Browsing by Author "Micheal Olaolu Arowolo"

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    A chi-square-SVM based pedagogical rule extraction method for microarray data analysis
    (International Journal of Advances in Applied Sciences (IJAAS), 2020-06-10) Mukhtar Damola Salawu; Micheal Olaolu Arowolo; Sulaiman Olaniyi Abdulsalam; Isiaka, Mope Rafiu; Bilkisu Jimada-Ojuolape; Mudashiru Lateef Olumide; Kazeem A. Gbolagade
    Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms.
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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].