Saravanan K

Department of Communication Engineering

School of Electronics Engineering

Vellore Campus

Balamurugan B

Venkata K.P

Rajya L.G.V

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Layered storage architecture for health system using cloud

2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies

Several classifiers are prevalent which act as a major drive for almost all supervised machine learning applications. These classifiers, though their objective working looks similar, they vary drastically in their performances. Some of the important factors that cause such variations are the scalability of the dataset, dataset nature, training time estimation, classifying time for the test data, prediction accuracy and the error rate computation. This paper focuses mainly on analyzing the performance of the existing four main classifiers: IF-THEN rule, C4.5 decision trees, naïve Bayes, and SVM classifier. The objective of this research article is to provide the complete statistical performance estimates of the four classifiers to the authenticated cloud users. These users have the access facility in obtaining the essential statistical information about the classifiers in study from the cloud server. Such statistical information might be helpful in choosing the best classifier for their personal or organizational benefits. The classifiers follow the traditional underlying algorithms for classification that is performed in the cloud server. These classifiers are tested on three different datasets namely PIMA, breast-cancer and liver-disorders dataset for performance analysis. The performance analysis indicators used in this research article to summarize the working of the various classifiers are training time, testing time, prediction accuracy and error rate computation. The proposed comparative analysis framework can be used to analyze the performances of the classifiers with respect to any input dataset.

International Journal of E-Health and Medical Communications

Comparative Performance Analysis of Various Classifiers for Cloud E-Health Users

2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom)

An Efficient Framework and Access Control Scheme for Cloud Health Care

International Journal of Communication Systems

SecCloudSharing: Secure data sharing in public cloud using ciphertext-policy attribute-based proxy re-encryption with revocation

Journal	Data powered by Typeset2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies
Publisher	Data powered by TypesetIEEE
Open Access	0