Browsing by Author "Isiaka, R.M."

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    A Chi-Square-SVM Based Pedagogical Rule Extraction Method for Microarray Data Analysis
    (Institute of Advanced Engineering and Science - International Journal of Advances in Applied Sciences. 9(2): 93 – 100, 2020) Salawu, M.D.; Arowolo, M.O.; Abdulsalam, S.O.; Isiaka, R.M.; Jimada-Ojuolape, B.; Mudashiru, L.O.; Gbolagade, K.A.
    Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms. Keywords: Machine learning, Medical diagnosis, Rule-extraction, SVMs
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    A Comparative Analysis of Feature Extraction Methods for Classifying Colon Cancer Microarray Data
    (EAI Publishing - EAI Endorsed Transactions on Scalable Information Systems, 4(14), 1 – 6, 2017) Arowolo, M.O.; Isiaka, R.M.; Abdulsalam, S.O; Saheed, Y.K.; Gbolagade, K.A.
    Feature extraction is a proficient method for reducing dimensions in the analysis and prediction of cancer classification. Microarray procedure has shown great importance in fetching informative genes that needs enhancement in diagnosis. Microarray data is a challenging task due to high dimensional-low sample dataset with a lot of noisy or irrelevant genes and missing data. In this paper, a comparative study to demonstrate the effectiveness of feature extraction as a dimensionality reduction process is proposed, and concludes by investigating the most efficient approach that can be used to enhance classification of microarray. Principal Component Analysis (PCA) as an unsupervised technique and Partial Least Square (PLS) as a supervised technique are considered, Support Vector Machine (SVM) classifier were applied on the dataset. The overall result shows that PLS algorithm provides an improved performance of about 95.2% accuracy compared to PCA algorithms. Keywords: Dimensionality Reduction, Principal Component Analysis, Partial Least Square, Support Vector Machine
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    A Comparative Analysis of Feature Selection and Feature Extraction Models for Classifying Microarray Dataset
    (School of Engineering and Computing, University of the West of Scotland - Computing and Information Systems Journal, 22(2), 29 – 38, 2018) Arowolo, M.O; Abdulsalam, S.O.; Isiaka, R.M.; Gbolagade, K.A.
    Purpose: The purpose of this research is to apply dimensionality reduction methods to fetch out the smallest set of genes that contributes to the efficient performance of classification algorithms in microarray data. Design/Methodology/Approach: Using colon cancer microarray dataset, One-Way- Analysis of Variance is used as a feature selection dimensionality reduction technique, due to its robustness and efficiency to select relevant information in a high-dimension of colon cancer microarray dataset. Principal Component Analysis (PCA) and Partial Least Square (PLS) are used as feature extraction techniques, by projecting the reduced high-dimensional data into efficient lowdimensional space. The classification capability of colon cancer datasets is carried out using a good classifier such as Support Vector Machine (SVM). The study is analyzed using MATLAB 2015. Findings: The study obtained high accuracies and the performances of the dimension reduction techniques used are compared. The PLS-Based attained 95% accuracy having edge over the other dimension reduction methods (One-Way- ANOVA and PCA). Practical Implications: The major implication of this research is getting the local dataset in the environments which lead to the usage of an open resource dataset. Originality: This study gives an insight and implications of high dimensional data in microarray gene analysis. The application of dimensionality reduction helps in fetching out irrelevant information that halts the performance of a microarray data technology. Keywords: Dimension Reduction, One-Way- ANOVA, PCA, PLS, Classification
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    A Machine Learning Approach to Dropout Early Warning System Modeling
    (International Association of Academicians - International Journal of Advanced Studies in Computer Science and Engineering, 8(2): 1 – 12, 2019) Isiaka, R.M.; Babatunde, R.S.; Ajao, J.F.; Abdulsalam, S.O.
    ABSTRACT 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 favourably 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 factorfor 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 managementtowards curtailing the menace. Keywords: dropout, adaptive-model, data mining, machine learning prediction
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    Classification of Leukemia Cancer Data using Correlation Based Feature Selection Model: A Comparative Approach
    (Faculty of Engineering, Osun State University, Osogbo, Nigeria - UNIOSUN Journal of Engineering and Environmental Sciences, 2025) Babatunde, R.S.; Isiaka, R.M.; Abdulsalam, S.O.; Arowolo, O.M.; Ajao, J.F.
    The abundance of data obtained from microarray experiments presents challenges related to the number of variables and the presence of random fluctuations. Despite the efforts that had been made by previous researchers, emphasizing how data mining aids the implementation of models to facilitate informed prediction, gaps are evident which requires improvement over the earlier models. Dimensionality reduction techniques, such as Correlation Based Feature Selection (CBFS), are good candidate solutions to these problems by selecting pertinent features for categorization. This research implements a model for classification of leukemia cancer using CBFS with Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Ensemble classifiers. The evaluation of the performance of these machine learning models was carried out using sensitivity, specificity, precision and accuracy. The findings indicate that the CBFS+DT model outperforms the other models in terms of sensitivity (96.75%), specificity (97.18%), precision (97.56%), accuracy (96.75%), and F1 score (96.97%), while also exhibiting a decreased computational time (0.4336). This demonstrates the efficacy of CBFS in improving classification accuracy and reducing computing load. Overall, this study highlights the effectiveness of CBFS in cancer research and underscores the importance of carefully choosing the most pertinent variables to enhance classification outcomes. Keywords: machine learning, classification, feature selection, pattern recognition
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    Development of an Intrusion Detection System in a Computer Network
    (Council for Innovative Research - International Journal of Computers and Technology, 12(5), 3479 – 3485, 2014) Babatunde, R.S.; Adewole, K.S.; Abdulsalam, S.O.; Isiaka, R.M.
    The development of network technologies and application has promoted network attack both in number and severity. The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection. This is because not all access to the network occurs through the firewall. This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using Microsoft Visual Basic. Indexing terms: Intrusion detection system, illegitimate, misuse, network.