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All Journal Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Jaiyeoba, Oluwayemisi
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Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Journal of Computing Theories and Applications

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Evaluation of University Websites in Nigeria using the Web Content Accessibility Guidelines Ogbuju, Emeka; Ihinkalu, Olalekan; Ajulo, Emmanuel; Jaiyeoba, Oluwayemisi; Yemi-Peters, Victoria
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9381

Abstract

Providing accessible open educational resources (OER) is essential for users with impairments to access university resources. To achieve this, web content accessibility guidelines (WCAG) have been developed. In this study, we used the AChecker web accessibility evaluation tool to assess the content of 42 federal university websites in Nigeria and recorded their conformance level to the WCAG. The findings show that at Level A (Minimal Compliance), there were 855 known problems, 55 likely problems, and 7536 potential problems. At Level AA (Acceptable Compliance), 2516 known problems, 58 likely problems, and 15537 potential problems were identified. At Level AAA (Optimal Compliance), 2679 known problems were found, while there were no likely problems, and 16772 potential problems. The results indicated that most websites did not conform to the accessibility guidelines, highlighting the need for educational institutions to comply with WCAG2.1 content standard. The study recommends introducing accessibility training courses in web design and development to ensure effective OER creation for people with diverse abilities. Furthermore, enforcing the implementation of these guidelines by flagging down non-compliant educational websites was suggested. There is a problem of lack of accessibility in federal university websites in Nigeria, leading to unequal access to web content for users with varying abilities. The study aimed to identify aspects of the websites where accessibility needs to be improved and promote diversity and inclusiveness for users with different abilities to have equal access to web content.
Development of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques Jaiyeoba, Oluwayemisi; Ogbuju, Emeka; Yomi, Owolabi Temitope; Oladipo, Francisca
Journal of Computing Theories and Applications Vol. 2 No. 1 (2024): JCTA 2(1) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10488

Abstract

Skin diseases are highly prevalent and transmissible. It has been one of the major health problems that most people face. The diseases are dangerous to the skin and tend to spread over time. A patient can be cured of these skin diseases if they are detected on time and treated early. However, it is difficult to identify these diseases and provide the right medications. This study's research objectives involve developing an ensemble machine learning based model for classifying Erythemato-Squamous Diseases (ESD). The ensemble techniques combine five different classifiers, Naïve Bayes, Support Vector Classifier, Decision Tree, Random Forest, and Gradient Boosting, by merging their predictions and utilizing them as input features for a meta-classifier during training. We tested and validated the ensemble model using the dataset from the University of California, Irvine (UCI) repository to assess its effectiveness. The Individual classifiers achieved different accuracies: Naïve Bayes (85.41%), Support Vector Machine (98.61%), Random Forest (97.91%), Decision Tree (95.13%), Gradient Boosting (95.83%). The stacking method yielded a higher accuracy of 99.30% and a precision of 1.00, recall of 0.96, F1 score of 0.97, and specificity of 1.00 compared to the base models. The study confirms the effectiveness of ensemble learning techniques in classifying ESD.
Co-Authors Ajulo, Emmanuel Ihinkalu, Olalekan Jaiyeoba, Oluwaseyi Ogbuju, Emeka Oladipo, Francisca Yemi-Peters, Victoria Yomi, Owolabi Temitope