| 1 |
Author(s):
Oti Eric U, Olusola Michael O..
Page No : 1-25
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Comparative Evaluation of Six Agglomerative Hierarchical Clustering Methods With a Robust Example
Abstract
The agglomerative hierarchical clustering methods are the most popular type of hierarchical clustering used to group objects in clusters based on their similarity. The methods are represented by a bottom-up approach where each object starts in its cluster and pairs of clusters are merged as it moves up the hierarchy. In this paper, we present six agglomerative hierarchical clustering methods namely: the single linkage method, complete linkage method, average linkage method, centroid method, median method, and Ward’s method. We also evaluated how these methods work on a practical basis using a matrix of distance pairs of five points. It was observed that the single linkage method through its dendrogram produced the most similarity measure between x_i and x_j, while Ward’s method produced the highest distance measure between x_i and x_j.
| 2 |
Author(s):
Obi Boniface Inalu.
Page No : 26-34
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Entropy Generation For Non-Newtonian Fluid With Constant Viscosity
Abstract
In this research, entropy generation for non-Newtonian fluid flow with constant viscosity is considered. The governing nonlinear equations of motion are solved analytically with regular perturbation techniques. Third grade fluid is employed to account for the non-Newtonian influence. The influence of some physical parameters involved in the analysis is studied. Results show that the parameter has the tendency of increasing the velocity of the fluid flow as well as the temperature of the cylindrical pipe. It is observed that as the Brinkman parameter increases, the cylindrical wall temperature is enhanced. Entropy generation number for both heat transfer and fluid friction for various values of and is examined. Results indicate that increase in these parameters increases the temperature of the cylinder, thereby increasing the entropy generation number.
| 3 |
Author(s):
Onuoha N.O..
Page No : 35-50
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Theoretical Study of Forced Van Der Pol Oscillator Equation Using Multiple Two-timing Regular Parameter Perturbation and Asymptotic Expansion Techniques
Abstract
This paper presents the theoretical study of forced Van der Pol oscillator equation. Oscillatory systems are studied to know measures that can reduce the amplitude of oscillation of the oscillatory system. Here, multiple two-timing regular parameter perturbation is applied since it is a kind of perturbation among other perturbation techniques that enables the study of the behaviour of a system under certain conditions. Asymptotic expansion technique was also applied. Excel Microsoft was used to analyse the uniformly valid asymptotic solution of the Van der Pol oscillator equation obtained. The uniformly valid asymptotic solution in the independent variable obtained, showed that damping alters the amplitude of the oscillatory system thereby affecting its motion. Increase in damping decreases the amplitude of oscillation of the system. With damping incorporated in the system though very small damping, the amplitude of oscillation reduces with time.
| 4 |
Author(s):
Adagba T. Titus, Ben O. Johnson, Auta T. Jonathan.
Page No : 51-68
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Analysıs on Propertıes and Structure of Dıhedral Groups
Abstract
The structure of groups plays an important role in the study of the nature of the groups. We examine some basic properties and structural characteristics of the dihedral group of degree n, where n is a natural number, by group-theoretic approach. We begin the exploration by providing a foundational understanding of dihedral groups, elucidating their definitions and essential properties. Furthermore, we investigated the algebraic and geometric aspects of these groups, highlighting their role in describing symmetries of n-gons and other mathematical entities. Special attention is given to the distinctive features that differentiate dihedral groups from other algebraic structures. The analytic expressions for the order of subgroups are obtained and the commutativity investigated. The groups are all represented for further analysis and applications.
| 5 |
Author(s):
Awoyemi Samson Oyebode, Taiwo Abass Ishola, Olatayo Timothy Olabisi.
Page No : 69-78
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Trend-Fourier Time Series Regression Model for Secular-Cyclical Datasets
Abstract
The study proposed a Trend-Fourier Regression (TFR) model to handle time series datasets with simultaneous trend and cyclical variations. The model steps involve identification, estimation, diagnosis and forecasting. The Nigerian monthly Crude Oil Price (NMCOP) was used to implement the model and NMCOP was identified as trend-cyclical. The model estimation using Ordinary Least Squares method indicates that an increase in time will result in changes in NMCOP. Durbin-Watson statistics, histogram and autocorrelation function of residual plots were used to diagnose and specify the model to be stable. The coefficient of determination (R^2) indicates that over 80% of dependent variable variations were explained, with an adjusted (R^2) indicating a predictive ability exceeding 80%. The model efficiency was confirmed through out-sample and forecast evaluations, revealing superiority due to its smaller MAE, RMSE, and MAPE values, indicating minimal error. Conclusively, the TFR model is suitable for datasets that exhibit trend-cyclical variations simultaneously.
| 6 |
Author(s):
George Obed Samuel, P. O. Ekoko (Prof.).
Page No : 79-95
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Formulation of Game Model as a Linear Programming Problem using Various Models
Abstract
Game theory is the examination of strategic interactions between two or more individuals known as players who act based on their individual self-interest within a framework known as the game. Every player possesses a set of possible actions referred to as strategies, from which they make selections. In a two-person zero sum game, each of the two players has at least two strategies. In such a game problem where both players have no inferior strategies, we can determine the optimal mixed strategies of the game problem by converting it to a linear programming problem and solving it using the simplex method or variations of it. In this paper, consideration of some existing models along with our proposed model on the conversion of game problem to LPP was made. We compared the results across the various models considered. The results obtained revealed that our proposed model on the conversion of game problem to LPP produced a higher value of the game compared to the others considered; and thus, produced better performance.
| 7 |
Author(s):
Achaku D. T., Sani B..
Page No : 96-111
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Application of Lexisearch Algorithm to Vehicle Routing Problem with Time Windows
Abstract
Vehicle Routing Problem with Time Windows (VRPTW) is an NP hard combinatorial scheduling optimization problem in which a minimum number of routes have to be determined so as to serve each of the destinations within their specified time windows. In this paper, the mail delivery of the Nigerian Postal Services (NIPOST) is modelled as a VRPTW in order to address the problem of delay in mail delivery occurring regularly in NIPOST. The Abuja Post Office is used as a case study and the Model of a related literature is applied with modifications to solve the problem. The problem is solved by applying Lexisearch algorithm using data that was obtained from Abuja Post Office and computational results on Solomon’s 100 instances were used to validate the algorithm.
| 8 |
Author(s):
Osuagwu Chidimma Udo, Okenwe Idochi.
Page No : 131-143
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Simple Regression Models: A Comparison using Criteria Measures
Abstract
The study is on simple regression models: a comparison using criteria measures. The source of the dataset used for this study was extracted from records of the Federal Medical Centre, Owerri, Imo State, on weight of babies and hemoglobin level of mothers. The response variable is weight of babies while the explanatory variable is hemoglobin level of mothers. Eleven simple regression models—Linear, Growth, Quadratic, Polynomial, Logarithmic, Hyperbolic, Power, Exponential Growth, Square Root, Sinusoidal and Arctangent—were stated and employed for the study. For ease of data analysis, E-views package was implemented. Three model selection criteria measures for comparison, known as Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC) and Hannan-Quinn Information Criterion (HQIC), were employed. The result of the study showed that, when it comes to analyzing the association between baby weight and mothers' hemoglobin levels, the exponential growth regression model performs better than the other ten models that were examined. Therefore, researchers should investigate other models that were not included in this analysis and compare the findings using goodness of fit metrics other than the criteria measures used in this work.
| 9 |
Author(s):
Mbanuzuru Ahoma Victor , Nwankwo Chike Henry.
Page No : 144-161
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Latent Demographic and Clinical Correlates of Drug Resistant Tuberculosis Among Treated Patients
Abstract
Demographic and Clinical variables (data) collected from tuberculosis patients whose cases were drug resistant were analysed. The tuberculosis patients studied were those treated in the 11 Local Governments Areas and a treatment centre of Anambra State, Nigeria, for six years (2017 – 2022). Data from 197 Drug Resistant Tuberculosis (DR-TB) patients were analysed.
The pair of data collected, being multivariate in nature, were analysed using the Canonical Correlation Analysis (CCA) and the Canonical loadings (structure coefficients) between the Demographic and Clinical Variables were extracted.
Data obtained showed that mean age of the study participants was 40.2 ± 18.9 years (95% Confidence Interval). Males were 60.9%. Participants with HIV co-infection was 22.3%. The CCA showed that the first canonical variate was significant with 79% contribution, extracting 28.5% of the variance from demographic variables and 6.7% variance from the clinical variables. The variables that significantly contributed to the relationship include Age, Location and Body Mass Index (BMI). Human Immuno-Deficiency Virus (HIV) negative was protective in the relationship but not statistically significant.
| 10 |
Author(s):
Oni Oluwabunmi Ayankemi, Iskilu Zainab Adesola, Lawrence Adeolu.
Page No : 162-171
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Comparative Analysis of Weather Prediction Using Classification Algorithm: Random Forest Classifier, Decision Tree Classifier and Extra Tree Classifier.
Abstract
Comparison of machine learning models is carried out in order to determine which models are best to deploy as a system. However, for the purpose of our research we carried out a comparative analysis on Random Forest classifier, Decision Tree classifier and Extra Tree classifier for weather prediction systems as we focused on seeking the classifier with the highest performance metrics. Based on the metrics, accuracy score, the best model for the system was determined. We carried out training, testing and validation of the three different models on the same dataset from the Kaggle dataset. We were able to implement Random Forest Classifier, Decision Tree Classifier and Extra Tree Classifier from Scikit-Learn to make weather prediction and using matplotlib to visualize the accuracy score of the implemented models. The Random Forest Classifier was chosen as the best able to achieve the highest at 66% accuracy.
| 11 |
Author(s):
Joy Ijeoma Adindu-Dick.
Page No : 172-182
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Portfolio Management Strategies using Knapsack Programming.
Abstract
One of the major tasks in portfolio management is to determine the number of stocks with relatively high net value on the stock market. This work presents a knapsack based portfolio selection model that considers the expected returns, prices and budget. It represents a typical resource allocation model in which limited resource is apportioned among a finite number of stocks. The objective is to maximize an associated return function. The work is implemented for some numerical data to illustrate the application of the model and demonstrate the effectiveness of the designed algorithm. Numerical results have shown that the optimization model yields promising results.
| 12 |
Author(s):
Oguntola Toyin Omoyeni, Adesina Oluwaseun Ayobami, Oke SAmuel Abayomi, Oladimeji Lukman Abiola.
Page No : 183-191
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Modeling the Accessibility to Electricity in Nigeria using Time Series Technique.
Abstract
Access to electricity in Nigeria has been a major issue for the country for many years. With a rapidly growing population and an increasing demand for electricity, the country has struggled to provide a reliable and sustainable source of power. This work focuses on modeling access to electricity in Nigeria spanning 1990 to 2020 extracted from the World Bank database. The data was subjected to Augmented Dickey-fuller test and the Box-Jenkins ARIMA time series methodology was used for analysis. The time plot showed a continuous fluctuation of access to electricity in an upward trend direction and the result of the augmented Dickey-Fuller (ADF) unit root test suggested that the series is not stationary at original level, but the model incorporates first differencing. The electricity accessibility series was modelled and predicted using the Autoregressive Integrated Moving Average Model (ARIMA). ARIMA (0,1,1) was selected as the appropriate optimal models based on the Akaike's Information Criterion and Bayesian Information Criterion. Likewise, from the result of the forecast the access to electricity in Nigeria will continue to rise for the next 10 years. It was recommended that the government should make efforts to address the issue by implementing reforms, privatizing the electricity sector, and investing in renewable energy sources such as solar and wind power.
| 13 |
Author(s):
Momoh Besiru, Raphael Michael Ugochukwu, Emwinloghosa Kenneth Guobadia, Precious Opoggen.
Page No : 192-207
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Kernel Construction for Exploring Trends in Probability Distribution Development.
Abstract
In this paper, we provided new methods that improve modeling flexibility of probability
distributions. The methods focus on the construction of kernels for possible development of new
probability models from (root) variable components or arbitrary functions. These approaches are
further grouped into two different categories including construction of kernels from existing
probability functions or directly using mathematical deterministic functions. The Direct
substitution approach, homogeneous and inhomogeneous interaction methods are captured under
kernel development from probabilistic functions. Two distributions namely, Lindley-Sine
Distribution (LSD) and Alpha Lindley Distribution (ALD) were developed from the variable
component of the Lindley distribution. More so, the combinations of normal and arcsine
distribution, and Gumbel and exponential distributions birthed the Double Censored NormalArcSine Distribution (DCNAD) and Left Censored Gumbel-Exponential Distribution (LCGED)
respectively. Interesting unconventional trends including decreasing sinusoidal, bathtub,
triangular and circular trends realized from these developments validates the relevance of the
approaches in probability forecasting. Finally, the asymptotic stability of the parameters of the
derived distributions was established through simulation study.
| 14 |
Author(s):
Abam Ayeni Omini, Isah Abubakar Salisu.
Page No : 208-224
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Constrained Maximization of the Economic Production Model of Lithium Ore Exploration in Nasarawa.
Abstract
Over 20,000 years ago, Mining was discovered as one of the oldest production industries generating over US$700 billions’ of revenue in the world by a few mining companies. For some time, mining work has resulted to very demanding affair as a result of greater depth, low-grade, limited resources, and complex geo-mining conditions. Therefore, optimization of mining system plays vital role in profit maximization with the satisfaction of many constraints. However, today’s mining industry uses complex and sophisticated systems whose reliability has become a critical issue. This work adopted the financial market theory of development to propose a maximized constrained optimization economic production model for lithium ore exploration in Nasarawa, Nigeria using three methods such as: the break-even principle method of cut-off grade between revenue earned and cost incurred; the mortimer’s method principle to determine ore based in two cut-off criteria (original and average good) and the lane method for net profit value thereby maximizing processing capacity. Data got from the field and the ministry of mines and solid minerals were analyzed using Energy dispersive x-ray fluorescence (EDXRF) showing the presence of lithium. The forth flotation technique used showed the beneficiation thereby achieving improved lithium concentrates and the inductively coupled atomic emission revealed a high presence of lithium of over 1859 parts per million (ppm) and other minerals.
| 15 |
Author(s):
Emmanuel Uchenna Ohaegbulem, Victor Chijindu Iheaka.
Page No : 225-261
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On Remedying the Presence of Heteroscedasticity in a Multiple Linear Regression Modelling .
Abstract
This study demonstrated the very essence of remedying the presence of heteroscedasticity, where it existed, in regression modelling. Two different hypothetical data, Data A (the Original) and Data B (the Original), were used in this study for the purpose of illustration. The normality, multicollinearity and autocorrelation assumptions were satisfied, but the Breusch-Pagan test and the White test established the existences of heteroscedasticity in the two datasets. The estimated multiple linear regression model for Data A (the Original) was statistically significant with an R-square value of 0.976, an AIC value of 332.5929, and an SBC value of 347.2533; and the one for Data B (the Original) was also statistically significant with an R-square value of 0.553, an AIC value of 69.89669, and an SBC value of 82.15499. The Log-transformation was applied on the variables in Data A (the Original) and Data B (the Original) to give rise to new sets of data, Data A (Now with Heteroscedasticity Remedied) and Data B (Now with Heteroscedasticity Remedied); which equally satisfied the normality, multicollinearity and autocorrelation assumptions, and also satisfied that there were no existences of heteroscedasticity in the two datasets. Now, the estimated multiple linear regression model for Data A (Now with Heteroscedasticity Remedied) was statistically significant with an R-square value of 0.986, an AIC value of -135.021, and an SBC value of -120.361; and the estimated model for Data B (Now with Heteroscedasticity Remedied) was statistically significant with an R-square value of 0.624, an AIC value of -32.0801, and an SBC value of -19.8218. From the points of view of the values of the R-square (0.986>0.976 and 0.624>0.553), AIC (-135.021<332.5929 and -32.0801<69.89669) and SBC (-120.361<347.2533 and -19.8218<82.15499), it was evident that the estimated regression models for Data A (Now with Heteroscedasticity Remedied) and Data B (Now with Heteroscedasticity Remedied) were, respectively, better models when compared to the regression models for Data A (the Original) and Data B (the Original).
| 16 |
Author(s):
Aminu Titilope Funmilayo , Daniel Akindunjoye Akinboro , Sanusi Akeem Olatoye .
Page No : 262-275
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Seasonality Prediction of Meningitis Using Artificial Neural Network (ANN).
Abstract
Communities are concerned about controlling, preventing and handling infectious diseases due to recent epidemic outbreaks . Meningitis, an inflammation of the membranes surrounding the brain and spinal cord, is a significant risk in Nigeria. It can cause death within hours of infection with average case fatality rate of 10 percent. To prevent meningitis outbreaks, this paper focuses on using an Artificial Neural network (ANN) to predict outbreak of meningitis based on climatological factors. Previous research has shown that climate plays a major role in these outbreaks. The study found that Levenberg-Marquarait ANN algorithm was the best with the lowest prediction error and fewer iterations . High temperature and low humidity were identified as major triggers for meningitis outbreaks. It is crucial to address these factors to prevent future outbreaks and protect the community.
| 17 |
Author(s):
Chukwudi Anderson Ugomma, Emmanuel Uchenna Ohaegbulem.
Page No : 276-286
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On Detecting an Appropriate Model in Time Series Analysis.
Abstract
This study sought to present yet another method of decomposition in time series data. The data for this study were of secondary source and obtained from https://ng.www.investing.com/equities/cocacola-bottle-historicaldata which comprised of both the open and the closed stock prices. The two data were firstly tested for randomness and they were confirmed fit for time series analysis. The two data were also subjected to trend curve analysis, and it was observed that both data were of exponential curve since the exponential trend curve exhibited the highest coefficient of determination (88%), among other trend curves which included linear, quadratic, cubic and logarithmic curves. In the decomposition of the two data series, using the exponential trend, it was revealed that the model, for each data were of multiplicative type since the multiplicative model had the Minimum Mean Squared Error (MSE) of 0.00827 and 0.003665 respectively for both Open and Closed Stock Prices of Coca-Cola Data. Hence, in this study, it was recommended that this traditional method of statistics should be applied in the decomposition of any time series data.
