DSpace Angular :: Browsing by Author "Babatunde, A.N."

Browsing by Author "Babatunde, A.N."

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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
Development of Iris Biometric Template Security Using Steganography
(School of Engineering and Computing, University of the West of Scotland - Computing and Information Systems Journal, 22(3), 8 – 17, 2018) Saheed, Y.K.; Abdulsalam, S.O.; Arowolo, M.O.; Babatunde, A.N.
Purpose: Traditional iris segmentation methods and strategies regularly contain an exhaustive search of a large parameter space, which is sensitive to noise, time-consuming and no longer secured enough. To address these challenges, this paper proposes a secured iris template. Approach: This paper proposes a technique to secure the iris template using steganography. The experimental analysis was carried out on matrix laboratory (MATLABR2015A) environment. The segmented iris region was normalized to decrease the dimensional inconsistencies between iris region areas with the aid of the usage of Hough transform (HT). The features of the iris were encoded by convolving the normalized iris region with 1D Log- Gabor filters in order to generate a bit-wise biometric template. Then, least significant bit (LSB) was used to secure the iris template. The Hamming distance was chosen as a matching metric, which gives the measure of how many bits disagreed between the templates of the iris. Findings: The system operated at a very good training time and high level of conditional testing signifying high optimization with recognition accuracy of 92% and error of 1.7%. The proposed system is reliable, secure and efficient with the computational complexity significantly reduced. Originality/value: The proposed technique provides an efficient approach for securing the iris template. Keywords: Biometric, Iris Recognition System (IRS), Least significant bit (LSB), Hough Transform, 1D Log-Gabor filters
Effectiveness of Contraceptive Usage among Reproductive Ages in Nigeria Using Artificial Neural Network
(School of Engineering and Computing, University of the West of Scotland - Computing and Information Systems Journal, 22(1), 24 – 34, 2018) Dauda, K.A.; Babatunde, A.N.; Olorede, K.O.; Abdulsalam, S.O.; Ogundokun, O.R.
Purpose: Contraceptive usage among women of reproductive age is a fertile area of research for the medical scientists, social scientists, and medical practitioners. However, not much work has been done in Nigeria to identify some of the contraceptive methods used in preventing unwanted pregnancy and sexually transmitted infection (STI) diseases, particularly to explicitly determine which of them are most effective. This paper tackles this task. Methodology: In this paper, we examine different types of contraceptive methods used by Nigeria women to prevent unwanted pregnancy. Datasets on contraceptive usage among 28,647 women of reproductive age from the 2008 Nigerian Demographic Health Survey (NDHS) were analyzed using Artificial Neural Network (ANN) approach in the R language. Finding/Results: The results of the analysis revealed that approximately 1%, 3% and 9% of the study population were using Folkloric, Traditional and Modern methods, respectively. Additionally, 12%, 3% and 9% of the women were found to be currently using the methods since last birth and before last birth. Furthermore, the ANN results through the Garson algorithm revealed that using the modern methods (IUD, Norplant and Female condom) and the traditional methods prevents the unwanted pregnancy negatively, while other modern methods (pill, male condom, female sterilizer, periodic abstinence, withdrawal, locational amenorrhea and foam or jelly) were found to be effective in preventing unwanted pregnancy positively. Interestingly, women that used the modern methods (i.e. Pill, IUD, Injection, Male condom, Female sterilization, Periodic abstinence, Withdrawal and Lactational amenorrhea) were found to be effective in preventing unwanted pregnancy. Research Limitation: The main limitation of this study is the inability to access the current data from NDHS. Therefore, the conclusion of this study is only based on the 2008 NDHS data. Originality/Value: This research work introduces artificial neural network (ANN) to determine and identify the effect of contraceptive methods usage among Nigeria women, and to determine their level of important using Garson's algorithm variable important. This introduction measures the nonlinear effect that exists between the methods and response variable (pregnant and no pregnant) which existing research approaches do not. Keywords: Contraceptive methods, Variable Important, Garson Algorithm, Artificial Neural Network (ANN) and Data mining
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
Knowledge Discovery from Educational Database Using Apriori Algorithm
(Georgian Technical University an Niko Muskhelishvili Institute of Computational Mathematics, Georgia - Georgian Electronic Scientific Journals (GESJ): Computer Science and Telecommunications, 1(51): 41 – 51, 2017) Abdulsalam, S.O.; Hambali, M.A.; Salau-Ibrahim, T.T.; Saheed, Y.K.; Babatunde, A.N.
Ability to predict student’s performance has become very crucial in educational environments and plays important role in producing the best quality graduates. There are several statistical tools for analyzing students' performance for knowledge discovery from available data. This study presents data mining in educational sector that identifies students’ failure pattern using Apriori algorithm. The results of 20 students in 25 courses taken in their 100 and 200 level of an educational institute in North Central Nigeria were considered as a case study. The patterns discovered were used to provide recommendations to academic planners so as to improve their level of decision making, restructuring of curriculum, and modifying the prerequisites of various courses. This study revealed some interesting patterns in failed courses as some failed courses have a relationship with other failed courses. A data mining software for mining student failed courses was developed, used to mine students result, and the analysis were described. Keywords: Association Rule Mining, Apriori Algorithm, Academic performance, Educational data mining, Curriculum, Educational database, Students' result repository
Student’s Performance Analysis Using Decision Tree Algorithms
(Faculty of Computers and Applied Computer Science, Tibiscus University of Trinisoara, Romania - Annals Computer Science Series Journal, Faculty of Computers and Applied Computer Science, Tibiscus University of Trinisoara, Romania, 15, 55– 62, 2017) Abdulsalam, S.O.; Saheed, Y.K.; Hambali, M.A.; Salau-Ibrahim, T.T.; Babatunde, A.N.
Educational Data Mining (EDM) is concerns with developing and modeling methods that discover knowledge from data originating from educational environments. This paper presents the use of data mining approach to study students’ performance in CSC207 (Internet Technology and Programming I) a 200 level course in the department of Computer, Library and Information Science. Data mining provides many approaches that could be used to study the students’ performance, classification task is used in this work to evaluate the student’s performance and as there are numbers of approaches that can be used for data classification, including decision tree method. In this work, decision trees were used which include BFTree, J48 and CART. Students’ attribute such as Attendance, Class test, Lab work, Assignment, Previous Semester Marks and End Semester Marks were collected from the students’ management system, to predict the performance at the end of semester examination. This paper also investigates the accuracy of different Decision tree algorithms used. The experimental results show that BFtree is the best algorithm for classification with correctly classified instance of 67.07% and incorrectly classified instance of 32.93%. KEYWORDS: Classification, Decision tree, Students’ Performance, Educational Data Mining

Browsing by Author "Babatunde, A.N."

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