Browsing by Author "Isiaka, R.M"

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    A Review of Spam Mail Genre and Spam Filtering Techniques
    (Computing, Information Systems, Development Informatics & Allied Research Journal, 2025-06-20) Oluwatunde, S.J.; Gbolagade, K.A; Isiaka, R.M
    Basically, the Internet was created for information purposes which include; education, entertainment, communication (emails), etc. However, the more useful the Internet gets, the more end users of the email boxes have this problem of Spam. Spam has annoyed, confused and frustrated email users and their administrators, because the major cost of spam is borne by the recipient and their mail servers. These costs are in the terms of bandwidth wastage, storage space that the spam emails utilize and most importantly, the likelihood that very important email/ information could be lost as anti-spam filters may wrongfully filter them out. Sometimes, the owner of the mailbox may get so overwhelmed by the volume of spam and deleting useful email. It is obvious that end users are no longer inundated with the same volume of unwanted email that they faced a few years ago, however spam is still a major problem for network operators because spam still accounts for around 70 percent of all Internet traffic, in spite of the numerous regulatory initiatives enacted throughout the world designed to combat the problem. In this paper, we review spamming techniques as it relates to different genre of spam mails and spam filers
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    A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis
    (Journal of Advances in Mathematical & Computational Sciences, 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