Browsing by Author "Abdulsalam, Olaniyi Sulaiman"

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    A 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 Kazeem
    In 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.
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    A Machine Learning Approach to Dropout Early Warning System Modeling
    (International Journal of Advanced Studies in Computer Science and Engineering (Ijascse.）, 2019-02-28) Isiaka, Mope Rafiu; Babatunde, Ronke Seyi; Abdulsalam, Olaniyi Sulaiman
    Dropout has been identified as a social problem with an immediate and long term consequences on the socio economic development of the society. A major element of a conceptual framework for dropout monitoring and control system is the prevention component. The component specified the need for automatic dropout early warning system. This paper presents the procedure for building the adaptive model, that is the core element for the realization of the prevention component of the framework. The model development is guided by the knowledge of the domain experts. An experimental approach was used to identify the K-Nearest Neighbors (KNN) as the best of the six algorithms considered for the adaptive models. Other algorithms explored are Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB) and Support Vector Machines (SVM). Historical dataset from a State University in the North Central region of Nigeria was used for building and testing the adaptive model. The average performance of the model on full attributes and the first year academic attributes compared favorably with 0.98 Precision, Recall and F1-Scores. Conversely, the Precision, Recall and F1-Scores on the entry attributes are 0.90, 0.95 and 0.92 respectively. The implication of this is that the academic performance is a very significant factor for dropping out of the educational system. The developed model can provide an early signal on the students with the propensity to dropout, thereby serving as an advisory system for the students, parents and school management towards curtailing the menace.
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    Assessing the Adequacy of Artificial Intelligence Tools for African Narratives: Towards Responsible and Culturally Inclusivity
    (FUW Trends in Science & Technology Journal, 2025-11-28) Isiaka, Mope Rafiu; Jimoh, Muhammed Kamaldeen; Abdulsalam, Olaniyi Sulaiman; Olanrewaju, Olaitan Toyyib
    The increasing reliance on Generative Artificial Intelligence (GenAI) across diverse domains has sparked global attention regarding its adequacy and accuracy. Specifically, its capacity to capture and represent African narratives remains underexplored. Most AI systems are developed within Western contexts fail to align with Africa's diverse sociocultural realities, thereby perpetuating biases, misrepresentation, and the erasure of indigenous knowledge systems. This study critically evaluates the performance of the ChatGPT, DeepSeek, Gemini, and Perplexity large language models in processing and representing African narratives. Using a mixed-methods approach, it incorporates systematic assessments of selected popular AI tools and qualitative input from domain experts in African linguistics, culture and technology. A survey involving academic staff from state and federal universities in Nigeria’s North-Central region contributed 24 relevant prompts, which were combined with 15 research-generated prompts, totalling 39. These prompts were executed concurrently on the selected AI models, and the resulting outputs were evaluated by subject-matter experts for accuracy, adequacy, and credibility. Perplexity consistently achieved the highest ratings across all parameters, whereas the other models displayed varying degrees of effectiveness. Notably, the findings revealed a “white as default” bias and a tendency to prioritise content from Eastern and Southern Africa. The study also identified serious gaps in the handling of African languages, idioms, and culturally embedded expressions, stemming from the poor representation of low-resource languages, limited infrastructure, skill deficiencies, and weak governance. In response, this study proposes roadmaps for responsible AI development tailored to African contexts, advancing ethical practices and amplifying African voices in the digital era.
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    Use of White Shark Optimization for Improving the Performance of Convolution Neural Network in Classification of Infected Citrus
    (University of Ibadan Journal of Science and Logics in ICT Research, 2023-06-09) Babatunde, Ronke Seyi; Isiaka, Mope Isiaka; Ajao, Jumoke Falilat; Abdulsalam, Olaniyi Sulaiman; Balogun, Bukola Fatimah
    Citrus plant diseases are major causes of reduction in the production of citrus fruits and their usage. Early detection of the onset of the diseases is very important to curb and reduce its spread. A number of researches have been done on the detection and classification of the diseases, most of which have identified poor labelling of symptoms which result into improper classification. Some researchers have also experimented on the effectiveness of convolution neural network and other deep learning techniques, most of which results into a faster convergence but suffers from low accuracy, computational overhead and overfitting as fundamental issues. To reduce the effects of overfitting, this research developed a White Shark Optimization-Convolution Neural Network (WSO-CNN) technique to address the aforementioned problem by introducing a regularization strategy via feature selection which selects more useful and distinguishing features for classification. As a result, the developed technique was able to detect and classify various types of citrus fruit diseases and label them accordingly with low false positive rate, high sensitivity, specificity, increased accuracy and reduced recognition time, based on all the experiments performed with the dataset used in the research. Hence WSOCNN performed better than CNN in classifying citrus plant disease having a reduced FPR of 3.57%, 8.34%, 3.89% and 9.00% for black spot, greasy spot, canker and healthy/non healthy dataset respectively.