Browsing by Author "Ajiboye, A. R."

Now showing 1 - 5 of 5
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    Frequent Pattern and Association Rule Mining from Inventory Database using Apriori Algorithm
    (African Journal of Computing & ICT, 2014) Adewole, K. S.; Akintola, A. G.; Abdulsalam, S. O.; Ajiboye, A. R.
    Recently, data mining has attracted a great deal of attention in the information industry and in a Society where data continue to grow on a daily basis. The availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge is the major focus of data mining. The information and knowledge obtained from large data can be used for applications ranging from market analysis, fraud detection, production control, customer retention, and science exploration. A record in such data typically consists of the transaction date and the items bought in the transaction. Successful organizations view such databases as important pieces of the marketing infrastructure. This paper considers the problem of mining association rules between items in a large database of sales transactions in order to understand customer-buying habits for the purpose of improving sales. Apriori algorithm was used for generating strong rules from inventory database. It was found that for a transactional database where many transaction items are repeated many times as a superset in that type of database, Apriori is suited for mining frequent itemsets. The algorithm was implemented using PHP, and MySQL database management system was used for storing the inventory data. The algorithm produces frequent itemsets completely and generates the accurate strong rules.
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    The Impact of Social Network Sites on Knowledge and Information Sharing to Students in the Open Distance Learning Scheme
    (Association for Scientific Computing and Electronic Engineering (ASCEE), 2018-02) Ajiboye, A. R.; Bakare, Maroofat; Usman-Hamza, Fatimah; Sulyman, Shakirat
    This study examines the impact of selected Social Network Platforms on the dissemination of knowledge and information for students of the National Open University of Nigeria. Three major social network platforms are considered in this study, they are: Facebook, Web-blogs and YouTube. Research questions were asked based on the objectives of this study and a number of hypotheses were formulated. The questionnaire used as the survey instrument was carefully designed and distributed to some students within the study domain. This comprised of the registered students for 2016/2017 Academic Session at the National Open University of Nigeria, Ilorin and Oshogbo Study Centres. Questionnaires were administered to a total of 600 students in the two centres. The data collected are analyzed using Statistical Package for Social Sciences (SPSS). Findings from this study show that, 80% of the respondents indicate the positive impact of the three social network platforms under study. The impacts of the three networks are also found to be significant. The study further reveals that, effective utilization of educational features of Social Network Platforms has a great and tremendous advantage to students if optimally put to use. It is recommended that students who find the use of Social Network Platforms distracting to academics, should reduce the time they dedicate to exploration of online resources and in particular on these platforms: Facebook, Web-blogs and YouTube.
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    Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier
    (Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics: Theories and Applications, 2021) Adewole, K. S.; Raheem, M. O.; Abikoye, O. C.; Ajiboye, A. R.; Oladele, Tinuke Omolewa; Jimoh, M. K.; Aremu, D. R.
    Within the multitude of security challenges facing the online community, malicious websites play a critical role in today’s cybersecurity threats. Malicious URLs can be delivered to users via emails, textmessages, pop-ups or advertisements. To recognize these malicious websites, blacklisting services have been created by the web security community. This method has been proven to be inefficient. This chapter proposed meta-heuristic optimization method for malicious URLs detection based on genetic algorithm (GA) and wolf optimization algorithm (WOA). Support vector machine (SVM) as well as random forest (RF) were used for classification of phishingweb pages. Experimental results showthatWOAreduced model complexity with comparable classification results without feature subset selection. RF classifier outperforms SVM based on the evaluation conducted. RF model without feature selection produced accuracy and ROC of 0.972 and 0.993, respectively, while RF model that is based onWOA optimization algorithm produced accuracy of 0.944 and ROC of 0.987. Hence, in view of the experiments conducted using two well-known phishing datasets, this research shows that WOA can produce promising results for phishing URLs detection task.
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    A novel approach to outliers removal in a noisy numeric dataset for efficient mining
    (Ilorin Journal of Computer Science and Information Technology, 2016) Ajiboye, A. R.; Adewole, K. S.; Babatunde, R. S.; Oladipo, I. D.
    Data pre-processing is a key task in the data mining process. The task generally consumes the largest portion of the total data engineering effort while unveiling useful patterns from datasets. Basically, data mining is about fitting descriptive or predictive models from data. However, the presence of outlier sometimes reduces the reliability of the models created. It is, therefore, essential to have raw data properly pre-processed before exploring them for mining. In this paper, an algorithm that detects and removes outliers in a numeric dataset is proposed. In order to establish the effectiveness of the proposed algorithm, the clean data obtained through the implementation of the proposed approach is used to create a prediction model. Similarly, the clean data obtained through the use of one of the existing techniques is also used to create a prediction model. Each of the models created is simulated using a set of untrained data and the error associated with each model is measured. The resulting outputs from the two approaches reveal that, the prediction model created using the output from the proposed algorithm has an error of 0.38, while the prediction model created using the cleaned data from the clustering method gives an error of 0.61. Comparison of the errors associated with the models created using the two approaches shows that, the proposed algorithm is suitable for cleaning numeric dataset. The results of the experiment also unveils that, the proposed approach is efficient and can be used as an alternative technique to other existing cleaning methods.
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    Stepwise biometric procedures for managing student attendance in higher institution of learning
    (2015) Adewole, K. S.; Jimoh, R. G.; Abikoye, O. C.; Ajiboye, A. R.
    Data and information security are very important issues in computing environment. Security of data prevents unauthorized users from accessing individual personal information. Biometric is one of the authentication methods used in a wide range of application domains such as airline and banking environment to secure confidential data. This technique is more reliable and capable of distinguishing between an authorized person and an impostor than traditional methods such as passwords. Large numbers of higher academic institutions in the developing countries are still using the process of manual attendance for both lecture and examination for students' authentication and authorization, hence, the need for automated system that can assist in this area. In this paper, stepwise biometric procedures for managing students' attendance for both lectures and examinations are presented. The various stages involved in student attendance management are discussed and simulated. These include enrollment, fingerprint matching and attendance management. The results show that the proposed system is able to identify those students who are qualified to sit for an examination.