Machine Learning Techniques and Analytics for Cloud Security. Группа авторов
Читать онлайн книгу.
with cloud, https://cio.economictimes.indiatimes.com/news/cloudcomputing/galvanizing-the-new-age-of-it-with-ai-and-hybrid-cloud/74722393, Accessed on 10th October, 2020.
14. Market Hybrid Cloud, https://www.mordorintelligence.com/industry-reports/hybrid-cloudmarket, Accessed on 10th October, 2020.
15. Hybrid Cloud Opportunity, https://www.thehindu.com/business/Industry/india-is-key-toibms-pursuit-of-a1-trillion-hybrid-cloud-opportunity/article33201906.ece. Accessed on 12th October, 2020.
16. Aryotejo, G., Kristiyanto, D.Y., Mufadhol, Hybrid cloud: bridging of private and public cloud computing. IOP Conf. Series: J. Physics: Conf. Ser., 1025, 012091, 2018.
17. Hybrid Cloud Providers, https://www.loomsystems.com/blog/hybrid-cloud-providers, Accessed on 10th November, 2020.
18. Top 5 Cloud Providers, https://www.cloudmanagementinsider.com/top-five-cloud-computing-news-june-2020, Accessed on 20th November, 2020.
19. SaaS-Cloud-Providers, https://www.zdnet.com/article/the-top-cloud-providers-of-2020-awsmicrosoft-azure-google-cloud-hybrid-saas. Accessed on 20th November, 2020.
20. Kumar, S., Kumar, S., Bajwa, M.S., Sharma, H., A Comparative Study of Various Cloud Service Platforms. J. Adv. Database Manage. Syst., 6, 1, pp. 7–12, 2020.
21. Islam, N. and Rehman, A., A comparative study of major service providers for cloud computing, in the Proceedings of 1st International Conference on Information and Communication Technology Trends, at Karachi Pakistan, 2014.
22. Kimmy, A Comparative Study Of Clouds In Cloud Computing. Int. J. Comput. Sci. Eng. Technol. (IJCSET), 4, pp. 843–849 2013.
23. 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
Influenza A is a widespread infectious disease caused by the influenza virus that can easily spread from one person to another by coughing, sneezing, etc. This virus infects hosts like humans, sea-mammals, and swine. The first reported pandemic from 1918 to 1919 and the other two pandemics occurred in the 20th century [1–3]. Every year, 250,000 to 500,000 deaths occurred worldwide for this virus. In the past few years, there are lots of disasters that occurred, for example, pandemic named “Spanish Flu” caused the global death of 60 to 100 million people in the 1918. In 2009, scientists recognized a particular strain of influenza A virus which is known as H1N1 [4, 5]. H1N1 is normally found in swine. For this reason, it is also called “Swine Flu” or “Pig Flu” or “Swine influenza viruses (SIVs)”. It is a human respiratory infection caused by the H1N1 influenza that is basically started in pigs and contains RNA virus with a segmented genome. It is also called orthomyxovirus because it contains haemagglutinin and neuraminidase glycoproteins. Transmission of swine flu to humans is rare. But, swine flu can be transmitted to humans via contact with infected swine or environments contaminated. Once a human gets infected, humans can then spread this virus to other humans, and in the same way, swine flu is spread (i.e., cough or sneezing). It is a mystery that when and where pandemic reassortments will happen. Lots of reports suggest that this type of reassortments