Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Scientific Journal of Informatics Journal of Information Technology and Computer Engineering
Oladimeji, Oladosu Oyebisi
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
A Comprehensive Survey On Cloud Computing Simulators Oladimeji, Oladosu Oyebisi; Oyeyiola, Dasola; Oladimeji, Olayanju; Oyeyiola, Pelumi
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.28878

Abstract

Cloud Computing is one of the upcoming technologies which has gotten the attention of many researchers and investor. But cloud computing still faces challenges because it is not economical and impractical for research institutions and industries to set up a physical cloud for research and experiments on it (cloud computing). Due to this, the researchers have chosen to test their contributions with simulators. Therefore, the purpose of this study is to perform a survey on existing cloud simulators. These cloud simulators aid in modeling cloud application through the creation of virtual machine, data Centre, and other thing which can be easily added and configured to it in order to provide stress free analysis. Till this present time, many cloud simulators with various features have been proposed and available for use. In this paper a comprehensive study has been performed on major cloud simulators by highlighting their features, strength and weakness through analysis. After which comparative analysis was done on the simulation, from the study, none of the simulators have the feature to simulate mobile cloud computing issues. This study has not been published anywhere else.
A Comprehensive Survey On Cloud Computing Simulators Oladimeji, Oladosu Oyebisi; Oyeyiola, Dasola; Oladimeji, Olayanju; Oyeyiola, Pelumi
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.28878

Abstract

Purpose: Cloud Computing is one of the upcoming technologies which has gotten the attention of many researchers and investor.  But cloud computing still faces challenges because it is not economical and impractical for research institutions and industries to set up a physical cloud for research and experiments on it (cloud computing). Due to this, the researchers have chosen to test their contributions with simulators. Therefore, the purpose of this study is to perform a survey on existing cloud simulators. Methods: These cloud simulators aid in modeling cloud application through the creation of virtual machine, data Centre, and other things which can be easily added and configured to it in order to provide stress free analysis. Result: Till this present time, many cloud simulators with various features have been proposed and available for use. Novelty: In this paper, a comprehensive study has been performed on major cloud simulators by highlighting their features, strengths, and weakness through analysis. After which comparative analysis was done on the simulation, from the study, none of the simulators have the feature to simulate mobile cloud computing issues. This study has not been published anywhere else.
Predicting Survival of Heart Failure Patients Using Classification Algorithms Oladimeji, Oladosu Oyebisi; Oladimeji, Olayanju
JITCE (Journal of Information Technology and Computer Engineering) Vol. 4 No. 02 (2020)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.90-94.2020

Abstract

Heart failure is a situation that occurs when the heart is unable to pump enough blood to meet the needs of other organs in the body. It is responsible for the annual death of approximately 17 million people worldwide. Series of studies have been done to predict heart failure survival with promising results. Hence, the purpose of this study is to increase the accuracy of previous works on predicting heart failure survival by selecting significant predictive features in order of their ranking and dealing with class imbalance in the classification dataset. In this study, we propose an integrated method using machine learning. The proposed method shows promising results as it performs better than previous works and this study confirms that dealing with imbalanced dataset properly increases accuracy of a model. The model was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at predicting if a patient will survive heart failure. Attention may be focused on mainly serum creatinine, ejection fraction, smoking status and age.
Co-Authors Oladimeji, Olayanju Oyeyiola, Dasola Oyeyiola, Pelumi