Browsing by Author "Jumoke Popoola"

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    Bayesian Additive Regression Trees for Predicting Colon Cancer: Methodological Study (Validity Study).
    (Turkiye klinikleri, 2022-05-01) Oyebayo Ridwan Olaniran; Saidat Fehintola Olaniran; Jumoke Popoola; Omekam Ifeyinwa Vivian
    The occurrence of colon cancer starts in the inner wall of the large intestine. The survival of colon cancer patients strongly relies on early detection. Diagnosing colon cancer using clinical approaches often takes longer, especially in most developing countries with limited facilities. The recent use of microarray technology has presented a new approach for the oncologist to diagnose cancer cells using non-clinical machine learning methods. In this paper, the aim is to predict the status of colon cancer tissues using the Bayesian Additive Regression Trees (BART) and 2 other machine learning methods. Material and Methods: The development and comparative analysis of BART alongside 2 other competing methods (Random Forest: RF and Gradient Boosting Machine: GBM) were implemented. The dataset used for the analysis is the microarray colon cancer data which consists of 2,000 gene expression …
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    Bayesian Regularized Neural Network for Forecasting Naira-USD Exchange Rate
    (Springer, 2022-05-04) Oyebayo Ridwan Olaniran; Saidat Fehintola Olaniran; Jumoke Popoola
    The Artificial Neural Network Autoregressive model (ANN-AR) is a recently adopted approach for forecasting Naira to the USD exchange rate. The Bayesian Regularized Neural Network (BRNN) is an alternative to ANN-AR based on the probabilistic interpretation of network weights. It is useful for solving overfitting problems inherent in ANN when large historical data are not available. In this paper, we developed a BRNN for modelling the monthly time series data of Naira to USD for four years. Performance analysis was observed using actual exchange rate values for the Year 2017 against the model predicted outcomes based on four evaluation metrics. Results from the analysis established the appropriateness of a BRNN for modelling short-term exchange rates in Nigeria.