Browsing by Author "Rafiu Mope Isiaka"

Now showing 1 - 4 of 4
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    Hybrid Algorithm for Symmetric Based Fully Homomorphic Encryption
    (ICAI: International Conference on Applied Informatics, 2021-10-23) Kamaldeen Jimoh Muhammed; Rafiu Mope Isiaka; Ayisat Wuraola Asaju-Gbolagade; Kayode Sakariyah Adewole; Kazeem Alagbe Gbolagade
    Fully Homomorphic Encryption (FHE) supports realistic computations on encrypted data and hence it is widely proposed to be used in cloud computing to protect the integrity and privacy of data stored in the cloud. The existing symmetric-based FHE schemes suffer from insecurity against known plaintext/ciphertext attacks and generate a large ciphertext size that required a large bandwidth to transfer the ciphertext over the network. To ameliorate these weaknesses is the aim of this paper. A hybrid algorithm for symmetric-based Fully Homomorphic Encryption that combines N-prime model and Matrix Operation for Randomization and Encryption with Secret Information Moduli Set (MORESIMS) is proposed. The results show that the proposed hybrid symmetric-based FHE framework satisfied homomorphism properties with robust inbuilt security that resists known plaintext, ciphertext and statistical attacks. The ciphertext size produced by the proposed hybrid framework is less than 4 times of its equivalent plaintext size with a considerable encryption execution time and fast decryption time. Thus, guaranteed to provide optimum performance and reliable solutions for securing integrity and privacy of user’s data in the cloud.
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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.
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    The critical role of hyperparameter tuning in machine learning: Implications for reproducibility and model comparison
    (Kwara State University, 2025-12-31) Rafiu Mope Isiaka; Shakirat Ronke Yusuff; Akinbowale Nathaniel Babatunde; Shuaib Babatunde Mohammed
    Despite being a fundamental aspect of machine learning model development, hyperparameter tuning remains underreported in the literature. This article highlights the importance of hyperparameter optimisation, outlines common hyperparameters across various algorithms, and discusses the consequences of inadequate hyperparameter documentation. We argue that the lack of transparency in hyperparameter settings impedes reproducibility, hinders fair model comparisons, and contributes to the hyperparameter deception. The importance of hyperparameter tuning in machine learning was demonstrated by comparing the performance of the decision tree, support vector machine and random forest models on Iris, Digits and Breast Cancer datasets using default and tuned hyperparameters. This further justifies the need to document and report the process and values of the hyperparameter settings used in the models. To facilitate this, an architecture that encourages the documentation of the hyperparameters has been proposed. By emphasising the need for comprehensive reporting, this study aims to raise awareness and encourage best practices in machine learning research
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    Towards adoption of information and communication technology in higher education - a structural equation model approach
    (Published by Inderscience Publishers, 2021) Rafiu Mope Isiaka; Ronke Seyi Babatunde; Kennedy Arebamen Eiriemiokhale; Damilola David Popoola