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10.1007/s12652-020-02119-3 http://scihub22266oqcxt.onion/10.1007/s12652-020-02119-3 C7239608!7239608!32837593     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Ambient+Intell+Humaniz+Comput 2021 ; 12 (1): 1023-37 Nephropedia Template TP {{tp|p=32837593|t=2021. Integrating machine learning and open data into social Chatbot for filtering information rumor |pdf=|usr=}}{{32837593}} gab.com Text Integrating machine learning and open data into social Chatbot for filtering information rumor JO: J Ambient Intell Humaniz Comput http://www.kidney.de/pget.php?&tw=1&pmid=32837593 #FOAvip Social networks have become a major platform for people to disseminate information, which can include negative rumors. In recent years, rumors on social networks has caused grave problems and considerable damages. We attempted to create a method to verify information from numerous social media messages. We propose a general architecture that integrates machine learning and open data with a Chatbot and is based cloud computing (MLODCCC), which can assist users in evaluating information authenticity on social platforms. The proposed MLODCCC architecture consists of six integrated modules: cloud computing, machine learning, data preparation, open data, chatbot, and intelligent social application modules. Food safety has garnered worldwide attention. Consequently, we used the proposed MLODCCC architecture to develop a Food Safety Information Platform (FSIP) that provides a friendly hyperlink and chatbot interface on Facebook to identify credible food safety information. The performance and accuracy of three binary classification algorithms, namely the decision tree, logistic regression, and support vector machine algorithms, operating in different cloud computing environments were compared. The binary classification accuracy was 0.769, which indicates that the proposed approach accurately classifies using the developed FSIP. Twit Text FOAVip Integrating machine learning and open data into social Chatbot for filtering information rumor ????J Ambient Intell Humaniz Comput http://www.kidney.de/pget.php?&tw=1&pmid=32837593 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32837593 #FOAvip English Wikipedia <ref>{{Cite pmid|32837593}}</ref> Integrating machine learning and open data into social Chatbot for filtering information rumor #MMPMID32837593 Hsu IC; Chang CC J Ambient Intell Humaniz Comput 2021[]; 12 (1): 1023-37 PMID32837593show ga Social networks have become a major platform for people to disseminate information, which can include negative rumors. In recent years, rumors on social networks has caused grave problems and considerable damages. We attempted to create a method to verify information from numerous social media messages. We propose a general architecture that integrates machine learning and open data with a Chatbot and is based cloud computing (MLODCCC), which can assist users in evaluating information authenticity on social platforms. The proposed MLODCCC architecture consists of six integrated modules: cloud computing, machine learning, data preparation, open data, chatbot, and intelligent social application modules. Food safety has garnered worldwide attention. Consequently, we used the proposed MLODCCC architecture to develop a Food Safety Information Platform (FSIP) that provides a friendly hyperlink and chatbot interface on Facebook to identify credible food safety information. The performance and accuracy of three binary classification algorithms, namely the decision tree, logistic regression, and support vector machine algorithms, operating in different cloud computing environments were compared. The binary classification accuracy was 0.769, which indicates that the proposed approach accurately classifies using the developed FSIP. äIntegrating machine learning and open data into social Chatbot for fil(epmc).pdf DeepDyve Pubget Overpricing