|
|
|
|
| |
|
|
| Taki, Morteza; Ajabshirchi, Yahya; Ranjbar, Seyed Faramarz; Matloobi, Mansour. |
| Artificial Neural Networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. After a comprehensive literature survey on the application of ANNs in greenhouses, this work describes the results of using ANNs to predict the roof temperature, inside air humidity, soil temperature and inside soil humidity (Tri, RHia, Tis, RHis), in a semi-solar greenhouse according to use some inside and outside parameters in the institute of renewable energy in East Azerbaijan province, Iran. For this purpose, a semi-solar greenhouse was designed and constructed for the first time in Iran. The model database selected beside on the main and important factors influence the four above variables inside... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Artificial Neural Networks; Semi-solar greenhouse; Multiple linear regression model; Iran. |
| Ano: 2016 |
URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/3672 |
| |
|
|
| SANTOS,PAULO C.C. DOS; LOPES,HELDER F.S.; ALCALDE,ROSANA; GONSALEZ,CLÁUDIO R.; ABE,JAIR M.; LOPEZ,LUIS F.. |
| ABSTRACT The high variability of HIV-1 as well as the lack of efficient repair mechanisms during the stages of viral replication, contribute to the rapid emergence of HIV-1 strains resistant to antiretroviral drugs. The selective pressure exerted by the drug leads to fixation of mutations capable of imparting varying degrees of resistance. The presence of these mutations is one of the most important factors in the failure of therapeutic response to medications. Thus, it is of critical to understand the resistance patterns and mechanisms associated with them, allowing the choice of an appropriate therapeutic scheme, which considers the frequency, and other characteristics of mutations. Utilizing Paraconsistents Artificial Neural Networks, seated in... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Artificial Neural Networks; HIV; Genotyping; Paraconsistent logic; Paraconsistents Artificial Neural Networks; Pattern recognition. |
| Ano: 2016 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652016000100323 |
| |
|
| |
|
|
|
