Browsing by Author "Jumoke Falilat Ajao"

Now showing 1 - 3 of 3
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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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    Development of an Intrusion Detection System using Mayfly Feature Selection and Artificial Neural Network Algorithms
    (LAUTECH Journal of Engineering and Technology, 2024-06-24) Sulaiman Olaniyi Abdulsalam; Rasheed Abiodun Ayofe; Michael Favour Edafeajiroke; Jumoke Falilat Ajao; Ronke Seyi Babatunde
    Protecting the privacy and confidentiality of information and devices in computer networks requires reliable methods of detecting intrusion. However, effective intrusion detection is made more difficult by the enormous dimensions of data available in computer networks. To boost intrusion detection classification performance in computer networks, this study proposed a feature selection mode for the classification task. The proposed model utilized the Mayfly feature selection algorithm and ANN as the classifier. The model was also tested without a Mayfly Algorithm (MA). The efficiency of the model was determined through a comparison of its Accuracy, Specificity, Precision, Negative predictive value, False positive rate, False discovery rate, False negative rate, Sensitivity, and F1- score. The experimental outcomes revealed that the proposed model is more efficient than existing models when implemented on the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CIC-IDS 2017) dataset. Accuracy of 99.94% (using Data+mayfly+ANN) and 90.17% (using Data+ANN) were attained after experimentation. The proposed model demonstrated superior accuracy compared to existing studies. Its robustness is due to employing mayfly techniques that combine the strengths of PSO, GA, and FA for optimal feature selection. This research presents a dependable dimensionality reduction model useful for intrusion detection and improving security in computer networks.
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    Optimized Convolution Neural Network-based Model for Detection and Classification of Pulmonary Diseases
    (LAUTECH Journal of Engineering and Technology, 2024-06-15) Anthony Taiwo Olajide; Ronke Seyi Babatunde; Sulaiman Olaniyi Abdulsalam; Jumoke Falilat Ajao; Rafiu Mope Isiaka; A. J. Kehinde
    Pelican Optimization Algorithm-based Convolutional Neural Network (POA-CNN) method for the automated identification of pulmonary disorders such as COVID-19 and pneumonia is proposed in this research. The study aims to enhance the efficiency of CNN models in diagnosing lung diseases by using the Pelican Optimization Algorithm (POA), and by addressing drawbacks like a lack of flexibility in hyperparameter modifications. The three primary phases of the model are feature extraction via POA-based hyperparameter optimization, image classification, and image pre-processing. This approach improves existing systems' performance in detecting pulmonary diseases, highlighting the potential of deep learning in identifying and categorizing human diseases. The study uses resizing, grayscale, and augmentation methods to optimize an existing CNN model. A Convolutional Neural Network (CNN) is then applied to classify Pneumonia and COVID-19 cases. The proposed model achieves an accuracy rate of 97.28% and 97.00%, outperforming existing models. This technique is effective in detecting and classifying other pulmonary diseases and can be used to automatically detect and classify these diseases. Higher accuracy findings show how successful the model is, making it a useful tool for pulmonary illness identification.