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10.3390/genes8110326 http://scihub22266oqcxt.onion/10.3390/genes8110326 C5704239!5704239!29149087     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genes+(Basel) 2017 ; 8 (11): ä Nephropedia Template TP {{tp|p=29149087|t=2017. Taxonomic Classification for Living Organisms Using Convolutional Neural Networks |pdf=|usr=}}{{29149087}} gab.com Text Taxonomic Classification for Living Organisms Using Convolutional Neural Networks JO: Genes (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=29149087 #FOAvip Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis. Twit Text FOAVip Taxonomic Classification for Living Organisms Using Convolutional Neural Networks ????Genes (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=29149087 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29149087 #FOAvip English Wikipedia <ref>{{Cite pmid|29149087}}</ref> Taxonomic Classification for Living Organisms Using Convolutional Neural Networks #MMPMID29149087 Khawaldeh S; Pervaiz U; Elsharnoby M; Alchalabi AE; Al-Zubi N Genes (Basel) 2017[Nov]; 8 (11): ä PMID29149087show ga Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis. äTaxonomic Classification for Living Organisms Using Convolutional Neur(epmc).pdf DeepDyve Pubget Overpricing