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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
Obinna Nwankwo
Novena University Ogume
Computer Science & IT Electrical & Electronics Engineering

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Assessing contributor features to phishing susceptibility amongst students of petroleum resources varsity in Nigeria Rume Elizabeth Yoro; Fidelis Obukohwo Aghware; Bridget Ogheneovo Malasowe; Obinna Nwankwo; Arnold Adimabua Ojugo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1922-1931

Abstract

In this observational quasi-experimental study, we recruited 200 participants during the Federal University of Petroleum Resources Effurun’s (FUPRE) orientation, who were exposed to socially engineered (phishing) attacks over nine months. Attacks sought to extract participants’ data and/or entice them to click (compromised) links. The study aims to determine phishing exposure and risks among undergraduates in FUPRE (Nigeria) by observing their responses to socially-engineered attacks and exploring their attitudes to cybercrime risks before and after phishing attacks. The study primed all students in place of cybercrime awareness to remain vigilant to scams and explored the various scam types with their influence on gender, age, status, and their perceived safety on susceptibility to scams. Results show that contrary to public beliefs, these factors have all been found to be associated with scam susceptibility and vulnerability of the participants.
Model development for pneumonia detection from chest radiograph using transfer learning Ojo Abayomi Fagbuagun; Obinna Nwankwo; Samson Adebisi Akinpelu; Olaiya Folorunsho
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.23296

Abstract

Accurate interpretation of chest radiographs outcome in epidemiological studies facilitates the process of correctly identifying chest-related or respiratory diseases. Despite the fact that radiological results have been used in the past and is being continuously used for diagnosis of pneumonia and other respiratory diseases, there abounds much variability in the interpretation of chest radiographs. This variability often leads to wrong diagnosis due to the fact that chest diseases often have common symptoms. Moreover, there is no single reliable test that can identify the symptoms of pneumonia. Therefore, this paper presents a standardized approach using convolutional neural network (CNN) and transfer learning technique for identifying pneumonia from chest radiographs that ensure accurate diagnosis and assist physicians in making precise prescriptions for the treatment of pneumonia. A training set consisting of 5,232 optical coherence tomography and chest X-ray images dataset from Mendelev public database was used for this research and the performance evaluation of the model developed on the test set yielded 88.14% accuracy, 90% precision, 85% recall and F1 score of 0.87.
Co-Authors Arnold Adimabua Ojugo Bridget Ogheneovo Malasowe Fidelis Obukohwo Aghware Ojo Abayomi Fagbuagun Olaiya Folorunsho Rume Elizabeth Yoro Samson Adebisi Akinpelu