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10.1016/j.compeleceng.2020.106765 http://scihub22266oqcxt.onion/10.1016/j.compeleceng.2020.106765 32834174!7372258!32834174     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       Comput+Electr+Eng 2020 ; 87 (ä): 106765 Nephropedia Template TP {{tp|p=32834174|t=2020. Deep learning disease prediction model for use with intelligent robots |pdf=|usr=}}{{32834174}} gab.com Text Deep learning disease prediction model for use with intelligent robots JO: Comput Electr Eng http://www.kidney.de/pget.php?&tw=1&pmid=32834174 #FOAvip Deep learning applications with robotics contribute to massive challenges that are not addressed in machine learning. The present world is currently suffering from the COVID-19 pandemic, and millions of lives are getting affected every day with extremely high death counts. Early detection of the disease would provide an opportunity for proactive treatment to save lives, which is the primary research objective of this study. The proposed prediction model caters to this objective following a stepwise approach through cleaning, feature extraction, and classification. The cleaning process constitutes the cleaning of missing values ,which is proceeded by outlier detection using the interpolation of splines and entropy-correlation. The cleaned data is then subjected to a feature extraction process using Principle Component Analysis. A Fitness Oriented Dragon Fly algorithm is introduced to select optimal features, and the resultant feature vector is fed into the Deep Belief Network. The overall accuracy of the proposed scheme experimentally evaluated with the traditional state of the art models. The results highlighted the superiority of the proposed model wherein it was observed to be 6.96% better than Firefly, 6.7% better than Particle Swarm Optimization, 6.96% better than Gray Wolf Optimization ad 7.22% better than Dragonfly Algorithm. Twit Text FOAVip Deep learning disease prediction model for use with intelligent robots ????Comput Electr Eng http://www.kidney.de/pget.php?&tw=1&pmid=32834174 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834174 #FOAvip English Wikipedia <ref>{{Cite pmid|32834174}}</ref> Deep learning disease prediction model for use with intelligent robots #MMPMID32834174 Koppu S; Maddikunta PKR; Srivastava G Comput Electr Eng 2020[Oct]; 87 (ä): 106765 PMID32834174show ga Deep learning applications with robotics contribute to massive challenges that are not addressed in machine learning. The present world is currently suffering from the COVID-19 pandemic, and millions of lives are getting affected every day with extremely high death counts. Early detection of the disease would provide an opportunity for proactive treatment to save lives, which is the primary research objective of this study. The proposed prediction model caters to this objective following a stepwise approach through cleaning, feature extraction, and classification. The cleaning process constitutes the cleaning of missing values ,which is proceeded by outlier detection using the interpolation of splines and entropy-correlation. The cleaned data is then subjected to a feature extraction process using Principle Component Analysis. A Fitness Oriented Dragon Fly algorithm is introduced to select optimal features, and the resultant feature vector is fed into the Deep Belief Network. The overall accuracy of the proposed scheme experimentally evaluated with the traditional state of the art models. The results highlighted the superiority of the proposed model wherein it was observed to be 6.96% better than Firefly, 6.7% better than Particle Swarm Optimization, 6.96% better than Gray Wolf Optimization ad 7.22% better than Dragonfly Algorithm. äDeep learning disease prediction model for use with intelligent robots(epmc).pdf DeepDyve Pubget Overpricing