Browsing by Author "Micheal Olaolu Arowolo"

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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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    Machine Learning Approach Using KPCA-SVMs for Predicting COVID-19
    (Springer Nature Switzerland AG 2022, 2022-07-22) Akeem Femi Kadri; Micheal Olaolu Arowolo; Sanjay Misra
    The world has met numerous epidemics in the past decades. Lately, a deadly sickness identified as COVID-19 has surfaced from China [1, 2]. Inimitable public health adversity is triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [2–4]. The World Health Organization (WHO) termed the new epidemic as COVID-19. It is acknowledged as a Public Health Emergency of International Concern since the beginning of 2020. It was considered an epidemic around the first quarter of 2020 [5–7], as Americans joined forces with investigative institutions and scientific concerns for global artificial intelligence (AI) investigators’ activities in evolving groundbreaking machine learning measures that will help tackle COVID-19 linked surveys [3]. COVID-19 is a novel solitary intelligent ribonucleic acid (RNA) germ comprising of a huge pathological genomic sequence. It alters advances fast with no specific limitation for evaluating or investigating techniques or suitable medications. Secluding infected individuals by quarantining is the utmost way of safeguarding the universe from more escalation of the deadly COVID-19 [8].