Browsing by Author "Isiaka R. M."

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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) Olajide, A. T.; Babatunde, R. S.; Abdulsalam, S. O.; Ajao, J. F.; Isiaka R. M.; Ayeni, J. K.
    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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    TSDL: A framework for Tip Spam Detection in Location Based Social Network
    (International Journal of Information Security, Privacy and Digital Forensic, 2017-03-14) Adewole K. S; Jimoh R. G; Isiaka R. M.; Ajiboye A. R.
    In Web 2.0 systems, Location Based Social Network (LBSN) has become increasingly popular because of its ability for locating places, such as restaurants, hotels and nearby facilities that can render services to the users. Among such LBSN is Apontador, a popular social network introduced in Brazil a couple of years ago. However, despite the numerous opportunities offered by Apontador LBSN, it has become attractive social network for tips spam distribution. In this work, an intelligent machine learning framework is proposed to detect tips spam in Apontador LBSN. The proposed framework explored three classification algorithms: Random Forest, Multilayer Perceptron, and Decorate. Bio-inspired feature identification was studied using Evolutionary algorithm (EA) and Particle Swamp Optimization (PSO) to identify the discriminating features for tip spam detection in LBSN. Three experiments were conducted based on sixty (60), twenty-two (22), and ten (10) features to ascertain the performance of the proposed framework for tip spam detection. Based on the various experiments conducted, Random Forest classification algorithm produced accuracy and F-measure of 90.5% and ROC of 96.7% using 60 features extracted from Apontador LBSN. The algorithm also outperformed the two other classifiers during EA evaluation. However, Decorate classifier produced the best results during the PSO evaluation, achieving F-measure and ROC of 86.3% and 92.9% respectively. The experimental results show that the proposed framework improved in performance for tips spam detection in Apontador LBSN.