Browsing by Author "Isiaka, Rafiu Mope"

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    A chi-square-SVM based pedagogical rule extraction method for microarray data analysis
    (Institute of Advanced Engineering and Science (IAES), 2020) Salawu, Mukhtar Damola; Arowolo, Micheal Olaolu; Abdulsalam, Sulaiman Olaniyi; Isiaka, Rafiu Mope; Jimada-Ojuolape, Bilkisu; Olumide, Mudashiru Lateef; Gbolagade, Kazeem A.
    Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
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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, Rafiu Mope; Babatunde, Ronke Seyi; Ajao, Jumoke Falilat; Yusuff, Shakirat Ronke; Popoola, Damilola David; Arowolo, Michael Olaolu; Adewole, Kayode
    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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    Development of an Intrusion Detection System in Web Applications Using C-Means and Decision Tree Algorithm
    (International Journal of Applied Science and Technology, 2023-03-01) Isiaka, Rafiu Mope; Popoola, Damilola David
    Intrusion detection is extremely important for online applications and for determining whether there has been a hostile entrance into the website. The aim of this research is to provide a machine learning technique for detecting intrusion in a web application. Machine learning models such as C-means, Decision Tree and Support Vector Machine were utilized to create an intrusion detection system. The study used the CIC-IDS 2018 intrusion dataset (Friday-Working Hours-Afternoon-Ddos.pcap ISCX). The data was initially sent to Decision tree and SVM which had accuracy of 99.97% and 99.77%, respectively. The raw data was next transferred into the c-means clustering approach, which had an accuracy of 99.99%. The goal of the clustering technique used is to improve the system’s accuracy, and the results were assessed using performance metrics like accuracy, sensitivity, precision, specificity, F1-score as well as accuracy comparison of the results obtained with the state of the art.
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    Towards adoption of information and communication technology in higher education – a structural equation model approach
    (International Journal of Learning Technology, 2021-06-02) Isiaka, Rafiu Mope; Babatunde, Ronke Seyi; Eiriemiokhale, Kennedy Arebamen; Popoola, Damilola David