O.S.S. Alsharif

Work place: Department of Material Science and Engineering, Kastamonu University, Kastamonu, Turkey

E-mail: osama.s.alsharif@gmail.com


Research Interests: Computational Science and Engineering, Computational Engineering, Computer systems and computational processes


Osama Alsharif, He received his B.Sc. in Information Technology Department from higher institute of instructors from Benghazi in Fall 2007/2008. His graduation project was The relationship between online computing systems and users. He is a master’s student at the department of computer engineering at Kastamonu University.


Author Articles
Evaluation of Different Machine Learning Methods for Caesarean Data Classification

By O.S.S. Alsharif K.M. Elbayoudi A.A.S. Aldrawi K. Akyol

DOI: https://doi.org/10.5815/ijieeb.2019.05.03, Pub. Date: 8 Sep. 2019

Recently, a new dataset has been introduced about the caesarean data. In this paper, the caesarean data was classified with five different algorithms; Support Vector Machine, K Nearest Neighbours, Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. The dataset is retrieved from California University website. The main objective of this study is to compare selected algorithms’ performances. This study has shown that the best accuracy that was for Naïve Bayes while the highest sensitivity which was for Support Vector Machine.

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