Department of Computer Sciences

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    Immediate Word Recall in Cognitive Assessment Can Predict Dementia Using Machine Learning Techniques
    (BMC, Alzheimer's Research & Therapy, 2023-06-15) Olowookere, Toluwase Ayobami
    Background Dementia, one of the fastest-growing public health problems, is a cognitive disorder known to increase in prevalence as age increases. Several approaches had been used to predict dementia, especially in building machine learning (ML) models. However, previous research showed that most models developed had high accuracies, and they suffered from considerably low sensitivities. The authors discovered that the nature and the scope of the data used in this study had not been explored to predict dementia based on cognitive assessment using ML techniques. Therefore, we hypothesized that using word-recall cognitive features could help develop models for the prediction of dementia through ML techniques and emphasized assessing the models’ sensitivity performance. Methods Nine distinct experiments were conducted to determine which responses from either sample person (SP)’s or proxy’s responses in the “word-delay,” “tell-words-you-can-recall,” and “immediate-word-recall” tasks are essential in the prediction of dementia cases, and to what extent the combination of the SP’s or proxy’s responses can be helpful in the prediction of dementia. Four ML algorithms (K-nearest neighbors (KNN), decision tree, random forest, and artificial neural networks (ANN)) were used in all the experiments to build predictive models using data from the National Health and Aging Trends Study (NHATS). Results In the first scenario of experiments using “word-delay” cognitive assessment, the highest sensitivity (0.60) was obtained from combining the responses from both SP and proxies trained KNN, random forest, and ANN models. Also, in the second scenario of experiments using the “tell-words-you-can-recall” cognitive assessment, the highest sensitivity (0.60) was obtained by combining the responses from both SP and proxies trained KNN model. From the third set of experiments performed in this study on the use of “Word-recall” cognitive assessment, it was equally discovered that the use of combined responses from both SP and proxies trained models gave the highest sensitivity of 1.00 (as obtained from all the four models). Conclusion It can be concluded that the combination of responses in a word recall task as obtained from the SP and proxies in the dementia study (based on the NHATS dataset) is clinically useful in predicting dementia cases. Also, the use of “word-delay” and “tell-words-you-can-recall” cannot reliably predict dementia as they resulted in poor performances in all the developed models, as shown in all the experiments. However, immediate-word recall is reliable in predicting dementia, as seen in all the experiments. This, therefore, shows the significance of immediate-word-recall cognitive assessment in predicting dementia and the efficiency of combining responses from both SP and proxies in the immediate-word-recall task.
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    XAPP: An Implementation of SAX-Based Method for Mapping XML Document to and from a Relational Database
    (The International Arab Journal of Information Technology, 2022) Oguntunde, Bosede
    Extensible Markup Language (XML) is the standard medium for data exchange among businesses over the Internet, hence the need for effective management. However, since XML was not designed for storage and retrieval, its management has become an open research area in the database community. Existing mapping techniques for XML-to-relational database adopt either the structural mapping or the model mapping. Though numerous mapping approaches have been developed, mapping and reconstruction time had been problematic, especially when the document size is large and can hardly fit into main memory. In this research, an application codenamed XAPP, a new lightweight application that adopts a novel model mapping approach was developed using Simple API for XML (SAX) parser. XAPP accepts a document with or without Document Type Definition (DTD). It implements two algorithms: one maps XML data to a relational database and improves mapping time, and the other reconstructs an XML document from a relational database to improve reconstruction time and minimise memory usage. The performance of XAPP was analysed and compared with the Document Object Model (DOM) algorithm. XAPP proves to perform significantly better than the DOM-based algorithm in terms of mapping and reconstruction time, and memory efficiency. The correctness of XAPP was also verified.
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    Performance Evaluation of Machine Learning Tools for Detection of Phishing Attacks on Web pages
    (Scientific African, 2022-03) Oguntunde, Bosede
    This paper analyses and implements a rule-based approach for phishing detcetion using the three machine learning models trained on a dataset consisting of fourteen (14) features. The machine learning algorithms are: K-Nearest Neighbour (KNN). random Forest and Support vector MAchine (SVM). Among the three algorithm 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 extension called PhishNet. PhishNet is build during the course of this research using web technologies such as HTML, CSS, and Javascript. As a result, PhishNet facilities highly efficient phishing detection for the web.
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    Modeling a Deep Transfer Learning Framework for the Classification of COVID-19 Radiology Dataset
    (PeerJ Computer Science, 2021) Oguntunde, Bosede
    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, Viral- Pneumonia 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 for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work.
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    The Design of a Hybrid Model-Based Journal Recommendation System
    (Advances in Science, Technology and Engineering Systems Journal, 2020) Oguntunde, Bosede
    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.