Machine Learning Techniques and Analytics for Cloud Security. Группа авторов

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Machine Learning Techniques and Analytics for Cloud Security - Группа авторов


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0.263 Fall out 0.011 False discovery rate 0.357 False omission rate 0.011 Threat score 0.583 Prevalence threshold 0.149 Accuracy 0.977 Balance accuracy 0.856 Matthews correlation coefficient 0.725 Fowlkes-Mallows index 0.736 Informedness or bookmarker informedness 0.712 Markedness −0.101 F-score 0.736 Schematic illustration of the performance measurements of the F-score, balance accuracy, and Matthews correlation coefficient. Schematic illustration of glycan cloud.

      2.3.4 Glycan Cloud

      2.4 Conclusions and Future Work

      In this paper, the deep study of epidemic models and methodologies has been investigated, and hence, we proposed a new model that has not been used in the earlier studies. This model will include unsupervised algorithms in which the system will be trained in order to capture and react to influenza-like activities so that the preventive measures can be taken to get rid of the epidemic as early as possible and we have established a proposed method for identifying of differentially expressed glycans. We will extend this work in future to find bonding between glycoproteins structure and moreover how the structure of glycan will change from host to host and estimate the mathematical parameters for the molecular insight of epidemiological characteristics in pandemic H1N1 influenza virus and also find the relationship between biosecurity and glycan. Biosecurity basically defines lots of attempts that secure biological dynamism, abnormality, and future of biology. In this article, we have to take some biosecurity concerns to control arising infectious diseases and pandemic flu and also organize some biosecurity events for the swine flu pandemic. The novel H1N1 virus has been recognized within swine in various countries like as Asia and Europe. Swine producers should be responsible to save themselves, farm workers, and swines from the further develop of H1N1. They should take biosecurity precautions that are respecting to the virus. The National Pork Board is requesting farm producers and veterinarians to maintain the precautions. At first, maintain biosecurity cultures to prevent the H1N1 to take entry their swineherd and also pay attention thoroughly for swines’ health, and take some necessary precautions to reduce its spread. Farm producers need to follow up all rules to prevent spread of the H1N1 virus between human to human.

      References

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      3. Solovyov, A., Palacios, G., Briese, T., Lipkin, I.W., Rabadan, R., cluster analysis of the origins of the new Influenza A (H1N1) virus, Eurosurveillance, 2009;14(21):19224.

      4. Eichelberger, M.C. and H.W., Influenza neuraminidase as a vaccine antigen. Curr. Top. Microbiol. Immunol., 386, 275–299, 2015.

      5. York, A.I., Stevens, J., Alymova, V.I., Influenza virus N-linked glycosylation and innate immunity. Biosci. Rep., 39, BSR20171505, https://doi.org/10.1042/BSR20171505, 2018.

      6. Wanzeck, K., Boyd, L.K., McCullers, A.J., Glycan Shielding of the Influenza Virus Hemagglutinin Contributes to Immunopathology in Mice. American journal of respiratory and critical care medicine, 183, 767–773, October 8, 2010, 2011.

      7. Rahul, R., Kannan, T., V.S., Ram, S., Glycan–protein interactions in viral pathogenesis.Curr. Opin. Struct. Biol., 40, 153–162, 2016.

      8. Mena, I., Origins of the 2009 H1N1 Influenza pandemic in swine in Mexico.


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