FACULTY OF SCIENCE

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Author: search.filters.author.Udomboso, C. G.Has files: Yes

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Now showing 1 - 10 of 28
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    Modeling students’ academic performance using artificial neural network
    (Federal University, Ndufu-Alike Ikwo (FUNAI), Nigeria, 2016) Asogwa, O. C.; Udomboso, C. G.
    Artificial Neural Network has been discovered as a better alternative to traditional models and that is why a model based on the Multilayer Perceptron algorithm was developed in this study. The appropriate number of hidden neurons that best modeled the academic performance of students was determined by the developed Network algorithm. Test data evaluation showed that Network Architecture 17-80 -1 was chosen among the numerous developed network architectures because of its model performances. The chosen network architecture gave the minimum value of Mean Square Error (MSE = 0.0718), minimum value of Network Information Criteria (NIC = 0.0743), maximum value of R- Square (R2=0.8975) and maximum value of Adjusted Network Information Criteria (ANIC= 0.8931). It was equally observed that there were patterns in the movement of hidden neurons against the model evaluation criteria. As the number of the hidden neurons appreciates the value of both MSE and NIC decreases down the plot, while that of ^-Square and ^MCvalues appreciate down the plot. The network was able to model the research problem with acceptable values judging from the model checking criteria considered in this work. Also the order of contribution of the predictor variables to the model was determined.
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    An adjusted network information criterion for model selection in statistical neural network models
    (JMASM, Inc., 2016) Udomboso, C. G.; Amahia, G. N.; Dontwi, I. K.
    In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC) criterion, based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The ANIC improves model selection in more sample sizes than does the NIC.
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    Volatility persistence and returns spillovers between oil and gold Prices: analysis before and after the global financial crisis
    (Elsevier Ltd, 2016) Yaya, O. S.; Tumala, M. T.; Udomboso, C. G.
    This paper investigated volatility persistence and returns spillovers between oil and gold markets using daily historical data from 1986 to 2015 partitioned into periods before the global crisis and after the crisis. The log-returns, absolute and squared log-returns series of these asset prices were used as proxy variables to investigate volatility persistence using the fractional persistence approach. The Constant Conditional Correlation (CCC) modelling framework was applied to investigate the spill over effects between the asset returns. The volatility in the gold market was found t be less than that at the oil market before and after the crisis periods. The returns spill over effect was bid irectional before the crisis period while it was unidirectional from gold to oil market after the crisis. The fact that there was no returns spill over running from oil to gold after the crisis suggested a measure of optimumal location weights and hedge ratio. The results obtained are of practical implications for port folio managers and decision managers in these two ways: gold market should be used as a hedge against oil price inflationary shocks; and the volatility at the oil market can be used to determine the behaviour of gold market.
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    Bio-social correlates of intention to use or not to use contraception: The case of Ghana and Nigeria
    (Union for African Population Studies, 2015) Udomboso, C. G.; Amoateng, A. Y.; Doegah, P. T.
    Based on the 2008 and 2013 Demographic and Health Survey data of Ghana and Nigeria respectively, statistical neural network and logit regression models were used to examine the effects of selected bio-social factors on the intention to use contraception among never married and ever married women in the two countries. The results showed that on the whole, the SNN model identified more biosocial factors affecting the intention to use contraception in the two countries than did the logic model. Educational attainment, exposure to media, and visitation to a health facility affected intention to use contraception significantly and positively in both countries. On the other hand, number of living children, infrequent sexual intercourse, postpartum amenorrhea, opposition to contraception and lack of access to contraceptives negatively affected intention to use contraception. The study findings have underscored the rational nature of the decisions women make in using contraception or not.
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    On the level of precision of the wavelet neural network in rainfall analysis
    (2014) Udomboso, C. G.; Amahia, G. N.; Dontwi, I. K.
    This research combines the efficiency of the artificial neural network and wavelet transform in modelling rainfall. The data used were decomposed into continuous wavelet signals on a scale of 48. Each of the decomposed series was subjected to correlation test with the original data. Instead of using all the series, ten series were selected on the basis of high correlation with die original data. These series included CWT 1, CWT 2, CWT 4, CWT 3, CWT 6, CWT 8, CWT 5, CWT 10, CWT 12, and CWT 7 (according to rank). The analysis showed that except in extremely rare cases, all the series performed optimally compared to the original data. The result of the study has been able to show' that using the continuous w'avelet transform in the ANN technique, a better performance of the network is observed.
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    On some properties of a hetereogeneous transfer function involving symmetric saturated linear (SATLINS) with hyperbolic tangent (TANH) transfer functions
    (JMASM, Inc., 2013-11) Udomboso, C. G.
    For transfer functions to map the input layer of the statistical neural network model to the output layer perfectly, they must lie within bounds that characterize probability distributions. The heterogeneous transfer function, SATLINS_TANH, is established as a Probability Distribution Function (p.d.f), and its mean and variance are shown.
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    On R2 contribution and statistical inference of the change in the hidden and input units of the statistical neural networks
    (Society of African Journal Editors, 2012-11) Udomboso, C. G.; James, T. O.; Odim, M. O.
    Determining the number of liitltlen units for obtaining optimal network performance has been a concern over the years ilespite empirical results showing that with higher neurons, the netivork error is retlucetl. This has led to indiscrimate increase in the hidden neurons, thereby bringing about overfitting. On the other hand, using too few hidden neurons leads to error bias, which can make neural network statistically unfit. In this paper, we developed a model for R1 for investigating changes in hidden and input units, as well as developed tests that can be used in determining the number of hidden and input units to obtain optimal performance. The result of the analyses shows that there is effect on the network model when there is an increase in the number of hidden neurons, as well as the number of input units.
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    Comparative analysis of rainfall prediction using statistical neural network and classical linear regression model
    (Medwell Journals, 2011) Udomboso, C. G.; Amahia., G. N.
    Different types of models have been used in modeling rainfall. Since 1990s however, interest has shifted from traditional models to ANN in rainfall modeling. Many researchers found out that the ANN performed better than such traditional models. In this study, we compared a traditional linear model and ANN in the modeling of rainfall in Ibadan, Nigeria. Ibadan is a city in West Africa, located in the tropical rainforest zone, using the data obtained from the Nigeria Meteorological (NIMET) station. Three variables were considered in this study rainfall, temperature and humidity. In selecting between the two models, we concentrated on the choice of adjusted R2 (R-2 ), Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC). Though, the MSE and R2 were also used, it was concluded from results that MSE is not a good choice for model selection. This is due to the nature of the rainfall data (which has wide variations). It was found that the Statistical Neural Network (SNN), generally performed better than the traditional (OLS).
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    Factors affecting learning in an open and distance learning programme
    (2010) Dontwi, I. K.; Amahia, G. N.; Chukwu, A. U.; Udomboso, C. G.
    There is bound to be a shift towards those courses that will provide the knowledge and skills for economic relevance and earning power. Commerce, science and technology are likely to be oversubscribed, once driven world, seems to be diminishing steadily. When designing instruction for distance education, attention is often focused on the cognitive domain, as it is in "traditional" (face-to-face) instruction. What do the students need to know? Which instructional strategies will be most appropriate? Upon what performance criteria will learners be evaluated?
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    Alternative goodness-of-fit test in logistic regression models
    (Medwell Journals, 2011) Nja, M. E.; Enang, E. I.; Chukwu, A. U.; Udomboso, C. G.
    The Deviance and the Pearson chi-square are two traditional goodness-of-fit tests in generalized linear models for which the logistic model is a special case. The effort involved in the computation of either the Deviance or Pearson chi-square statistic is enormous and this provides a reason for prospecting an alternative goodness-of-fit test in logistic regression models with discrete predictor variables. The Deviance is based on the log likelihood function while the Pearson chi-square derives from the discrepancies between observed and predicted counts. Replacing observed and predicted counts with observed proportions and predicted probabilities, respectively in a cross-classification data arrangement, the standard error of estimate is proposed as an alternative goodness-of-fit test in logistic regression models. The illustrative example returns favourable comparisons with Deviance and the Pearson chi-square statistics.