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- ItemComparative Approach of Back Propagation Neural Network and Decision Tree on Breast Cancer Classification. .(Proceedings of the 8th International Conference on Mobile e-Services (ICoMeS) – Lautech, Ogbomoso. Oyo State. Nigeria, 2019-10-18) Babatunde, R. S, Adewole, K. S., and Ajiboye, A. R. (2019)The use of data mining methods in incorporating decision making has been increasing in the past decades. Data mining simply refers to extracting or mining knowledge from large amount of data. Over the years, medical image processing has benefited immensely from data mining techniques including breast cancer diagnosis. Sonography (also known as ultrasound) has become a great addition to mammography and magnetic resonance imaging (MRI) as imaging techniques dedicated to providing breast cancer screening. This technique is time-consuming and often characterized with low accuracy. Hence, the need to develop a robust classification model with high performance accuracy and reduced false alarm. In this paper, the performance of back propagation neural networks (BPNN) and C4.5 decision tree (DT) for breast cancer prediction was carried out. Filter based feature selection approach using correlation filter was employed for ranking features according to their predictive power. The model was simulated using WEKA data mining tools and extensive comparative study was performed based on the standard evaluation metrics. The performance of the two classifiers was compared based on their predictive accuracy, precision, recall, kappa statistic and other relevant statistical measures. The simulation results shows that C4.5 outperforms BPNN in terms of training time (0.16 secs) and accuracy (94.2857% ) while BPNN has 46.9secs training time and accuracy of 90.9524%. However, the result also reveals that BPNN outperforms C4.5 in terms of error rate, with BPNN having mean absolute error of 0.0542 while C4.5 has mean absolute error of 0.0834. It can therefore be deduced from the comparison that C4.5 can be a good option for prediction task considering the fast training time of the algorithm as well as the high accuracy of prediction.
- ItemClassification of Diabetes Types using Machine Learning.(International Journal of Advanced Computer Science and Applications, 2022-04-22) Adigun, O., Okikiola, F., Yekini, N. and Babatunde R. (2022)Machine learning algorithms have aided health workers (including doctors) in the processing, analysis, and diagnosis of medical problems, as well as the detection of disease patterns and other patient data. Diabetes mellitus (DM), commonly referred to as diabetes, is a gathering of a syndrome issue that is portrayed by high glucose levels in the blood over a drawn-out period. It is a long-term illness that is a great threat to humanity and causes death. Most of the existing machine learning algorithms used for the classification and prediction of diabetes suffer from embodying redundant or inessential medical procedures that cause complications and wastage of time and resources. The absence of a correct diagnosis scheme, deficiency of economic means, and a general lack of awareness represent the main reasons for these negative effects. Hence, preventing the sickness altogether through early detection may doubtless cut back a considerable burden on the economy and aid the patient in diabetes management. This study developed diabetes classification using machine learning techniques that will minimize the aforementioned drawbacks in the prediction of diabetes systems. Decision tree classifiers, logistic regression, random forest, and support vector machines are all examples of predictive algorithms that were tested in this paper. 1009 records of data set were obtained from the Diabetes dataset of Abelvikas, Data World. We used a confusion matrix to visualize the performance evaluation of the classifiers. The experimental result shows that the four machine learning algorithms perform well. However, Random Forest outperforms the other three, with a prediction accuracy of 100% and has a better prediction level when compared with others and existing work.
- ItemImplication of variation in Ant Colony Optimization Algorithm (ACO) parameters on Feature Subset Size.(Computing and Information System Journal, University of West Scotland, 2017-05-20) Babatunde, R. S., Isiaka, R.M., Adigun, O.J. and Babatunde, A.N. (2017)Purpose: The purpose of this research is to apply ant colony optimization (ACO), a nature inspired computational (NIC) technique to achieve optimal feature subset selection, for the purpose of data dimensionality reduction, which will remove redundancy, provide a reduced storage space, improve memory utilization and subsequently, classification task. Design/Methodology/Approach: The ACO feature subset selection process was filter-based, accomplished by using correlation coefficient as the heuristic information which guides the search to determine the pixel (feature) attractiveness. The intensity of the pixel represents the pheromone level . Also, ρ, φ, and are key parameters which were tuned to various arbitrary values at each iteration to determine the best combination of parameters which can result in a reduced feature subset (window size). The procedure was simulated in MATLAB 2012. Findings: The study has been able to demonstrate that experimental results obtained by tuning the parameters of ACO at each run gave a reduced feature subset (window size), at parameter settings of α (0.10), β (0.45), ρ (0.50), φ (1.0) which was salient for efficient face recognition thereby demonstrating the effectiveness and superiority of filter-based over wrapper–based feature selection. Research Limitation: The major limiting factor in this research was time and materials which hindered the further testing and implementation of innovative computational intelligence. Originality/Value: This research work is valuable because it gives the implication of the variation of the ACO parameters which determines the extent to which the optimal solution construction that results in a reduced feature subset can be realized.
- ItemA Systematic Review of Feature Dimensionality Reduction Techniques for Face Recognition Systems.(Journal of Digital Innovations & Contemp Res. In Sc., Eng & Tech., 2017-09-07) Babatunde, R.S. (2017)In this research, a comparison of the various techniques used for face recognition is shown in tabular format to give a precise overview of what different authors have already projected in this particular field. A systematic review of 40 journal articles pertaining to feature dimensionality reduction was carried out. The articles were reviewed to appraise the methodology and to identify the key parameters that were used for testing and evaluation. The dates of publication of the articles were between 2007 and 2015. Ten percent (10%) of the articles reported the training time for their system while twenty four percent (24%) reported their testing time. Sixty seven percent (67%) of the reviewed articles reported the image dimension used in the research. Also, only forty eight percent (48%) of the reviewed articles compared their result with other existing methods. The main emphasis of this survey is to identify the major trade-offs of parameters and (or) metrics for evaluating the performance of the techniques employed in dimensionality reduction by existing face recognition systems. Findings from the review carried out showed that major performance metrics reported by vast amount of researchers in this review is recognition accuracy in which eighty six percent (86%) of the authors reported in their experiment.
- ItemFramework on Enhancing Biometric Template Protection Transformation Scheme Using Residue Number System. .(Published by Institute of Electrical and Electronics Engineers (IEEE), 2023-10-16) Omotosho. F. S., Babatunde, R. S., and Gbolagade, K. A. (2023)Biometric technology was introduced into our applications and smart devices to provide a dependable approach to the problem of user authentication. However, study has proven that there are susceptible points in typical biometric systems which are prone to manipulation by hackers. The most concerned is biometric template attacks, reason for various biometric template techniques proposed by researchers in the literature to secure biometric raw template. The main challenge of most biometric template protection techniques is decrease in recognition performance when compare to unprotected system. Also, most of them required Auxiliary Data (AD) for verification. This study will leverage on features of Residue Number System (RNS) by integrating it with Biometric Template Protection (BTP) Transformation technique. The Model simulation was carried-out, the result generated shows that there was no reduction in recognition performance as compare to unprotected biometric system. Since RNS is a carry-free arithmetic and parallel operations, the overall computational time is less when compared to unprotected biometric system. .