Browsing by Author "Oladipo, I. D."
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Item Customer Churn Prediction in Telecommunications Using Ensemble Technique(Department of Computer Science, University of Ibadan, Ibadan, Nigeria, 2023) Oladipo, I. D.; Awotunde, J. B.; AbdulRaheem, M.; Taofeek-Ibrahim, F. A.; Obaje, O.; Ndunagu, J. N.Item Design and Implementation of Information Retrieval System for Laboratory Equipments(Journal of Mathematics Association of Nigeria, ABACUS, 2016) Oladipo, I. D.; Adewole, K. S.; Babatunde, A. O.; Abati, A. I.; Tomori, A. R.; Awotunde, J. B.Getting information relating to user's search criteria on equipment within a University laboratory has posed many challenges since there is no specific system to serve this purpose. It is difficult to handle the whole system manually. The aim of this paper is therefore to design and implement a web-based information retrieval system for University Laboratory equipment. This system offers a powerful internet-based search engine for locating and identifying equipment within a University laboratory while eliminating all unrelated content that a general-purpose search engine would retrieve. The method used involved collecting information needed from the technologist on various equipment in the laboratory and inserted into the retrieval system using SQL server to make query on the database to manage the data and a PHP programming language was used as the server-side scripting language for establishing an efficient querying of information from the database. Java scripting was also used as client-side scripting language for the purpose of adding more interactions to the proposed system. The developed system having been evaluated and assessed thoroughly was found to be efficient, easy to use for locating laboratory equipment in a University.Item 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.