Browsing by Author "Ajao, J. F."

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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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    Offline yoruba word recognition system based on capsule neural network
    (Ilorin Journal of Computer Science and Information Technology, 2022-06-30) Ajao, J. F.; Olorunmaiye, E; Mabayoje, M. A.; Yusuff, S. R.; Bajeh, A.
    The need for Digital Document archiving has resulted in several efforts in Image Document Analysis, where scanned historical/ indigenous documents can be stored in digitized form and made readily accessible and available for future use. Visual system in human uses an highly space variant in sampling, coding, processing and understanding of variations in human handwritings, which possess lots of challenges or difficulty for machine to understand. Consequently, it is important to preserve our indigenous heritage values by way of digitization. In this regard, the research work models a Yoruba Script Recognition System using Capsnet, based on the peculiar features of Yoruba alphabet. Yoruba bird names were gathered from the Yoruba dictionary and were written by 20 indigenous literate writers in 50 different writing styles. This was used to model HYSR system. The training time and testing after training different epoch produces 89.2222 and the testing time generated is 0.8667. The model accuracy gives 85.49%, which shows a promising result.
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    Optimized Convolution Neural Network-based Model for Detection and Classification of Pulmonary Diseases
    (LAUTECH Journal of Engineering and Technology, 2024-06-15) Olajide, A. T.; Babatunde, R. S.; Abdulsalam, S. O.; Ajao, J. F.; Isiaka R. M.; Ayeni, J. K.
    Pelican Optimization Algorithm-based Convolutional Neural Network (POA CNN) method for the automated identification of pulmonary disorders such as COVID-19 and pneumonia is proposed in this research. The study aims to enhance the efficiency of CNN models in diagnosing lung diseases by using the Pelican Optimization Algorithm (POA), and by addressing drawbacks like a lack of flexibility in hyperparameter modifications. The three primary phases of the model are feature extraction via POA-based hyperparameter optimization, image classification, and image pre-processing. This approach improves existing systems' performance in detecting pulmonary diseases, highlighting the potential of deep learning in identifying and categorizing human diseases. The study uses resizing, grayscale, and augmentation methods to optimize an existing CNN model. A Convolutional Neural Network (CNN) is then applied to classify Pneumonia and COVID-19 cases. The proposed model achieves an accuracy rate of 97.28% and 97.00%, outperforming existing models. This technique is effective in detecting and classifying other pulmonary diseases and can be used to automatically detect and classify these diseases. Higher accuracy findings show how successful the model is, making it a useful tool for pulmonary illness identification.