Browsing by Author "Isiaka, R. M."

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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 Exploratory Data Analysis of Growth, Pest Infestation and Treatment Dataset Towards Improved Prediction of Cowpea Yield
    (FUOYE Journal of Pure and Applied Sciences, 2025-04-10) Babatunde, R. S; Musa, A.; Ajao, J. F.; Isiaka, R. M.; Ojo, J. A.; Abdulrahman, L. O; Mohammend, A. K.
    The rising need for food security requires creative agricultural methods, especially in the farming of essential crops such as cowpea (Vigna unguiculata). This research introduces an improved automated forecasting system for cowpea production utilizing machine learning methods, drawing on an extensive dataset comprising factors like plant growth metrics, pest presence, and treatment methods. Utilizing regression models and sophisticated algorithms, the system examines past growth data to determine significant factors affecting yield results. Preliminary findings indicate a strong relationship between growth indicators like plant height at different developmental phases and the overall yield. The predictive model will not only enable prompt actions in crop management but also assists farmers in making refined choices to enhance production. Ultimately, this study aids in crafting precision agriculture techniques that improve cowpea production while tackling issues arising from pests and environmental factors. This automated method indicates a way to enhance agricultural output and sustainability in areas dependent on cowpea as an essential food source.
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    Classification of Leukemia Cancer Data using Correlation Based Feature Selection Model: A Comparative Approach
    (UNIOSUN Journal of Engineering and Environmental Sciences, 2025-03-15) Babatunde, R. S.; Isiaka, R. M.; Abdulsalam, S. O.; Arowolo, O. M.; Ajao, J. F.
    The abundance of data obtained from microarray experiments presents challenges related to the number of variables and the presence of random fluctuations. Despite the efforts that had been made by previous researchers, emphasizing how data mining aids the implementation of models to facilitate informed prediction, gaps are evident which requires improvement over the earlier models. Dimensionality reduction techniques, such as Correlation Based Feature Selection (CBFS), are good candidate solutions to these problems by selecting pertinent features for categorization. This research implements a model for classification of leukemia cancer using CBFS with Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Ensemble classifiers. The evaluation of the performance of these machine learning models was carried out using sensitivity, specificity, precision and accuracy. The findings indicate that the CBFS+DT model outperforms the other models in terms of sensitivity (96.75%), specificity (97.18%), precision (97.56%), accuracy (96.75%), and F1 score (96.97%), while also exhibiting a decreased computational time (0.4336). This demonstrates the efficacy of CBFS in improving classification accuracy and reducing computing load. Overall, this study highlights the effectiveness of CBFS in cancer research and underscores the importance of carefully choosing the most pertinent variables to enhance classification outcomes.
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    Learner Activities Evaluation Model: A Neuro-Fuzzy Approach
    (International Journal of e-Education, e-Business, e-Management and e-Learning, 2013-10-14) Omidiora, E. O.; Olabiyisi, S. O.; Okediran, O. O.; Isiaka, R. M.
    Computer Mediated Communication (CMC) platforms are incorporated in e-learning systems to actualize effective learning activities. Visualization techniques are been used to communicate the unstructured complex dataset of learners’ activities in the log files to the actors. The effectiveness of this approach as bases for teaching-learning support and learning analytics however relies on the commitment of the teacher. The teacher being human can become overwhelmed when the enrolment is large and/or when the Internet access is a problem. This paper presents a technique for capturing the teacher’s knowledge for monitoring learners’ activities in Neuro-fuzzy model for online automatic monitoring. The model intelligently provides inbuilt competence assessment and promptly takes decisions. The IEEE-LTSA is modified to reflect the initiative. Similarly, the integration of the model in an architecture based on Actuator- Indicator Models was demonstrated.