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10.3389/fpubh.2021.675801 http://scihub22266oqcxt.onion/10.3389/fpubh.2021.675801 33898386!8062709!33898386     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33898386&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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       Front+Public+Health 2021 ; 9 (ä): 675801 Nephropedia Template TP {{tp|p=33898386|t=2021. Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models |pdf=|usr=}}{{33898386}} gab.com Text Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models JO: Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33898386 #FOAvip This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods. Twit Text FOAVip Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models ????Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33898386 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33898386 #FOAvip English Wikipedia <ref>{{Cite pmid|33898386}}</ref> Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models #MMPMID33898386 Pan WT; Huang QY; Yang ZY; Zhu FY; Pang YN; Zhuang ME Front Public Health 2021[]; 9 (ä): 675801 PMID33898386show ga This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods. |*COVID-19[MESH] |*Deep Learning[MESH] |*Tourism[MESH] |Algorithms[MESH] |China[MESH]Determinants of Tourism Stocks During the COVID-19: Evidence From the (epmc).pdf DeepDyve Pubget Overpricing