Department of Computer Engineering

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https://repository.run.edu.ng/handle/123456789/2323

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Browsing Department of Computer Engineering by Author "Omolayo Abegunde"

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    A Comprehensive Analysis of COVID-19 Spread in Nigeria
    (Baze Universityc, 2021) Omolayo Abegunde
    The COVID-19 pandemic emanated from China was not only unexpected by the rest of the world, but it also resulted in an economic downturn. In Nigeria, attempts have been made at different levels of government to combat the virus' spread, with some promising results. In this paper, we looked at the impact of the spread from February 29 to December 27, 2020 to see what it was like. The findings were based on data from reported cases, deaths, recoveries, and active cases. The data was preprocessed and feature engineering was performed by adding new features (active, days, month). The pandas library were used to analyse the two sets of data. The results of the analysis provide us with a description of the COVID-19 pandemic's spread in Nigeria and the need to slow it down even further.
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    Authorship Verification of Yorùbá Blog Posts using Character N-grams
    (ICMCECS (IEEE), 2020) Omolayo Abegunde
    The task of determining whether a pair (or more) documents were written by the same author comes under authorship verification. N-grams are sequences of elements appearing in texts; they can be words, POS tags, characters, or some other elements that can be encountered one after another in texts. The tasks in authorship verification were more challenging as it focused on whether the target author and the text to be used have a closely related style. In this paper, an authorship verification task on Yorùbá blog posts is hereby presented. N-grams features were extracted from the corpus, and inductive learning techniques were applied to build feature-based models in order to perform the automatic author identification. The K-means clustering algorithm was used in the study since the supervised algorithm cannot be applied to the one-class classification of the dataset. The evaluation was done with the Silhouette Coefficient algorithm, which is used to evaluate unlabeled data. The result obtained is positive, which indicates the data points have a strong relationship with the dataset. The obtained result signifies a yes relationship between the posts. This signifies that the posts were from the same author.
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    Design and Implementation of Mobile Information System for Federal Road Safety Corps (FRSC) of Nigeria
    (International Journal of Sensor Networks and Data Communications, 2021) Omolayo Abegunde
    With a daily increase in the use of mobile devices in the 21st century, handheld devices are fast reaching the unreached and information is now easily disseminated. Nigeria, as a developing nation in the western Africa needs to be all information technology compliant. Far from this, vehicles have been registered manually. This mobile information system is designed to aid the every member of the Nigeria community in building an information network with the Federal Road Safety Corps (FRSC). Motorists, drivers and others who had registered their vehicles manually would be able to register their vehicles number plates and report accident victims to the Corps with ease from their mobile devices. This work focuses mainly on the vehicle registration, issuing number plates and information dissemination to the Federal Road Safety Corps, Nigeria.
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    Design Issues in Sentiment Analysis for Yorùbá Written Text
    (Ife Journal of Science and Technology, 2019) Omolayo Abegunde
    Sentiment Analysis (SA) is an exciting and important field in Artificial Intelligence combining Human Language Processing, Machine Learning and Psychology. It is a means of understanding a user’s opinion about an event. The goal of SA is to get opinion expressed in implied text, targets of the opinion and reason for the opinion. Conversely, a great number of research efforts are dedicated to English language data, while a countless share of information is obtainable in other languages as well but none yet for Yorùbá. This work examines the design issues with respect to automating SA for standard Yorùbá language. The process of SA which includes data cleaning, data annotation etc. is highlighted. The structure of the Yorùbá text is described and a text corpus design for Yorùbá sentiment analysis system is presented. The outcome of this work provided suitable requirements for the design.
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    Predicting Sentiment in Yorùbá Written Texts: A Comparison of Machine Learning Models
    (Springer Nature Switzerland, 2020) Omolayo Abegunde
    Sentiment analysis (SA) provides a rich set of tools and techniques for extracting and evaluating subjective information from large datasets. Users' opinions concerning an event are what determine the user perspective of such event, whether it is good or bad. This study compared three machine learning models (logistic regression, Naı̈ve Bayes, and support vector machine) with a view to identifying the best model for predicting sentiment in Yorùbá written texts at the sentence level. The corpus of Yorùbá records was created from several online and offline sources such as dictionaries, experts, the Bible, and social media, as well as the Awayoruba blog, and processed using Tákàdá. The system was implemented using the Python programming language and evaluated using mean opinion score and receiver operating characteristics. The research concludes that Naı̈ve Bayes (NB) outperforms other algorithm for analysis of sentiments for Yorùbá sentences.