Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Adeyinka Adewale
Covenant University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Internet service providers responsibilities in botnet mitigation: a Nigerian perspective Julius Olatunji Okesola; Marion Adebiyi; Tochukwu Osi-Okeke; Adeyinka Adewale; Ayodele Adebiyi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (389.065 KB) | DOI: 10.11591/ijece.v10i4.pp4168-4175

Abstract

Botnet-based attack is dangerous and extremely difficult to overcome as all the primary mitigation methods are passive and limited in focus. A combine efforts of Internet Service Providers (ISPs) are better guides since they can monitor the traffic that traverse through their networks. However, ISPs are not legally banded to this role and may not view security as a primary concern. Towards understudying the involvement of ISPs in Botnet mitigation in Nigeria, this study elicited and summarized mitigation measures from scientific literatures to create a reference model which was validated by structured interview. Although, ISPs role is seen to be voluntary and poorly incentivized, the providers still take customers security very serious but concentrate more on preventive and notification measures.
Determining the operational status of a three phase induction motor using a predictive data mining model Aderibigbe Israel Adekitan; Adeyinka Adewale; Alashiri Olaitan
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v10.i1.pp93-103

Abstract

The operational performance of a three phase induction motor is impaired by unbalanced voltage supply due to the generation of negative sequence currents, and negative sequence torque which increase motor losses and also trigger torque pulsations. In this study, data mining approach was applied in developing a predictive model using the historical, simulated operational data of a motor for classifying sample motor data under the appropriate type of voltage supply i.e. balanced (BV) and unbalance voltage supply (UB = 1% to 5%). A dataset containing the values of a three phase induction motor’s performance parameter values was analysed using KNIME (Konstanz Information Miner) analytics platform. Three predictive models; the Naïve Bayes, Decision Tree and the Probabilistic Neural Network (PNN) Predictors were deployed for comparative analysis. The dataset was divided into two; 70% for model training and learning, and 30% for performance evaluation. The three predictors had accuracies of 98.649%, 100% and 98.649% respectively, and this confirms the suitability of data mining methods for predictive evaluation of a three phase induction motor’s performance using machine learning
Co-Authors Aderibigbe Israel Adekitan Alashiri Olaitan Ayodele Adebiyi Julius Olatunji Okesola Marion Adebiyi Tochukwu Osi-Okeke