—This paper deals with the optimized Multi class SVM classifier (OMSC) with Named entity extraction in cloud environment. The proposed OMSC handles with scheduling workflow in cloud computing where the data and files are transferred between the participants based on the different set of rules, with having the additional advantage of rules formation capability in which it is going to follow 22 rules templates. This has shown an improved performance against the traditional Multi class SVM classifier. The average f-score for the tested data sets is 81.04 % when compared to the existing classifier it is improved sign. The time complexity is decreased and as per the scheduling is concerned the execution time and response time is improved.
—OMSC, workflow model, NE extraction and rules formation.
Jyothi Bellary is with Aditya College of Engineering, Madanapalle, India (e-mail: firstname.lastname@example.org).
E Keshava Reddy was with Jawaharlal Nehru Technological University Ananthapuramu. He is now with the Department of Mathematics JNTUCEA ,Jawaharlal Nehru Technological University Ananthapuramu, Ananthapuramu, India (e-mail: email@example.com).
Cite: Jyothi Bellary and E. Keshava Reddy, "Optimized Multi Class SVM Classifier for Named Entity Extraction for Workflow Scheduling in Cloud," International Journal of Engineering and Technology vol. 8, no. 6, pp. 453-457, 2016.