Work place: Department of Computer Science, Joseph Ayo Babalola University, Ikeji Arakeji, Osun State, NIGERIA
Research Interests: Computer Architecture and Organization, Software Design, Management and Marketing, Web Technologies, Computer Networking
Dr Adekunle A. Eludire received the MSc. and PhD degrees in Computer Systems and Engineering from Kiev Polytechnic Institute in Kiev, Ukraine. He majors in Computer Networking, Architecture and Organization and is a certified Oracle Database Professional and MCSE on Windows Systems. He is a member of IEEE, Nigeria Computer Society, Computer Professionals Registration Nigeria and Nigerian Society of Engineers. Currently, he lectures at the Department of Computer Science of Joseph Ayo Babalola University, Ikeji Arakeji and also involved in designing, implementing ICT and e-governance systems and web-based applications.
DOI: https://doi.org/10.5815/ijitcs.2016.02.03, Pub. Date: 8 Feb. 2016
This work documents the development and implementation of a commercial bank's loan classification database system. It employed multiple discriminant analysis models to assess the relationship between relevant loan variables and existing bad loan problem. It also made use of mathematical model to replicate the Examiner's classification process to classify loans in a more objective and sober way. Classification of loan is grouping of loans in accordance to their likelihood of ultimate recovery from borrowers. Banking business is one of the most highly levered businesses especially on loan accounts. It is likely to collapse in case of a slight deterioration in quality of loans. Six important factors (propriety of use of funds borrowed; operation of Borrower's overdraft account; cooperation with the Bank, collateral and number of days the loan is past due) were identified and grouped as variables in determining the quality of loan portfolio. The developed classification model shows that there exists a linear relation between loan classification and the six variables considered. Four classification functions were developed and implemented in Microsoft Access database to assist in effective classification. The implementation of a database system makes it easy to store relevant classification information and revert to them whenever needed for comparative analysis on quarterly, half-yearly and annual basis.[...] Read more.
DOI: https://doi.org/10.5815/ijitcs.2014.02.03, Pub. Date: 8 Jan. 2014
This work employed Artificial Neural Networks and Decision Trees data analysis techniques to discover new knowledge from historical data about accidents in one of Nigeria’s busiest roads in order to reduce carnage on our highways. Data of accidents records on the first 40 kilometres from Ibadan to Lagos were collected from Nigeria Road Safety Corps. The data were organized into continuous and categorical data. The continuous data were analysed using Artificial Neural Networks technique and the categorical data were also analysed using Decision Trees technique .Sensitivity analysis was performed and irrelevant inputs were eliminated. The performance measures used to determine the performance of the techniques include Mean Absolute Error (MAE), Confusion Matrix, Accuracy Rate, True Positive, False Positive and Percentage correctly classified instances. Experimental results reveal that, between the machines learning paradigms considered, Decision Tree approach outperformed the Artificial Neural Network with a lower error rate and higher accuracy rate. Our research analysis also shows that, the three most important causes of accident are Tyre burst, loss of control and over speeding.[...] Read more.
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