Machine Learning Techniques and Analytics for Cloud Security. Группа авторов
Чтение книги онлайн.

Читать онлайн книгу Machine Learning Techniques and Analytics for Cloud Security - Группа авторов страница 21

СКАЧАТЬ Sharma, A. and Garg, S., Comparative Study of Cloud Computing Solutions. IJCST, 6, 4, pp. 231–233, Oct - Dec 2015.

      24. Data Protection in Cloud, https://www.nutanix.com/theforecastbynutanix/technology/protecting-data-when-running-ai-in-the-cloud, Accessed on 10th December, 2020.

      25. AI and Hybrid Cloud, https://www.hcltech.com/blogs/growing-bond-between-ai-hybridcloud, Accessed on 5th December, 2020.

      26. Pervasive Encryption, https://www.ibm.com/support/z-content-solutions/pervasive-encryption/, Accessed on 6th December, 2020.

      27. Nutanix, https://www.nutanix.com/, Accessed on 10th December, 2020.

      28. Quantifi, https://www.quantifisolutions.com/overview, Accessed on 10th December, 2020.

      29. IBM Watson, https://www.ibm.com/in-en/watson, Accessed on 7th December, 2020.

      30. Watson Blog, https://www.ibm.com/blogs/watson/2019/09/gartner-names-ibm-a-leader-in-2019-magic-quadrant-for-insight-engines/, Accessed on 7th December, 613, pp. 480–489, 2020.

      31. Hybrid cloud in Education, https://www.intel.fr/content/dam/www/public/us/en/documents/education/hybrid-cloud-in-education.pdf, Accessed on 10th December, 2020.

      32. Dhinakaran, K., Kirtana, R., Gayathri, K., Devisri, R., Enhance hybrid cloud security using Vulnerability Management. Adv. Intell. Syst. Comput., 613, pp. 480–489, December 2018.

      33. Vaishnnave, M.P., Suganya Devi, K., Srinivasan, P., A Survey on Cloud Computing and Hybrid Cloud. Int. J. Appl. Eng. Res., 2019.

      34. Cearley, W. and Hilgendorf, K., Cloud Computing Innovation Key Initiative Overview, Gartner Research Database, Volume 15 pp. 45–52, 2014.

      1 *Corresponding author: [email protected]

      2 †Corresponding author: [email protected]

      2

      Recognition of Differentially Expressed Glycan Structure of H1N1 Virus Using Unsupervised Learning Framework

       Shillpi Mishrra

       Department of Computer Science and Engineering, Techno India University, Kolkata, India

       Abstract

      Influenza A (H1N1) virus created a pandemic situation around the world from 1918 to 1919. More than 10,000 cases have been reported to the World Health Organization (WHO). It affects species and sometimes in humans. Binding of hemagglutinin and some types of glycan receptors is the major ingredients for virus infections. In this work, we take both H1N1 infected human and non-infected human glycan datasets and identify differentially expressed glycans. In this work, we narrate a computational frame work using the cluster algorithm, namely, k-means, hierarchical, and fuzzy c-means. The entire methodology has been demonstrated on glycan datasets and recognizes the set of glycans that are significantly expressed from normal state to infected state. The result of the methodology has been validated using t-test and F-score.

      Keywords: Glycan receptors, differentially expressed glycan, clustering, k-means, fuzzy, F-score, glycan cloud

      2.1 Introduction