently, the false information about the new coronavirus disease 2019 (COVID-19) has spread like a virus around the world. The state of the Internet is forcing the world's tech giants to take unprecedented action to protect the "information health" of the public. Despite many existing fake news datasets, comprehensive and effective algorithms for detecting fake news have become one of the major obstacles. In order to address this issue, we designed a self-learning semi-supervised deep learning network by adding a confidence network layer, which made it possible to automatically return and add correct results to help the neural network to accumulate positive sample cases, thus improving the accuracy of the neural network. Experimental results indicate that our network is more accurate than the existing mainstream machine learning methods and deep learning methods. Twit Text FOAVip A novel self-learning semi-supervised deep learning network to detect fake news on social media ????Multimed Tools Appl http://www.kidney.de/pget.php?&tw=1&pmid=34093070 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34093070 #FOAvip English Wikipedia <ref>{{Cite pmid|34093070}}</ref> A novel self-learning semi-supervised deep learning network to detect fake news on social media #MMPMID34093070 Li X; Lu P; Hu L; Wang X; Lu L Multimed Tools Appl 2022[]; 81 (14): 19341-19349 PMID34093070show ga Social media has become a popular means for people to consume and share news. However, it also enables the extensive spread of fake news, that is, news that deliberately provides false information, which has a significant negative impact on society. Especially recently, the false information about the new coronavirus disease 2019 (COVID-19) has spread like a virus around the world. The state of the Internet is forcing the world's tech giants to take unprecedented action to protect the "information health" of the public. Despite many existing fake news datasets, comprehensive and effective algorithms for detecting fake news have become one of the major obstacles. In order to address this issue, we designed a self-learning semi-supervised deep learning network by adding a confidence network layer, which made it possible to automatically return and add correct results to help the neural network to accumulate positive sample cases, thus improving the accuracy of the neural network. Experimental results indicate that our network is more accurate than the existing mainstream machine learning methods and deep learning methods. äA novel self-learning semi-supervised deep learning network to detect (epmc).pdf DeepDyve Pubget Overpricing