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- ItemImage Restoration: A LSHADE-GAN Approach for Yoruba Documents(Proceedings of International Computing and Communication Conference (13C 2024), 2024-04-22) Lawal, Olamilekan Lawal; Ajao, Jumoke Falilat; Isiaka, Mope RafiuThe paper introduces an innovative approach to reconstructing historical handwritten texts, specifically focusing on Yoruba documents. This method combines a generative adversarial network (GAN) with the LSHADE algorithm to create the LSHADE-GAN model. Trained on a curated dataset of five degraded Yoruba documents, this model surpasses traditional image-processing techniques and deep learning-based methods in performance. Evaluation of the LSHADE-GAN model using two samples reveals F-measures of 61.83% and 78.02%, showcasing its superiority over DE-GAN (58.24% and 75.23%) and PSO-GAN (51.67% and 66.46%) approaches. Additionally, the model demonstrates enhanced PSNR and visual quality, underscoring its effectiveness in preserving cultural heritage through accurate reconstruction of historical texts.
- ItemA Hybrid Dimensionality Reduction Model for Classification of Microarray Dataset(I.J. Information Technology and Computer Science, 2017-11-08) Abdulsalam, Olaniyi Sulaiman; Isiaka, Mope Rafiu; Gbolagade, Alade KazeemIn this paper, a combination of dimensionality reduction technique, to address the problems of highly correlated data and selection of significant variables out of set of features, by assessing important and significant dimensionality reduction techniques contributing to efficient classification of genes is proposed. One-Way ANOVA is employed for feature selection to obtain an optimal number of genes, Principal Component Analysis (PCA) as well as Partial Least Squares (PLS) are employed as feature extraction methods separately, to reduce the selected features from microarray dataset. An experimental result on colon cancer dataset uses Support Vector Machine (SVM) as a classification method. Combining feature selection and feature extraction into a generalized model, a robust and efficient dimensional space is obtained. In this approach, redundant and irrelevant features are removed at each step; classification presents an efficient performance of accuracy of about 98% over the state of art.
- ItemDevelopment of a Web-Based Platform Utilizing a Hybrid CNN Architecture for Real-Time Fecal Image Classification(Ajayi Crowther Journal of Pure and Applied Sciences, 2025-10-29) Omideyi, Damilare Andrew; Isiaka, Mope Rafiu; Babatunde, Ronke SeyiAccurate classification of fecal images offers a non-invasive and scalable approach to detecting enteric diseases such as Coccidiosis, Salmonellosis, and Newcastle disease. Traditional diagnostic methods remain limited by their dependence on expert analysis, time consumption, and cost. This study presents the development of a web-based platform utilizing a hybrid Convolutional Neural Network (CNN) architecture for the real-time classification of fecal images. The proposed model combines MobileNetV2 for lightweight inference and VGG-16 for deep feature extraction, ensuring both accuracy and computational efficiency. Following training on a curated and augmented dataset of labeled fecal images, the model was quantized and converted into TensorFlow Lite format for mobile compatibility. The final system enables users to upload images via a browser or Android application and receive instant classification into four categories: Coccidiosis, Salmonella, Newcastle disease, or Healthy. Performance evaluations confirm high diagnostic accuracy and responsiveness under real-world conditions. This study demonstrates that hybrid deep learning models, when integrated into web-based platforms, can support effective classification of fecal images for field-ready disease detection. It is recommended that future work expands the dataset across multiple geographic regions and incorporates additional diagnostic inputs to enhance system robustness and adaptability.
- ItemEmpirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection(MDPI electronics, 2022-09-22) Kayode S. Adewole; Taofeekat T. Salau-Ibrahim; Agbotiname Lucky Imoize; Idowu Dauda Oladipo; Muyideen AbdulRaheem; Joseph Bamidele Awotunde; Abdullateef O. Balogun; Isiaka Mope Rafiu; Taye Oladele AroNetwork intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA(82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended.
- ItemA Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis(Journal of Advances in Mathematical & Computational Sciences, 2022-12-04) Isiaka, R.M; Babatunde, R.S.; Ajao, J.F.; Yusuff, S.R.; Popoola, D.D.; Arowolo, M.O.; Adewole, K.S.Following the identification of Coronavirus Disease 2019 (COVID-19) in Wuhan, China in December 2019, AI researchers have teamed up with a health specialist to combat the virus. This study explores the medical and non-medical areas of COVID-19 that AI has impacted: the prevalence of the AI technologies adopted across all stages of the pandemic, the collaboration networks of global AI researchers, and the open issues. 21,219 papers from ACM Digital, Science Direct and Google Scholar were examined. Adherence to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and utilizing the PICO (population, intervention, comparison, and outcome) paradigm, guided the inclusion of researches in the review. Tables and graphs were utilized to display the results. Analysis revealed that AI has impacted 4 molecular, 4 clinical, and 7 societal areas of COVID-19. Deep Learning among other AI technologies was traced to all aspects of the pandemic. 2173 authors and co-authors were traced to these achievements, while 32 of the most connected 51 authors were affiliated with institutions in China, 18 to the United States, and 1 to Europe. The open issues identified had to do with the quality of datasets, AI model deployment, and privacy issues. This study demonstrates how AI may be utilized for COVID-19 diagnosis, prediction, medication and vaccine identification, prognosis, and contact person monitoring. This investigation began at the beginning of the epidemic and continued until the first batch of vaccinations received approval. The study provided collaboration opportunities for AI researchers and revealed open issues that will spike further research toward preparing the world for any future pandemic