Browsing by Author "Babatunde, R.S."

Now showing 1 - 7 of 7
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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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    Comparative Analysis of Decision Tree Algorithms for Predicting Undergraduate Students’ Performance in Computer Programming
    (Faculty of Science, Adeleke University, Ede, Nigeria - Journal of Advances in Scientific Research & Applications, A Multidisciplinary Journal Publication of the Faculty of Science, Adeleke University, Ede, Nigeria, 2015) Abdulsalam, S.O.; Babatunde, A.N.; Hambali, M.A.; Babatunde, R.S.
    Educational data are increasing tremendously with little exploration by the education managers. Hidden knowledge can be discovered from the huge data sets available in educational databases through the use of data mining techniques. Educational data mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that reside in educational databases. One of the significant areas of the application of EDM is the development of student models for predicting students’ performances in their educational institutions. The focus of this work is to identify the optimal decision tree algorithms for predicting students’ performance in a computer programming course taken in 200 level based on their ordinary level results in Mathematics and Physics and their 100 level results in Mathematics and Physics courses. One hundred and thirty one (131) students’ records from computer science programme at Kwara State University (KWASU) between 2009 and 2013 were used. The attributes used are students’ ordinary level scores in Mathematics and Physics, 100 level results in Mathematics and Physics courses and the score in a 200 level computer programming course (CSC 203). C4.5 (known as J48 in WEKA) Classification and Regression Tree (CART), and Best-First Tree (BF Tree) decision tree algorithms were used in Waikato Environment for Knowledge Analysis data mining software to generate three classification models employed in predicting students’ performance in CSC 203. The results of these algorithms were compared using 10-fold cross validation method in terms of prediction accuracy and computational time. Our results showed that J48 tree has the highest prediction accuracy of 70.37% and least execution time of 0.02 seconds while CART and BFTree has prediction accuracy of 60.44% and 60.30% respectively and both having execution time of 0.22 seconds based on the data set used in this study. This study also revealed that previous knowledge of Mathematics and Physics both at Ordinary level and 100 level are essential determinants of students’ performance in a computer programming course. Keywords: Data Mining, Educational Data Mining, Decision Tree algorithm, Students’ Performance
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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.
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    Development of an Intrusion Detection System using ANOVA Feature Selection and Support Vector Machine Algorithms
    (Journal Press India - COMPUTOLOGY: Journal of Applied Computer Science and Intelligent Technologies, 4(1), 89 – 108, 2024) Edafeajiroke, M.F.; Abdulsalam, S.O.; Shuaib, M.U.; Babatunde, R.S.
    The escalating sophistication and frequency of cyber-attacks have necessitated the development of more advanced intrusion detection systems (IDS). This research presents the development of an innovative IDS employing Analysis of Variance (ANOVA) for feature selection and a Support Vector Machine (SVM) algorithm for intrusion detection. The aim is to enhance detection accuracy while reducing computational overhead. ANOVA was used to identify significant features from vast and complex network traffic data, simplifying the high dimensional data and improving the detection system’s efficiency. The selected features are then classified using the Radial Basis Function (RBF) SVM algorithm, renowned for its high accuracy and robustness in handling high-dimensional data. A comparative analysis with existing IDS models demonstrates the improved efficiency and accuracy of the proposed model. This work provides an advanced methodology for cyber security, contributing to the ever-evolving battle against cyber threats. Keywords: Intrusion detection; Machine learning; Feature selection; Support vector machine; ANOVA.
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    Gender Recognition Using Local Binary Pattern and Naive Bayes Classifier
    (Nigeria Computer Society (NCS), Lagos, Nigeria - The Journal of Computer Science and Its Application, An International Journal of the Nigeria Computer Society. 23(1), 65 – 74, 2016) Babatunde, R.S.; Abdulsalam, S.O.; Yusuff, S.R.; Babatunde, A.N.
    Human face provides important visual information for gender perception. Ability to recognize a particular gender is very important for the purpose of differentiation. Automatic gender classification has many important applications, for example, intelligent user interface, surveillance, identity authentication, access control and human-computer interaction amongst others. Gender recognition is a fundamental task for human beings, as many social functions critically depend on the correct gender perception. Consequently, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this study, Local Binary Pattern is used to detect the occurrence of a face in a given image by reading the texture change within the regions of the image, while Naive Bayes Classifier was used for the gender classification. From the results obtained, the gender correlation was 100% and the highest accuracy of the result obtained was 99%.The system can be employed for use in scenarios where real time gender recognition is required. Keywords: Gender, Local Binary Pattern, Naïve Bayes, Recognition
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    Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM
    (Springer Nature Switzerland, Lecture Notes in Business Information Processing 402: 480 – 492 - Proceedings of 17th European, Mediterranean, and Middle Eastern Conference, EMCIS 2020, Dubai, United Arab Emirates, November 25–26, 2020, 2020) Abdulsalam, S.O.; Abubakar, A.M.; Ajao, J.F.; Babatunde, R.S.; Ogundokun, R.O.; Nnodim, C.T.; Arowolo, M.O.
    A significant application of microarray gene expression data is the classification and prediction of biological models. An essential component of data analysis is dimension reduction. This study presents a comparison study on a reduced data using Analysis of Variance (ANOVA) and Recursive Feature Elimination (RFE) feature selection dimension reduction techniques, and evaluates the relative performance evaluation of classification procedures of Support Vector Machine (SVM) classification technique. In this study, an accuracy and computational performance metrics of the processes were carried out on a microarray colon cancer dataset for classification, SVM-RFE achieved 93% compared to ANOVA with 87% accuracy in the classification output result. Keywords: SVM-RFE; ANOVA; Microarray; SVM; Cancer