Computer Science -Scholarly Publications
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Browsing Computer Science -Scholarly Publications by Author "Agwuegbo, S.O."
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- ItemOpen AccessDigital Filter Design Using Artificial Neural Network.(Journal of Computer Science and its Applications, 2010-06) Adewole, A.P.; Agwuegbo, S.O.In this paper, Feed Forward Multi-Layer Perceptron neural network was adapted as a digital filtering tool in modelling communications systems that were corrupted by noise or interference. Discrete-Fourier Transform was used to reduce error in transmission. The input and target output data from the study were generated using ionosphere data radar, and this proved to be essential and necessary for training and testing the network. The network was trained using MATLAB R2008a and the training resulted to the minimisation of the error. The result of digit filtration shows a near error-free output. In conclusion, the forward-feed multilayered neural network can be used to build a functional digital filter.
- ItemOpen AccessPredicting Insurance Investment: A Factor Analytic Approach(Journal of Mathematics and Statistics, 2010) Agwuegbo, S.O.; Adewole, A.P.; Maduegbuna, A.N.Problem statement: In the last decade growing attention has been paid to the pattern of investments by the insurance industry and the question of how to evaluate such investments. In an economy where the capital market is huge and active, mathematical considerations come into play in the selection of investments to ensure yield maximisation. Approach: This study examined the use of factor analysis as an emerging technique for the analysis of insurance investment in Nigeria. Results: The proposed technique described a number of methods designed to analyze interrelationships within the investment variables in terms of few underlying but unobservable random quantities called factors. The factors were constructed in a way that reduces the overall complexity of the data by taking advantage of inherent interdependencies. Conclusion: The result obtained through this approach were promising and shows that two principal components of the factor loadings have a cumulative proportion of variance accounted for 94.5% of the total variations of the investments pattern.