Browsing by Author "Ojewumi, Theresa O."

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    The Design of a Hybrid Model-Based Journal Recommendation System
    (Advances in Science, Technology and Engineering Systems Journal Vol. 5, No. 6, 1153-1162 (2020), 2020-11-16) Ojewumi, Theresa O.
    There is currently an overload of information on the internet, and this makes information search a challenging task. Researchers spend a lot of man-hour searching for journals related to their areas of research interest that can publish their research output on time. In, this study, a recommender system that can assist researchers access relevant journals that can publish their research output on time based on their preferences is developed. This system uses the information provided by researchers and previous authors’ research publications to recommend journals with similar preferences. Data were collected from 867 respondents through an online questionnaire and from existing publication sources and databases on the web. The scope of the research was narrowed down to computer science-related journals. A hybrid model-based recommendation approach that combined Content-Based and Collaborative filtering was employed for the study. The Naive Bayes and Random Forest algorithms were used to model the recommender. WEKA, a machine learning tool, was used to implement the system. The result of the study showed that the Naïve Bayes produced a shorter training time (0.01s) and testing time (0.02s) than the Random forest training time (0.41) and testing time (0.09). On the other hand, the classification accuracy of the Random forest algorithm outperformed the naïve Bayes with % correctly classified instance of 89.73 and 72.66; kappa of 0.893 and 0.714; True Positive of 0.897 and 0.727 and ROC area of 0.998 and 0.977, respectively, among other metrics. The model derived in this work was used as a knowledge-base for the development of a web-based application, named “Journal Recommender” which allowed academic authors to input their preferences and obtain prompt journal recommendations. The developed system would help researchers to efficiently choose suitable journals to help their publication quest.
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    Development of a Simple Graphical Interface Based Software for Machine Learning and Data Visualization
    (International Journal of Recent Technology and Engineering (IJRTE), 2019-07-02) Ojewumi, Theresa O.
    Machine learning has become one of the foremost techniques used for extracting knowledge from large amounts of data. The programming expertise required to implement machine learning algorithms has led to the rise of software products that simplify the process. Many of these systems however, have sacrificed simplicity as they evolved and included more features. In this study, a machine learning software with a simple graphical user interface was developed with a special focus on enhancing usability. The system made use of basic graphical interface elements such as buttons and textboxes. Comparison of the system with other similar open-source tools revealed that the developed system showed an improvement in usability over the other tools.
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    Emergence of More Female Role Models in the Sciences: The Case of Students’ Academic Performance in a Nigerian Private University
    (The International Journal of Science & Technology, 2020-12) Ojewumi, Theresa O.
    In this article, we present an empirical study to provide more evidence on the strength of the female gender in the sciences. It is on record that the percentage of women in the sciences is still low compared to the male counterpart. This paper is thus intended to make use of the performance of female undergraduates in the sciences to encourage more participation from this group. The study focused on six science courses that already graduated students from Redeemer’s university for at least 4 years as at the time of the research. We compared the average performance of female students with that of male students in each program over 5 years. The results obtained suggest that female students also have great potentials in the sciences, with better performance in some instances than male students. Consequently, we encourage more women participation in Science, Technology, Engineering and Mathematics (STEM) disciplines.
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    Modeling a Deep Transfer Learning Framework for the Classification of COVID-19 Radiology Dataset
    (PeerJ Computer Science, 2021-08-03) Ojewumi, Theresa O.
    Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class COVID-19, ViralPneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models’ predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics of accuracy, as shown in this work.
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    Performance Evaluation of Machine Learning Tools for Detection of Phishing Attacks on Web Pages
    (Scientific African, 2022-03-19) Ojewumi, Theresa O.
    This paper analyses and implements a rule-based approach for phishing detection using the three machine learning models trained on a dataset consisting of fourteen (14) features. The machine learning algorithms are; k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). Among the three algorithms used, it was discovered that Random Forest model proved to deliver the best performance. Rules were extracted from the Random Forest Model and embedded into a Google chrome browser extension called PhishNet. PhishNet is built during the course of this research using web technologies such as HTML, CSS, and Javascript. As a result, PhishNet facilitates highly efficient phishing detection for the web.
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    Securing and Monitoring of Bandwidth Usage in Multi-Agents Denial of Service Environment
    ((IJACSA) International Journal of Advanced Computer Science and Applications,, 2018) Ojewumi, Theresa O.
    The primary purpose of Denial of Service attack (DoS) is to cripple resources so that the resources are made unavailable to the legitimate users. Due to the inadequate monitoring of activities on the network, it has resulted into huge financial losses. Bandwidth which is one of the resources being used on the network, if not properly monitored could result into misused and attack. This paper proposes a real time system for securing and monitoring the amount of bandwidth consumed on the network using the multi-agent framework technology. It also keeps a record of internet protocol (IP) addresses visiting the network and may be used as a starting point for the aspect of response in providing a comprehensive solution to DoS attacks. The bandwidth is pre-entered and an agent is assigned to monitor bandwidth consumption rate against the set threshold. If the bandwidth is consumed above the bandwidth limit and time set, then a DoS attack is suspected taking into considerations the DoS attack framework. This framework can be used as a replicate of what happen in the network scenario environment.