Browsing by Author "Babatunde, R. S."

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    A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis
    (An International Pan-African Multidisciplinary Journal of the SMART Research Group International Centre for IT & Development (ICITD) USA © Creative Research Publishers, 2022-12-04) Isiaka, R. M.; Babatunde, R. S.; Ajao, J. F.; Yusuff, S. R.; Popoola, D. D.; Arowolo, M. O.; Adewole, K. S.
    Following the identification of Coronavirus Disease 2019 (COVID-19) in Wuhan, China in December 2019, AI researchers have teamed up with a health specialist to combat the virus. This study explores the medical and non-medical areas of COVID-19 that AI has impacted: the prevalence of the AI technologies adopted across all stages of the pandemic, the collaboration networks of global AI researchers, and the open issues. 21,219 papers from ACM Digital, Science Direct and Google Scholar were examined. Adherence to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and utilizing the PICO (population, intervention, comparison, and outcome) paradigm, guided the inclusion of researches in the review. Tables and graphs were utilized to display the results. Analysis revealed that AI has impacted 4 molecular, 4 clinical, and 7 societal areas of COVID-19. Deep Learning among other AI technologies was traced to all aspects of the pandemic. 2173 authors and co-authors were traced to these achievements, while 32 of the most connected 51 authors were affiliated with institutions in China, 18 to the United States, and 1 to Europe. The open issues identified had to do with the quality of datasets, AI model deployment, and privacy issues. This study demonstrates how AI may be utilized for COVID-19 diagnosis, prediction, medication and vaccine identification, prognosis, and contact person monitoring. This investigation began at the beginning of the epidemic and continued until the first batch of vaccinations received approval. The study provided collaboration opportunities for AI researchers and revealed open issues that will spike further research toward preparing the world for any future pandemic
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    An Evaluation of performance of k-NN and Mahalanobis Distance on Local Binary Pattern Feature representation for Access Control in Face Recognition System.
    (The Creative Research Networks for The African Society for Information & Communication Technology, USA, 2017-03) Yusuff, S. R.; Balogun, F. B.; Babtunde A. N.; Babatunde, R. S.
    A vast majority of the existing face recognition techniques encounter misclassifications when there is a large variation of the subject in terms of pose, illumination and expression as well as artefact such as occlusion. With the usage of Local Binary Pattern (LBP), local feature representation which preserves local primitives and texture information, robust against illumination, expression and occlusion was realized in the feature extraction process. Mahalanobis distance and k-NN classifier were employed for recognition as basis for comparison. An assessment of both algorithms was considered. It was observed that Mahalanobis distance measure outperformed the k-NN classifier for access control and recognition system due to its high recognition rate (98.4%), genuine acceptance rate (98.37) and its stern adherence to both FAR and FRR (0.0144, 1.568) than kNN with 94.9% and (0.392 and 2.895) respectively. This result demonstrates the robustness of Mahalanobis distance classifier for use access control system