Browsing by Author "Bukola Fatimah Balogun"

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    A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms
    (EAI Endorsed Transactions on Moblie Communications and Applications, 2022-07-27) Sulaiman Olaniyi Abdulsalam; Jumoke Falilat Ajao; Bukola Fatimah Balogun; Micheal Olaolu Arowolo
    INTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that influence customer churn to yield effective solutions to minimize churn. OBJECTIVES: The major purpose of this work is to create a model of churn prediction that assists telecom operatives to envisage clients that are more probably to be prone to churn. METHODS: The experimental strategy for this study leverages the machine learning techniques on the telecom churn dataset, employing an improved Relief-F feature selection algorithm to extract related features from the enormous dataset. RESULTS: The result demonstrates that CNN has a high prediction capability of 94 percent compared to the 91 percent Random Forest classifier. CONCLUSION: The results are of enormous relevance to the telecommunication business in improving churners and loyal clients.
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    Use of White Shark Optimization for Improving the Performance of Convolution Neural Network in Classification of Infected Citrus
    (University of Ibadan Journal of Science and Logics in ICT Research, 2023) Ronke Seyi Babatunde; Abdulrafiu Mope Isiaka; Jumoke F. Ajao; Sulaiman Olaniyi Abdulsalam; Bukola Fatimah Balogun
    Citrus plant diseases are major causes of reduction in the production of citrus fruits and their usage. Early detection of the onset of the diseases is very important to curb and reduce its spread. A number of researches have been done on the detection and classification of the diseases, most of which have identified poor labelling of symptoms which result into improper classification. Some researchers have also experimented on the effectiveness of convolution neural network and other deep learning techniques, most of which results into a faster convergence but suffers from low accuracy, computational overhead and overfitting as fundamental issues. To reduce the effects of overfitting, this research developed a White Shark Optimization-Convolution Neural Network (WSO-CNN) technique to address the aforementioned problem by introducing a regularization strategy via feature selection which selects more useful and distinguishing features for classification. As a result, the developed technique was able to detect and classify various types of citrus fruit diseases and label them accordingly with low false positive rate, high sensitivity, specificity, increased accuracy and reduced recognition time, based on all the experiments performed with the dataset used in the research. Hence WSOCNN performed better than CNN in classifying citrus plant disease having a reduced FPR of 3.57%, 8.34%, 3.89% and 9.00% for black spot, greasy spot, canker and healthy/non healthy dataset respectively.